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Yang, Dan, Jing Zhang, Sifeng Wang, and XueDong Zhang. "A Time-Aware CNN-Based Personalized Recommender System." Complexity 2019 (December 18, 2019): 1–11. http://dx.doi.org/10.1155/2019/9476981.
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Recommender system has received tremendous attention and has been studied by scholars in recent years due to its wide applications in different domains. With the in-depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. The success of modern recommender system mainly depends on the understanding and application of the context of recommendation requests. However, when leveraging deep learning algorithms for recommendation, the impact of context information such as recommendation time and location is often neglected. In this paper, a time-aware convolutional neural network- (CNN-) based personalized recommender system TC-PR is proposed. TC-PR actively recommends items that meet users’ interests by analyzing users’ features, items’ features, and users’ ratings, as well as users’ time context. Moreover, we use Tensorflow distributed open source framework to implement the proposed time-aware CNN-based recommendation algorithm which can effectively solve the problems of large data volume, large model, and slow speed of recommender system. The experimental results on the MovieLens-1m real dataset show that the proposed TC-PR can effectively solve the cold-start problem and greatly improve the speed of data processing and the accuracy of recommendation.
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Beheshti, Amin, Shahpar Yakhchi, Salman Mousaeirad, Seyed Mohssen Ghafari, Srinivasa Reddy Goluguri, and Mohammad Amin Edrisi. "Towards Cognitive Recommender Systems." Algorithms 13, no. 8 (July 22, 2020): 176. http://dx.doi.org/10.3390/a13080176.
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Intelligence is the ability to learn from experience and use domain experts’ knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts’ knowledge and experience, as it is vital to know the domain that the items will be recommended. Traditionally, Recommender Systems have been recognized as playlist generators for video/music services (e.g., Netflix and Spotify), e-commerce product recommenders (e.g., Amazon and eBay), or social content recommenders (e.g., Facebook and Twitter). However, Recommender Systems in modern enterprises are highly data-/knowledge-driven and may rely on users’ cognitive aspects such as personality, behavior, and attitude. In this paper, we survey and summarize previously published studies on Recommender Systems to help readers understand our method’s contributions to the field in this context. We discuss the current limitations of the state of the art approaches in Recommender Systems and the need for our new approach: A vision and a general framework for a new type of data-driven, knowledge-driven, and cognition-driven Recommender Systems, namely, Cognitive Recommender Systems. Cognitive Recommender Systems will be the new type of intelligent Recommender Systems that understand the user’s preferences, detect changes in user preferences over time, predict user’s unknown favorites, and explore adaptive mechanisms to enable intelligent actions within the compound and changing environments. We present a motivating scenario in banking and argue that existing Recommender Systems: (i) do not use domain experts’ knowledge to adapt to new situations; (ii) may not be able to predict the ratings or preferences a customer would give to a product (e.g., loan, deposit, or trust service); and (iii) do not support data capture and analytics around customers’ cognitive activities and use it to provide intelligent and time-aware recommendations.
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Yang, Dan, Zheng Tie Nie, and Fajun Yang. "Time-Aware CF and Temporal Association Rule-Based Personalized Hybrid Recommender System." Journal of Organizational and End User Computing 33, no. 3 (May 2021): 19–34. http://dx.doi.org/10.4018/joeuc.20210501.oa2.
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Most recommender systems usually combine several recommendation methods to enhance the recommendation accuracy. Collaborative filtering (CF) is a best-known personalized recommendation technique. While temporal association rule-based recommendation algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key problems affecting the recommendation performance. Aiming at the above problems, a time-aware CF and temporal association rule-based personalized hybrid recommender system, TP-HR, is proposed. The proposed time-aware CF algorithm considers evolving features of users' historical feedback. And time-aware users' similar neighbors selecting measure and time-aware item rating prediction function are proposed to keep track of the dynamics of users' preferences. The proposed temporal association rule-based recommendation algorithm considers the time context of users' historical behaviors when mining effective temporal association rules. Experimental results on real datasets show the feasibility and performance improvement of the proposed hybrid recommender system compared to other baseline approaches.
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Javed, Umair, Kamran Shaukat, Ibrahim A. Hameed, Farhat Iqbal, Talha Mahboob Alam, and Suhuai Luo. "A Review of Content-Based and Context-Based Recommendation Systems." International Journal of Emerging Technologies in Learning (iJET) 16, no. 03 (February 12, 2021): 274. http://dx.doi.org/10.3991/ijet.v16i03.18851.
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In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.
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Campos, Pedro G., Fernando Díez, and Iván Cantador. "Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols." User Modeling and User-Adapted Interaction 24, no. 1-2 (February 15, 2013): 67–119. http://dx.doi.org/10.1007/s11257-012-9136-x.
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Ahn, Hyun Chul, and Kyoung Jae Kim. "Context-Aware Recommender System for Location-Based Advertising." Key Engineering Materials 467-469 (February 2011): 2091–96. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.2091.
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Demand for context-aware systems continues to grow due to the diffusion of mobile devices. This trend may represent good market opportunities for mobile service industries. Thus, context-aware or location-based advertising (LBA) has been an interesting marketing tool for many companies. However, some studies reported that the performance of context-aware marketing or advertising has been quite disappointing. In this study, we propose a novel context-aware recommender system for LBA. Our proposed model is designed to apply a modified collaborative filtering (CF) algorithm with regard to the several dimensions for the personalization of mobile devices – location, time and the user’s needs type. In particular, we employ a classification rule to understand user’s needs type using a decision tree algorithm. We empirically validated the effectiveness of the proposed model by using a real-world dataset. Experimental results show that our model makes more accurate and satisfactory advertisements than comparative systems.
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Sundermann, Camila, Marcos Domingues, Roberta Sinoara, Ricardo Marcacini, and Solange Rezende . "Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review." Information 10, no. 2 (January 28, 2019): 42. http://dx.doi.org/10.3390/info10020042.
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Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user’s current context (e.g., location and time). Moreover, the advent of Web 2.0 and the growing popularity of social and e-commerce media sites have encouraged users to naturally write texts describing their assessment of items. There are increasing efforts to incorporate the rich information embedded in user’s reviews/texts into the recommender systems. Given the importance of this type of texts and their usage along with opinion mining and contextual information extraction techniques for recommender systems, we present a systematic review on the recommender systems that explore both contextual information and opinion mining. This systematic review followed a well-defined protocol. Its results were based on 17 papers, selected among 195 papers identified in four digital libraries. The results of this review give a general summary of the current research on this subject and point out some areas that may be improved in future primary works.
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Al-Ghossein, Marie, Talel Abdessalem, and Anthony BARRÉ. "A Survey on Stream-Based Recommender Systems." ACM Computing Surveys 54, no. 5 (June 2021): 1–36. http://dx.doi.org/10.1145/3453443.
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Recommender Systems (RS) have proven to be effective tools to help users overcome information overload, and significant advances have been made in the field over the past two decades. Although addressing the recommendation problem required first a formulation that could be easily studied and evaluated, there currently exists a gap between research contributions and industrial applications where RS are actually deployed. In particular, most RS are meant to function in batch: they rely on a large static dataset and build a recommendation model that is only periodically updated. This functioning introduces several limitations in various settings, leading to considering more realistic settings where RS learn from continuous streams of interactions. Such RS are framed as Stream-Based Recommender Systems (SBRS). In this article, we review SBRS, underline their relation with time-aware RS and online adaptive learning, and present and categorize existing work that tackle the corresponding problem and its multiple facets. We discuss the methodologies used to evaluate SBRS and the adapted datasets that can be used, and finally we outline open challenges in the area.
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Li, Hongzhi, and Dezhi Han. "A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering." Mobile Information Systems 2020 (October 22, 2020): 1–16. http://dx.doi.org/10.1155/2020/8896694.
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Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.
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Lozano Murciego, Álvaro, Diego M. Jiménez-Bravo, Adrián Valera Román, Juan F. De Paz Santana, and María N. Moreno-García. "Context-Aware Recommender Systems in the Music Domain: A Systematic Literature Review." Electronics 10, no. 13 (June 27, 2021): 1555. http://dx.doi.org/10.3390/electronics10131555.
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The design of recommendation algorithms aware of the user’s context has been the subject of great interest in the scientific community, especially in the music domain where contextual factors have a significant impact on the recommendations. In this type of system, the user’s contextual information can come from different sources such as the specific time of day, the user’s physical activity, and geolocation, among many others. This context information is generally obtained by electronic devices used by the user to listen to music such as smartphones and other secondary devices such as wearables and Internet of Things (IoT) devices. The objective of this paper is to present a systematic literature review to analyze recent work to date in the field of context-aware recommender systems and specifically in the domain of music recommendation. This paper aims to analyze and classify the type of contextual information, the electronic devices used to collect it, the main outstanding challenges and the possible opportunities for future research directions.
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Symeonidis, Panagiotis, Ludovik Coba, and Markus Zanker. "Improving Time-Aware Recommendations in Open Source Packages." International Journal on Artificial Intelligence Tools 28, no. 06 (September 2019): 1960007. http://dx.doi.org/10.1142/s0218213019600078.
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Collaborative filtering techniques have been studied extensively during the last decade. Many open source packages (Apache Mahout, LensKit, MyMediaLite, rrecsys etc.) have implemented them, but typically the top-N recommendation lists are only based on a highest predicted ratings approach. However, exploiting frequencies in the user/item neighborhood for the formation of the top-N recommendation lists has been shown to provide superior accuracy results in offline simulations. In addition, most open source packages use a time-independent evaluation protocol to test the quality of recommendations, which may result to misleading conclusions since it cannot simulate well the real-life systems, which are strongly related to the time dimension. In this paper, we have therefore implemented the time-aware evaluation protocol to the open source recommendation package for the R language — denoted rrecsys — and compare its performance across open source packages for reasons of replicability. Our experimental results clearly demonstrate that using the most frequent items in neighborhood approach significantly outperforms the highest predicted rating approach on three public datasets. Moreover, the time-aware evaluation protocol has been shown to be more adequate for capturing the life-time effectiveness of recommender systems.
