Journal articles on the topic 'Basket Ranking Algorithm'

Recent e commerce website does not assure shopping within a stipulated budget. Many a times when our basket cart contains multiple products our budget gets collapsed. This somewhere affects customer shopping experience. Hence in this research we propose a methodology where a different combination of products, that customer wants to purchase is made. Then these combinations or these baskets are further evaluated based on customer's given budget. While suggesting readymade baskets to customer, we make sure that quality of product is not hampered. During these transactions it is verified that both seller and customer point of view is taken into consideration. Hence due to our proposed system seller's profit margin too is considered while suggesting the basket to customer. This system enables easy shopping within less time. Many a times it happens that buyers are not satisfied because selection of products go beyond the budget and buyer is not able to spend perfect price on product on which buyer should look forward to spend. Hence our system helps in managing purchases according to preferences. Todays e commerce website does not provide the shopping within buyers or customers stipulated budget. In our work we produce a win win condition for both, seller and buyer customer. Sellers profit margin is also considered while pairing products and product ratings are consider to ensure product quality. Hence our work aims to benefit both seller and buyer without compromising product quality. Deep Thakkar | Shalaka Sonar | Niddhi Bhangdia | Digvijay Sonawane "Budget Management Shopping Strategy for an E-Commerce Website" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: https://www.ijtsrd.com/papers/ijtsrd11308.pdf

Wireless sensor network are often installed in unattended environment for monitoring and sending information to base station. If nodes in the network are compromised then the security of the network degrades quickly. There have been many approaches researched to tackle this issue. This paper introduces three node categorisation algorithms named Global Ranking Algorithm, Stepwise Ranking Algorithm and Hybrid Ranking Algorithm, which can identify misbehaving forwarders that drop or modify packets. In network each packet is padded and encrypted so as to hide the source of the packet. The packet mark, a small number of extra bits, is added to each packet such that the sink node can recover the source of the packet and then figure out the dropping ratio associated with every sensor node. Finally, the node categorization algorithms can identify nodes that are packet droppers for sure, suspiciously packet droppers, not packet droppers based on dropping ratio. This paper analyses and compares the three ranking algorithms on the basis of detection rate and false positive probability. Extensive analysis is conducted by running each algorithm in java eclipse.

Searching on the web can be considered as a process of user enters the query and search system returns a set of most relevant pages in response to user’s query. But results returned are not mostly relevant to user’s query and ranking of the pages are not efficient according to user requirement. In order to improve the precision of ranking of the web pages, after analyzing the different algorithms like Page Rank, Weighted Page Rank, Page Rank based on VOL, Weighted Page Rank algorithm based on VOL. In this paper, we are proposing enhancement by including “User Activities Time” and “Page Reading Time” in Weighted Page Rank based on VOL algorithm (WPRVOL). Page Reading Time (PRT) is the total time page remains focused in browser tab. User Activities Time (UAT) is the total time user does activities like Key Press, Mouse Click, Touch the Screen and Scrolling the page etc. WPRVOL Algorithm signifies the importance of a web page for a user and thus helps in increasing the accuracy of web page ranking. Our proposed Extended Weighted Page Rank based on Visit of links (EWPRvolT) algorithm is a page ranking mechanism, which considers user browsing behavior / user using trends into account. Other algorithms discussed in literature are either link or content oriented. WPRVOL has already being devised for search engines, which works very much similar to weighted page rank algorithm and takes number of visits of inbound links of web pages into account. Also we are making one more improvement in our algorithm (EWPRvolT) by storing the no of visits on links, PRT and UAT information directly on Search Engine database server instead of storing it on client’s web server in the form of logs which was suggested in earlier literature. The proposed improvement in algorithm finds more relevant information according to user’s query. So, this concept is very useful to display most important and useful pages on the top of the result list on the basis of user usage trends, which reduce the search space to a large scale for user.

Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. The parameters of three types of photovoltaic modules (two polycrystalline and one monocrystalline) are estimated using the proposed algorithm. The estimated parameters show good agreement with the measured data for three modules at different irradiance levels. Performance of the developed opposition-based tunicate swarm algorithm is compared with other predefined algorithms in terms of robustness, statistical, and convergence analysis. The root mean square error values are minimum (6.83×10-4, 2.06×10-4, and 4.48×10-6) compared to the tunicate swarm algorithm and other predefined algorithms. Proposed algorithm decreases the function cost by 30.11%, 97.65%, and 99.80% for the SS2018 module, SolarexMSX-60 module, and Leibold solar module, respectively, as compared to the basic tunicate swarm algorithm. The statistical results and convergence speed depicts the outstanding performance of the anticipated approach. Furthermore, the Friedman ranking tests confirm the competence and reliability of the developed approach.

Data mining is one of the developing sciences and it is very suitable for analyzing database. Data mining is used in many sciences, such as business intelligence, shopping basket analysis, and medicine. The main algorithms of data mining are 4 categories that 2 main categories are feature ranking and classification algorithms. In this study, we propose a method for predicting the dividend of market price using data mining technique. A new method has been provided for classifying the data, and then the accuracy of each method has been achieved by implementing the above approach on a database with 371 companies in different industries and obtaining the precision of each model according to the inputs. We used stock data to apply this research, and based on the approach, we predicted the rate of change in the companies' dividends in 2015 according to the data of companies. The results indicated the high accuracy and high speed of the proposed approach.

Social networks have ensured the expanding disproportion between the face of WWW stored traditionally in search engine repositories and the actual ever changing face of Web. Exponential growth of web users and the ease with which they can upload contents on web highlights the need of content controls on material published on the web. As definition of search is changing, socially-enhanced interactive search methodologies are the need of the hour. Ranking is pivotal for efficient web search as the search performance mainly depends upon the ranking results. In this paper new integrated ranking model based on fused rank of web object based on popularity factor earned over only valid interlinks from multiple social forums is proposed. This model identifies relationships between web objects in separate social networks based on the object inheritance graph. Experimental study indicates the effectiveness of proposed Fusion based ranking algorithm in terms of better search results.

