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Articles de revues sur le sujet « Algorithmes online »

Auteur : Grafiati

Publié le 25 mai 2024

Mis à jour le 31 juillet 2025

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1

Tornede, Alexander, Viktor Bengs, and Eyke Hüllermeier. "Machine Learning for Online Algorithm Selection under Censored Feedback." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 10370–80. http://dx.doi.org/10.1609/aaai.v36i9.21279.

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In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. As the latter is known to exhibit a heavy-tail distribution, an algorithm is normally stopped when exceeding a predefined upper time limit. As a consequence, machine learning methods used to optimize an algorithm selection strategy in a data-driven manner need to d

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Lange, Tomer, Joseph (Seffi) Naor, and Gala Yadgar. "Offline and Online Algorithms for SSD Management." ACM SIGMETRICS Performance Evaluation Review 50, no. 1 (2022): 89–90. http://dx.doi.org/10.1145/3547353.3522630.

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The abundance of system-level optimizations for reducing SSD write amplification, which are usually based on experimental evaluation, stands in contrast to the lack of theoretical algorithmic results in this problem domain. To bridge this gap, we explore the problem of reducing write amplification from an algorithmic perspective, considering it in both offline and online settings. In the offline setting, we present a near-optimal algorithm. In the online setting, we first consider algorithms that have no prior knowledge about the input. We present a worst case lower bound and show that the gre

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Putri, Salsa Della Guitara, Eko Priyo Purnomo, and Tiara Khairunissa. "Echo Chambers and Algorithmic Bias: The Homogenization of Online Culture in a Smart Society." SHS Web of Conferences 202 (2024): 05001. http://dx.doi.org/10.1051/shsconf/202420205001.

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The rise of smart societies, characterized by extensive use of technology and data-driven algorithms, promises to improve our lives. However, this very technology presents a potential threat to the richness and diversity of online culture. This thesis explores the phenomenon of echo chambers and algorithmic bias, examining how they contribute to the homogenization of online experiences. Social media algorithms personalize content feeds, presenting users with information that reinforces their existing beliefs. This creates echo chambers, where users are isolated from diverse viewpoints. Algorit

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Xu, Chenyang, and Benjamin Moseley. "Learning-Augmented Algorithms for Online Steiner Tree." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8744–52. http://dx.doi.org/10.1609/aaai.v36i8.20854.

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This paper considers the recently popular beyond-worst-case algorithm analysis model which integrates machine-learned predictions with online algorithm design. We consider the online Steiner tree problem in this model for both directed and undirected graphs. Steiner tree is known to have strong lower bounds in the online setting and any algorithm’s worst-case guarantee is far from desirable. This paper considers algorithms that predict which terminal arrives online. The predictions may be incorrect and the algorithms’ performance is parameterized by the number of incorrectly predicted terminal

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Smale, Steve, and Yuan Yao. "Online Learning Algorithms." Foundations of Computational Mathematics 6, no. 2 (2005): 145–70. http://dx.doi.org/10.1007/s10208-004-0160-z.

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BARBAKH, WESAM, and COLIN FYFE. "ONLINE CLUSTERING ALGORITHMS." International Journal of Neural Systems 18, no. 03 (2008): 185–94. http://dx.doi.org/10.1142/s0129065708001518.

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We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum. We derive online learning algorithms and illustrate their convergence to optimal solutions which K-means fails to find. We then extend the algorithm by underpinning it with a latent space which enables a topology preserving mapping to be found. We show visualisation results on some standard data sets.

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Sharma, Vishal, Kirsten E. Bray, Neha Kumar, and Rebecca E. Grinter. "Romancing the Algorithm." Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022): 1–29. http://dx.doi.org/10.1145/3555651.

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Many romance novelists have shifted to self-publishing mediated through online technologies, such as online retailer platforms for selling novels and social media for marketing. However, engagement with such complex algorithmic systems has posed challenges, including understanding continually changing algorithms, frequently changing silently, impacting novelists' successful professionalization and monetization. We conducted surveys and interviews with romance novelists to examine how they experience, interpret, and navigate algorithms. Our findings detail interviewees' efforts to comprehend al

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K, Kousalya, and Balasubramanie P. "Online Grid Scheduling Using Ant Algorithm." International Journal of Engineering and Technology 1, no. 1 (2009): 21–26. http://dx.doi.org/10.7763/ijet.2009.v1.4.

