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Littérature scientifique sur le sujet « Algorithmes online »

Auteur : Grafiati

Publié le 25 mai 2024

Mis à jour le 19 juillet 2025

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Articles de revues sur le sujet "Algorithmes online"

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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Thèses sur le sujet "Algorithmes online"

Sentenac, Flore. "Learning and Algorithms for Online Matching." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG005.

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Cette thèse se concentre principalement sur les problèmes d'appariement en ligne, où des ensembles de ressources sont alloués séquentiellement à des flux de demandes. Nous les traitons à la fois du point de vue de l'apprentissage en ligne et de l'analyse compétitive, toujours lorsqueEn ce qui concerne l'apprentissage en ligne, nous étudions comment la structure spécifique de l'appariement influence l'apprentissage dans la première partie, puis comment les effets de report dans le système affectent ses performances.En ce qui concerne l'analyse compétitive, nous étudions le problème de l'apparie

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Liu, Ming. "Design and Evaluation of Algorithms for Online Machine Scheduling Problems." Phd thesis, Ecole Centrale Paris, 2009. http://tel.archives-ouvertes.fr/tel-00453316.

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Dans cette thèse, nous proposons et évaluons des algorithmes pour résoudre des problèmes d'ordonnancement en ligne. Pendant des décennies, les études en ordonnancement considèrent des modèles déterministes où toutes les informations nécessaires pour la définition du problème sont supposées connues à l'avance. Cette hypothèse n'est généralement pas réaliste. Ceci a motivé les études sur l'ordonnancement en ligne. Dans un problème d'ordonnancement en ligne, un algorithme doit prendre des décisions sans connaissance du futur. L'analyse compétitive est généralement la méthode utilisée pour évaluer

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Jin, Shendan. "Online computation beyond standard models." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS152.

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Dans le cadre standard du calcul en ligne, l’entrée de l’algorithme n’est pas entièrement connue à l’avance, mais elle est révélée progressivement sous forme d’une séquence de requêtes. Chaque fois qu'une requête arrive, l'algorithme en ligne doit prendre des décisions irrévocables pour servir la demande, sans connaissance des requêtes futures. Dans le domaine des algorithmes en ligne, le cadre standard utilisé pour évaluer les performances des algorithmes en ligne est l’analyse compétitive. De manière informelle, le concept d’analyse compétitive consiste à comparer les performances d’un algor

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Ben, Mazziane Younes. "Analyse probabiliste pour le caching." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4014.

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Les caches sont de petites mémoires qui accélèrent la récupération des données. L'un des objectifs des politiques de mise en cache est de sélectionner le contenu du cache afin de minimiser le temps de réponse aux requêtes d'objets. Un problème plus général permet de répondre approximativement à la requête d'un objet par un objet similaire mis en cache. Ce concept, appelé "mise en cache par similarité", s'avère utile pour les systèmes de recommandation. L'objectif est de minimiser le temps de latence tout en fournissant des réponses satisfaisantes. La compréhension théorique des algorithmes de

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Liu, Ming Chu Chengbin. "Design and Evaluation of Algorithms for Online Machine Scheduling Problems." S. l. : S. n, 2009. http://theses.abes.fr/2009ECAP0028.

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Nouinou, Hajar. "Ordonnancement semi-online sur machine unitaire pour l’industrie du futur." Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0028.

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Nous étudions la valeur de l’information dans des problèmes d’ordonnancement semi-online sur machine unitaire. Nous proposons ainsi des algorithmes semi-online pour résoudre ces problèmes et nous évaluons leurs performances. Contrairement aux problèmes d’ordonnancement classiques offline où le décideur connaît toutes les caractéristiques de l’instance à ordonnancer, dans les problèmes d’ordonnancement online ou semi-online la prise de décision est effectuée sans aucune information ou uniquement avec des informations partielles sur l’instance. Notre travail consiste à distinguer les information

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Renault, Marc Paul. "Lower and upper bounds for online algorithms with advice." Paris 7, 2014. http://www.theses.fr/2014PA077196.

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Les algorithmes en ligne fonctionnent dans un contexte où l'entrée est révélé au fur et à mesure du temps; chaque morceau révélé est appelé une demande. Après réception de chaque demahde, les algorithmes en ligne doivent prendre une action avant que la prochaine demande soit révélée, c'est-à-dire que les algorithmes en ligne doivent prendre une décision irrévocable basée sur les demandes déjà révélées sans aucune connaissance des demandes à venir. Le but est d'optimiser une fonction de coût dépendante de l'entrée. L'analyse compétitive est la méthode standard utilisée pour analyser la qualité

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Teiller, Alexandre. "Aspects algorithmiques de l'optimisation « multistage »." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS471.

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En optimisation combinatoire classique, étant donné une instance d’un problème, il est demandé de trouver une bonne solution réalisable. Cependant, dans de nombreux cas, les données peuvent évoluer au cours du temps et il est demandé de résoudre une séquence d’instances. Gupta et al. (2014) et Eisenstat et al. (2014) ont proposé un modèle multistage où étant donné un horizon de temps, l’entrée est une séquence d’instances (une pour chaque pas de temps), et l’objectif est de trouver une séquence de solutions (une pour chaque pas de temps) qui atteindrait un compromis entre la qualité des soluti

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Vallée, Sven. "Algorithmes d’optimisation pour un service de transport partagé à la demande." Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0063.