Social networks have ensured the expanding disproportion between the face of WWW stored traditionally in search engine repositories and the actual ever changing face of Web. Exponential growth of web users and the ease with which they can upload contents on web highlights the need of content controls on material published on the web. As definition of search is changing, socially-enhanced interactive search methodologies are the need of the hour. Ranking is pivotal for efficient web search as the search performance mainly depends upon the ranking results. In this paper new integrated ranking model based on fused rank of web object based on popularity factor earned over only valid interlinks from multiple social forums is proposed. This model identifies relationships between web objects in separate social networks based on the object inheritance graph. Experimental study indicates the effectiveness of proposed Fusion based ranking algorithm in terms of better search results.

<p>In recent years, with the rise of numerous Internet service industries, recommender systems have been widely used as never before. Users can easily obtain the information, products or services they need from the Internet, and businesses can also increase additional revenue through the recommender system. However, in today’s recommender system, the data scale is very large, and the sparsity of the scoring data seriously affects the quality of the recommendation. Thus, this study intends to propose a recommendation algorithm based on evolutionary algorithm, which combines user characteristic clustering and matrix factorization. In addition, the exponential ranking selection technology is employed for evolutionary algorithm. The experiment result shows that the proposed algorithm can obtain better result in terms of four indicators, mean square error, precision, recall, and F score for two benchmark datasets.</p> <p> </p>

This research aims to compare and analyze the effectiveness of four popular fuzzy multi-criteria decision-making methods (FMCDMMs) for quality of service (QoS)-based web service selection. These methods are fuzzy DEMATEL (FD), fuzzy TOPSIS (FT), fuzzy VIKOR (FV), and fuzzy PROMETHEE (FP), including three ranking versions of FV. We assess the ranking similarities among these methods using Spearman's relationship figure. We describe the algorithms of these six FMCDMs in the methods section. In a case study, we collected primary data from five experts who rated nine QoS factors of nine web services. We used modified online software for analysis. The results showed that S6 ranked first in all FMCDMs, except for FD and FP, where it was ranked 2nd and 8th, respectively. The highest association coefficient (Rs) was found between FT and FV ranking in S techniques (0.983), FV ranking in S and FV ranking in Q (0.883), and FT and FV ranking Q (0.833) when comparing the similarity measure of the FMCDMMs. This analysis helps decision-makers and researchers choose the most suitable methods for integrated FMCDMs studies and real-world problem-solving.

<em>While searching a visit place, users invariably have specific preferences concerning their visits rather than limiting users to restricted question choices like locations, activities, or time periods; proposed system have a tendency regarding contemplate take into account discretionary text descriptions as keywords about personalized needs.</em> <em>A various and representative set of counseled travel routes is required. Previous works have careful on mining and ranking existing routes from arrival information to satisfy the necessity for automatic trip organization; proposed system have a tendency to claim that a lot of options of places of interest (POIs) ought to be extracted.</em> <em>In this paper, proposed framework tend to propose partner conservative keyword-mindful representative travel route system utilizing K-NN Algorithm that utilizes data extraction from clients&#39; verifiable quality records and social collaborations.</em> <em>Explicitly, proposed framework venture work have planned a watchword extraction module to group the POI-related labels, for successful coordinating with inquiry keywords.The proposed framework venture calculation have any structured course remaking recipe to develop course hopefuls that satisfy the necessities. To evaluate the effectiveness and potency of the planned algorithms, proposed system project work had conducted in depth experiments on real location-based social network datasets, and also the experiment results show that proposed system strategies do so demonstrate sensible performance compared to progressive works.</em>

With the increase in internet of things (IoT) applications&#39; range and scale, it is essential to test the applications before deploying them in the real world. Most common approaches utilize simulations and testbeds; however, these methods lack real-time failure scenarios and the capability to scale, respectively. A virtual environment is a suitable approach that overcomes these drawbacks further, IoT applications using cloud computing have evolved to shift some computing and storage capabilities to the edge networks for ensuring adherence to strict latency constraints for real-time applications. This led to the emergence of fog computing which provides lower latency and better security, among other advantages. As for any processing tasks, scheduling becomes a critical concern for matching the tasks with the devices having appropriate resources. This paper analyzes a hybridized fog scheduling algorithm based on a ranking approach considering latency as the main parameter. It builds a software layer for scheduling on top of the validating internet of things at large scales (VIoLET) infrastructure. The results are compared with the round-robin scheduling algorithm, and it is found that the hybridized algorithm provides closer actual latency values to the expected one.

This article explores recent advancements in addressing bias within recommendation systems, focusing on three key approaches: Ranking-based Equal Opportunity (RBEO), post-processing adjustments, and future challenges in implementation. The article examines how these methods effectively reduce demographic disparities while maintaining recommendation quality across various platforms. It investigates the technical architecture of RBEO, the flexibility of post-processing techniques, and the complex balance between fairness and system performance. The article also addresses critical challenges in scaling bias mitigation techniques and managing ethical considerations in algorithmic decision-making, providing insights into future directions for developing more equitable recommendation systems.

&lt;p&gt;Computer-aided diagnosis of retinopathy is a hot research topic in the field of medical image classification, where optical coherence tomography (OCT) is an important basis for the diagnosis of ophthalmic diseases. Traditional approaches to multi-label image classification learn independent classifiers for each category and employ ranking or thresholding on the classification results. These techniques, although working well, fail to explicitly exploit the label dependencies in an image. In this paper, two publicly available retinal OCT image datasets are integrated and screened. Then, an end-to-end deep learning algorithmic framework based on CNN-RNN Unified Neural Networks was proposed to automatically and reliably classify six categories of retinal OCT images. Numerical results suggest that the proposed algorithm works well in terms of accuracy, precision, sensitivity and specificity, approaching or even partially surpassing the performance of clinical experts. It is valuable in promoting computer-aided diagnosis towards practical clinical applications and improving the efficiency of clinical diagnosis of retinal diseases.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

The paper deals with issues of designing and creating knowledge bases in the field of nuclear science and technology. The authors present the results of searching for and testing optimal classification and semantic annotation algorithms applied to the textual network content for the convenience of computer-aided filling and updating of scalable semantic repositories (knowledge bases) in the field of nuclear physics and nuclear power engineering and, in the future, for other subject areas, both in Russian and English. The proposed algorithms will provide a methodological and technological basis for creating problem-oriented knowledge bases as artificial intelligence systems, as well as prerequisites for the development of semantic technologies for acquiring new knowledge on the Internet without direct human participation. Testing of the studied machine learning algorithms is carried out by the cross-validation method using corpora of specialized texts. The novelty of the presented study lies in the application of the Pareto optimality principle for multi-criteria evaluation and ranking of the studied algorithms in the absence of a priori information about the comparative significance of the criteria. The project is implemented in accordance with the Semantic Web standards (RDF, OWL, SPARQL, etc.). There are no technological restrictions for integrating the created knowledge bases with third-party data repositories as well as metasearch, library, reference or information and question-answer systems. The proposed software solutions are based on cloud computing using DBaaS and PaaS service models to ensure the scalability of data warehouses and network services. The created software is in the public domain and can be freely replicated.