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Ahmadian, Sajad, Nima Joorabloo, Mahdi Jalili, and Milad Ahmadian. "Alleviating data sparsity problem in time-aware recommender systems using a reliable rating profile enrichment approach." Expert Systems with Applications 187 (January 2022): 115849. http://dx.doi.org/10.1016/j.eswa.2021.115849.
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Perera, Dilruk, and Roger Zimmermann. "Towards Comprehensive Recommender Systems: Time-Aware Unified Recommendations Based on Listwise Ranking of Implicit Cross-Network Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 189–97. http://dx.doi.org/10.1609/aaai.v34i01.5350.
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The abundance of information in web applications make recommendation essential for users as well as applications. Despite the effectiveness of existing recommender systems, we find two major limitations that reduce their overall performance: (1) inability to provide timely recommendations for both new and existing users by considering the dynamic nature of user preferences, and (2) not fully optimized for the ranking task when using implicit feedback. Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users. Furthermore, we consider the ranking problem under implicit feedback as a classification task, and propose a generic personalized listwise optimization criterion for implicit data to effectively rank a list of items. We illustrate our cross-network model using Twitter auxiliary information for recommendations on YouTube target network. Extensive comparisons against multiple time aware and cross-network baselines show that the proposed solution is superior in terms of accuracy, novelty and diversity. Furthermore, experiments conducted on the popular MovieLens dataset suggest that the proposed listwise ranking method outperforms existing state-of-the-art ranking techniques.
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Ullah, Farman, Ghulam Sarwar, and Sungchang Lee. "N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/679849.
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This paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation seeks to improve the user experience with recommended content by considering the user N-screen device attributes such as screen resolution, media codec, remaining battery time, and access network and the user temporal usage pattern information that are not considered in existing recommender systems. For N-screen aware recommendation support, this work introduces a user device profile collaboration agent, manager, and N-screen control server to acquire and manage the user N-screen devices profile. Furthermore, a multicriteria hybrid framework is suggested that incorporates the N-screen devices information with user preferences and demographics. In addition, we propose an individual feature and subspace weight based clustering (IFSWC) to assign different weights to each subspace and each feature within a subspace in the hybrid framework. The proposed system improves the accuracy, precision, scalability, sparsity, and cold start issues. The simulation results demonstrate the effectiveness and prove the aforementioned statements.
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CIARAMELLA, ALESSANDRO, MARIO G. C. A. CIMINO, BEATRICE LAZZERINI, and FRANCESCO MARCELLONI. "A SITUATION-AWARE RESOURCE RECOMMENDER BASED ON FUZZY AND SEMANTIC WEB RULES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 04 (August 2010): 411–30. http://dx.doi.org/10.1142/s0218488510006623.
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Nowadays, a huge quantity of resources for mobile users are made available on the most important marketplaces. Further, handheld devices can accommodate plenty of these resources, such as applications, documents and web pages, locally. Thus, to search for resources suitable for specific circumstances often requires a considerable effort and rarely brings to a completely satisfactory result. A tool able to recommend suitable resources at the right time in each situation would be of great help for the mobile users and would make the use of the handheld devices less boring and more attractive. To this aim, new levels of granularity, together with some degree of self-awareness, are needed to assist mobile users in managing and using resources. In this paper, we propose an efficient situation-aware resource recommender (SARR), which helps mobile users to timely locate resources proactively. Situations are determined by a semantic reasoner that exploits domain knowledge expressed in terms of ontologies and semantic rules. This reasoner works in synergy with a fuzzy engine, which is in charge of handling the vagueness of some conditions in the semantic rules, computing a certainty degree for each inferred situation. These degrees are used to rank the situations and consequently to assign a priority to the resources associated with the specific situations. The application of SARR to two real business cases is also shown and discussed.
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Polatidis, Nikolaos, Christos K. Georgiadis, Elias Pimenidis, and Emmanouil Stiakakis. "Privacy-preserving recommendations in context-aware mobile environments." Information & Computer Security 25, no. 1 (March 13, 2017): 62–79. http://dx.doi.org/10.1108/ics-04-2016-0028.
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Purpose This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of Web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They use recommendation methods, such as collaborative filtering or content-based filtering and use a considerable amount of contextual information to provide relevant recommendations. However, because of privacy concerns, users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable. Design/methodology/approach This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method has been designed with privacy protection in mind, which is done by using realistic dummy parameter creation. To demonstrate the applicability of the method, a relevant context-aware data set has been used to run performance and usability tests. Findings The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance, user privacy can be protected. Originality/value This is a novel research paper that proposed a method for protecting the privacy of mobile recommender systems users when context parameters are used.
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Rabiu, Idris, Naomie Salim, Aminu Da’u, and Akram Osman. "Recommender System Based on Temporal Models: A Systematic Review." Applied Sciences 10, no. 7 (March 25, 2020): 2204. http://dx.doi.org/10.3390/app10072204.
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Over the years, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users’ needs through mapping the available products to users based on their observed interests towards items. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended, a problem known as concept drift or sometimes called temporal dynamics in RS. Although the traditional techniques of RS have attained significant success in providing recommendations, they are insufficient in providing accurate recommendations due to concept drift problems. These issues have triggered a lot of researches on the development of dynamic recommender systems (DRSs) which is focused on the design of temporal models that will account for concept drifts and ensure more accurate recommendations. However, in spite of the several research efforts on the DRSs, only a few secondary studies were carried out in this field. Therefore, this study aims to provide a systematic literature review (SLR) of the DRSs models that can guide researchers and practitioners to better understand the issues and challenges in the field. To achieve the aim of this study, 87 papers were selected for the review out of 875 total papers retrieved between 2010 and 2019, after carefully applying the inclusion/exclusion and the quality assessment criteria. The results of the study show that concept drift is mostly applied in the multimedia domain, then followed by the e-commerce domain. Also, the results showed that time-dependent neighborhood models are the popularly used temporal models for DRS followed by the Time-dependent Matrix Factorization (TMF) and time-aware factors models, specifically Tensor models, respectively. In terms of evaluation strategy, offline metrics such as precision and recalls are the most commonly used approaches to evaluate the performance of DRS.
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Sánchez-Moreno, Diego, Yong Zheng, and María N. Moreno-García. "Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach." Applied Sciences 10, no. 15 (July 31, 2020): 5324. http://dx.doi.org/10.3390/app10155324.
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Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest them from the vast amount of music available. However, many are not reliable as they may not take into account contextual aspects or the ever-evolving user behavior. Therefore, it is necessary to develop systems that consider these aspects. In the field of music, time is one of the most important factors influencing user preferences and managing its effects, and is the motivation behind the work presented in this paper. Here, the temporal information regarding when songs are played is examined. The purpose is to model both the evolution of user preferences in the form of evolving implicit ratings and user listening behavior. In the collaborative filtering method proposed in this work, daily listening habits are captured in order to characterize users and provide them with more reliable recommendations. The results of the validation prove that this approach outperforms other methods in generating both context-aware and context-free recommendations.
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Cao, Junyu, Wei Sun, Zuo-Jun (Max) Shen, and Markus Ettl. "Fatigue-Aware Bandits for Dependent Click Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3341–48. http://dx.doi.org/10.1609/aaai.v34i04.5735.
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As recommender systems send a massive amount of content to keep users engaged, users may experience fatigue which is contributed by 1) an overexposure to irrelevant content, 2) boredom from seeing too many similar recommendations. To address this problem, we consider an online learning setting where a platform learns a policy to recommend content that takes user fatigue into account. We propose an extension of the Dependent Click Model (DCM) to describe users' behavior. We stipulate that for each piece of content, its attractiveness to a user depends on its intrinsic relevance and a discount factor which measures how many similar contents have been shown. Users view the recommended content sequentially and click on the ones that they find attractive. Users may leave the platform at any time, and the probability of exiting is higher when they do not like the content. Based on user's feedback, the platform learns the relevance of the underlying content as well as the discounting effect due to content fatigue. We refer to this learning task as “fatigue-aware DCM Bandit” problem. We consider two learning scenarios depending on whether the discounting effect is known. For each scenario, we propose a learning algorithm which simultaneously explores and exploits, and characterize its regret bound.
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Abu-Salih, Bilal, Hamad Alsawalqah, Basima Elshqeirat, Tomayess Issa, Pornpit Wongthongtham, and Khadija Khalid Premi. "Toward a Knowledge-based Personalised Recommender System for Mobile App Development." JUCS - Journal of Universal Computer Science 27, no. 2 (February 28, 2021): 208–29. http://dx.doi.org/10.3897/jucs.65096.
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Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users’ query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user’s query with the minimum mismatches.
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Huang, Ruo, Shelby McIntyre, Meina Song, Haihong E, and Zhonghong Ou. "An Attention-Based Recommender System to Predict Contextual Intent Based on Choice Histories across and within Sessions." Applied Sciences 8, no. 12 (November 29, 2018): 2426. http://dx.doi.org/10.3390/app8122426.
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Recent years have witnessed the growth of recommender systems, with the help of deep learning techniques. Recurrent Neural Networks (RNNs) play an increasingly vital role in various session-based recommender systems, since they use the user’s sequential history to build a comprehensive user profile, which helps improve the recommendation. However, a problem arises regarding how to be aware of the variation in the user’s contextual preference, especially the short-term intent in the near future, and make the best use of it to produce a precise recommendation at the start of a session. We propose a novel approach named Attention-based Short-term and Long-term Model (ASLM), to improve the next-item recommendation, by using an attention-based RNNs integrating both the user’s short-term intent and the long-term preference at the same time with a two-layer network. The experimental study on three real-world datasets and two sub-datasets demonstrates that, compared with other state-of-the-art methods, the proposed approach can significantly improve the next-item recommendation, especially at the start of sessions. As a result, our proposed approach is capable of coping with the cold-start problem at the beginning of each session.