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Möhlmann, Mareike, Lior Zalmanson, Ola Henfridsson, and Robert Wayne Gregory. "Algorithmic Management of Work on Online Labor Platforms: When Matching Meets Control." MIS Quarterly 45, no. 4 (2021): 1999–2022. http://dx.doi.org/10.25300/misq/2021/15333.

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Online labor platforms (OLPs) can use algorithms along two dimensions: matching and control. While previous research has paid considerable attention to how OLPs optimize matching and accommodate market needs, OLPs can also employ algorithms to monitor and tightly control platform work. In this paper, we examine the nature of platform work on OLPs, and the role of algorithmic management in organizing how such work is conducted. Using a qualitative study of Uber drivers’ perceptions, supplemented by interviews with Uber executives and engineers, we present a grounded theory that captures the alg

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Lange, Tomer, Joseph (Seffi) Naor, and Gala Yadgar. "Offline and Online Algorithms for SSD Management." Communications of the ACM 66, no. 7 (2023): 129–37. http://dx.doi.org/10.1145/3596205.

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Flash-based solid-state drives (SSDs) are a key component in most computer systems, thanks to their ability to support parallel I/O at sub-millisecond latency and consistently high throughput. At the same time, due to the limitations of the flash media, they perform writes out-of-place, often incurring a high internal overhead which is referred to as write amplification. Minimizing this overhead has been the focus of numerous studies by the systems research community for more than two decades. The abundance of system-level optimizations for reducing SSD write amplification, which is typically

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Shao, Zhiyuan. "Algorithmic Resistance and Online Privacy: Extending the Meta-UTAUT Model with Particular Privacy Concerns." International Journal of Applied Science 8, no. 1 (2025): p72. https://doi.org/10.30560/ijas.v8n1p72.

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This study delved into the perceived benefits and privacy concerns individuals face when interacting with algorithms, and explored their relation to algorithmic resistance. Based on technology acceptance research and online privacy studies, an extended Meta-UTAUT model was proposed. A total of 434 valid samples were obtained in China. The results show that perceived benefits (including performance expectancy, effort expectancy, social influence, and facilitating conditions) are negatively related to algorithmic resistance attitude. Moreover, concerns for technology and financial privacy are po

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Ying, Y., and D. X. Zhou. "Online Regularized Classification Algorithms." IEEE Transactions on Information Theory 52, no. 11 (2006): 4775–88. http://dx.doi.org/10.1109/tit.2006.883632.

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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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Christianson, Nicolas, Bo Sun, Steven Low, and Adam Wierman. "Risk-Sensitive Online Algorithms." ACM SIGMETRICS Performance Evaluation Review 52, no. 2 (2024): 6–8. http://dx.doi.org/10.1145/3695411.3695415.

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Albers, Susanne. "Online algorithms: a survey." Mathematical Programming 97, no. 1 (2003): 3–26. http://dx.doi.org/10.1007/s10107-003-0436-0.

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Ying, Yiming, and Ding-Xuan Zhou. "Online Pairwise Learning Algorithms." Neural Computation 28, no. 4 (2016): 743–77. http://dx.doi.org/10.1162/neco_a_00817.

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Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS) that we refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to existing works (Kar, Sriperumbudur, Jain, & Karnick, 2013 ; Wang, Khardon, Pechyony, & Jones, 2012 ), which require that the i

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R, Velvizhi, and Jayapriya D. "Decoupling Online Algorithms from Symmetric Encryption in Hierarchical Databases." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (2019): 1004–9. http://dx.doi.org/10.5373/jardcs/v11/20192664.

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Song, Lixun, Changhao Chen, and Federick Lin. "AI Algorithmic Pricing for Online Platforms: A Literature Review." Advances in Economics, Management and Political Sciences 196, no. 1 (2025): 125–39. https://doi.org/10.54254/2754-1169/2025.bj24935.

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Rapid advances in Artificial Intelligence (AI) are reshaping the economic landscape, creating opportunities and challenges for businesses, consumers, and policymakers. These technologies are changing market dynamics by optimizing collusive pricing and altering the competitive landscape. Therefore, we summarized the relevant literature and explored the following questions in depth: Will algorithmic decision-making promote competition or lead to new market concentration and collusion forms? How will AI-driven automation affect dynamic pricing on online platforms? How does price discrimination co

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Cheney-Lippold, John. "A New Algorithmic Identity." Theory, Culture & Society 28, no. 6 (2011): 164–81. http://dx.doi.org/10.1177/0263276411424420.