The stochastic process is one the most important tools to overcome uncertainties of supply chain problems. Being a lack of studies on constrained reliable facility location problems (RFLP) with multiple capacity levels, this paper develops a bi-objective RFLP with multiple capacity levels in a three echelon supply chain management while there is a constraint on the coverage levels. Moreover, there is a provider-side uncertainty for distribution-centers (DCs). The aim of this paper is to find a near-optimal solution including suitable locations of DCs and plants, the fraction of satisfied customer demands, and the fraction of items sent to DCs to minimize the total cost and to maximize fill rate, simultaneously. As the proposed model belongs to NP-Hard problems, a meta-heuristic algorithm called multi-objective biogeography-based optimization (MOBBO) is employed to find a near-optimal Pareto solution. Since there is no benchmark in the literature to compare provided solutions, a non-dominated ranking genetic algorithm (NRGA) and a multi objective simulated annealing (MOSA) are used to verify the solution obtained by MOBBO while a two-stage stochastic programming (2-SSP) is employed to capture randomness of DCs. This paper uses the adapted concepts of expected value of perfect information (EVPI) and the value of stochastic solution (VSS) in order to validate 2-SSP. Moreover, the parameters of algorithms are tuned by the response surface methodology (RSM) in the design of experiments. Besides, an exact method, called branch-and-bound method via GAMS optimization software, is used to compare lower and upper bounds of Pareto fronts to optimize two single-objective problems separately.

A group recommender system aim&#39;s to provide relevant information to all members of the group. To determine group preferences, the majority of existing studies use aggregation approaches. An aggregation method is a strategy for recommending products to a group by combining the individual preferences of group members. So far, a slew of different types of aggregation algorithms has been discovered. However, they only aggregate one component of the offered ratings (e.g., counts, rankings, high averages), which limits their ability to capture group members&#39; proclivities. This study proposes a novel aggregation method called weighted count that aggregates ratings by providing weights equal to the number of users who provide ratings to an item (location). In addition, the study proposes combining additive utilitarian and weighted count approaches to highlight popular locations on which group members agreed. Experiments on a benchmark check-in dataset demonstrated that the proposed blended technique surpasses the existing methods significantly.

Purpose Query-based summarization approaches might not be able to provide summaries compatible with the user&rsquo;s information need, as they mostly rely on a limited source of information, usually represented as a single query by the user.&lrm; This issue becomes even more challenging when dealing with scientific documents, as they contain more specific subject-related terms, while the user may not be able to express his/her specific information need in a query with limited terms.&lrm; This study aims to propose an interactive multi-document text summarization approach that generates an eligible summary that is more compatible with the user&rsquo;s information need.&lrm; This approach allows the user to interactively specify the composition of a multi-document summary.&lrm; Design/methodology/approach This approach exploits the user&rsquo;s opinion in two stages.&lrm; The initial query is refined by user-selected keywords/keyphrases and complete sentences extracted from the set of retrieved documents.&lrm; It is followed by a novel method for sentence expansion using the genetic algorithm, and ranking the final set of sentences using the maximal marginal relevance method.&lrm; Basically, for implementation, the Web of Science data set in the artificial intelligence (AI) category is considered.&lrm; Findings The proposed approach receives feedback from the user in terms of favorable keywords and sentences.&lrm; The feedback eventually improves the summary as the end.&lrm; To assess the performance of the proposed system, this paper has asked 45 users who were graduate students in the field of AI to fill out a questionnaire.&lrm; The quality of the final summary has been also evaluated from the user&rsquo;s perspective and information redundancy.&lrm; It has been investigated that the proposed approach leads to higher degrees of user satisfaction compared to the ones with no or only one step of the interaction.&lrm; Originality/value The interactive summarization approach goes beyond the initial user&rsquo;s query, while it includes the user&rsquo;s preferred keywords/keyphrases and sentences through a systematic interaction.&lrm; With respect to these interactions, the system gives the user a more clear idea of the information he/she is looking for and consequently adjusting the final result to the ultimate information need.&lrm; Such interaction allows the summarization system to achieve a comprehensive understanding of the user&rsquo;s information needs while expanding context-based knowledge and guiding the user toward his/her information journey.&lrm;

To carry out a comparative analysis of the EU countries’ national innovation systems (NIS), a feature vector has been compiled, covering three modules, namely, science, education, and innovation. The feature vector is a valid multidimensional data set of sixteen official statistics indices and two sub-indices of the Global Innovation Index. The development of a cognitive model for managing the NIS parameters required a preliminary three-stage empirical study to determine its elements. In the first stage, cluster analysis was performed (the k-means, metric – Euclidean distance algorithm was used). As a result, the EU countries were divided into four clusters (following multidimensional scaling estimates). In the second stage, a classification analysis (using decision trees) was carried out, which allowed determining three parameters that distinguish clusters (or classes) optimally. These parameters are recognized as important ones in terms of positioning the countries in the general ranking; that is, they can be considered as a priority for the NIS development and improving the countries’ positions in international comparisons. In the third stage, based on the authors’ approach, the significance (information content) of each key parameter is estimated. As a result, a cognitive model was compiled, taking into account the parameter significance. The model can be used in managing the NIS parameters, seeking to increase the system performance and improve the international position of a specific country. The model can also be used by partner countries, for example, Ukraine, as it demonstrates the landscape of EU innovative development and outlines the directions for priority development of NIS towards the European progress.