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Zhang, Zhi-Peng, Yasuo Kudo, Tetsuya Murai, and Yong-Gong Ren. "Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting." Applied Sciences 9, no. 9 (May 10, 2019): 1928. http://dx.doi.org/10.3390/app9091928.
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Recommender systems (RS) analyze user rating information and recommend items that may interest users. Item-based collaborative filtering (IBCF) is widely used in RSs. However, traditional IBCF often cannot provide recommendations with good predictive and classification accuracy at the same time because it assigns equal weights to all items when computing similarity and prediction. However, some items are more relevant and should be assigned greater weight. To address this problem, we propose a niche approach to realize item-variance weighting in IBCF in this paper. In the proposed approach, to improve the predictive accuracy, a novel time-related correlation degree is proposed and applied to form time-aware similarity computation, which can estimate the relationship between two items and reduce the weight of the item rated over a long period. Furthermore, a covering-based rating prediction is proposed to increase classification accuracy, which combines the relationship between items and the target user’s preference into the predicted rating scores. Experimental results suggest that the proposed approach outperforms traditional IBCF and other existing work and can provide recommendations with satisfactory predictive and classification accuracy simultaneously.
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Abderrahim, Naziha, and Sidi Mohamed Benslimane. "STRESS." International Journal of Information Systems in the Service Sector 7, no. 3 (July 2015): 40–58. http://dx.doi.org/10.4018/ijisss.2015070103.
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Recommender systems help users find relevant Web service based on peers' previous experiences dealing with Web services (WSs). However, with the proliferation of WSs, recommendation has become “questionable”. Social computing seems offering innovative solutions to improve the quality of recommendations. Social computing is at the crossroad of computer sciences and social sciences disciplines by looking into ways of improving application design and development using elements that people encounter daily such as collegiality, friendship and trust. In this paper, the authors propose a social trust-aware system for recommending WS based on social qualities of WSs that they exhibit towards peers at run-time, and trustworthiness of the users who provide feedback on their overall experience using WSs. A set of experiments to assess the fairness and accuracy of the proposed system are reported in the paper, showing promising results.
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Xu, Yanan, Yanmin Zhu, and Jiadi Yu. "Modeling Multiple Coexisting Category-Level Intentions for Next Item Recommendation." ACM Transactions on Information Systems 39, no. 3 (May 6, 2021): 1–24. http://dx.doi.org/10.1145/3441642.
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Purchase intentions have a great impact on future purchases and thus can be exploited for making recommendations. However, purchase intentions are typically complex and may change from time to time. Through empirical study with two e-commerce datasets, we observe that behaviors of multiple types can indicate user intentions and a user may have multiple coexisting category-level intentions that evolve over time. In this article, we propose a novel Intention-Aware Recommender System (IARS) which consists of four components for mining such complex intentions from user behaviors of multiple types. In the first component, we utilize several Recurrent Neural Networks (RNNs) and an attention layer to model diverse user intentions simultaneously and design two kinds of Multi-behavior GRU (MGRU) cells to deal with heterogeneous behaviors. To reveal user intentions, we carefully design three tasks that share representations from MGRUs. The next-item recommendation is the main task and leverages attention to select user intentions according to candidate items. The remaining two (item prediction and sequence comparison) are auxiliary tasks and can reveal user intentions. Extensive experiments on the two real-world datasets demonstrate the effectiveness of our models compared with several state-of-the-art recommendation methods in terms of hit ratio and NDCG.
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Meehan, Kevin, Tom Lunney, Kevin Curran, and Aiden McCaughey. "Aggregating social media data with temporal and environmental context for recommendation in a mobile tour guide system." Journal of Hospitality and Tourism Technology 7, no. 3 (August 1, 2016): 281–99. http://dx.doi.org/10.1108/jhtt-10-2014-0064.
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Purpose Manufacturers of smartphone devices are increasingly utilising a diverse range of sensors. This innovation has enabled developers to accurately determine a user’s current context. One area that has been significantly enhanced by the increased use of context in mobile applications is tourism. Traditionally, tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions and tourists experiencing information overload. These problems can be mitigated if appropriate personalisation and content filtering is performed. This research proposes an intelligent context-aware recommender system that aims to minimise the highlighted problems. Design/methodology/approach Intelligent reasoning was performed to determine the weight or importance of different types of environmental and temporal context. Environmental context such as the weather outside can have an impact on the suitability of tourist attractions. Temporal context can be the time of day or season; this is particularly important in tourism as it is largely a seasonal activity. Social context such as social media can potentially provide an indication of the “mood” of an attraction. These types of contexts are combined with location data and the context of the user to provide a more effective recommendation to tourists. The evaluation of the system is a user study that utilised both qualitative and quantitative methods, involving 40 participants of differing gender, age group, number of children and marital status. Findings This study revealed that the participants selected the context-based recommendation at a significantly higher level than either location-based recommendation or random recommendation. It was clear from analysing the questionnaire results that location is not the only influencing factor when deciding on a tourist attraction to visit. Research limitations/implications To effectively determine the success of the recommender system, various combinations of contextual conditions were simulated. Simulating contexts provided the ability to randomly assign different contextual conditions to ensure an effective recommendation under all circumstances. This is not a reflection of the “real world”, because in a “real world” field study the majority of the contextual conditions will be similar. For example, if a tourist visited numerous attractions in one day, then it is likely that the weather conditions would be the same for the majority of the day, especially in the summer season. Practical implications Utilising this type of recommender system would allow the tourists to “go their own way” rather than following a prescribed route. By using this system, tourists can co-create their own experience using both social media and mobile technology. This increases the need to retain user preferences and have it available for multiple destinations. The application will be able to learn further through multiple trips, and as a result, the personalisation aspect will be incrementally refined over time. This extensible aspect is increasingly important as personalisation is gradually more effective as more data is collated. Originality/value This paper contributes to the body of knowledge that currently exists regarding the study of utilising contextual conditions in mobile recommender systems. The novelty of the system proposed by this research is the combination of various types of temporal, environmental and personal context data to inform a recommendation in an extensible tourism application. Also, performing sentiment analysis on social media data has not previously been integrated into a tourist recommender system. The evaluation concludes that this research provides clear evidence for the benefits of combining social media data with environmental and temporal context to provide an effective recommendation.
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Uria-Rivas, Rodriguez-Sanchez, Santos, Vaquero, and Boticario. "Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios." Sensors 19, no. 20 (October 17, 2019): 4520. http://dx.doi.org/10.3390/s19204520.
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Physiological sensors can be used to detect changes in the emotional state of users with affective computing. This has lately been applied in the educational domain, aimed to better support learners during the learning process. For this purpose, we have developed the AICARP (Ambient Intelligence Context-aware Affective Recommender Platform) infrastructure, which detects changes in the emotional state of the user and provides personalized multisensorial support to help manage the emotional state by taking advantage of ambient intelligence features. We have developed a third version of this infrastructure, AICARP.V3, which addresses several problems detected in the data acquisition stage of the second version, (i.e., intrusion of the pulse sensor, poor resolution and low signal to noise ratio in the galvanic skin response sensor and slow response time of the temperature sensor) and extends the capabilities to integrate new actuators. This improved incorporates a new acquisition platform (shield) called PhyAS (Physiological Acquisition Shield), which reduces the number of control units to only one, and supports both gathering physiological signals with better precision and delivering multisensory feedback with more flexibility, by means of new actuators that can be added/discarded on top of just that single shield. The improvements in the quality of the acquired signals allow better recognition of the emotional states. Thereof, AICARP.V3 gives a more accurate personalized emotional support to the user, based on a rule-based approach that triggers multisensorial feedback, if necessary. This represents progress in solving an open problem: develop systems that perform as effectively as a human expert in a complex task such as the recognition of emotional states.
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Purificato, Erasmo, Sabine Wehnert, and Ernesto William De Luca. "Dynamic Privacy-Preserving Recommendations on Academic Graph Data." Computers 10, no. 9 (August 25, 2021): 107. http://dx.doi.org/10.3390/computers10090107.
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In the age of digital information, where the internet and social networks, as well as personalised systems, have become an integral part of everyone’s life, it is often challenging to be aware of the amount of data produced daily and, unfortunately, of the potential risks caused by the indiscriminate sharing of personal data. Recently, attention to privacy has grown thanks to the introduction of specific regulations such as the European GDPR. In some fields, including recommender systems, this has inevitably led to a decrease in the amount of usable data, and, occasionally, to significant degradation in performance mainly due to information no longer being attributable to specific individuals. In this article, we present a dynamic privacy-preserving approach for recommendations in an academic context. We aim to implement a personalised system capable of protecting personal data while at the same time allowing sensible and meaningful use of the available data. The proposed approach introduces several pseudonymisation procedures based on the design goals described by the European Union Agency for Cybersecurity in their guidelines, in order to dynamically transform entities (e.g., persons) and attributes (e.g., authored papers and research interests) in such a way that any user processing the data are not able to identify individuals. We present a case study using data from researchers of the Georg Eckert Institute for International Textbook Research (Brunswick, Germany). Building a knowledge graph and exploiting a Neo4j database for data management, we first generate several pseudoN-graphs, being graphs with different rates of pseudonymised persons. Then, we evaluate our approach by leveraging the graph embedding algorithm node2vec to produce recommendations through node relatedness. The recommendations provided by the graphs in different privacy-preserving scenarios are compared with those provided by the fully non-pseudonymised graph, considered as the baseline of our evaluation. The experimental results show that, despite the structural modifications to the knowledge graph structure due to the de-identification processes, applying the approach proposed in this article allows for preserving significant performance values in terms of precision.
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Chen, Wanyu, Pengjie Ren, Fei Cai, Fei Sun, and Maarten De Rijke. "Multi-interest Diversification for End-to-end Sequential Recommendation." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–30. http://dx.doi.org/10.1145/3475768.
