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Lee, Russell, Jessica Maghakian, Mohammad Hajiesmaili, Jian Li, Ramesh Sitaraman, and Zhenhua Liu. "Online peak-aware energy scheduling with untrusted advice." ACM SIGEnergy Energy Informatics Review 1, no. 1 (2021): 59–77. http://dx.doi.org/10.1145/3508467.3508473.
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This paper studies the online energy scheduling problem in a hybrid model where the cost of energy is proportional to both the volume and peak usage, and where energy can be either locally generated or drawn from the grid. Inspired by recent advances in online algorithms with Machine Learned (ML) advice, we develop parameterized deterministic and randomized algorithms for this problem such that the level of reliance on the advice can be adjusted by a trust parameter. We then analyze the performance of the proposed algorithms using two performance metrics: robustness that measures the competitive ratio as a function of the trust parameter when the advice is inaccurate, and consistency for competitive ratio when the advice is accurate. Since the competitive ratio is analyzed in two different regimes, we further investigate the Pareto optimality of the proposed algorithms. Our results show that the proposed deterministic algorithm is Pareto-optimal, in the sense that no other online deterministic algorithms can dominate the robustness and consistency of our algorithm. Furthermore, we show that the proposed randomized algorithm dominates the Pareto-optimal deterministic algorithm. Our large-scale empirical evaluations using real traces of energy demand, energy prices, and renewable energy generations highlight that the proposed algorithms outperform worst-case optimized algorithms and fully data-driven algorithms.
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Bianchi, Maria Paola, Hans-Joachim Böckenhauer, Tatjana Brülisauer, Dennis Komm, and Beatrice Palano. "Online Minimum Spanning Tree with Advice." International Journal of Foundations of Computer Science 29, no. 04 (2018): 505–27. http://dx.doi.org/10.1142/s0129054118410034.
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In the online minimum spanning tree problem, a graph is revealed vertex by vertex; together with every vertex, all edges to vertices that are already known are given, and an online algorithm must irrevocably choose a subset of them as a part of its solution. The advice complexity of an online problem is a means to quantify the information that needs to be extracted from the input to achieve good results. For a graph of size [Formula: see text], we show an asymptotically tight bound of [Formula: see text] on the number of advice bits to produce an optimal solution for any given graph. For particular graph classes, e.g., with bounded degree or a restricted edge weight function, we prove that the upper bound can be drastically reduced; e.g., [Formula: see text] advice bits allow to compute an optimal result if the weight function equals the Euclidean distance; if the graph is complete and has two different edge weights, even a logarithmic number suffices. Some of these results make use of the optimality of Kruskal’s algorithm for the offline setting. We also study the trade-off between the number of advice bits and the achievable competitive ratio. To this end, we perform a reduction from another online problem to obtain a linear lower bound on the advice complexity for any near-optimal solution. Using our results finally allows us to give a lower bound on the expected competitive ratio of any randomized online algorithm for the problem, even on graphs with three different edge weights.
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Boyar, Joan, Lene M. Favrholdt, Christian Kudahl, Kim S. Larsen, and Jesper W. Mikkelsen. "Online Algorithms with Advice." ACM Computing Surveys 50, no. 2 (2017): 1–34. http://dx.doi.org/10.1145/3056461.
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Barrière, Lali, Xavier Muñoz, Janosch Fuchs, and Walter Unger. "Online Matching in Regular Bipartite Graphs." Parallel Processing Letters 28, no. 02 (2018): 1850008. http://dx.doi.org/10.1142/s0129626418500081.
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In an online problem, the input is revealed one piece at a time. In every time step, the online algorithm has to produce a part of the output, based on the partial knowledge of the input. Such decisions are irrevocable, and thus online algorithms usually lead to nonoptimal solutions. The impact of the partial knowledge depends strongly on the problem. If the algorithm is allowed to read binary information about the future, the amount of bits read that allow the algorithm to solve the problem optimally is the so-called advice complexity. The quality of an online algorithm is measured by its competitive ratio, which compares its performance to that of an optimal offline algorithm. In this paper we study online bipartite matchings focusing on the particular case of bipartite matchings in regular graphs. We give tight upper and lower bounds on the competitive ratio of the online deterministic bipartite matching problem. The competitive ratio turns out to be asymptotically equal to the known randomized competitive ratio. Afterwards, we present an upper and lower bound for the advice complexity of the online deterministic bipartite matching problem.
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Chen, Li-Hsuan, Ling-Ju Hung, Henri Lotze, and Peter Rossmanith. "Online Node- and Edge-Deletion Problems with Advice." Algorithmica 83, no. 9 (2021): 2719–53. http://dx.doi.org/10.1007/s00453-021-00840-9.
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AbstractIn online edge- and node-deletion problems the input arrives node by node and an algorithm has to delete nodes or edges in order to keep the input graph in a given graph class $$\Pi $$ Π at all times. We consider only hereditary properties $$\Pi $$ Π , for which optimal online algorithms exist and which can be characterized by a set of forbidden subgraphs $${{\mathcal{F}}}$$ F and analyze the advice complexity of getting an optimal solution. We give almost tight bounds on the Delayed Connected$${{\mathcal{F}}}$$ F -Node-Deletion Problem, where all graphs of the family $${\mathcal{F}}$$ F have to be connected and almost tight lower and upper bounds for the Delayed$$H$$ H -Node-Deletion Problem, where there is one forbidden induced subgraph H that may be connected or not. For the Delayed$$H$$ H -Node-Deletion Problem the advice complexity is basically an easy function of the size of the biggest component in H. Additionally, we give tight bounds on the Delayed Connected$${\mathcal{F}}$$ F -Edge-Deletion Problem, where we have an arbitrary number of forbidden connected graphs. For the latter result we present an algorithm that computes the advice complexity directly from $${\mathcal{F}}$$ F . We give a separate analysis for the Delayed Connected$$H$$ H -Edge-Deletion Problem, which is less general but admits a bound that is easier to compute.
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Lykouris, Thodoris, and Sergei Vassilvitskii. "Competitive Caching with Machine Learned Advice." Journal of the ACM 68, no. 4 (2021): 1–25. http://dx.doi.org/10.1145/3447579.
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Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon unconditional worst-case lower bounds when the predictor has low error. Our approach treats the predictor as a complete black box and is not dependent on its inner workings or the exact distribution of its errors. We apply this framework to the traditional caching problem—creating an eviction strategy for a cache of size k . We demonstrate that naively following the oracle’s recommendations may lead to very poor performance, even when the average error is quite low. Instead, we show how to modify the Marker algorithm to take into account the predictions and prove that this combined approach achieves a competitive ratio that both (i) decreases as the predictor’s error decreases and (ii) is always capped by O (log k ), which can be achieved without any assistance from the predictor. We complement our results with an empirical evaluation of our algorithm on real-world datasets and show that it performs well empirically even when using simple off-the-shelf predictions.