<em>One of the important areas of action in Ukraine is the restoration of the sphere of activity of hotels with the reduction of restrictions after COVID-19 in the conditions of the russian federation&#39;s war against Ukraine. Using the experience of hotels in other countries will be useful in restoring the tourism industry in Ukraine. Disadvantages in the operation of hotels arise from the fact that management decisions are sometimes made intuitively, modern innovative management methods, such as SWOT analysis, are not used.</em> <em>The object of the research is the influence of the owner and management on the review of the business strategy in the critical use of the hotel&#39;s capabilities with the help of SWOT analysis. The problem of improving business management was solved at the stage of considering options for improving the hotel&#39;s operations. The results were to show the chance of choosing the optimal business strategy when systematically considering options for management decisions regarding the future of the hotel using SWOT analysis techniques.</em>&nbsp;<em>When applying a SWOT analysis, the weaknesses and strengths of the hotel are modeled; but it requires special statistical knowledge.</em> <em>In the paper, based on the processing of studied of the hospitality industry, examples of SWOT analysis experience for the implementation of business strategy by hotels in countries with a developed tourism market are indicated</em><em>.</em>&nbsp;<em>Consolidated information is formed on the components of a SWOT analysis of a hotel business strategy according to the authors&#39; proposals</em><em>.</em>&nbsp;<em>The process of SWOT analysis in improving the activity of Ukrainian hotels at the stage of planning and implementation of strategic directions is revealed</em><em>.</em>&nbsp;<em>The formation of an information base is outlined with an integrated approach in the process of implementation</em><em>.</em>&nbsp;<em>Organizational measures for implementing the hotel strategy according to SWOT analysis are indicated</em><em>.</em>&nbsp;<em>Conclusions are drawn and directions for further research are shown.</em> <em>The obtained results are the process of making management decisions based on a set of market data, which determines the application of a certain sequence of recognized stages (algorithm). A SWOT analysis, which was prepared by eliminating all the ideas of the participants and ranking them in order of importance, will combine a large problem into a report that is more understandable to individuals and must be put into practice by them.</em>

In this paper, we study the location of emergency centres considering cooperative and backup coverage while natural disasters occur which can result in facility disruption. In this regard, a reliable version of cooperative covering problem is presented considering two types of candidate sites, i.e., reliable and unreliable. To achieve a fortified system against disaster, reliable candidate sites are selected from areas which are far away from the disaster harms. Furthermore, backup coverage is considered to compensate unsatisfied coverage of the demand zones due to facility disruption. The performance of the model is investigated solving numerical examples with different approaches utilising commercial software. The results confirm accurate performance of the model. They also show that both facility failure and backup coverage considerations lead to a more efficient network by incurring some additional cost.

In this manuscript, three contributions are proposed. First contribution is proposing a good evaluation between the fuzzy and neutrosophic approaches using a novel fuzzy-neutrosophic transfer. Second contribution is introducing a general framework for solving the neutrosphic linear programming problems using the advantages of the method of Abdel-Basset et al. and the advantages of Singh et al.&#39;s method. Third contribution is proposing a new neutrosophic exterior point simplex algorithm NEPSA and its fuzzy version FEPSA. NEPSA has two paths to get optimal solutions. One path consists of basic not feasible solutions but the other path is feasible. Finally, the numerical examples and results analysis show that NEPSA more than accurate FEPSA.

Nowadays Social Media focus on users to billions of images, famous e commerce web sites such as Flipkart, Amazon etc. Tag primarily based definitely image are seeking for is an essential method to find photos shared with the aid of manner of clients in social networks. But, a manner to make the top ranked result applicable and with range is difficult. In this paper, we advocate a topic diverse ranking method for tag primarily based photo retrieval with the eye of selling the situation insurance overall performance. First, we bring together a tag graph based totally absolutely at the similarity among every tag. Then network detection approach is accomplished to mine the subject community of every tag. After that, inter community and intra network ranking are added to gather the very last retrieved results. Inside the inter network rating way, an adaptive random stroll model is employed to rank the network primarily based at the multi information of every topic community D. Dhayalan | M. Queen Mary Vidya | B. Gowri Priya &quot;Image Tagging With Social Assistance&quot; Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Active Galaxy , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd14566.pdf Paper URL: http://www.ijtsrd.com/engineering/computer-engineering/14566/image-tagging-with-social-assistance/d-dhayalan

The modern researchers have right to use to large library of procedural literatures in the publish system. These collection are to enhance as new article placed online. It is a user request for researchers to frequently detect their research topics. These methods are very popular due to their relative simplicity, efficiency and the results which are often easy to interpret using the scheme, topics about user preferences can be correctly extract and useful to personalized travel recommendation. The popularity of each POI is measured by how many users have uploaded photos of this POI. We are developing a model, SBPR (Social Bayesian Personalized Ranking), based on the simple remark that users tend to assign higher Ranks in addition to ranking-based paradigms are key in real-world recommendation setting. Combining tags and guitars would be an out of the ordinary and hard task. To less famous places, the data would be even sparser and noisier, which poses an even greater do research challenge. Our effort building the lead preceding do research on recommender systems, look at belongings of hint list as entities in their own wrong to a certain extent than in particular focus on the exactness of human being recommendations initiate the intra-list parallel metric to evaluate the contemporary diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. Recommending technical commentary to user based on individually content and other users’ ratings. Our schoolwork showed that this approach works well comparative to traditional medium factorization methods and makes good guess on absolutely unrated articles. Further, our algorithm provides interpretable user profiles. Such profiles could be useful in real-world recommender systems if a particular user recognizes her profile as on behalf of singular topics, she can choose some topics after in search of recommendations about a subject.

Over the past decades, automatic face recognition has become a highly active research area, mainly due to the countless application possibilities in both the private as well as the public sector. Numerous algorithms have been proposed in the literature to cope with the problem of face recognition, nevertheless, a group of methods commonly referred to as appearance based have emerged as the dominant solution to the face recognition problem. Many comparative studies concerned with the performance of appearance based methods have already been presented in the literature, not rarely with inconclusive and often with contradictory results. No consent has been reached within the scientific community regarding the relative ranking of the efficiency of appearance based methods for the face recognition task, let alone regarding their susceptibility to appearance changes induced by various environmental factors. To tackle these open issues, this paper assess the performance of the three dominant appearance based methods: principal component analysis, linear discriminant analysis and independent component analysis, and compares them on equal footing (i.e., with the same preprocessing procedure, with optimized parameters for the best possible performance, etc.) in face verification experiments on the publicly available XM2VTS database. In addition to the comparative analysis on the XM2VTS database, ten degraded versions of the database are also employed in the experiments to evaluate the susceptibility of the appearance based methods on various image degradations which can occur in &quot;real-life&quot; operating conditions. Our experimental results suggest that linear discriminant analysis ensures the most consistent verification rates across the tested databases.