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Sequential recommenders capture dynamic aspects of users’ interests by modeling sequential behavior. Previous studies on sequential recommendations mostly aim to identify users’ main recent interests to optimize the recommendation accuracy; they often neglect the fact that users display multiple interests over extended periods of time, which could be used to improve the diversity of lists of recommended items. Existing work related to diversified recommendation typically assumes that users’ preferences are static and depend on post-processing the candidate list of recommended items. However, those conditions are not suitable when applied to sequential recommendations. We tackle sequential recommendation as a list generation process and propose a unified approach to take accuracy as well as diversity into consideration, called multi-interest, diversified, sequential recommendation . Particularly, an implicit interest mining module is first used to mine users’ multiple interests, which are reflected in users’ sequential behavior. Then an interest-aware, diversity promoting decoder is designed to produce recommendations that cover those interests. For training, we introduce an interest-aware, diversity promoting loss function that can supervise the model to learn to recommend accurate as well as diversified items. We conduct comprehensive experiments on four public datasets and the results show that our proposal outperforms state-of-the-art methods regarding diversity while producing comparable or better accuracy for sequential recommendation.
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Swain, Sujata, and Rajdeep Niyogi. "SmartMedicist: a context-aware system for recommending an alternative medicine." International Journal of Pervasive Computing and Communications 14, no. 2 (June 4, 2018): 147–64. http://dx.doi.org/10.1108/ijpcc-d-18-00021.
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PurposeThis study aims to discuss a context-aware system, SmartMedicist, which can recommend an alternative medicine from a set of available medicines present at a patient’s home for an unavailable medicine. The system is applied to the chronic disease patients only. The system requires only a smartphone, and provides a reminder to the patient to take medicine at appropriate times and to procure medicines from drug store. The system discusses the output method for the physically challenged patient. Although there are existing systems that can remind a patient for taking medicines, the authors are not aware of any such system that has the capability to recommend an alternative medicine for the prescribed medicine.Design/methodology/approachThe study developed a pharmacology knowledge base that consists of a representation of a set of diseases, according to family, type and medicines, in a k-ary tree. An alternative medicine is recommended based on the set of available medicines and knowledge base.FindingsWe considered four diseases: Hypertension, Gastritis, Alzheimer’s disease, and Parkinson; and performed several experiments for each disease for the different number of available medicines. The execution time to find an alternative medicine (if any) in each case is around four seconds.Originality/valueThe proposed system is cost effective and affordable for most families in India. Although the proposed system is not a substitute of a doctor, this system will enhance the safety golden period for a patient to consult a doctor in the emergency exhaustion of the prescribed medicines.
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Attiogbé, Christian, Flavio Ferrarotti, and Sofian Maabout. "Advances and Challenges for Model and Data Engineering." JUCS - Journal of Universal Computer Science 27, no. 7 (July 28, 2021): 646–49. http://dx.doi.org/10.3897/jucs.70972.
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Following the stimulating discussions in the workshops held during the 9th International Conference on Model and Data Engineering (MEDI 2019), we proposed to edit a special issue compiling the fruitful research resulting from those discussions. This special issue on current research in model and data engineering of the Journal of Universal Computer Science is the outcome of that proposal. As such, it contains thoroughly revised and significantly extended versions of key papers discussed at MEDI 2019 workshops. The main objective of MEDI is to provide a forum for the dissemination of research accomplishments and to promote the interaction and collaboration between the models and data research communities. MEDI provides an international platform for the pre- sentation of research on models and data theory, development of advanced technologies related to models and data and their advanced applications. This international scientific event, initiated by researchers from Euro-Mediterranean countries in 2011, aims also at promoting the creation of north-south scientific networks, projects and faculty/student exchanges. The following seven accepted papers nicely reflect the wide range of topics covered by MEDI conferences. In their paper “Enhancing GDPR Compliance Through Data Sensitivity and Data Hiding Tools”, Xabier Larrucea, Micha Moffie and Dan Mor consider the problem of fulfilling the rules set by the General Data Protection Regulation (GDPR) of the EU within the framework of the reference architectural model industry 4.0 for the healthcare sector. This is challenging due to the highly sensitive data managed by this sector and the need to share this data between different national healthcare providers within the EU. The authors propose and implement a series of valuable tools to enhance security and privacy in this context as well as compliance with the GDPR. They also illustrate through a case study the use of the proposed tools for sharing health records and their integration within the reference framework. In their paper “BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks”, Lamia Berkani, Lylia Betit and Louiza Belarif present a new approach to improve accuracy and coverage of clustering based recommendations systems for social networks. The approach is based on improving the results of multiview clustering by combining it with a bees swarm optimization algorithm. Through extensive experimentation with two real-world datasets, they are able to demonstrate the effectiveness of the proposed approach to significantly improve accuracy, outperforming others clustering-based approaches. In their paper “A Formal Model for Configurable Business Process with Optimal Cloud Resource Allocation”, Abderrahim Ait Wakrime, Souha Boubaker, Slim Kallel, Emna Guermazi and Walid Gaaloul propose a formal approach to analyse and verify con- figurable business process models as well as to optimize the cost of their implementation in the Cloud. The mechanism consists on transforming the problem into an equivalent Boolean satisfiability problem (SAT) which is then feed to a solver. This transformation is done by means of translation rules from configurable business processes to SAT. This model formalizes the different configurable process behaviors including control-flow and cloud resource allocations, enabling the derivation of correct configuration variants. Weighted partial SAT formulae are integrated in the model in order to optimize the global cloud resource allocation cost. In their paper “Towards a Semantic Graph-based Recommender System: A Case Study of Cultural Heritage”, Sara Qassimi and El Hassan Abdelwahed present a semantic graph-based recommender system of cultural heritage places. Their approach consists on first constructing an emergent description that semantically augments the information about the places of interest and then model through graphs the semantic relationships between similar cultural heritage places and their associated tags. Note that the unsuper- vised nature of folksonomy’s tags semantically weakens the description of resources, which in turn hinders their indexing and decreases the quality of their classification and clustering. The semantic augmentation produced by the proposed method in the case study of cultural heritage places in Marrakesh city shows to be an effective tool to fight information overload and to produce better recommendations in this context. As such, the paper presents a valuable contribution that can be used to improve the quality of recommender systems in general. In their paper “Assembling the Web of Things and Microservices for the Management of Cyber-Physical Systems”, Manel Mena, Javier Criado, Luis Iribarne and Antonio Corral face the challenge of facilitating communication between the diverse devices and protocols used by Cyber-Physical Systems (CPS) and the Internet of Things (IoT). They propose an approach based on the concept of digital dice (an abstraction of various objects). The digital dice builds on the web of things standard. It is based on microservices and capable of handling the interaction and virtualization of IoT devices. This work introduces a technique to build, transform and compose digital dices from descriptions of “things”. A full transformation flow is presented and a case study is used to illustrate its implementation. The proposal is shown to be effective and flexible, improving the state of the art. In their paper “Model-Driven Engineering for End-Users in the Loop in Smart Ambient Systems”, Sylvie Trouilhet, Jean-Paul Arcangeli, Jean-Michel Bruel and Maroun Koussaifi present a Model-Driven Engineering (MDE) approach to involve the user in the process of constructing at run time component based applications, adapted to a situation and user needs, in the context of ambient systems. The proposed solution relies on several domain-specific languages and a transformation process, based on established MDE tools (Gemoc Studio, Eclipse Modeling Framework, EcoreTools, Sirius and Acceleo). In this context, the authors describe an innovative way of reinforcing the place of the user in the engineering loop. The authors propose an editor that allows the end user to be aware of the emerging applications resulting of this process, to understand their function and use, and to modify them if desired. From these actions, feedback data are extracted to improve the process. In their paper “An Approach for Testing False Data Injection Attack on Data Depen- dent Industrial Devices”, Mathieu Briland and Fabrice Bouquet present a domain specific language (DSL) for generating test data for IoT devices/environments. The DSL is proposed for testing and simulating false data injection attacks (FDIA). First, the paper outlines a generic approach for FDIA and presents a list of possible sensor types and a categorization schema for data obtained from sensors. Then, the application of the DSL is illustrated using two examples; a simple one altering the data obtained from a temperature sensor and a more complex one concurrently altering the data obtained from three particle sensors. The authors show that their approach works well in the case study of the Flowbird parking meter system and discuss how it can be adapted to different application domains. We are grateful to all authors of journal articles in this issue, who contributed to a fine collection of research in model and data engineering. We would like to express our greatest thanks to all reviewers, who put in a lot of time reading the articles and making substantial suggestions for improvement, which at the end led to the high quality. We also would like to thank J.UCS evaluation committee for the opportunity to publish this collection of research articles as a special issue of the Journal of Universal Computer Science and in particular to the publishing managers Dana Kaiser and Johanna Zeisberg for their timeless assistance during the whole process. Last but not least, we would like to acknowledge to our host institutions, the University of Nantes and the Software Competence Center Hagenberg (SCCH) for their support and sponsoring of this special issue. In particular, Prof. Yamine Ait-Ameur and its host institute IRIT/INP-ENSEEIHT have significantly collaborated with this special issue in the framework of the COMET scientific partnership agreement with SCCH, and have also supported the MEDI confer- ence from which it originated. Christian Attiogbé, Flavio Ferrarotti and Sofian Maabout (July, 2021)
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Venkatachalaappaswamy, Mareeswari, Vijayan Ramaraj, and Saranya Ravichandran. "Location-Based Collaborative Filtering for Web Service Recommendation." Recent Patents on Computer Science 12, no. 1 (January 10, 2019): 34–40. http://dx.doi.org/10.2174/2213275911666181025130059.
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Background: In many modern applications, information filtering is now used that exposes users to a collection of data. In such systems, the users are provided with recommended items’ list they might prefer or predict the rate that they might prefer for the items. So that, the users might be select the items that are preferred in that list. Objective: In web service recommendation based on Quality of Service (QoS), predicting QoS value will greatly help people to select the appropriate web service and discover new services. Methods: The effective method or technique for this would be Collaborative Filtering (CF). CF will greatly help in service selection and web service recommendation. It is the more general way of information filtering among the large data sets. In the narrower sense, it is the method of making predictions about a user’s interest by collecting taste information from many users. Results: It is easy to build and also much more effective for recommendations by predicting missing QoS values for the users. It also addresses the scalability problem since the recommendations are based on like-minded users using PCC or in clusters using KNN rather than in large data sources. Conclusion: In this paper, location-aware collaborative filtering is used to recommend the services. The proposed system compares the prediction outcomes and execution time with existing algorithms.