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Boyar, Joan, Lene M. Favrholdt, Christian Kudahl, Kim S. Larsen, and Jesper W. Mikkelsen. "Online Algorithms with Advice: A Survey." ACM SIGACT News 47, no. 3 (2016): 93–129. http://dx.doi.org/10.1145/2993749.2993766.
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Böckenhauer, Hans-Joachim, Dennis Komm, Rastislav Královič, Richard Královič, and Tobias Mömke. "Online algorithms with advice: The tape model." Information and Computation 254 (June 2017): 59–83. http://dx.doi.org/10.1016/j.ic.2017.03.001.
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Aumayr, Erik, Jeffrey Chan, and Conor Hayes. "Reconstruction of Threaded Conversations in Online Discussion Forums." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (2021): 26–33. http://dx.doi.org/10.1609/icwsm.v5i1.14122.
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Online discussion boards, or Internet forums, are a significant part of the Internet. People use Internet forums to post questions, provide advice and participate in discussions. These online conversations are represented as threads, and the conversation trees within these threads are important in understanding the behaviour of online users. Unfortunately, the reply structures of these threads are generally not publicly accessible or not maintained. Hence, in this paper, we introduce an efficient and simple approach to reconstruct the reply structure in threaded conversations. We contrast its accuracy against three baseline algorithms, and show that our algorithm can accurately recreate the in and out degree distributions of forum reply graphs built from the reconstructed reply structures.
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Zhao, Xiaofan, and Hong Shen. "Online algorithms for 2D bin packing with advice." Neurocomputing 189 (May 2016): 25–32. http://dx.doi.org/10.1016/j.neucom.2015.11.035.
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Amballa, Durga Prasad. "AI-Powered Online Symptom Checkers: Enhancing Accuracy and Guiding Users to Appropriate Medical Care." Journal of Artificial Intelligence & Cloud Computing 3, no. 1 (2024): 1–5. http://dx.doi.org/10.47363/jaicc/2024(3)292.
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Online symptom checkers have emerged as valuable tools for individuals seeking health advice and guidance. Powered by artificial intelligence (AI) algorithms, these symptom checkers aim to provide more accurate and personalized health assessments, directing users to the appropriate level of medical care. This study presents an innovative architecture that combines Vector Database (VDB), Faiss similarity search algorithm, and GPT (Generative Pre-trained Transformer) API to analyze user inputs, identify intents, and generate tailored responses. The proposed system leverages the power of VDB to store symptom and test data as embeddings, enabling efficient similarity searches. The GPT model is employed to analyze user inputs, determining whether the user is seeking symptom analysis, test suggestions, booking details, booking help, or general health queries. Based on the identified intent, the Faiss algorithm searches for relevant tests and returns appropriate recommendations. The GPT model then generates personalized responses by considering the user's input, suggested tests, intent, and a summary of the chat history. The chatbot also incorporates a memory mechanism to store conversation summaries, providing context for subsequent interactions. Experimental results demonstrate the effectiveness of the proposed architecture in delivering accurate and context-aware health advice, guiding users to the most suitable medical care options. This study highlights the potential of AI-powered online symptom checkers in improving healthcare accessibility, reducing unnecessary medical visits, and empowering individuals to make informed decisions about their health.
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Zhao, Fen, Penghua Li, Yuanyuan Li, Jie Hou, and Yinguo Li. "Semi-Supervised Convolutional Neural Network for Law Advice Online." Applied Sciences 9, no. 17 (2019): 3617. http://dx.doi.org/10.3390/app9173617.
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With the rapid developments of Internet technology, a mass of law cases is constantly occurring and needs to be dealt with in time. Automatic classification of law text is the most basic and critical process in the online law advice platform. Deep neural network-based natural language processing (DNN-NLP) is one of the most promising approaches to implement text classification. Meanwhile, as the convolutional neural network-based (CNN-based) methods developed, CNN-based text classification has already achieved impressive results. However, previous work applied amounts of manually-annotated data, which increased the labor cost and reduced the adaptability of the approach. Hence, we present a new semi-supervised model to solve the problem of data annotation. Our method learns the embedding of small text regions from unlabeled data and then integrates the learned embedding into the supervised training. More specifically, the learned embedding regions with the two-view-embedding model are used as an additional input to the CNN’s convolution layer. In addition, to implement the multi-task learning task, we propose the multi-label classification algorithm to assign multiple labels to an instance. The proposed method is evaluated experimentally subject to a law case description dataset and English standard dataset RCV1 . On Chinese data, the simulation results demonstrate that, compared with the existing methods such as linear SVM, our scheme respectively improves by 7.76%, 7.86%, 9.19%, and 2.96% the precision, recall, F-1, and Hamming loss. Analogously, the results suggest that compared to CNN, our scheme respectively improves by 4.46%, 5.76%, 5.14% and 0.87% in terms of precision, recall, F-1, and Hamming loss. It is worth mentioning that the robustness of this method makes it suitable and effective for automatic classification of law text. Furthermore, the design concept proposed is promising, which can be utilized in other real-world applications such as news classification and public opinion monitoring.
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Stockdale, Michael, and Rebecca Mitchell. "Legal advice privilege and artificial legal intelligence: Can robots give privileged legal advice?" International Journal of Evidence & Proof 23, no. 4 (2019): 422–39. http://dx.doi.org/10.1177/1365712719862296.
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Legal professional privilege entitles parties to legal proceedings to object to disclosing communications. The form of legal professional privilege that is now commonly known as ‘legal advice privilege’ attaches to communications between a client and its lawyers in connection with the provision of legal advice. The provision of legal advice increasingly involves the use of technology across a wide spectrum of activities with varying degrees of human interaction or supervision. Use of technology ranges from a lawyer conducting a keyword search of a legal database to legal advice given online by fully automated systems. With technology becoming more integrated into legal practice, an important issue that has not been explored is whether legal advice privilege attaches to communications between client and legal services provider regardless of the degree of human involvement and even if the ‘lawyer’ might constitute a fully automated advice algorithm. In essence, our central research question is: If a robot gives legal advice, is that advice privileged? This article makes an original and distinctive contribution to discourse in this area through offering novel perspectives on and solutions to a question which has not previously been investigated by legal academics.
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Khadiev, K., A. Khadieva, and I. Mannapov. "Quantum Online Algorithms with Respect to Space and Advice Complexity." Lobachevskii Journal of Mathematics 39, no. 9 (2018): 1377–87. http://dx.doi.org/10.1134/s1995080218090421.
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Renault, Marc P., Adi Rosén, and Rob van Stee. "Online algorithms with advice for bin packing and scheduling problems." Theoretical Computer Science 600 (October 2015): 155–70. http://dx.doi.org/10.1016/j.tcs.2015.07.050.