<strong>Background: </strong>Technology-based objective measures (TOMs) recently gained relevance to support clinicians in the assessment of motor function in Parkinson&rsquo;s disease (PD), although limited data are available in the early phases. <strong>Objective: </strong>To assess motor performances of a population of newly diagnosed, drug free PD patients using wearable inertial sensors and to compare them to healthy controls (HC) and differentiate different PD subtypes [tremor dominant (TD), postural instability gait disability (PIGD), and mixed phenotype (MP)]. <strong>Methods: </strong>We enrolled 65 subjects, 36 newly diagnosed, drug-free PD patients and 29 HCs. PD patients were clinically defined as tremor dominant, postural instability-gait difficulties or mixed phenotype. All 65 subjects performed seven MDSUPDRS III motor tasks wearing inertial sensors: rest tremor, postural tremor, rapid alternating hand movement, foot tapping, heel-to-toe tapping, Timed-Up-and-Go test (TUG) and pull test. The most relevant motor tasks were found combining ReliefF ranking and Kruskal&ndash;Wallis feature-selection methods.We used these features, linked to the relevant motor tasks, to highlight differences between PD from HC, by means of Support Vector Machine (SVM) classifier. Furthermore, we adopted SVM to support the relevance of each motor task on the classification accuracy, excluding one task at time. <strong>Results: </strong>Motion analysis distinguished PD from HC with an accuracy as high as 97%, based on SVM performed with measured features from tremor and bradykinesia items, pull test and TUG. Heel-to-toe test was the most relevant, followed by TUG and pull test. <strong>Conclusions: </strong>In this pilot study, we demonstrate that the SVM algorithm successfully distinguishes <em>de novo </em>drug-free PD patients from HC. Surprisingly, pull test and TUG tests provided relevant features for obtaining high SVM classification accuracy, differing from the report of the experienced examiner. The use of TOMs may improve diagnostic accuracy for these patients.

Реальные условия, в которых функционирует вуз насыщены многими присутствующими НЕ-факторами, неблагоприятное сочетание которых способно нести угрозу как психическому и физическому здоровью всех участников образовательного процесса, так и создавать риски их жизни. В работе рассматриваются общие подходы к выявлению информационных составляющих этих рисков и, за счет нечеткого агрегирования соответствующих показателей, своевременно принимать решения по корректировки управлений вузами. Излагается обобщенная методика формирования оценки состояния вуза с точки зрения безопасности. Для уточнения используемых групп оценки рисков приводится их классификация, а также экспертные оценки показателей. В случае, когда нормативные значения представляют собой нечеткие вектора требований, необходимо применять особый подход. Таким образом, предлагается учитывать большое количество параметров и оптимизировать вариант реализации системы по векторам требований. В первую очередь вводятся критерии, оценивается значимость, определяются нормативы и ограничения. Во вторую, находится уровень отклонений варианта альтернативы от эталона. В итоге происходит ранжирование по рассогласованиям, расставляются приоритеты, которые и принимаются за решение. Предлагаемый инструмент выполнения - Python (библиотеки: TKinter, matplotlib, openpyxl), позволяет построить интерфейс, графики, таблицы и интерпретировать результаты анализа. Разработанный алгоритм позволяет получить гармоничный набор итоговых решений и упростить процесс экспертной оценки, а также обеспечить выбор комплексных систем безопасности. The real conditions in which the university operates are saturated with many non-factors present, an unfavorable combination of which can pose a threat to both the mental and physical health of all participants in the educational process and create risks to their lives. The paper considers general approaches to identifying the information components of these risks and, due to fuzzy aggregation of the relevant indicators, make timely decisions on adjusting the management of universities. A generalized method of forming an assessment of the state of the university from the point of view of safety is presented. To clarify the used risk assessment groups, their classification is given, as well as expert assessments of indicators. In the case when the normative values are fuzzy vectors of requirements, a special approach must be applied. The article proposes to take into account numerous parameters and optimize the implementation of the system according to the vectors of requirements. First of all, criteria are introduced, significance is assessed, standards and restrictions are determined. Secondly, the level of deviations of the alternative variant from the standard is found. As a result of the execution, there is a ranking according to mismatches, priorities are set, which are taken for a decision. The suggested execution tool is Python (libraries: TKinter, matplotlib, openpyxl). It allows you to build an interface, graphs, tables and interpret the analysis results. The developed algorithm makes it possible to obtain a harmonious set of final solutions and to simplify the expert assessment process, as well as to ensure the choice of integrated security systems.