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Marketing and web analytic companies have implemented sophisticated algorithms to observe, analyze, and identify users through large surveillance networks online. These computer algorithms have the capacity to infer categories of identity upon users based largely on their web-surfing habits. In this article I will first discuss the conceptual and theoretical work around code, outlining its use in an analysis of online categorization practices. The article will then approach the function of code at the level of the category, arguing that an analysis of coded computer algorithms enables a supple

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Carkner, Brandon. "The Rhythm of The Algorithm: Behavioural Influences and TikTok Users." Emerging Library & Information Perspectives 6, no. 1 (2024): 7–23. http://dx.doi.org/10.5206/elip.v6i1.16753.

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TikTok’s ubiquity, with over two billion downloads, has made the social media platform one of the most popular in the world. Such popularity necessitates information experts to be aware of the technological composition and effects that can be induced into user populations through algorithmic processes which modify and alter behaviour. The composition and purpose of algorithms are explored within a sociotechnical space. Correlations between algorithms, user activity, and user behaviour can be examined as a product of algorithmic influence. Algorithmic procedures have the potential to shape user

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Guo, Yueqi. "Algorithmic Discrimination Causes Fewer Positive Expectations of Punishment Effects Than Human Discrimination." Lecture Notes in Education Psychology and Public Media 12, no. 1 (2023): 144–49. http://dx.doi.org/10.54254/2753-7048/12/20230797.

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People have different attitudes toward algorithmic discrimination than human discrimination. This study collected 179 data through an online experiment, comparing peoples expectations of the effect of punishing algorithms and humans due to discriminatory behaviors in recruitment. It turns out that people have fewer positive expectations about the effects of punishing algorithms than punishing humans. This may be because people dont trust algorithms. The findings contribute to a better understanding of peoples responses to algorithmic discrimination and provide new evidence for algorithm aversi

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Bienkowski, Marcin, David Fuchssteiner, Jan Marcinkowski, and Stefan Schmid. "Online Dynamic B-Matching." ACM SIGMETRICS Performance Evaluation Review 48, no. 3 (2021): 99–108. http://dx.doi.org/10.1145/3453953.3453976.

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This paper initiates the study of online algorithms for the maximum weight b-matching problem, a generalization of maximum weight matching where each node has at most b≥1 adjacent matching edges. The problem is motivated by emerging optical technologies which allow to enhance datacenter networks with reconfigurable matchings, providing direct connectivity between frequently communicating racks. These additional links may improve network performance, by leveraging spatial and temporal structure in the workload. We show that the underlying algorithmic problem features an intriguing connection to

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Berry, Pauline. "Troubleshooting algorithms: A book review of Weapons of Math Destruction by Cathy O’Neil." McMaster Journal of Communication 12, no. 2 (2020): 91–96. http://dx.doi.org/10.15173/mjc.v12i2.2450.

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Fact: we no longer control our lives, algorithms do. Mortgage-backed securities, college rankings, online advertising, law enforcement, human resources, credit lending, insurance, social media, politics, and consumer marketing; algorithms live within each one of these – collecting, segmenting, defining, and planting each one of us into arbitrary, unassailable buckets. The algorithms and the data that feed this process is what data scientist and international bestselling author, Cathy O’Neil, calls Weapons of Math Destruction (WMDs). In her captivating and frankly, bone-chilling account of the

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Prof. Dipti A. Mirkute, Prasad D. Banarase, and Geeta K. Rathod. "Online Writeup Categorization using a Machine Learning Strategy." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 5 (2024): 242–47. http://dx.doi.org/10.32628/ijsrset24115118.

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A variety of online sources produces a huge amount of daily news; thus, it is important to categorize the news items to make the information accessible to consumers easily and quickly scraping is used to gather existing news items from news websites and then categorize them automatically using a variety of classification algorithms. As a result, news categorization is a technique for discovering untracked news themes and providing specific recommendations based on the user's historical interests. The BBC News dataset, which includes articles from five different categories including Business, E

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Kang, Chowon, Yoonseo Choi, Yongjae Sohn, Hyunseung Lim, and Hwajung Hong. "Beyond Swipes and Scores: Investigating Practices, Challenges and User-Centered Values in Online Dating Algorithms." Proceedings of the ACM on Human-Computer Interaction 8, CSCW2 (2024): 1–30. http://dx.doi.org/10.1145/3687025.