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Renault, Marc P., and Adi Rosén. "On Online Algorithms with Advice for the k-Server Problem." Theory of Computing Systems 56, no. 1 (2012): 3–21. http://dx.doi.org/10.1007/s00224-012-9434-z.
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Renault, Marc P. "Online algorithms with advice for the dual bin packing problem." Central European Journal of Operations Research 25, no. 4 (2016): 953–66. http://dx.doi.org/10.1007/s10100-016-0450-y.
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Trella, Anna L., Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, and Susan A. Murphy. "Reward Design for an Online Reinforcement Learning Algorithm Supporting Oral Self-Care." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15724–30. http://dx.doi.org/10.1609/aaai.v37i13.26866.
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While dental disease is largely preventable, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients. Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors. In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors. One of the main challenges in developing such an algorithm is ensuring that the algorithm considers the impact of current actions on the effectiveness of future actions (i.e., delayed effects), especially when the algorithm has been designed to run stably and autonomously in a constrained, real-world setting characterized by highly noisy, sparse data. We address this challenge by designing a quality reward that maximizes the desired health outcome (i.e., high-quality brushing) while minimizing user burden. We also highlight a procedure for optimizing the hyperparameters of the reward by building a simulation environment test bed and evaluating candidates using the test bed. The RL algorithm discussed in this paper will be deployed in Oralytics. To the best of our knowledge, Oralytics is the first mobile health study utilizing an RL algorithm designed to prevent dental disease by optimizing the delivery of motivational messages supporting oral self-care behaviors.
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A. Al-Hunaiyyan, Ahmed, Andrew Thomas Bimba, and Salah Alsharhan. "A Cognitive Knowledge-based Model for an Academic Adaptive e-Advising System." Interdisciplinary Journal of Information, Knowledge, and Management 15 (2020): 247–63. http://dx.doi.org/10.28945/4633.
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Aim/Purpose: This study describes a conceptual model, based on the principles of concept algebra that can provide intelligent academic advice using adaptive, knowledge-based feedback. The proposed model advises students based on their traits and academic history. The system aims to deliver adaptive advice to students using historical data from previous and current students. This data-driven approach utilizes a cognitive knowledge-based (CKB) model to update the weights (values that indicate the strength of relationships between concepts) that exist between student’s performances and recommended courses. Background: A research study conducted at the Public Authority for Applied Education and Training (PAAET), a higher education institution in Kuwait, indicates that students’ have positive perceptions of the e-Advising system. Most students believe that PAAET’s e-Advising system is effective because it allows them to check their academic status, provides a clear vision of their academic timeline, and is a convenient, user-friendly, and attractive online service. Student advising can be a tedious element of academic life but is necessary to fill the gap between student performance and degree requirements. Higher education institutions have prioritized assisting undecided students with career decisions for decades. An important feature of e-Advising systems is personalized feedback, where tailored advice is provided based on students' characteristics and other external parameters. Previous e-Advising systems provide students with advice without taking into consideration their different attributes and goals. Methodology: This research describes a model for an e-Advising system that enables students to select courses recommended based on their personalities and academic performance. Three algorithms are used to provide students with adaptive course selection advice: the knowledge elicitation algorithm that represents students' personalities and academic information, the knowledge bonding algorithm that combines related concepts or ideas within the knowledge base, and the adaptive e-Advising model that recommends relevant courses. The knowledge elicitation algorithm acquires student and academic characteristics from data provided, while the knowledge bonding algorithm fuses the newly acquired features with existing information in the database. The adaptive e-Advising algorithm provides recommended courses to students based on existing cognitive knowledge to overcome the issues associated with traditional knowledge representation methods. Contribution: The design and implementation of an adaptive e-Advising system are challenging because it relies on both academic and student traits. A model that incorporates the conceptual interaction between the various academic and student-specific components is needed to manage these challenges. While other e-Advising systems provide students with general advice, these earlier models are too rudimentary to take student characteristics (e.g., knowledge level, learning style, performance, demographics) into consideration. For the online systems that have replaced face-to-face academic advising to be effective, they need to take into consideration the dynamic nature of contemporary students and academic settings. Findings: The proposed algorithms can accommodate a highly diverse student body by providing information tailored to each student. The academic and student elements are represented as an Object-Attribute-Relationship (OAR) model. Recommendations for Practitioners: The model proposed here provides insight into the potential relationships between students’ characteristics and their academic standing. Furthermore, this novel e-Advising system provides large quantities of data and a platform through which to query students, which should enable developing more effective, knowledge-based approaches to academic advising. Recommendation for Researchers: The proposed model provides researches with a framework to incorporate various academic and student characteristics to determine the optimal advisory factors that affect a student’s performance. Impact on Society: The proposed model will benefit e-Advising system developers in implementing updateable algorithms that can be tested and improved to provide adaptive advice to students. The proposed approach can provide new insight to advisors on possible relationships between student’s characteristics and current academic settings. Thus, providing a means to develop new curriculums and approaches to learning. Future Research: In future studies, the proposed algorithms will be implemented, and the adaptive e-Advising model will be tested on real-world data and then further improved to cater to specific academic settings. The proposed model will benefit e-Advising system developers in implementing updateable algorithms that can be tested and improved to provide adaptive advisory to students. The approach proposed can provide new insight to advisors on possible relationships between student’s characteristics and current academic settings. Thus, providing a means to develop new curriculums and approaches to course recommendation.
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Kozitsin, Viacheslav, Iurii Katser, and Dmitry Lakontsev. "Online Forecasting and Anomaly Detection Based on the ARIMA Model." Applied Sciences 11, no. 7 (2021): 3194. http://dx.doi.org/10.3390/app11073194.
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Real-time diagnostics of complex technical systems such as power plants are critical to keep the system in its working state. An ideal diagnostic system must detect any fault in advance and predict the future state of the technical system, so predictive algorithms are used in the diagnostics. This paper proposes a novel, computationally simple algorithm based on the Auto-Regressive Integrated Moving Average model to solve anomaly detection and forecasting problems. The good performance of the proposed algorithm was confirmed in numerous numerical experiments for both anomaly detection and forecasting problems. Moreover, a description of the Autoregressive Integrated Moving Average Fault Detection (ARIMAFD) library, which includes the proposed algorithms, is provided in this paper. The developed algorithm proves to be an efficient algorithm and can be applied to problems related to anomaly detection and technological parameter forecasting in real diagnostic systems.
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Gao, Qiang, Ganggang Li, and Xiwen Lu. "Online and semi-online scheduling to minimize makespan on single machine with an availability constraint." Discrete Mathematics, Algorithms and Applications 07, no. 03 (2015): 1550021. http://dx.doi.org/10.1142/s1793830915500214.