Content warning: although it was kept to a minimum, this text displays instances of (anti-Semitic) hate speech. During the 2016 U.S. election and its aftermath, multiple journalistic accounts reported on “alt-right trolls” emanating from anonymous online spaces like the imageboard 4chan (e.g. Abramson; Ellis). Having gained infamy for its nihilist trolling subcultures (Phillips, This Is Why) and the loose hacktivist movement Anonymous (Coleman), 4chan now drew headlines because of the alt-right’s “genuinely new” concoction of white supremacy, ironic Internet humour, and a lack of clear leadership (Hawley 50). The alt-right “anons”, as imageboard users call themselves, were said to primarily manifest on the “Politically Incorrect” subforum of 4chan: /pol/. Gradually, a sentiment arose in the titles of several news articles that the pro-Trump “alt-right trolls” had successfully won the metapolitical battle intertwined with the elections (Phillips, Oxygen 5). For instance, articles titled that “trolls” were “The Only True Winners of this Election” (Dewey) or even “Plotting a GOP Takeover” (Stuart).The headlines were as enticing as questionable. As trolling-expert Whitney Phillips headlined herself, the alt-right did not attain political gravity solely through its own efforts but rather was “Conjured Out of Pearl Clutching and Media Attention” (“The Alt-Right”), with news outlets being provoked to criticise, debunk, or sensationalise its trolling activities (Faris et al. 131; Phillips, “Oxygen” 5-6). Even with the right intentions, attempts at denouncement through using vague, structuralist notions–from “alt-right” and “trolls” to “the basket of deplorables” (Robertson) – arguably only strengthened the coherence of those it was meant to disavow (Phillips, Oxygen; Phillips et al.; Marantz). Phillips et al. therefore lamented such generalisations, arguing attributing Trump’s win to vague notions of “4chan”, “alt-right”, or “trolls” actually bestowed an “atemporal, almost godlike power” to what was actually an “ever-reactive anonymous online collective”. Therefore, they called to refrain from making claims about opaque spaces like 4chan without first “plotting the landscape” and “safeguarding the actual record”. Indeed, “when it comes to 4chan and Anonymous”, Phillips et al. warned, “nobody steps in the same river twice”.This text answers the call to map anonymous online groups by engaging with the complexity of testing the muddy waters of the ever-changing and dissimulative 4chan-current. It first argues how anti-structuralist research outlooks can answer to many of the pitfalls arising from this complex task. Afterwards, it traces the word trump as it was used on 4chan/pol/ to problematise some of the above-mentioned media narratives. How did anons consider Trump, and how did the /pol/-current change during the build-up of the 2016 U.S. elections and afterwards?On Researching Masked and Dissimulative ExtremistsWhile potentially playing into the self-imagination of malicious actors (Phillips et al.), the frequent appearance of overblown narratives on 4chan is unsurprising considering the peculiar affordances of imageboards. Imageboards are anonymous – no user account is required to post – and ephemeral – posts are deleted after a certain amount of activity, sometimes after days, sometimes after minutes (Bernstein et al.; Hagen). These affordances complicate studying collectives on imageboards, with the primary reasons being that 1) they prevent insights into user demographics, 2) they afford particularly dissimulative, playful discourse that can rarely be taken at face value (Auerbach; de Zeeuw and Tuters), and 3) the sheer volume of auto-deleted activity means one has to stay up-to-date with a rapid waterfall of subcultural ephemera. Additionally, the person stepping into the muddy waters of the chan-river also changes their gaze over time. For instance, Phillips bravely narrates how she once saw parts of the 4chan-stream as “fun” to only later realise the blatantly racist elements present from the start (“It Wasn’t Just”).To help render legible the changing currents of imageboard activity without relying on vague understandings of the “alt-right”, “trolls”, or “Anonymous”, anti-structuralist research outlooks form a possible answer. Around 1900, sociologists like Gabriel Tarde already argued to refrain from departing from structuralist notions of society and instead let social compositions arise through iterative tracing of minute imitations (11). As described in Bruno Latour’s Reassembling the Social, actor-network theory (ANT) revitalises the Tardean outlook by similarly criticising the notion of the “social” and “society” as distinct, sui-generis entities. Instead, ANT advocates tracing “flat” networks of agency made up of both human and non-human actors (165-72). By tracing actors and describing the emerging network of heterogeneous mediators and intermediaries (105), one can slowly but surely get a sense of collective life. ANT thus takes a page from ethnomethodology, which advocates a similar mapping of how participants of a group produce themselves as such (Garfinkel).For multiple reasons, anti-structuralist approaches like ANT can be useful in tracing elusive anonymous online groups and their changing compositions. First, instead of grasping collectives on imageboards from the outset through structuralist notions, as networked individuals, or as “amorphous and formless entities” (see e.g. Coleman 113-5), it only derives its composition after following where its actors lead. This can result in an empirical and literally objective mapping of their collectivity while refraining from mystifications and non-existent connections–so often present in popular narratives about “trolls” and the “alt-right”. At the same time, it allows prominent self-imaginations and mythologizations – or, in ANT-parlance, “localisations of the global” (Latour 173-190) – rise to the surface whenever they form important actors, which, as we will see, tends to happen on 4chan.Second, ANT offers a useful lens with which to consider how non-human actors can uphold a sense of collectivity within anonymous imageboards. This can include digital objects as part of the infrastructure–e.g. the automatically assigned post numbers having mythical value on 4chan (Beran, It Came From 69)–but also cultural objects like words or memes. Considering 4chan’s anonymity, this focus on objects instead of individuals is partly a necessity: one cannot know the exact amount and flow of users. Still, as this text seeks to show, non-human actors like words or memes can form suitable actors to map the changing collectivity of anonymous imageboard users in the absence of demographic insights.There are a few pitfalls worth noting when conducting ANT-informed research into extremist spaces like 4chan/pol/. The aforementioned ironic and dissimulative rhetoric of anonymous forum culture (de Zeeuw and Tuters) means tracing is complicated by implicit (yet omnipresent) intertextual references undecipherable to the untrained eye. Even worse, when misread or exaggerated, such tracing efforts can play into trolling tactics. This can in turn risk what Phillips calls “giving oxygen” to bigoted narratives by amplifying their presence (“Oxygen”). Since ANT does not prescribe what sort of description is needed (Latour 149), this exposure can be limited and/or critically engaged with by the researcher. Still, it is inevitable that research on extremist collectives adds at least some garbage to already polluted information ecologies (Phillips and Milner 2020), even when “just” letting the actors speak (Venturini). Indeed, this text will unfortunately also show hate speech terms below.These complications of irony and amplification can be somewhat mitigated by mixing ethnographic involvement with computational