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The reliability of online dating algorithms has sparked considerable debate, particularly regarding skepticism about their excessive emphasis on evaluating and getting evaluated, which often overshadows the quest for authentic romantic connections. To understand the multifaceted influence of dating algorithms on end-users and explore avenues for algorithmic features considering the dynamics of human relationships, we conducted a mixed-methods study comprising in-depth interviews (N = 22) and a metaphoric co-design workshop (N = 12) with active users of online dating platforms. Interviews revea

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Le Thi, Hoai An, and Vinh Thanh Ho. "Online Learning Based on Online DCA and Application to Online Classification." Neural Computation 32, no. 4 (2020): 759–93. http://dx.doi.org/10.1162/neco_a_01266.

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We investigate an approach based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) for online learning techniques. The prediction problem of an online learner can be formulated as a DC program for which online DCA is applied. We propose the two so-called complete/approximate versions of online DCA scheme and prove their logarithmic/sublinear regrets. Six online DCA-based algorithms are developed for online binary linear classification. Numerical experiments on a variety of benchmark classification data sets show the efficiency of our proposed algorithms in comparison wi

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Gruber, Jonathan, and Eszter Hargittai. "The importance of algorithm skills for informed Internet use." Big Data & Society 10, no. 1 (2023): 205395172311681. http://dx.doi.org/10.1177/20539517231168100.

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Using the Internet means encountering algorithmic processes that influence what information a user sees or hears. Existing research has shown that people's algorithm skills vary considerably, that they develop individual theories to explain these processes, and that their online behavior can reflect these understandings. Yet, there is little research on how algorithm skills enable people to use algorithms to their own benefit and to avoid harms they may elicit. To fill this gap in the literature, we explore the extent to which people understand how the online systems and services they use may

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Zarouali, Brahim, Natali Helberger, and Claes H. De Vreese. "Investigating Algorithmic Misconceptions in a Media Context: Source of a New Digital Divide?" Media and Communication 9, no. 4 (2021): 134–44. http://dx.doi.org/10.17645/mac.v9i4.4090.

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Algorithms are widely used in our data-driven media landscape. Many misconceptions have arisen about how these algorithms work and what they can do. In this study, we conducted a large representative survey (<em>N</em> = 2,106) in the Netherlands to explore algorithmic misconceptions. Results showed that a significant part of the general population holds (multiple) misconceptions about algorithms in the media. We found that erroneous beliefs about algorithms are more common among (1) older people (vs. younger people), (2) lower-educated people (vs. higher-educated), and (3) women (

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Shkodzinsky, Oleh, and Mykhailo Lutskiv. "Automated ai-based proctoring for online testing in e-learning system." Scientific journal of the Ternopil national technical university 107, no. 3 (2022): 76–85. http://dx.doi.org/10.33108/visnyk_tntu2022.03.076.

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Based on the analysis of existing on the market algorithmic solutions for identity verification during knowledge control in electronic learning systems, the requirements for the target system were formed. The main algorithms and approaches to the detection and recognition of faces were considered, as a result of which an effective combination of algorithms was chosen. The system of photo fixation and identity verification during knowledge control in LMS ATutor was designed and implemented. Its effectiveness was verified on the basis of a sample of test passes during its work in the real condit

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Ross, S., J. Pineau, S. Paquet, and B. Chaib-draa. "Online Planning Algorithms for POMDPs." Journal of Artificial Intelligence Research 32 (July 29, 2008): 663–704. http://dx.doi.org/10.1613/jair.2567.

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Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP is often intractable except for small problems due to their complexity. Here, we focus on online approaches that alleviate the computational complexity by computing good local policies at each decision step during the execution. Online algorithms generally consist of a lookahead search to find the best action to execute at each time step in an environment. Our objectives here are to survey the various existing online P

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Ashlagi, Itai, Brendan Lucier, and Moshe Tennenholtz. "Equilibria of Online Scheduling Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 67–73. http://dx.doi.org/10.1609/aaai.v27i1.8631.