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Online and semi-online scheduling problems on a single machine with an availability constraint are considered in this paper. The machine has one unavailable interval in which jobs cannot be processed. Preemption is not allowed. Jobs arrive over time. The objective is to minimize makespan. First we discuss the online version of the problem. After giving its lower bound, we prove that Earliest Release Date (ERD) algorithm is an optimal algorithm. Then we study some semi-online problems in which the largest processing time, the total processing time, the largest release date, or the optimal makespan is known in advance. For these semi-online problems, we give their lower bounds, design semi-online algorithms and prove their competitive ratios, respectively.
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Zhao, Zichun. "Evolution of the encryption and analysis of algorithm." Applied and Computational Engineering 52, no. 1 (2024): 292–95. http://dx.doi.org/10.54254/2755-2721/52/20241674.
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This paper examines how cryptography has developed from the ancient kingdom to the present and examines what modern humans understand by an acceptable encryption scheme. The significance of encryption is evident; it is typically present in data, banks and governments in offline procedures, or VPNs in online ones. Additionally, the research shows how cryptography manifests itself in the world and highlights the confidentiality and universality of encryption techniques. However, the article also discusses the similarities and differences between pure mathematics and cryptography. It presents the two most widely used encryption techniques in computer science, RSA and Hush, and outlines their benefits to show why these techniques are the most often used as well as how challenging it is to comprehend and apply. It also provides some advice and recommendations to achieve the purpose of improving ability.
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CAI, SHENG-YI. "SEMI-ONLINE MACHINE COVERING." Asia-Pacific Journal of Operational Research 24, no. 03 (2007): 373–82. http://dx.doi.org/10.1142/s0217595907001255.
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This paper investigates two different semi-online versions of the machine covering, which is the problem of assigning a set of jobs to a system of m(m ≥ 3) identical parallel machines so as to maximize the earliest machine completion time. In the first case, we assume that the largest processing times is known in advance. In the second case, we assume that the total processing times of all jobs is known in advance. For each version we propose a semi-online algorithm and investigate its competitive ratio. The competitive ratio of each algorithm is [Formula: see text], which is shown to be the best possible competitive ratio for each semi-online problem.
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Khadiev, Kamil, and Aliya Khadieva. "Quantum and Classical Log-Bounded Automata for the Online Disjointness Problem." Mathematics 10, no. 1 (2022): 143. http://dx.doi.org/10.3390/math10010143.
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We consider online algorithms with respect to the competitive ratio. In this paper, we explore one-way automata as a model for online algorithms. We focus on quantum and classical online algorithms. For a specially constructed online minimization problem, we provide a quantum log-bounded automaton that is more effective (has less competitive ratio) than classical automata, even with advice, in the case of the logarithmic size of memory. We construct an online version of the well-known Disjointness problem as a problem. It was investigated by many researchers from a communication complexity and query complexity point of view.
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ZHANG, YONG, YUXIN WANG, FRANCIS Y. L. CHIN, and HING-FUNG TING. "COMPETITIVE ALGORITHMS FOR ONLINE PRICING." Discrete Mathematics, Algorithms and Applications 04, no. 02 (2012): 1250015. http://dx.doi.org/10.1142/s1793830912500152.
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Given a seller with m items, a sequence of users {u1, u2, …} come one by one, the seller must set the unit price and assign some items to each user on his/her arrival. Items can be sold fractionally. Each ui has his/her value function vi(⋅) such that vi(x) is the highest unit price ui is willing to pay for x items. The objective is to maximize the revenue by setting the price and number of items for each user. In this paper, we have the following contributions: if the highest value h among all vi(x) is known in advance, we first show the lower bound of the competitive ratio is ⌊ log h⌋/2, then give an online algorithm with competitive ratio 4⌊ log h⌋ + 6; if h is not known in advance, we give an online algorithm with competitive ratio 2⋅h log -1/2 h + 8⋅h3 log -1/2 h.
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Li, Jing, and Zhou Ye. "Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm." Complexity 2020 (December 24, 2020): 1–10. http://dx.doi.org/10.1155/2020/6619249.
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In this paper, a personalized online education platform based on a collaborative filtering algorithm is designed by applying the recommendation algorithm in the recommendation system to the online education platform using a cross-platform compatible HTML5 and high-performance framework hybrid programming approach. The server-side development adopts a mature B/S architecture and the popular development model, while the mobile terminal uses HTML5 and framework to implement the function of recommending personalized courses for users using collaborative filtering and recommendation algorithms. By improving the traditional recommendation algorithm based on collaborative filtering, the course recommendation results are more in line with users' interests, which greatly improves the accuracy and efficiency of the recommendation. On this basis, online teaching on this platform is divided into two modes: one mode is the original teacher uploads recorded teaching videos and students can learn by purchasing online or offline download; the other mode is interactive online live teaching. Each course is a separate online classroom; the teacher will publish online class information in advance, and students can purchase to get classroom number and password information online.
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Wang, Run Ying, and Lin Xu. "Multi-Agent Dam Management Model Based on Improved Reinforcement Learning Technology." Applied Mechanics and Materials 198-199 (September 2012): 922–26. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.922.
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In order to achieve efficient management of the dam, the new algorithms such as reinforcement learning, Synergetic, Structural Risk Minimization and Particle Swarm Optimization are used to establish a Cooperative Wavelet Least Squares Support Vector Machine Model. To improve the convergence rate and make full use of knowledge and advice of mechanics and hydraulics of the dam, WLS-SVRM and WLS-SVCM models are used cooperatively. Before the training online, mapping provides training samples for WLS-SVCM. During the course of training online, the numerical simulation and WLS-SVCM will provide knowledge and advices for WLS-SVRM. Case study shows that the model can provide timely information of gate opening and management information of the dam so as to provide decision support for engineering management.
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Zuo, Long, Shuo Xiong, Xin Qi, Zheng Wen, and Yiwen Tang. "Communication-Based Book Recommendation in Computational Social Systems." Complexity 2021 (January 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/6651493.
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This paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary research focus. Some improvements in its application performance are analyzed. First, for the calculation of user similarity, the introduction of computational social system attributes can help to determine users’ neighbors more accurately. Second, computational social system strategies can be adopted to penalize popular items. Third, the network community, identity, and trust can be combined as there is a close relationship. Therefore, this paper proposes a new method that uses a computational social system, including a trust model based on community relationships, to improve the user similarity calculation accuracy to enhance personalized recommendation. Finally, the improved algorithm in this paper is tested on the online reading website dataset. The experimental results show that the enhanced collaborative filtering algorithm performs better than the traditional algorithm.
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Hole, Prof K. R. "Fraud Detection and Prevention in E-commerce using Decision Tree Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 2187–96. http://dx.doi.org/10.22214/ijraset.2024.60307.