methods. Together, they can render implicit references explicit while also mapping broad patterns in imitation and preventing singular (misleading) actors from over-dominating the description. When done well, such descriptions do not only have to amplify but can also marginalise and trivialise. An accurate mapping can thereby counter sensationalist media narratives, as long as that is where the actors lead. It because of this potentiality that anti-structuralist tracing of extremist, dissimulative online groups should not be discarded outright.Stopping Momentarily to Test the WatersTo put the above into practice, what follows is a brief case study on the term trump on 4chan/pol/. Instead of following users, here the actor trump is taken an entry point for tracing various assemblages: not only referring to Donald J. Trump as an individual and his actions, but also to how /pol/-anons imagine themselves in relation to Trump. In this way, the actor trump is a fluid one: each of its iterations contains different boundaries and variants of its environment (de Laet and Mol 252). By following these environments, can we make sense of how the delirious 2016 U.S. election cycle played out on /pol/, a space described as the “skeleton key to the rise of Trump” (Beran, 4chan)?To trace trump, I use the 4plebs.com archive, containing almost all posts made on /pol/ between late-2013 and early 2018 (the time of research). I subsequently use two text mining methods to trace various connections between trump and other actors and use this to highlight specific posts. As Latour et al. note, computational methods allow “navigations” (593) of different data points to ensure diverse empirical perspectives, preventing both structuralist “zoomed-out” views and local contexts from over-dominating. Instead of moving between micro and macro views, such a navigation should therefore be understood as a “circulation” around the data, deploying various perspectives that each assemble the actors in a different way. In following this, the case study aims to demonstrate how, instead of a lengthy ethnographic account, a brief navigation using both quali- and quantitative perspectives can quickly demystify some aspects of seemingly nebulous online groups.Tracing trump: From Meme-Wizard to Anti-Semitic TargetTo get a sense of the centrality of Trump on /pol/, I start with post frequencies of trump assembled in two ways. The first (Figure 1) shows how, soon after the announcement of Trump’s presidential bid on 16 June 2015, around 100,000 comments mention the word (2% of the total amount of posts). The frequencies spike to a staggering 8% of all comments during the build-up to Trump’s win of the Republican nomination in early 2016 and presidential election in November 2016. Figure 1: The absolute and relative amount of posts on 4chan/pol/ containing the word trump (prefixes and suffixes allowed).To follow the traces between trump and the more general discourse surrounding it, I compiled a more general “trump-dense threads” dataset. These are threads containing thirty or more posts, with at least 15% of posts mentioning trump. As Figure 2 shows, at the two peaks, 8% of any thread on /pol/ was trump-dense, accounting for approximately 15,000 monthly threads. While Trump’s presence is unsurprising, these two views show just how incredibly central the former businessman was to /pol/ at the time of the 2016 U.S. election. Figure 2: The absolute and relative amount of threads on 4chan/pol/ that are “trump-dense”, meaning they have thirty comments or more, out of which at least 15% contain the word trump (prefixes and suffixes allowed).Instead of picking a certain moment from these aggregate overviews and moving to the “micro” (Latour et al.), I “circulate” further with Figure 3, showing another perspective on the trump­-dense thread dataset. It shows a scatter plot of trump-dense threads grouped per week and plotted according to how similar their vocabulary is. First, all the words per week are weighted with tf-idf, a common information retrieval algorithm that scores units on the basis if they appear a lot in one of the datasets but not in others (Spärck-Jones). The document sets are then plotted according to the similarity of their weighted vocabulary (cosine similarity). The five highest-scoring terms for the five clusters (identified with K-means) are listed in the bottom-right corner. For legibility, the scatterplot is compressed by the MDS algorithm. To get a better sense of specific vocabulary per week, terms that appeared in all weeks are filtered out (like trump or hillary). Read counterclockwise, the nodes roughly increase in time, thus showing a clear temporal change of discourse, with the first clusters being more similar in vocabulary than the last, and the weeks before and after the primary election (orange cluster) showing a clear gap. Figure 3: A scatterplot showing cosine distances between tf-idf weighted vocabularies of trump-dense threads per week. Compressed with MDS and coloured by five K-means clusters on the underlying tf-idf matrix (excluding terms that appeared in all weeks). Legend shows the top five tf-idf terms within these clusters. ★ denotes the median week in the cluster.With this map, we can trace other words appearing around trump as significant actors in the weekly documents. For instance, Trump-supportive words like stump (referring to “Can’t Stump the Trump”) and maga (“Make America Great Again”) are highly ranked in the first two clusters. In later weeks, less clearly pro-Trump terms appear: drumpf reminds of the unattractive root of the Trump family name, while impeached and mueller show the Russia probe in 2017 and 2018 were significant in the trump-dense threads of that time. This change might thus hint at growing scepticism towards Trump after his win, but it is not shown how these terms are used. Fortunately, the scatterplot offers a rudder with which to navigate to further perspectives.In keeping with Latour’s advice to keep “aggregate structures” and “local contexts” flat (165-72), I contrast the above scatterplot with a perspective on the data that keeps sentence structures intact instead of showing abstracted keyword sets. Figure 4 uses all posts mentioning trump in the median weeks of the first and last clusters in the scatterplot (indicated with ★) and visualises word trees (Wattenberg and Viégas) of most frequent words following “trump is a”. As such, they render explicit ontological associations about Trump; what is Trump, according to /pol/-anons? The first word tree shows posts from 2-8 November 2015, when fifteen Republican competitors were still in the race. As we have seen in Figure 1, Trump was in this month still “only” mentioned in around 50,000 posts (2% of the total). This word tree suggests his eventual nomination was at this point seen as an unlikely and even undesirable scenario, showing derogatory associations like retard and failure, as well as more conspiratorial words like shill, fraud, hillary plant, and hillary clinton puppet. Notably, the most prominent association, meme, and others like joke and fucking comic relief, imply Trump was not taken too seriously (see also Figure 5). Figure 4: Word trees of words following “trump is a” in the median weeks of the first and last clusters of the scatterplot. Made with Jason Davies’s Word Tree application. Figure 5: Anons who did not take Trump seriously. Screencapture taken from archive.4plebs.org (see post 1 and post 2 in context).The first word tree contrast dramatically with the one from the last median week from 18 to 24 December 2017. Here, most associations are anti-Semitic or otherwise related to Judaism, with trump most prominently related to the hate speech term kike. This prompts several questions: did /pol/ become increasingly anti-Semitic? Did already