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Journal articles on the topic 'Online algorithm with advice'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

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 competiti

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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 parti

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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 com

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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 $${\mathcal{F}}$$

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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 uncondition

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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 ac

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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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11

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 s

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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 dat

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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

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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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18

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

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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 recommende

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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 fore

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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 makes

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22

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

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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 b

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24

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

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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

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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

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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 kno

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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 adopt

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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 i

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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 st

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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 conve

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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 me

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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 reso

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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 con

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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 significantl

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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 effe

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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 Pro

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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

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40

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

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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

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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 modif

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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 manag

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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.

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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 d

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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 a

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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

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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 algor

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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 central

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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 develo

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