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Abstract: Fraud detection is an important part of e-commerce because it helps prevent fraud such as illegal transactions, identity theft, and money laundering. Recently, there has been a lot of literature on the application of machine learning algorithms to identify e-commerce fraud. These algorithms work by learning patterns in data that indicate fraud. Pattern checking deals with discovering differences in data, such as unusual products, locations, or behavior outside the norm for certain users, through machine learning. In this project, we propose a decision tree algorithm to detect fraud in e-commerce using newly generated data from various online products on e-commerce sites. In addition to fraud detection, we also provide advice on fraud prevention. We propose a new security model that will prove the user's identity. In this security model, users are required to register their profile with some questions. Our security systems will display relevant images in response to the registration question. The user has to click on the correct answer image within the time limit. We will ask the user 3 questions in graphic format. If the user selects the correct answer, the user will be considered a real user.
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Chen, Yuting, and Ming Li. "An Effective Online Sequential Stochastic Configuration Algorithm for Neural Networks." Sustainability 14, no. 23 (2022): 15601. http://dx.doi.org/10.3390/su142315601.
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Random Vector Functional-link (RVFL) networks, as a class of random learner models, have received careful attention from the neural network research community due to their advantages in obtaining fast learning algorithms and models, in which the hidden layer parameters are randomly generated and remain fixed during the training phase. However, its universal approximation ability may not be guaranteed if the random parameters are not properly selected in an appropriate range. Moreover, the resulting random learner’s generalization performance may seriously deteriorate once the RVFL network’s structure is not well-designed. Stochastic configuration (SC) algorithm, which incrementally constructs a universal approximator by obtaining random hidden parameters under a specified supervisory mechanism, instead of fixing the selection scope in advance and without any reference to training information, can effectively circumvent these awkward issues caused by randomness. This paper extends the SC algorithm to an online sequential version, termed as an OSSC algorithm, by means of recursive least square (RLS) technique, aiming to copy with modeling tasks where training observations are sequentially provided. Compared to the online sequential learning of RVFL networks (OS-RVFL in short), our proposed OSSC algorithm can avoid the awkward setting of certain unreasonable range for the random parameters, and can also successfully build a random learner with preferable learning and generalization capabilities. The experimental study has shown the effectiveness and advantages of our OSSC algorithm.
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Liang, Shihan. "The Future of Finance: Fintech and Digital Transformation." Highlights in Business, Economics and Management 15 (June 28, 2023): 20–26. http://dx.doi.org/10.54097/hbem.v15i.9222.
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The realm of financial technology, or fintech, has been increasingly gaining attention and interest as it pertains to the integration of technology in financial services. This paper endeavors to provide an overview of various facets of fintech, including its impact on online payments, Peer-to-Peer (P2P) lending, Robo-advice, and blockchain technology. One significant effect of fintech is observed in the area of online payments, where traditional banking methods are being substituted with more advanced online payment systems. Fintech has emerged as a promising solution to the challenge of convenient, secure, and fast payment transactions for goods and services. P2P lending, another fintech innovation, enables borrowers to obtain loans without having to go through traditional financial institutions. This process has been simplified and made more accessible through the use of fintech platforms that connect borrowers with lenders. The paper also addresses the use of Robo-advice in fintech, which utilizes algorithms and artificial intelligence to provide financial advice to clients. Robo-advice technology has increased accessibility to financial advice for a more extensive range of people, reducing the barriers that might have limited access to this type of financial service. Finally, this paper examines the importance of blockchain technology in fintech, with its potential to streamline processes and ensure security in financial transactions. The decentralized nature of blockchain technology offers a robust solution to financial security, ensuring that transactions are secure, transparent, and immutable. In conclusion, the essay underlines the transformative potential of fintech in the financial industry with its numerous innovations.
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Zhou, Chenchen, Shaoqi Wang, Yi Cao, Shuang-Hua Yang, and Bin Bai. "Online Pyrometry Calibration for Industrial Combustion Process Monitoring." Processes 10, no. 9 (2022): 1694. http://dx.doi.org/10.3390/pr10091694.
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Temperature and its distribution are crucial for combustion monitoring and control. For this application, digital camera-based pyrometers become increasingly popular, due to its relatively low cost. However, these pyrometers are not universally applicable due to the dependence of calibration. Compared with pyrometers, monitoring cameras exist in all most every combustion chamber. Although these cameras, theologically, have the ability to measure temperature, due to lack of calibration they are only used for visualization to support the decisions of operators. Almost all existing calibration methods are laboratory-based, and hence cannot calibrate a camera in operation. This paper proposes an online calibration method. It uses a pre-calibrated camera as a standard pyrometer to calibrate another camera in operation. The calibration is based on a photo taken by the pyrometry-camera at a position close to the camera in operation. Since the calibration does not affect the use of the camera in operation, it sharply reduces the cost and difficulty of pyrometer calibration. In this paper, a procedure of online calibration is proposed, and the advice about how to set camera parameters is given. Besides, the radio pyrometry is revised for a wider temperature range. The online calibration algorithm is developed based on two assumptions for images of the same flame taken in proximity: (1) there are common regions between the two images taken at close position; (2) there are some constant characteristic temperatures between the two-dimensional temperature distributions of the same flame taken from different angles. And those two assumptions are verified in a real industrial plants. Based on these two verified features, a temperature distribution matching algorithm is developed to calibrate pyrometers online. This method was tested and validated in an industrial-scale municipal solid waste incinerator. The accuracy of the calibrated pyrometer is sufficient for flame monitoring and control.
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Stein, Clifford, Van-Anh Truong, and Xinshang Wang. "Advance Service Reservations with Heterogeneous Customers." Management Science 66, no. 7 (2020): 2929–50. http://dx.doi.org/10.1287/mnsc.2019.3364.
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We study a fundamental model of resource allocation in which a finite number of resources must be assigned in an online manner to a heterogeneous stream of customers. The customers arrive randomly over time according to known stochastic processes. Each customer requires a specific amount of capacity and has a specific preference for each of the resources with some resources being feasible for the customer and some not. The system must find a feasible assignment of each customer to a resource or must reject the customer. The aim is to maximize the total expected capacity utilization of the resources over the horizon. This model has application in services, freight transportation, and online advertising. We present online algorithms with bounded competitive ratios relative to an optimal off-line algorithm that knows all stochastic information. Our algorithms perform extremely well compared with common heuristics as demonstrated on a real data set from a large hospital system in New York City. This paper was accepted by Yinyu Ye, optimization.
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Rao Jetti, Chandrasekhar, Rehamatulla Shaik, and Sadhik Shaik. "Disease Prediction using Naïve Bayes - Machine Learning Algorithm." International Journal of Science and Healthcare Research 6, no. 4 (2021): 17–22. http://dx.doi.org/10.52403/ijshr.20211004.