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B D, Deebak, Fadi Al-Turjman, and Leonardo Mostarda. "A Hash-Based RFID Authentication Mechanism for Context-Aware Management in IoT-Based Multimedia Systems." Sensors 19, no. 18 (September 4, 2019): 3821. http://dx.doi.org/10.3390/s19183821.
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With the technological advances in the areas of Machine-To-Machine (M2M) and Device-To-Device (D2D) communication, various smart computing devices now integrate a set of multimedia sensors such as accelerometers, barometers, cameras, fingerprint sensors, gestures, iris scanners, etc., to infer the environmental status. These devices are generally identified using radio-frequency identification (RFID) to transfer the collected data to other local or remote objects over a geographical location. To enable automatic data collection and transition, a valid RFID embedded object is highly recommended. It is used to authorize the devices at various communication phases. In smart application devices, RFID-based authentication is enabled to provide short-range operation. On the other hand, it does not require the communication device to be in line-of-sight to gain server access like bar-code systems. However, in existing authentication schemes, an adversary may capture private user data to create a forgery problem. Also, another issue is the high computation cost. Thus, several studies have addressed the usage of context-aware authentication schemes for multimedia device management systems. The security objective is to determine the user authenticity in order to withhold the eavesdropping and tracing. Lately, RFID has played a significant for the context-aware sensor management systems (CASMS) as it can reduce the complexity of the sensor systems, it can be available in access control, sensor monitoring, real time inventory and security-aware management systems. Lately, this technology has opened up its wings for CASMS, where the challenging issues are tag-anonymity, mutual authentication and untraceability. Thus, this paper proposes a secure hash-based RFID mechanism for CASMS. This proposed protocol is based on the hash operation with the synchronized secret session-key to withstand any attacks, such as desynchronization, replay and man-in-the-middle. Importantly, the security and performance analysis proves that the proposed hash-based protocol achieves better security and performance efficiencies than other related schemes. From the simulation results, it is observed that the proposed scheme is secure, robust and less expensive while achieving better communication metrics such as packet delivery ratio, end-to-end delay and throughput rate.
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Mohd Ekhsan, Hawa, Jiwa Noris Hamid, and Nurul Syakilah Mazlan. "Integrating Primary School Notification System with SMS Technology." Journal of Computing Research and Innovation 3, no. 1 (September 29, 2020): 1–6. http://dx.doi.org/10.24191/jcrinn.v3i1.96.
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This study presents the use of web SMS technology as an alternative method for primary school notification system. By integrating the system with SMS gateway, it will enable the primary school notification system to send information to parents’ mobile phone directly. The traditional methods of notifying parents about the school matters by sending letter and written memo are time consuming and the updated information may not reach the parents. This integration allows information to be disseminated from computer to mobile phone at any time without requiring face to face meeting or the use of other media such as paper-based notice or verbal notice from the students. Furthermore, parents can always be alert and aware of the latest announcement or information no matter where they are. A usability testing was conducted to 20 users to survey the feedback on SMS technology to notify parents. The results from the testing stated that 100% of users give positive feedback about the ease of use and satisfied that the system can replace old systems. Based on the results, it shows that the integration of primary school notification system with SMS technology is highly recommended.
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Majgi, Sumanth Maliikarjuna, and Swathi Kamal S. "Preference of tertiary care centres over peripheral health centres for routine NCD care: a cross-sectional study in hypertensive and diabetic individuals." International Journal Of Community Medicine And Public Health 7, no. 7 (June 26, 2020): 2667. http://dx.doi.org/10.18203/2394-6040.ijcmph20202995.
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Background: Although there is a program for non-communicable diseases (NCD) control and facilities for their management at the peripheral health centres (PHCs), many patients visit tertiary health care centres (THCs), spending 25 -35% of their income for health care on a long term basis for routine NCD care. Objective of the study was to identify the reasons for these patients not visiting the nearest PHC facility and to estimate the money and the time spent by the patients visiting the THC for such unwarranted visits.Methods: This cross-sectional study was conducted on 207 patients with diabetes mellitus and/or hypertension at Medicine OPD at K. R. Hospital, Mysuru, THC and the patients were interviewed with the help of a questionnaire.Results: All 207 (100%) were routine NCD care. 44% of the participants stated that they felt satisfied with services at THC while 5.8% had no specific reason to state for choosing to seek NCD care at THCs. Also, many patients are not aware of the facilities available at the PHC and hence visit THCs, even for routine NCD care. The average overall expense incurred per person per visit to the THC was approximately Rs. 640. The costs incurred on transportation and on drugs were statistically significant. The major contributing component for the total expense incurred was found to be the money spent on the drugs.Conclusions: Strengthening health systems are recommended by improvement in availability and prescription of essential NCD drugs along with creating awareness about various government schemes that offer good financial coverage for the poor households.
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Ma, Jiaqi, Fang Zhou, Zhitong Huang, Christopher L. Melson, Rachel James, and Xiaoxiao Zhang. "Hardware-in-the-Loop Testing of Connected and Automated Vehicle Applications: A Use Case for Queue-Aware Signalized Intersection Approach and Departure." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 22 (September 9, 2018): 36–46. http://dx.doi.org/10.1177/0361198118793001.
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Most existing studies on connected and automated vehicle (CAV) applications apply simulation to evaluate system effectiveness. Model accuracy, limited data for calibration, and simulation assumptions limit the validity of evaluation results. One alternative approach is to use emerging hardware-in-the-loop (HIL) testing methods. HIL test environments enable physical test vehicles to interact with virtual vehicles from traffic simulation models, providing an evaluation environment that can replicate deployment conditions at early stages of CAV technology implementation without incurring excessive costs related to large field tests. In this study, a HIL testing system for vehicle-to-infrastructure (V2I) CAV applications is developed. The involved software and hardware includes a physical CAV controlled in real time, a traffic signal controller, communication devices, and a traffic simulator (VISSIM). Such HIL systems increase validity by considering the physical vehicle’s trajectories—which are constrained by real-world factors such as GPS accuracy, communication delay, and vehicle dynamics—in a simulated traffic environment. The developed HIL system is applied to test a representative early deployment CAV application: queue-aware signalized intersection approach and departure (Q-SIAD). The Q-SIAD algorithm generates recommended speed profiles based on the vehicle’s status, signal phase and timing (SPaT), downstream queue length, and system constraints and parameters (e.g., maximum acceleration and deceleration). The algorithm also considers the status of other vehicles in designing the speed profiles. The experiment successfully demonstrated this functionality with one test CAV driving through one intersection controlled by a fixed-timing traffic signal under various simulated traffic conditions.
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McMullin, Richard Troy, and Christopher J. Lewis. "The unusual lichens and allied fungi of Sandbanks Provincial Park, Ontario." Botany 92, no. 2 (February 2014): 85–92. http://dx.doi.org/10.1139/cjb-2013-0227.
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Sandbanks Provincial Park contains one of the world’s largest freshwater bay mouth sandbar and dune systems. To better understand the lichen biota of this provincially rare ecosystem, we inventoried the species at Sandbanks and its surrounding area. We found 128 species of lichens and allied fungi in 58 genera. Two species are new to Canada, Hyperphyscia syncolla (Tuck. ex Nyl.) Kalb and Minutoexcipula mariana V. Atienza. One additional species is new to Ontario, Physcia biziana (A. Massal.) Zahlbr. Sixteen species are provincially ranked as S1 (critically imperiled), S2 (imperiled), or S3 (vulnerable) by the Natural Heritage Information Centre: Acrocordia cavata (Ach.) R.C. Harris, Anaptychia crinalis (Schaer.) Vězda, Arthrosporum populorum A. Massal., Bacidia rubella (Hoffm.) A. Massal., Cresponea chloroconia (Tuck.) Egea & Torrente, Diploschistes muscorum (Scop.) R. Sant. ssp. muscorum, Flavopunctelia soredica (Nyl.) Hale, Heterodermia obscurata (Nyl.) Trevis., Lecanora carlottiana Lewis & Śliwa, Leptogium tenuissimum (Dicks.) Körb., Phaeophyscia hirsuta (Mereschk.) Essl., Physconia enteroxantha (Nyl.) Poelt, Ramalina pollinaria (Westr.) Ach., Staurothele drummondii (Tuck.) Tuck., Teloschistes chrysophthalmus (L.) Th. Fr., and Trypethelium virens Tuck. ex Michener. Unranked species collected for the second time in Ontario are Arthonia diffusa Nyl., Cladonia norvegica Tønsberg & Holien, and Lecanora juniperina Śliwa. Other provincially rare and unranked species are Caloplaca pollinii (A. Massal.) Jatta and Xanthomendoza weberi (S. Kondr. & Kärnefelt) L. Lindblom. Of these rare and unranked species, the following are reported (published) for the first time in the province: A. diffusa, C. pollinii, and L. juniperina. Some lichens require specific ecological conditions for colonization, and the rare ecosystem at Sandbanks appears to be the reason for the large number of rare species. We recommend a lichen education program for park visitors to make them more aware of these unusually rare and sensitive lichens in the park.
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Hanganu, Bianca, Irina Smaranda Manoilescu, and Beatrice Gabriela Ioan. "Claims of Medical Malpractice in the Age of Information Technology." Studia Universitatis Babeş-Bolyai Bioethica 66, Special Issue (September 9, 2021): 86–87. http://dx.doi.org/10.24193/subbbioethica.2021.spiss.52.