active users radicalise, or were more anti-Semites drawn to /pol/? Or was this nefarious current always there, with Trump merely drawing anti-Semitic attention after he won the election? Although the navigation did not depart from a particular critical framework, by “just following the actors” (Venturini), it already stumbled upon important questions related to popular narratives on 4chan and the alt-right. While it is tempting to stop here and explain the change as “radicalisation”, the navigation should continue to add more empirical perspectives. When doing so, the more plausible explanation is that the unlikely success of Trump briefly attracted (relatively) more diverse and playful visitors to /pol/, obscuring the presence and steady growth of overt extremists in the process.To unpack this, I first focus on the claim that a (relatively) diverse set of users flocked to /pol/ because of the Trump campaign. /pol/’s overall posting activity rose sharply during the 2016 election, which can point to already active users becoming more active, but is likely mostly caused by new users flocking to /pol/. Indeed, this can be traced in actor language. For instance, many anons professed to be “reporting in” from other 4chan boards during crucial moments in the campaing. One of the longest threads in the trump-dense threads dataset (4,504 posts) simply announces “Cruz drops out”. In the comments below, multiple anons state they arrived from other boards to join the Trump-infused activity. For instance, Figure 6 shows an anon replying “/v/ REPORTING IN”, to which sixty other users reacted by similarly affirming themselves as representatives from other boards (e.g. “/mu/ here. Ready to MAGA”). While but another particular view, this implies Trump’s surprising nomination stimulated a crowd-like gathering of different anons jumping into the vortex of trump-related activity on /pol/. Figure 6: Replies by outside-anons “reporting in” the sticky thread announcing Ted Cruz's drop out, 4 May 2016. Screenshots taken from 4plebs.org (see post 1 and post 2 in context).Other actor-language further expresses Trump’s campaign “drew in” new and unadjusted (or: less extreme) users. Notably, many anons claimed the 2016 election led to an “invasion of Reddit users”. Figure 7 shows one such expression: an annotated timeline of /pol/’s posting activity graph (made by 4plebs), posted to /pol/ on 26 February 2016 and subsequently reposted 34 times. It interprets 2016 as a period where “Trump shit goes into overdrive, meme shit floods /pol/, /pol/ is now reddit”. Whether these claims hold any truth is difficult to establish, but the image forms an interesting case of how the entirety “/pol/” is imagined and locally articulated. Such simplistic narratives relate to what Latour calls “panoramas”: totalising notions of some imagined “whole” (188-90) that, while not to be “confused with the collective”, form crucial data since they express how actors understand their own composition (190). Especially in the volatile conditions of anonymous and ephemeral imageboards, repeated panoramic narratives can help in constructing a sense of cohesion–and thereby also form interesting actors to trace. Indeed, following the panoramic statement “/pol/ is now reddit”, other gatekeeping-efforts are not hard to find. For instance, phrases urging other anons to go “back to reddit” (occurring in 19,069 posts in the total dataset) or “back to The_Donald” (a popular pro-Trump subreddit, 1,940 posts) are also particularly popular in the dataset. Figure 7: An image circulated on /pol/ lamenting that "/pol/ is now reddit" by annotating 4plebs’s posting metrics. Screenshot taken from archive.4plebs.org (see posts).Did trump-related activity on /pol/ indeed become more “meme-y” or “Reddit-like” during the election cycle, as the above panorama articulates? The activity in the trump-dense threads seems to suggest so. Figure 8 again uses the tf-idf terms from these threads, but here with the columns denoting the weeks and the rows the top scoring tf-idf terms of their respective week. To highlight relevant actors, all terms are greyed out (see the unedited sheet here), except for several keywords that indicate particularly playful or memetic vernacular: the aforementioned stump, emperor, referring to Trump’s nickname as “God Emperor”; energy, referring to “high energy”, a common catchphrase amongst Trump supporters; magic, referring to “meme magic”, the faux-ironic belief that posting memes affects real-life events; and pepe, the infamous cartoon frog. In both the tf-idf ranking and the absolute frequencies, these keywords flourish in 2016, but disappear soon after the presidential election passes. The later weeks in 2017 and 2018 rarely contain similarly playful and memetic terms, and if they do, suggest mocking discourse regarding Trump (e.g. drumpf). This perspective thus pictures the environment around trump in the run-up to the election as a particularly memetic yet short-lived carnival. At least from this perspective, “meme shit” thus indeed seemed to have “flooded /pol/”, but only for a short while. Figure 8: tf-idf matrix of trump-dense threads, columns denoting weeks and rows denoting the top hundred most relevant terms per week. Download the full tf-idf matrix with all terms here.Despite this carnivalesque activity, further perspectives suggest it did not go at the expense of extremist activity on /pol/. Figure 9 shows the absolute and relative counts of the word "jew" and its derogatory synonym "kike". Each of these increases from 2015 onwards. As such, it seems to align with claims that Trump’s success and /pol/ becoming increasingly extremist were causally related (Thompson). However, apart from possibly confusing correlation with causation, the relative presence remains fairly stable, even slightly decreasing during the frenzy of the Trump campaign. Since we also saw Trump himself become a target for anti-Semitic activity, these trendlines rather imply /pol/’s extremist current grew proportionally to the overall increase in activity, and increased alongside but not but necessarily as a partisan contingent as a result of Trump’s campaign. Figure 9: The absolute and relative frequency of the terms "jew" and "kike" on 4chan/pol/.ConclusionCombined, the above navigation implies two main changes in 4chan/pol/’s trump-related current. First, the climaxes of the 2016 Republican primaries and presidential elections seem to have invoked crowd-like influxes of (relatively) heterogeneous users joining the Trump-delirium, marked by particularly memetic activity. Second, /pol/ additionally seemed to have formed a welcoming hotbed for anti-Semites and other extremists, as the absolute amount of (anti-Semitic) hate speech increased. However, while already-present and new users might have been energised by Trump, they were not necessarily loyal to him, as professed by the fact that Trump himself eventually became a target. Together with the fact that anti-Semitic hate speech stayed relatively consistent, instead of being “countercultural” (Nagle) or exclusively pro-Trump, /pol/ thus seems to have been composed of quite a stable anti-Semitic and Trump-critical contingent, increasing proportionally to /pol/’s general growth.Methodologically, this text sought to demonstrate how a brief navigation of trump on 4chan/pol/ can provide provisional yet valuable insights regarding continuously changing current of online anonymous collectives. As the cliché goes, however, this brief exploration has left more many questions, or rather, it did not “deploy the content with all its connections” (Latour 147). For instance, I have not touched on how many of the trump-dense threads are distinctly separated and pro-Trump “general threads” (Jokubauskaitė and Peeters). 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