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It can occur on many occasions that you or a loved one requires urgent medical assistance, but they are unavailable due to unforeseen circumstances, or that we are unable to locate the appropriate doctor for the care. As a result, we will try to incorporate an online intelligent Smart Healthcare System in this project to solve this issue. It's a web-based programmed that allows patients to get immediate advice about their health problems. The aim of the smart healthcare system is to create a web application that can take a user's symptoms and predict diseases, as well as serve as an online consultant for various diseases. We created an expert system called Smart Health Care System, which is used to make doctors' jobs easier. A machine examines a patient at a basic level and recommends diseases that may be present. It begins by inquiring about the patient's symptoms; if the device is able to determine the relevant condition, it then recommends a doctor in the patient's immediate vicinity. The system will show the result based on the available accumulated data. We're going to use some clever data mining techniques here. We use several intelligent data mining techniques to guess the most accurate illness that could be associated with a patient's symptoms, and we use an algorithm (Naive Bayes) to map the symptoms with potential diseases based on a database of many patients' medical records. This system not only makes doctors' jobs easier, but it also benefits patients by getting them the care they need as soon as possible. Keywords: Disease Prediction, Naïve Bayes, Machine Learning Algorithm, Smart Healthcare System.
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Wu, Yong, Min Ji, and Qi Fan Yang. "Semi-Online Machine Covering under a Grade of Service Provision." Applied Mechanics and Materials 101-102 (September 2011): 484–87. http://dx.doi.org/10.4028/www.scientific.net/amm.101-102.484.
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Two semi-online scheduling problems on two parallel identical machines under a grade of service (GoS) provision were studied. The goal is to maximize the minimum machine load. For the semi-online version where the largest processing time of all jobs is known in advance, we show that no competitive algorithm exists. For the semi-online version where the optimal offline value is known in advance, we propose an optimal algorithm with competitive ratio 2.
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Mitra, Siddharth, and Aditya Gopalan. "On Adaptivity in Information-Constrained Online Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5199–206. http://dx.doi.org/10.1609/aaai.v34i04.5964.
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We study how to adapt to smoothly-varying (‘easy’) environments in well-known online learning problems where acquiring information is expensive. For the problem of label efficient prediction, which is a budgeted version of prediction with expert advice, we present an online algorithm whose regret depends optimally on the number of labels allowed and Q* (the quadratic variation of the losses of the best action in hindsight), along with a parameter-free counterpart whose regret depends optimally on Q (the quadratic variation of the losses of all the actions). These quantities can be significantly smaller than T (the total time horizon), yielding an improvement over existing, variation-independent results for the problem. We then extend our analysis to handle label efficient prediction with bandit (partial) feedback, i.e., label efficient bandits. Our work builds upon the framework of optimistic online mirror descent, and leverages second order corrections along with a carefully designed hybrid regularizer that encodes the constrained information structure of the problem. We then consider revealing action-partial monitoring games – a version of label efficient prediction with additive information costs – which in general are known to lie in the hard class of games having minimax regret of order T2/3. We provide a strategy with an O((Q*T)1/3 bound for revealing action games, along with one with a O((QT)1/3) bound for the full class of hard partial monitoring games, both being strict improvements over current bounds.
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Su, Dai Zhong, and Wen Jie Peng. "Remote Machine Condition Monitoring Using Wireless Web Technology." Key Engineering Materials 419-420 (October 2009): 745–48. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.745.
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A remote real-time machine condition monitoring system is reported in this paper, which is applied for diagnosis and prognosis of gearboxes’ working condition. Within the system, the diagnostic classification is performed by pattern recognition using statistic parameters, and remote diagnostic capability is enhanced by applying Wireless Web technology. An online signal-processing scheme is adopted based on time-frequency analysis, digital filtering and statistic parameter algorithm to detect early fault signals of gears and to provide expert advice for decision making for maintenance. The effectiveness of the developed remote diagnostic system is verified via experimental investigation of monitoring a gearbox on a test rig under different conditions.
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Li, Wan-Yue, Ya-Nan Song, Ling Luo, Chuang Nie, and Mao-Nian Zhang. "An online diabetic retinopathy screening tool for patients with type 2 diabetes." International Journal of Ophthalmology 14, no. 11 (2021): 1748–55. http://dx.doi.org/10.18240/ijo.2021.11.15.
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AIM: To develop a useful diabetic retinopathy (DR) screening tool for patients with type 2 diabetes mellitus (T2DM). METHODS: A DR prediction model based on the Logistic regression algorithm was established on the development dataset containing 778 samples (randomly assigned to the training dataset and the internal validation dataset at a ratio of 7:3). The generalization capability of the model was assessed using an external validation dataset containing 128 samples. The DR risk calculator was developed through WeChat Developer Tools using JavaScript, which was embedded in the WeChat Mini Program. RESULTS: The model revealed risk factors (duration of diabetes, diabetic nephropathy, and creatinine level) and protective factors (annual DR screening and hyperlipidemia) for DR. In the internal and external validation, the recall ratios of the model were 0.92 and 0.89, respectively, and the area under the curve values were 0.82 and 0.70, respectively. CONCLUSION: The DR screening tool integrates education, risk prediction, and medical advice function, which could help clinicians in conducting DR risk assessments and providing recommendations for ophthalmic referral to increase the DR screening rate among patients with T2DM.
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Wang, Yingjie, Ming Zeng, and Zesong Fei. "Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems." Electronics 12, no. 11 (2023): 2441. http://dx.doi.org/10.3390/electronics12112441.
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With the rapid growth of data traffic, low earth orbit (LEO) satellite communication networks have gradually ushered in a new trend of development due to its advantages of low latency, wide coverage, and high capacity. However, as a result of the limited on-board resources and rapidly changing traffic demand, it is increasingly urgent to design an efficient resource-allocation scheme to satisfy the traffic demand. In this paper, we propose two resource allocation algorithms in the multi-satellite system based on beam-hopping technology. In the offline case, it is assumed that the channel gains in all time-slots are known in advance, and we propose a heuristic algorithm to allocate time and frequency resources, and a successive convex approximation (SCA) algorithm to allocate power resources. In the online case, it is assumed that only the instant channel gains information is known; therefore, we apply the dynamic programming (DP) algorithm to maximize the system throughput. The simulation results prove that the proposed resource-allocation algorithms based on beam-hopping technology have better performance than the traditional average allocation method, and the online algorithm has acceptable performance loss compared with the offline algorithm.
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Chambers, Duncan, Anna J. Cantrell, Maxine Johnson, et al. "Digital and online symptom checkers and health assessment/triage services for urgent health problems: systematic review." BMJ Open 9, no. 8 (2019): e027743. http://dx.doi.org/10.1136/bmjopen-2018-027743.