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"Introduction. Medical practice is almost constantly bending to new technologies, and in recent years, the health care system has been increasingly dominated by advances in information technology. Its use offers many advantages, but it also has its own risks. Material and method. The authors conducted a literature review to see to what extent the accessibility and effective use of information technology, i.e. electronic health records (EHR) influence risk of malpractice. Results. The literature refers both to how EHR use can prevent malpractice claims, and how it can increase their number. Thus, EHR can prevent medical errors and associated complaints by: instant access to complete patient information (including laboratory and imaging results); improving communication between medical team members; reducing drug errors (e.g. drug interactions, allergic reactions); prompt request for further investigations. However, the misuse of EHR can create new problems: inadequate training with errors from implementation and accommodation; automatic or unexpected deletion of the recommended medication; the temptation to use the information obtained previously and the circumvention of the stage of obtaining a new medical history or the temptation to copy and paste the information from the previous consultations to the current consultation - which will lead to the perpetuation of errors and omissions from the previous consultations; increased risk of privacy and confidentiality breach. Likewise, certain facilities that these systems allow may be ambivalent, and may both reduce or increase the risk of complaints, depending on how they are used: communication between doctor and patient through messages, including updating prescriptions and reporting symptoms that require prompt evaluation but at the same time, delay in response may dissatisfy the patient. Conclusions. The implementation of EHR brings many advantages, both for the patient and for the medical staff in terms of accessing information, facilitating communication and carrying out treatment plans, but the medical staff must be constantly aware of the risks involved, especially related to their proper use. "
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Alaber, Omar Ali, Apoorva Krishna Chandar, Basma Ali Dahash, Sohi N. Mistry, Stanley Martin Cohen, Amitabh Chak, and Ankit Mangla. "Anticoagulation Increases Bleeding Rates in Portal Vein Thrombosis: A National Database Study." Blood 134, Supplement_1 (November 13, 2019): 5860. http://dx.doi.org/10.1182/blood-2019-131226.
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Introduction: Liver cirrhosis is being increasingly recognized as a hypercoagulable state, mainly due to disproportionate reduction in antithrombotic factors (protein C, S and anti-thrombin III). Portal vein thrombosis (PVT) is a frequent sequala of liver cirrhosis that can lead to numerous complications and potential exclusion from transplant lists, at least until the resolution of the thrombus. The current evidence for anticoagulation (AC) for PVT in liver cirrhosis is limited to small retrospective studies. Major liver societies recommend limited anticoagulation with enoxaparin based on expert opinion (Category C). We sought to examine the incidence of bleeding after AC in cirrhotic patients with PVT. Methods: Data were obtained from a commercial de-identified database (Explorys, IBM, Inc.) that integrates electronic health records from 27 major integrated U.S. healthcare systems from 1999 to July 2019. Cases were defined as adult patients aged >=18 years having a new Systematized Nomenclature of Medicine Clinical Terms (SNOMED) diagnosis of PVT, had been anticoagulated for the first time following PVT and had experienced bleeding for the first time following AC. Controls were adults who had a diagnosis of PVT and did not get AC. We included older anticoagulants (warfarin and enoxaparin) as well as newer anticoagulants (apixaban, fondaparinux and rivaroxaban). We compared the incidence of bleeding (defined as bleeding from any site) in those with PVT who received AC to those with PVT who did not receive AC. In addition, we also compared the incidence of bleeding between older and newer anticoagulants, and also examined whether there were differences in gender, race, and insurance status for those who bled while on AC. Analysis comprised of calculating odds ratios (OR) and confidence intervals (CI) for the OR. Results: A total of 213,810 patients had liver cirrhosis and out of those, 7,570 (3.5%) patients had PVT. Four hundred and ten cases out of 1,430 patients who received AC bled for the first time whereas 980 cases out of 3,880 patients who did not receive AC bled for the first time. Cases on AC had 1.18 times higher odds of bleeding when compared to controls who did not receive AC (CI = 1.04 - 1.36). Newer AC were less likely to increase bleeding when compared to older AC (OR = 0.6, CI = 0.4 - 0.9). Females were significantly more likely to be first time bleeders on AC when compared to male first-time bleeders on AC (Table 1). However, race and insurance status did not seem to affect bleeding rates (Table 1). Conclusion: Anticoagulation for PVT in liver cirrhosis increases bleeding events. Newer AC were significantly less likely to increase bleeding when compared to older AC. Females were more likely to bleed on newer AC than males, but race and insurance status did not affect bleeding rates. Limitations of the study include the retrospective nature of the analysis that relied on diagnosis coding, and smaller numbers in our subgroup analyses which limits generalizability. Clinicians should be aware of the significant risk of bleeding when prescribing AC, particularly older AC to cirrhotic patients with PVT. Disclosures No relevant conflicts of interest to declare.
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Dmitriev, A. V., N. V. Fedinа, T. G. Tkachenko, R. A. Gudkov, V. I. Petrova, and А. L. Zaplatnikov. "Preventive vaccination compliance among medical students and pediatricians during the COVID-19 pandemic." Meditsinskiy sovet = Medical Council, no. 11 (August 12, 2021): 202–9. http://dx.doi.org/10.21518/2079-701x-2021-11-202-209.
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The novel coronavirus disease (COVID-19) pandemic has become a strength test for the national healthcare systems and medical professional communities. The pandemic gave a revealing insight into the provision of resuscitation care, a shortage of personnel and protective equipment, and the lack of effective drugs to treat a novel, poorly studied infection. Objective. To identify the attitude towards immunoprophylaxis in general and against the coronavirus infection (COVID-19) in particular among medical students and pediatricians and to assess the dynamics of compliance to vaccination in these groups. Materials and methods. The survey was conducted among the 5-6-year students of the medical and pediatric faculties of the Ryazan State Medical University (RSMU) of the Ministry of Health of Russia in 2017 and 2021, the groups of students included 250 and 225 people, respectively. The children’s polyclinic pediatrician groups included 45 and 60 people in 2017 and 2021, respectively. The survey was conducted anonymously, face-to-face, and without compulsion. The identical questionnaires asked students 13 questions and pediatricians 10 questions with suggested response options. Results and discussion. The percentage of students who correctly named the number of vaccine preventable diseases in the National Immunisation Schedule has significantly decreased; the number of students who were not aware of the availability of the chickenpox vaccine has increased by 1.9 times. In 2021, the number of students giving priority to domestic vaccines decreased by 2.3 times, and the number of those choosing imported drugs as better and safer ones has increased by 1.5 times. In 2021, the number of senior students who were vaccinated against influenza doubled as compared to 2017. At the same time, the students agreed to recommend their patients to be vaccinated against influenza 1.4 times more often. During the 2021 pediatrician survey, the number of respondents, who preferred imported vaccines as better and safer ones, increased by 4 times, and the number of domestic vaccine advocates decreased by 1.8 times. In addition, the number of pediatricians who did not decide on this issue has increased by 9.8 times. Сonclusion. The results of the survey showed that there is still a lack of compliance to vaccine prophylaxis in certain groups of the professional medical community. The student cohort showed a decrease in knowledge and confidence in the vaccine prophylaxis. Among practitioners, there has been a positive trend towards an increase in the percentage of pediatricians, who considered it expedient to vaccinate children with chronic pathology.
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Mathibe, Motshedisi S., and Johan H. Van Zyl. "The Impact Of Business Support Services To SMMEs In South Africa." International Business & Economics Research Journal (IBER) 10, no. 11 (October 27, 2011): 101. http://dx.doi.org/10.19030/iber.v10i11.6410.
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Small Medium and Micro Enterprises in South Africa were operating in the era of the apartheid regime but were not given enough support and were not a priority in the government of those days. Before democratic transition, the South African government was mainly giving attention to large businesses as well as state-owned enterprises. It was only in the late 1970s and the early 1980s that the South African government realised the importance of the small enterprise sector and its contribution to the countrys economy. The democratic regime of the early 1990s gave SMMEs an opportunity to participate in the South African economy. A White Paper of 1995 introduced the strategy to promote and develop SMMEs in South Africa and to design an SMME policy framework that will focus its attention on supporting and developing SMMEs. The aim was to enable SMMEs to grow as a way of creating a balance in the economy (away from state-owned and large enterprises) As a result, different support mechanisms have been implemented to support and develop SMMEs in the country since the democratic government took over in 1994. This paper evaluates the business support programmes that have been implemented in South Africa since 1994, specifically in the Free State Province for the development and support of SMMEs. In this respect, the paper compares and evaluates the ability of the business support programmes that have been put in place to develop and support SMMEs in order to grow and become sustainable. This is done by means of a brief overview of the international business support services and an assessment of the South African SMME policy environment. The study also conducted five structured interviews with the management representative of the five different business support programmes in the Free State Province. Some of the key findings indicate that not many SMMEs are aware of business support programmes in the Free State and are even less informed as to where to access finances. At the same time, it was found that staff responsible for business support programmes appears to be incompetent, and therefore, deliver poor quality services. From this study it can be concluded that of all the programmes the government-driven initiatives to develop and support SMMEs struggle the most to become operational. The business support programmes focused on markets to some degree, but seldom made it an inherent requirement. This paper recommends that there should be monitoring and evaluation systems available to document the quality of the service delivery to SMMEs in the Province, and to train the staff of the business support programmes to deliver high-quality services to entrepreneurs. The study compared five business support programmes in the Free State with regards to developing and supporting SMMEs in the province. As a result, the value of the findings might well be considered in terms of future provincial planning documents and policy.
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(Louis) Burgyan, Lajos, and Yuji Kakizaki. "Reverse Engineering for the Purpose of Intellectual Property Protection and Accelerated Product Development in SOC and SIP Structures." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2014, DPC (January 1, 2014): 000436–58. http://dx.doi.org/10.4071/2014dpc-ta13.