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We describe a model for competitive online scheduling algorithms. Two servers, each with a single observable queue, compete for customers. Upon arrival, each customer strategically chooses the queue with minimal expected wait time. Each scheduler wishes to maximize its number of customers, and can strategically select which scheduling algorithm, such as First-Come-First-Served (FCFS), to use for its queue. This induces a game played by the servers and the customers. We consider a non-Bayesian setting, where servers and customers play to maximize worst-case payoffs. We show that there is a uniq

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Sun, Peng, and Teng Zhao. "Deploying Robots and Online Algorithms." Applied Mechanics and Materials 325-326 (June 2013): 1058–61. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1058.

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The improvement of 802.11 mesh networks is a robust quagmire. In this work, we show the construction of link-level acknowledgements. In order to answer this grand challenge, we use linear-time epistemologies to disconfirm that 802.11 mesh networks and architecture can collaborate to fix this riddle.

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Blum, Avrim, Tuomas Sandholm, and Martin Zinkevich. "Online algorithms for market clearing." Journal of the ACM 53, no. 5 (2006): 845–79. http://dx.doi.org/10.1145/1183907.1183913.

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Lagrée, Paul, Olivier Cappé, Bogdan Cautis, and Silviu Maniu. "Algorithms for Online Influencer Marketing." ACM Transactions on Knowledge Discovery from Data 13, no. 1 (2019): 1–30. http://dx.doi.org/10.1145/3274670.

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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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Devanur, Nikhil R. "Online algorithms with stochastic input." ACM SIGecom Exchanges 10, no. 2 (2011): 40–49. http://dx.doi.org/10.1145/1998549.1998558.

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Bienkowski, Marcin, Martin Böhm, Jaroslaw Byrka, et al. "Online Algorithms for Multilevel Aggregation." Operations Research 68, no. 1 (2020): 214–32. http://dx.doi.org/10.1287/opre.2019.1847.

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Gatto, Michael, and Peter Widmayer. "On robust online scheduling algorithms." Journal of Scheduling 14, no. 2 (2009): 141–56. http://dx.doi.org/10.1007/s10951-009-0115-y.

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Ying, Yiming. "Convergence analysis of online algorithms." Advances in Computational Mathematics 27, no. 3 (2006): 273–91. http://dx.doi.org/10.1007/s10444-005-9002-z.

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Ying, Yiming, and Massimiliano Pontil. "Online Gradient Descent Learning Algorithms." Foundations of Computational Mathematics 8, no. 5 (2007): 561–96. http://dx.doi.org/10.1007/s10208-006-0237-y.

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Kumar, Sandeep, and Deepak Garg. "Online Financial Algorithms: Competitive Analysis." International Journal of Computer Applications 40, no. 7 (2012): 8–14. http://dx.doi.org/10.5120/4974-7228.

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Susanto, Muhammad Riza Radyaka, Husni Thamrin, and Naufal Azmi Verdikha. "PERFORMANCE OF TEXT SIMILARITY ALGORITHMS FOR ESSAY ANSWER SCORING IN ONLINE EXAMINATIONS." Jurnal Teknik Informatika (Jutif) 4, no. 6 (2023): 1515–21. http://dx.doi.org/10.52436/1.jutif.2023.4.6.1025.

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The purpose of assessment is to determine learning success. Exams with question descriptions have several advantages, including ease of preparation and the ability to reveal student comprehension and originality. The problem with space is that it takes time to fix. Therefore, it is important to develop algorithms and software that automatically evaluate space. With the help of this algorithm and this software, you can solve some exam and assessment problems. This study aims to investigate similarity algorithms that approximate human patterns in evaluating ambiguous answers. This study examines

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Mastelini, Saulo Martiello, and André Carlos Ponce de Leon Ferreira De Carvalho. "Efficient online tree, rule-based and distance-based algorithms." Journal of the Brazilian Computer Society 31, no. 1 (2025): 426–34. https://doi.org/10.5753/jbcs.2025.5117.

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The fast development of technology resulted in the constant production of data in different forms and from different sources. Contrary to what was observed in the first machine learning (ML) research works, there might be too much data to handle with traditional algorithms. Changes in the underlying data distributions might also render traditional ML solutions useless in real-world applications. Online ML (OML) aims to create solutions able to process data incrementally, with limited computation resource usage, and to deal with time-changing data distributions. Unfortunately, we have seen a re

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Dixit, Rishabh, Amrit Singh Bedi, Ruchi Tripathi, and Ketan Rajawat. "Online Learning With Inexact Proximal Online Gradient Descent Algorithms." IEEE Transactions on Signal Processing 67, no. 5 (2019): 1338–52. http://dx.doi.org/10.1109/tsp.2018.2890368.