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ObjectivesIn England, the NHS111 service provides assessment and triage by telephone for urgent health problems. A digital version of this service has recently been introduced. We aimed to systematically review the evidence on digital and online symptom checkers and similar services.DesignSystematic review.Data sourcesWe searched Medline, Embase, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Health Management Information Consortium, Web of Science and ACM Digital Library up to April 2018, supplemented by phrase searches for known symptom checkers and citation searching of key studies.Eligibility criteriaStudies of any design that evaluated a digital or online symptom checker or health assessment service for people seeking advice about an urgent health problem.Data extraction and synthesisData extraction and quality assessment (using the Cochrane Collaboration version of QUADAS for diagnostic accuracy studies and the National Heart, Lung and Blood Institute tool for observational studies) were done by one reviewer with a sample checked for accuracy and consistency. We performed a narrative synthesis of the included studies structured around pre-defined research questions and key outcomes.ResultsWe included 29 publications (27 studies). Evidence on patient safety was weak. Diagnostic accuracy varied between different systems but was generally low. Algorithm-based triage tended to be more risk averse than that of health professionals. There was very limited evidence on patients’ compliance with online triage advice. Study participants generally expressed high levels of satisfaction, although in mainly uncontrolled studies. Younger and more highly educated people were more likely to use these services.ConclusionsThe English ‘digital 111’ service has been implemented against a background of uncertainty around the likely impact on important outcomes. The health system may need to respond to short-term changes and/or shifts in demand. The popularity of online and digital services with younger and more educated people has implications for health equity.PROSPERO registration numberCRD42018093564.
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Li, Zihao, Hao Wang, and Zhenzhen Yan. "Fully Online Matching with Stochastic Arrivals and Departures." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 12014–21. http://dx.doi.org/10.1609/aaai.v37i10.26417.
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We study a fully online matching problem with stochastic arrivals and departures. In this model, each online arrival follows a known identical and independent distribution over a fixed set of agent types. Its sojourn time is unknown in advance and follows type-specific distributions with known expectations. The goal is to maximize the weighted reward from successful matches. To solve this problem, we first propose a linear program (LP)-based algorithm whose competitive ratio is lower bounded by 0.155 under mild conditions. We further achieve better ratios in some special cases. To demonstrate the challenges of the problem, we further establish several hardness results. In particular, we show that no online algorithm can achieve a competitive ratio better than 2/3 in this model and there is no LP-based algorithm (with respect to our proposed LP) with a competitive ratio better than 1/3. Finally, we demonstrate the effectiveness and efficiency of our algorithm numerically.
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Jeon, Wonbo, Wonsop Kim, Heoncheol Lee, and Cheol-Hoon Lee. "Online Slack-Stealing Scheduling with Modified laEDF in Real-Time Systems." Electronics 8, no. 11 (2019): 1286. http://dx.doi.org/10.3390/electronics8111286.
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In hard real-time task systems where periodic and aperiodic tasks coexist, the object of task scheduling is to reduce the response time of the aperiodic tasks while meeting the deadline of periodic tasks. Total bandwidth server (TBS) and advanced TBS (ATBS) are used in dynamic priority systems. However, these methods are not optimal solutions because they use the worst-case execution time (WCET) or the estimation value of the actual execution time of the aperiodic tasks. This paper presents an online slack-stealing algorithm called SSML that can make significant response time reducing by modification of look-ahead earliest deadline first (laEDF) algorithm as the slack computation method. While the conventional slack-stealing method has a disadvantage that the slack amount of each frame must be calculated in advance, SSML calculates the slack when aperiodic tasks arrive. Our simulation results show that SSML outperforms the existing TBS based algorithms when the periodic task utilization is higher than 60%. Compared to ATBS with virtual release advancing (VRA), the proposed algorithm can reduce the response time up to about 75%. The performance advantage becomes much larger as the utilization increases. Moreover, it shows a small performance variation of response time for various task environments.
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Balseiro, Santiago, Christian Kroer, and Rachitesh Kumar. "Online Resource Allocation under Horizon Uncertainty." ACM SIGMETRICS Performance Evaluation Review 51, no. 1 (2023): 63–64. http://dx.doi.org/10.1145/3606376.3593559.
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We study stochastic online resource allocation: a decision maker needs to allocate limited resources to stochastically-generated sequentially-arriving requests in order to maximize reward. At each time step, requests are drawn independently from a distribution that is unknown to the decision maker. Online resource allocation and its special cases have been studied extensively in the past, but prior results crucially and universally rely on the strong assumption that the total number of requests (the horizon) is known to the decision maker in advance. In many applications, such as revenue management and online advertising, the number of requests can vary widely because of fluctuations in demand or user traffic intensity. In this work, we develop online algorithms that are robust to horizon uncertainty. In sharp contrast to the known-horizon setting, no algorithm can achieve even a constant asymptotic competitive ratio that is independent of the horizon uncertainty. We introduce a novel generalization of dual mirror descent which allows the decision maker to specify a schedule of time-varying target consumption rates, and prove corresponding performance guarantees. We go on to give a fast algorithm for computing a schedule of target consumption rates that leads to near-optimal performance in the unknown-horizon setting. In particular, our competitive ratio attains the optimal rate of growth (up to logarithmic factors) as the horizon uncertainty grows large. Finally, we also provide a way to incorporate machine-learned predictions about the horizon which interpolates between the known and unknown horizon settings.
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Ding, Zhen, Chifu Yang, Zhipeng Wang, Xunfeng Yin, and Feng Jiang. "Online Adaptive Prediction of Human Motion Intention Based on sEMG." Sensors 21, no. 8 (2021): 2882. http://dx.doi.org/10.3390/s21082882.
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Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction. The adaptation network ensures the proposed prediction method can still maintain a certain prediction accuracy even the sEMG signals distribution changes by adjusting some parameters of the feature extraction network and the prediction network online. Ten subjects were recruited to collect surface EMG data from nine muscles on the treadmill. The proposed prediction algorithm can predict the knee angle 101.25 ms in advance with 2.36 degrees accuracy. The proposed prediction algorithm also can predict the occurrence time of initial contact 236±9 ms in advance. Meanwhile, the proposed feature extraction method can achieve 90.71±3.42% accuracy of sEMG reconstruction and can guarantee 73.70±5.01% accuracy even when the distribution of sEMG is changed without any adjustment. The online adaptation network enhances the accuracy of sEMG reconstruction of CAE to 87.65±3.83% and decreases the angle prediction error from 4.03∘ to 2.36∘. The proposed method achieves effective motion prediction in advance and alleviates the influence caused by the non-stationary of sEMG.
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Lechowicz, Adam, Nicolas Christianson, Bo Sun, et al. "Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms." ACM SIGMETRICS Performance Evaluation Review 52, no. 1 (2024): 45–46. http://dx.doi.org/10.1145/3673660.3655074.
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We introduce and study online conversion with switching costs, a family of online problems that capture emerging problems at the intersection of energy and sustainability. In this problem, an online player attempts to purchase (alternatively, sell) fractional shares of an asset during a fixed time horizon with length T. At each time step, a cost function (alternatively, price function) is revealed, and the player must irrevocably decide an amount of asset to convert. The player also incurs a switching cost whenever their decision changes in consecutive time steps, i.e., when they increase or decrease their purchasing amount. We introduce competitive (robust) threshold-based algorithms for both the minimization and maximization variants of this problem, and show they are optimal among deterministic online algorithms. We then propose learning-augmented algorithms that take advantage of untrusted black-box advice (such as predictions from a machine learning model) to achieve significantly better average-case performance without sacrificing worst-case competitive guarantees. Finally, we empirically evaluate our proposed algorithms using a carbon-aware EV charging case study, showing that our algorithms substantially improve on baseline methods for this problem.