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Technical analysis of intellectual property (IP) is conducted for the purpose of legal protection and product development. A brief review of the process of IP analysis and associated terminology is provided along with examples illustrating the significant potential for monetary benefits to be derived. The evolution of the reverse engineering (RE) process in the semiconductor industry is briefly reviewed from a historical perspective. It is shown how the objective of RE, while continuing its traditional engagement in IP protection, has shifted away from “second sourcing” activities to become an active participant by providing valuable services to technology and product development. The assertion is made that the negative connotation often associated with “reverse engineering” is no longer justified; and the legitimacy, usefulness, and respectability of that process is reaffirmed. The effects of international diffusion of technology are described. It is shown that being aware of technology content in competing high-tech products is now greater than ever before. The process of RE and the “toolbox” of career IP analysts are described through the analysis example of an advanced SOC and SIP structure. The dual utility of the analyst's toolbox and skill set is examined as it is being applied a) to the discovery process aimed at intellectual property protection and b) as a means to accelerate product development. Special attention is given to technical IP analysis conducted in association with new product research and development. Practical examples involving the analysis of advanced 3D structures are provided from the field of 3D integrated product development in order to demonstrate how technical IP analysis can a) help avoid costly mistakes, b) capture design wins, and c) accelerate new product development. The synergistic relationship between IP analysis applied to IP protection and product development is explored; and a coordinated and comprehensive approach to technical IP analysis is recommended whenever practical. A high-tech company will realize maximum benefits from a technical analyst's work if IP analysis of competing products is performed for the purpose of product development with the analyst remaining mindful and attentive of the need to protect corporate patent portfolio. Conversely, knowledge gained from technical analysis aimed at protecting the company's patents can be quite useful to the development engineer. Regardless of whether or not the analyst is an employee of the company or a hired sub-contractor, proper description of the task is crucial from the outset. The analyst should be encouraged to take a dual track approach with primary focus directed towards the main intent (IP protection or engineering analysis of a competing product or technology) without ignoring the secondary purpose. At the end of a project, an assessment should be made as to what part of the acquired knowledge is relevant to the engineering community and what portion of the report needs to be directed to the IP department. Technical IP analysis conducted with this dual purpose in mind is a cost-effective way to maximize return on investment (ROI) in RE. It can also be a powerful tool to reduce the cost of new product development while improving time to market. A new area of technical IP analysis, the extraction of parasitic R, L, C elements from SOC and SIP structures, is explored in detail. This field is believed to be of great importance in 3D integration due to the loss or breakup of ground and power delivery planes as a result of increased reliance on vertical interconnections such as interposers and TSVs. These structures introduce troublesome interconnect inductances, resistances, and capacitances. Both power distribution networks (PDN) and high-speed signal paths are affected by interconnect parasitic elements in component modules such as deep sub-micron 22nm ARM processors, multi-stack memories, and multilayer PCBs of high speed communication devices and systems. It is essential for circuit designers, package designers, and system designers to be aware of these risks as early in the design phase as possible. Practical examples are given how an entire PDN of a larger system including complex 2.5D and 3D packages, substrates, and PCB can be reconstructed from the power source down to individual components, including high-speed data paths. Such reconstruction is done using two-dimensional layer images and via structures. The reconstructed file can be 2D or 3D representation. Depending on the objective, the data residing in those files is then imported into state-of-the art circuit simulation tools familiar to the circuit or package designer. At that point, the circuit, package, or system designer can analyze the entire system and extract all parasitic interconnect elements. The circuit designer can then incorporate all those interconnect and passive component parasitic R, L, C, and M elements or their S-parameter representation into a top-level circuit simulation of an integrated circuit and obtain an accurate circuit performance that is truly representative of the final hardware. In summary, the need for precise modeling of the PDN section and certain high-speed data paths of SOC and SIP structures is reaffirmed, and a case is made for making this sometimes labor intensive process available as part of the technical analysis process. The synergy between reverse engineering conducted for the purpose of IP protection and product development is further emphasized. It is concluded that technical IP analysis, competitor product (hardware) analysis, and product development are activities complementary to one another. These activities, if executed thoughtfully, consistently, and systematically, can not only protect IP, increase intellectual asset value, but can also accelerate product development, guide and fuel innovation, and help in setting the direction of research and development.
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Lin, Zuoquan, and Hanxuan Chen. "Recommendation over time: a probabilistic model of time-aware recommender systems." Science China Information Sciences 62, no. 11 (October 9, 2019). http://dx.doi.org/10.1007/s11432-018-9915-8.
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Zeng, Daniel, Yong Liu, Ping Yan, and Yanwu Yang. "Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers." INFORMS Journal on Computing, February 25, 2021. http://dx.doi.org/10.1287/ijoc.2020.1020.
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Providing real-time product recommendations based on consumer profiles and purchase history is a successful marketing strategy in online retailing. However, brick-and-mortar (BAM) retailers have yet to utilize this important promotional strategy because it is difficult to predict consumer preferences as they travel in a physical space but remain anonymous and unidentifiable until checkout. In this paper, we develop such a recommender approach by leveraging the consumer shopping path information generated by radio frequency identification technologies. The system relies on spatial-temporal pattern discovery that measures the similarity between paths and recommends products based on measured similarity. We use a real-world retail data set to demonstrate the feasibility of this real-time recommender system and show that our approach outperforms benchmark methods in key recommendation metrics. Conceptually, this research provides generalizable insights on the correlation between spatial movement and consumer preference. It makes a strong case that the emerging location and path data and the spatial-temporal pattern discovery methods can be effectively utilized for implementable marketing strategies. Managerially, it provides one of the first real-time recommender systems for BAM retailers. Our approach can potentially become the core of the next-generation intelligent shopping environment in which the stores customize marketing efforts to provide real-time, location-aware recommendations.
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Arabi, Hossein, Vimala Balakrishnan, and Nor Liyana Mohd Shuib. "A Context-Aware Personalized Hybrid Book Recommender System." Journal of Web Engineering, July 17, 2020. http://dx.doi.org/10.13052/jwe1540-9589.19343.
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Contextual information such as emotion, location and time can effectively improve product or service recommendations, however, studies incorporating them are lacking. This paper presents a context-aware recommender system, personalized based on several user characteristics and product features. The recommender system which was customized to recommend books, was aptly named as a Context-Aware Personalized Hybrid Book Recommender System, which utilized users’ personality traits, demographic details, location, review sentiments and purchase reasons to generate personalized recommendations. Users’ personality traits were determined using the Ten Item Personality Inventory. The results show an improved recommendation accuracy compared to the existing algorithms, and thus indicating that the integration of several filtering techniques along with specific contextual information greatly improves recommendations.
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Kasting, Monica L., Julie Rathwell, Kaitlyn M. Gabhart, Jennifer Garcia, Richard G. Roetzheim, Olveen Carrasquillo, Anna R. Giuliano, and Susan T. Vadaparampil. "There’s just not enough time: a mixed methods pilot study of hepatitis C virus screening among baby boomers in primary care." BMC Family Practice 21, no. 1 (December 2020). http://dx.doi.org/10.1186/s12875-020-01327-2.
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Abstract Background Liver cancer rates are rising and hepatitis C virus (HCV) is the primary cause. The CDC recommends a one-time HCV screening for all persons born 1945–1965 (baby boomers). However, 14% of baby boomers have been screened. Few studies have examined primary care providers’ (PCP) perspectives on barriers to HCV screening. This study examines current HCV screening practices, knowledge, barriers, and facilitators to HCV screening recommendation for baby boomers among PCPs. Methods We conducted a mixed methods pilot study of PCPs. Quantitative: We surveyed PCPs from 3 large academic health systems assessing screening practices, knowledge (range:0–9), self-efficacy to identify and treat HCV (range:0–32), and barriers (range:0–10). Qualitative: We conducted interviews assessing patient, provider, and clinic-level barriers to HCV screening for baby boomers in primary care. Interviews were audio recorded, transcribed, and analyzed with content analysis. Results The study sample consisted of 31 PCPs (22 survey participants and nine interview participants). All PCPs were aware of the birth cohort screening recommendation and survey participants reported high HCV testing recommendation, but qualitative interviews indicated other priorities may supersede recommending HCV testing. Provider knowledge of viral transmission was high, but lower for infection prevalence. While survey participants reported very few barriers to HCV screening in primary care, interview participants provided a more nuanced description of barriers such as lack of time. Conclusions There is a need for provider education on both HCV treatment as well as how to effectively recommend HCV screening for their patients. As HCV screening guidelines continue to expand to a larger segment of the primary care population, it is important to understand ways to improve HCV screening in primary care.
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Wamsley, Christine E., John Hoopman, and Jeffrey M. Kenkel. "The Role of the Laser Safety Officer and Laser Safety Programs in Clinical Practice." Aesthetic Surgery Journal, February 5, 2021. http://dx.doi.org/10.1093/asj/sjaa239.
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Abstract Recent advancements in laser technology have led to its expanded utilization in smaller clinical settings and medical spas, particularly for facial rejuvenation and the treatment of other aesthetic concerns. Despite the increasing popularity of this technology, discussion of laser safety programs has remained limited, mostly to operating rooms at larger clinical institutions. Although smaller facilities do not operate at the same capacity as a large hospital or medical center, the requirements for utilizing a laser are no less stringent. Employers must comply with local and federal regulations, the Occupational Safety and Health Administration (OSHA) General Duty Clause, American National Standards Institute (ANSI) standards, and professional recommended practices applicable to their business. Although the laser safety officer (LSO) is often a full-time position within larger facilities, smaller clinical settings and medical spas may be limited in staff number. It is important, therefore, that clinical practices establish laser policies and procedures with consideration of their individual needs and capabilities. In this paper, we will define a laser safety program, highlight basic requirements needed to establish this program, and outline the specific responsibilities of the LSO. To ensure that safe laser practices are being conducted at the healthcare facility, it is imperative that small business owners are aware of these regulations and standards in place for the operation of laser systems.
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Zhang, Wenyan, Ling Xu, Meng Yan, Ziliang Wang, and Chunlei Fu. "A Probability Distribution and Location-aware ResNet Approach for QoS Prediction." Journal of Web Engineering, July 8, 2021. http://dx.doi.org/10.13052/jwe1540-9589.20415.