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Cárdenas-Haro, José Antonio, Mohamed Salem, Abraham N. Aldaco-Gastélum, Roberto López-Avitia, and Maurice Dawson. "Enhancing Security in Social Networks through Machine Learning: Detecting and Mitigating Sybil Attacks with SybilSocNet." Algorithms 17, no. 10 (2024): 442. http://dx.doi.org/10.3390/a17100442.

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This study contributes to the Sybil node-detecting algorithm in online social networks (OSNs). As major communication platforms, online social networks are significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. An additional exploration of distinct reputation systems and their practical applications led to this study’s discovery of machine learning algorithms, i.e., the KNN, support vector machine, and random forest algorithms, as part of our SybilSocNet. This study details the data-cleansing process for

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Lee, Min Kyung. "Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management." Big Data & Society 5, no. 1 (2018): 205395171875668. http://dx.doi.org/10.1177/2053951718756684.

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Algorithms increasingly make managerial decisions that people used to make. Perceptions of algorithms, regardless of the algorithms' actual performance, can significantly influence their adoption, yet we do not fully understand how people perceive decisions made by algorithms as compared with decisions made by humans. To explore perceptions of algorithmic management, we conducted an online experiment using four managerial decisions that required either mechanical or human skills. We manipulated the decision-maker (algorithmic or human), and measured perceived fairness, trust, and emotional res

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Pratibha, Sharma. "Algorithms and strategies for fraud prevention on online platforms." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2220–25. https://doi.org/10.5281/zenodo.14881312.

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The topic of fraud prevention on online platforms is an urgent area in the field of information security and user protection. Modern fraud prevention algorithms and strategies include an integrated approach combining machine learning, analytical tools, and multi-layered protection systems. The key aspects are the detection of anomalies in user behavior, the use of machine learning algorithms to identify suspicious activities, as well as the integration of data verification and verification systems. Behavioral analytical models that help predict and prevent potential threats also play an import

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Doc, Nguyen Van, Nguyen Minh Giam, Nguyen Thi Hoai Nam, Ngo Tu Thanh, and Nguyen Thi Huong Giang. "Applying Algorithmic Thinking to Teaching Graphs of Functions For Students Through Geogebra." Journal of Education For Sustainable Innovation 1, no. 2 (2023): 85–94. http://dx.doi.org/10.56916/jesi.v1i2.554.

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Algorithmic thinking is a term that is of interest to many educators and teachers. Algorithmic thinking plays an important role not only in problem solving but also in solving real world problems. The article presents some concepts of algorithmic thinking; propose the process of applying algorithmic thinking to teaching function graphs for students through GeoGebra online, helping students to draw all functions in the fastest way. GeoGebra is integrated with algorithms used to graph any function online that students cannot do. GeoGebra is used effectively, interactively and actively supported

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Kim, Wangdo, and Eunice Ortiz. "Algorithmic Implementation of Visually Guided Interceptive Actions: Enhancing Motion Perception in Virtual and Augmented Reality Systems." International Journal of Media and Networks 2, no. 10 (2024): 01–17. https://doi.org/10.33140/ijmn.02.10.03.

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This research introduces an innovative algorithmic framework designed to enhance motion perception and visually guided interceptive actions in virtual and augmented reality (VR/AR) environments. By applying harmonic ratios and stimulation invariants, the proposed algorithms enable real-time prediction of interception points and improve the responsiveness of VR/AR systems. This methodology translates complex theories of visual perception and motion into practical algorithmic solutions, providing dynamic prediction capabilities critical for applications such as online gaming, virtual simulations

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Iyer, Ravi. "Crowdsourcing Objective Answers to Subjective Questions Online." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 1 (November 3, 2013): 93–94. http://dx.doi.org/10.1609/hcomp.v1i1.13053.

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In this demonstration, we show how Ranker’s algorithms use diverse sampling, measurement, and algorithmic techniques to crowdsource answers to subjective questions in a real-world online environment where user behavior is difficult to control. Ranker receives approximately 8 million visitors each month, as of September 2013, and collects over 1.5 million monthly user opinions. Tradeoffs between computational complexity, projected user engagement, and accuracy are required in such an environment, and aggregating across diverse techniques allows us to mitigate the sizable errors specific to indi

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