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Zhou, Bingjie, Susan B. Roberts, Sai Krupa Das, and Elena N. Naumova. "Weight Loss Trajectories and Short-Term Prediction in an Online Weight Management Program." Nutrients 16, no. 8 (2024): 1224. http://dx.doi.org/10.3390/nu16081224.
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The extent to which early weight loss in behavioral weight control interventions predicts long-term success remains unclear. In this study, we developed an algorithm aimed at classifying weight change trajectories and examined its ability to predict long-term weight loss based on weight early change. We utilized data from 667 de-identified individuals who participated in a commercial weight loss program (Instinct Health Science), comprising 69,363 weight records. Sequential polynomial regression models were employed to classify participants into distinct weight trajectory patterns based on key model parameters. Next, we applied multinomial logistic models to evaluate if early weight loss in the first 14 days and prolonged duration of participation were significantly associated with long-term weight loss patterns. The mean percentage of weight loss was 7.9 ± 5.1% over 133 ± 69 days. Our analysis revealed four main weight loss trajectory patterns: a steady decrease over time (30.6%), a decrease to a plateau with subsequent decline (15.8%), a decrease to a plateau with subsequent increase (46.9%), and no substantial decrease (6.7%). Early weight change rate and total participating duration emerged as significant factors in differentiating long-term weight loss patterns. These findings contribute to support the provision of tailored advice in the early phase of behavioral interventions for weight loss.
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Kim, Hyo Young, and Young Soo Park. "Trust Dynamics in Financial Decision Making: Behavioral Responses to AI and Human Expert Advice Following Structural Breaks." Behavioral Sciences 14, no. 10 (2024): 964. http://dx.doi.org/10.3390/bs14100964.
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This study explores the trust dynamics in financial forecasting by comparing how individuals perceive the credibility of AI and human experts during significant structural market changes. We specifically examine the impact of two types of structural breaks on trust: Additive Outliers, which represent a single yet significant anomaly, and Level Shifts, which indicate a sustained change in data patterns. Grounded in theoretical frameworks such as attribution theory, algorithm aversion, and the Technology Acceptance Model (TAM), this research investigates psychological responses to AI and human advice under uncertainty. This experiment involved 157 participants, recruited via Amazon Mechanical Turk (MTurk), who were asked to forecast stock prices under different structural break scenarios. Participants were randomly assigned to either the AI or human expert treatment group, and the experiment was conducted online. Through this controlled experiment, we find that, while initial trust levels in AI and human experts are comparable, the credibility of advice is more severely compromised following a structural break in the Level Shift condition, compared to the Additive Outlier condition. Moreover, the decline in trust is more pronounced for human experts than for AI. These findings highlight the psychological factors influencing decision making under uncertainty and offer insights into the behavioral responses to AI and human expert systems during structural market changes.
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Lapatta, Nouval Trezandy. "Ecotourism Recommendations based on Sentiments Using Skyline Query and Apache-Spark." Journal of Sosial Science 3, no. 3 (2022): 534–46. http://dx.doi.org/10.46799/jss.v3i3.333.
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The selection of an ecotourism destination is a challenging service in an online transaction. The process must consider personal considerations, such as costs or distance and interesting eco-points like specific sceneries or the rare and unique picturesque landscapes. Only a few tourists have such required information for any particular local resources. A proposed recommender system is a solution for tourists to get advice on appropriate ecotourism destinations based on sentiments according to their preferences. This work proposed the skyline query method based on the Skyline Sort Filter algorithm in the Apache Spark cluster computing framework to build recommendations. The sentiment analysis process using the SentiStrength algorithm obtain an accuracy of 78.3% and F-arithmetic of 84.5%. These results indicate the proposed recommender system can detect positive responses from visitors to ensure best ecotourism recommendations with positive sentiments for tourist. Apache Spark with three computer nodes has 213.7 times faster execution time on correlated data, 240 times faster on independent data, and 288.1 times faster on anti-correlated data than a single computing method.
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Si, JiaShuai, and MingRui Hao. "Online Weapon-target Assignment based on Distributed Auction Mechanism." Journal of Physics: Conference Series 2456, no. 1 (2023): 012044. http://dx.doi.org/10.1088/1742-6596/2456/1/012044.
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Abstract To solve the problem of online weapon-target assignment (OWTA) in the integration of large-scale search and attack in unknown environment, an OWTA algorithm based on distributed auction mechanism is presented. Aiming at the problem that the traditional combinatorial optimization algorithm needs to set up the global battlefield situation in advance, considering the consumability of resources in the attack process, the integrated search and attack task flow is established. Considering the communication restricted environment, the unmanned aerial vehicles (uavs) are grouped, with centralized architecture within the group and distributed structure between the groups, and the corresponding distributed auction mechanism is constructed to achieve OWTA within the communication range limited. In order to solve the problem that it is difficult to ensure the time consistency of the coordinated attack target, a dubins cooperative path planning based on cooperative particle swarm optimization (CPSO) algorithm is proposed. Particle swarm optimization algorithm is used to adjust the radius of the dubins path of each bomb, so that the uav in the same group can hit the target simultaneously without collision and have the shortest flight range. The simulation results show that the designed distributed auction algorithm takes into account the consumption of attack resources, and quickly redistributes the firepower to the new targets in the dynamic uncertain environment, which ensures the maximization of the execution efficiency of the multi-machine cluster fire allocation task.
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Bhushan, Neha, Saad Mekhilef, Kok Soon Tey, Mohamed Shaaban, Mehdi Seyedmahmoudian, and Alex Stojcevski. "Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies." Sustainability 14, no. 23 (2022): 15912. http://dx.doi.org/10.3390/su142315912.
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The online battery management system (BMS) is very critical for the safe and reliable operation of electric vehicles (EVs) and renewable energy storage applications. The primary responsibility of BMS is data assembly, state monitoring, state management, state safety, charging control, thermal management, and information management. The algorithm and control development for smooth and cost-effective functioning of online BMS is challenging research. The complexity, stability, cost, robustness, computational cost, and accuracy of BMS for Li-ion batteries (LiBs) can be enhanced through the development of algorithms. The model-based and non-model-based data-driven methods are the most suitable for developing algorithms and control for online BMS than other methods present in the literatures. The performance analysis of algorithms under different current, thermal, and load conditions have been investigated. The objective of this review is to advance the experimental design and control for online BMS. The comprehensive overview of present techniques, core issues, technical challenges, emerging trends, and future research opportunities for next-generation BMS is covered in this paper with experimental and simulation analysis.