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In recent years, the number of online services has grown rapidly, invoking the required services through the cloud platform has become the primary trend. How to help users choose and recommend high-quality services among huge amounts of unused services has become a hot issue in research. Among the existing QoS prediction methods, the collaborative filtering (CF) method can only learn low-dimensional linear characteristics, and its effect is limited by sparse data. Although existing deep learning methods could capture high-dimensional nonlinear features better, most of them only use the single feature of identity, and the problem of network deepening gradient disappearance is serious, so the effect of QoS prediction is unsatisfactory. To address these problems, we propose an advanced probability distribution and location-aware ResNet approach for QoS Prediction (PLRes). This approach considers the historical invocations probability distribution and location characteristics of users and services, and first uses the ResNet in QoS prediction to reuses the features, which alleviates the problems of gradient disappearance and model degradation. A series of experiments are conducted on a real-world web service dataset WS-DREAM. At the density of 5%–30%, the experimental results on both QoS attribute response time and throughput indicate that PLRes performs better than the existing five state-of-the-art QoS prediction approaches.
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R. Kweka, John, and Rashid A. Chikoyo. "Electronic Revenue Collection System for Improving Local Government Authorities Revenue in Tanzania: A Case of Moshi Municipal Council, Kilimanjaro Region." Journal of Media & Management, June 30, 2021, 1–6. http://dx.doi.org/10.47363/jmm/2021(3)125.
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The aim of the study was to evaluate whether electronic revenue collection systems improves Municipal revenues. The study specifically aimed to determine the effective sources of revenues; to compare the revenue collection before and after the introduction of electronic revenues collection system; and to analyze the challenges encountered with the use of electronic revenue collection system. A descriptive research design was adopted in conducting the study. The study sample was 79 respondents whereby simple random sampling was employed. Data were collected using questionnaires to provide quantitative data. Data was analyzed using descriptive statistics and Pearson correlation coefficient. The findings revealed that the major sources of revenues to Municipal Council are Guest house/hotel levy, Land rent fees, Markets fees, Intoxicating liquor licenses, Abattoirs charges, Revenue from agricultural and forestry products and Meat inspection charges. The study finding revealed that electronic revenue collection system improves Municipal revenue collection in the financial year 2015/2016 compare to that of 2014/2015 revenues. The major challenges encountered the e-payment is access to internet and lack of civic education to revenues payers on the use of online payments. The study concludes that online revenue collection system gives accurate revenue computations to a very great extent and the revenue payers is satisfied with the electronic revenues collection service which assured no queuew is experienced on revenue due dates. The study recommends to the Municipal officials that it‘s high time for Moshi Municipal Council to put much efforts to encourage revenue payers to adopt electronic revenue collection system as the findings shows it has reduced queuing and increased revenue. Also civic education should be provided to other revenue payers who did not registered in order to be aware and help the Municipal to improve their revenues collection status.
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R. Kweka, John, and Rashid A. Chikoyo. "Electronic Revenue Collection System for Improving Local Government Authorities Revenue in Tanzania: A Case of Moshi Municipal Council, Kilimanjaro Region." Journal of Media & Management, June 30, 2021, 1–6. http://dx.doi.org/10.47363/jmm/2020(3)126.
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The aim of the study was to evaluate whether electronic revenue collection systems improves Municipal revenues. The study specifically aimed to determine the effective sources of revenues; to compare the revenue collection before and after the introduction of electronic revenues collection system; and to analyze the challenges encountered with the use of electronic revenue collection system. A descriptive research design was adopted in conducting the study. The study sample was 79 respondents whereby simple random sampling was employed. Data were collected using questionnaires to provide quantitative data. Data was analyzed using descriptive statistics and Pearson correlation coefficient. The findings revealed that the major sources of revenues to Municipal Council are Guest house/hotel levy, Land rent fees, Markets fees, Intoxicating liquor licenses, Abattoirs charges, Revenue from agricultural and forestry products and Meat inspection charges. The study finding revealed that electronic revenue collection system improves Municipal revenue collection in the financial year 2015/2016 compare to that of 2014/2015 revenues. The major challenges encountered the e-payment is access to internet and lack of civic education to revenues payers on the use of online payments. The study concludes that online revenue collection system gives accurate revenue computations to a very great extent and the revenue payers is satisfied with the electronic revenues collection service which assured no queuew is experienced on revenue due dates. The study recommends to the Municipal officials that it‘s high time for Moshi Municipal Council to put much efforts to encourage revenue payers to adopt electronic revenue collection system as the findings shows it has reduced queuing and increased revenue. Also civic education should be provided to other revenue payers who did not registered in order to be aware and help the Municipal to improve their revenues collection status.
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Tan, Yvonne, Ammar Mohamedalhadi, and Fiona Wood. "EP07 Eosinophilic granulomatosis with polyangiitis: diagnostic and therapeutic challenges during COVID-19 pandemic." Rheumatology Advances in Practice 4, Supplement_1 (October 1, 2020). http://dx.doi.org/10.1093/rap/rkaa052.006.
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Abstract Case report - Introduction COVID-19 pandemic affected medical practise significantly and caused difficulties in accessing necessary investigations at the appropriate time. As of March 2020, NHS England issued measures to redirect staffs and resources in preparation for the rising cases of coronavirus. As a result of this, non-urgent tests/treatments were put on hold. We present a new case of EGPA admitted to our district general hospital during the COVID-19 pandemic to highlight the challenges faced. The diagnosis was reached based on clinical judgment in the absence of some confirmatory tests as well as the decision of starting immunosuppressant treatment during the pandemic. Case report - Case description A 41-years-old lady with a background of well-controlled asthma, presented with five days history of paraesthesia and swelling in both legs. She also reported mild pleuritic chest pain, which radiated to her left arm. Physical examination revealed left foot drop. She had reduced sensation on the L5-S1 dermatomal distribution with absent ankle reflex and reduced knee reflex of her left leg. Her left calf was swollen and tender. The rest of her examination was unremarkable. Baseline blood revealed raised WCC of 19.3 with significant eosinophilia (10). CRP and ESR were 135 mg/L and 48mm/hr, respectively. Electrocardiogram showed new T-wave inversion in the anterolateral leads with significantly raised troponin levels. There was ground glass appearance in both lungs, keeping with suspected COVID-19 and no evidence of pulmonary embolus was found on CTPA. MRI spine confirmed no evidence of cauda equina compression. Deep vein thrombosis was also excluded with US doppler. She was treated as myocarditis and pneumonia secondary to probable COVID-19 infection. Echocardiogram revealed severe LVSD (EF < 35%) with no LV hypertrophy. Three days later, she became acutely breathless and required high flow oxygen. New bilateral basal crackles were found on auscultation. Her antibiotic regimes were escalated to intravenous infusion. A revised CT report suggested the findings may correlate with eosinophilic pneumonia or EGPA. MRI of lower legs proved muscular oedema in bilaterally, which was suggestive of myositis with fasciitis. There was no significant change on the thigh musculature. CK level was slightly elevated (403 IU/L). Urinalysis was positive for blood (3+). Given the strong clinical suspicion of EPGA, a decision to start high dose steroid therapy was made, despite the pending immunology results. After the third dose of the methylprednisolone, pulsed cyclophosphamide was started along with high dose oral prednisolone. The patient was discharged home following significant clinical improvement. Case report - Discussion This patient has fulfilled 4 out of 6 criteria of ACR 1990 classification for EGPA, which are eosinophilia, bronchial asthma, mononeuritis multiplex and pulmonary infiltrates on radiological images. However, in the context of current pandemic, these changes on chest CT findings could also be suggestive of COVID-19 pneumonitis. At present, there is no reliable test for COVID-19. Even though RT-PCR testing has been the gold standard for diagnosing suspected cases, the clinical sensitivity and specificity of these tests are variable. A negative test may not rule out infection. In our case, the patient was tested twice at separate times to rule out the possibility of COVID-19 infection. During the pandemic, there is extremely limited access to some confirmatory tests. We were not able to perform nerve conduction studies on our patient as the service was suspended, instead, we sought neurologist’s review to confirm the mononeuritis multiplex. We also sought advice from haematologist to rule out the possibility of hyper-eosinophilic syndrome as bone marrow biopsy was unavailable. The screen for atypical pneumonia, aspergillosis, viruses, and tuberculosis were negative. By excluding the alternative diagnoses related to eosinophilia, we concluded that this was likely to be a case of first presentation EGPA. Our next obstacle was introducing remission–induction regimens during COVID-19 pandemic. BSR does not recommend starting new treatment due to the increased risk of infection. We had to weigh out the benefits and risks of initiating immunosuppression. Our patient was made aware of the potential risks involved which include severe infection with COVID-19. She was also shifted to a side room with strict infection control precautions and PCP prophylaxis prescribed before starting pulsed methylprednisolone and cyclophosphamide. Fortunately, her neurological symptoms resolved after three days of steroid therapy. Eosinophils count dropped within 1 day to zero, after the first dose of IV methylprednisolone. Case report - Key learning points Despite the rising cases of COVID-19 infection, it is essential to keep an open mind and consider alternative diagnosis if a patient did not respond to conventional treatment. As EGPA and COVID-19 pneumonia share similar clinical and radiological presentation, clinical judgement is essential when making the diagnosis as the treatments for both conditions are vastly different. When EGPA is suspected, a multidisciplinary team should be involved in the evaluation of different organ involvements as well as ruling out other causes of eosinophilia. The role of specialists’ inputs is extremely important in reaching the diagnosis, especially with limited access to the usual confirmatory tests due to reduced services during the pandemic. In addition, when there is an increased risk of infection such as during the COVID-19 pandemic, it is essential to weigh up the benefits and risks of commencing immunosuppressant treatment carefully. Patients need to be involved in the decision-making process as well as take precautions to minimise the risk of infection. The decision to start remission induction regimes should not be delayed if there is a presence of life or organ threatening disease manifestations in EGPA patients. Our patient has had a life-threatening disease because of multi-organ involvements (cardiac, pulmonary, and neurological systems).