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L'objectif de cette thèse est de proposer des algorithmes d'optimisation efficaces pour un système de tranport en commun à la demande proposé par Padam Mobility, une start-up Parisienne. Après avoir modélisé le problème comme un DARP dynamique, trois modules d'optimisation sont présentés : un module online destiné à répondre aux requêtes en temps réel, un module de réinsertion pour insérer les requêtes rejetées par le module online et enfin un module offline basé sur une métaheuristique permettant d'optimiser en continue les itinéraires<br>The purpose of this thesis is to propose efficient opt

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Lu, Wei. "Μéthοdes stοchastiques du secοnd οrdre pοur le traitement séquentiel de dοnnées massives". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMIR13.

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Avec le développement rapide des technologies et l'acquisition de données de plus en plus massives, les méthodes capables de traiter les données de manière séquentielle (en ligne) sont devenues indispensables. Parmi ces méthodes, les algorithmes de gradient stochastique se sont imposés pour estimer le minimiseur d'une fonction exprimée comme l'espérance d'une fonction aléatoire. Bien qu'ils soient devenus incontournables, ces algorithmes rencontrent des difficultés lorsque le problème est mal conditionné. Dans cette thèse, nous nous intéressons sur les algorithmes stochastiques du second ordre

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Livres sur le sujet "Algorithmes online"

Evripidis, Bampis, Jansen Klaus, and Kenyon Claire, eds. Efficient approximation and online algorithms: Recent progress on classical combinatorical optimization problems and new applications. Springer, 2006.

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Fiat, Amos, and Gerhard J. Woeginger, eds. Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029561.

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WAOA 2008 (2008 Karlesruhe, Germany). Approximation and online algorithms: 6th international workshop, WAOA 2008, Karlsruhe, Germany, September 18-19, 2008 : revised papers. Springer, 2009.

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Kaklamanis, Christos, and Asaf Levin, eds. Approximation and Online Algorithms. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80879-2.

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Koenemann, Jochen, and Britta Peis, eds. Approximation and Online Algorithms. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92702-8.

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Chalermsook, Parinya, and Bundit Laekhanukit, eds. Approximation and Online Algorithms. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18367-6.

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Bampis, Evripidis, and Ola Svensson, eds. Approximation and Online Algorithms. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18263-6.

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Sanità, Laura, and Martin Skutella, eds. Approximation and Online Algorithms. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28684-6.

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Erlebach, Thomas, and Giuseppe Persiano, eds. Approximation and Online Algorithms. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38016-7.

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Chapitres de livres sur le sujet "Algorithmes online"

Fiat, Amos, and Gerhard J. Woeginger. "Competitive analysis of algorithms." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029562.

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Albers, Susanne, and Jeffery Westbrook. "Self-organizing data structures." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029563.

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Irani, Sandy. "Competitive analysis of paging." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029564.

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Chrobak, Marek, and Lawrence L. Larmore. "Metrical task systems, the server problem and the work function algorithm." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029565.

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Bartal, Yair. "Distributed paging." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029566.

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Aspnes, James. "Competitive analysis of distributed algorithms." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029567.

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Csirik, János, and Gerhard J. Woeginger. "On-line packing and covering problems." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029568.

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Azar, Yossi. "On-line load balancing." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029569.

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Sgall, JiŘí. "On-line scheduling." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029570.

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Berman, Piotr. "On-line searching and navigation." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029571.

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Actes de conférences sur le sujet "Algorithmes online"

Degroote, Hans. "Online Algorithm Selection." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/746.

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Algorithm selection approaches have achieved impressive performance improvements in many areas of AI. Most of the literature considers the offline algorithm selection problem, where the initial selection model is never updated after training. However, new data from running algorithms on instances becomes available while an algorithm selection method is in use. In this extended abstract, the online algorithm selection problem is considered. In online algorithm selection, additional data can be processed, and the selection model can change over time. This abstract details the online algorithm se

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Soma, Tasuku, and Yuichi Yoshida. "Online Risk-Averse Submodular Maximization." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/411.

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We present a polynomial-time online algorithm for maximizing the conditional value at risk (CVaR) of a monotone stochastic submodular function. Given T i.i.d. samples from an underlying distribution arriving online, our algorithm produces a sequence of solutions that converges to a (1−1/e)-approximate solution with a convergence rate of O(T −1/4 ) for monotone continuous DR-submodular functions. Compared with previous offline algorithms, which require Ω(T) space, our online algorithm only requires O( √ T) space. We extend our on- line algorithm to portfolio optimization for mono- tone submodul

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Sardarmehni, Tohid, and Ali Heydari. "Approximate Solution for Optimal Control of Continuous-Time Switched Systems." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9745.

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Two approximate solutions for optimal control of switched systems with autonomous subsystems and continuous-time dynamics are developed. The proposed solutions consist of online training algorithms with recursive least squares training laws. The first solution is the classic policy iteration algorithm which imposes heavy computational burden (full back-up). In order to relax the computational burden in the policy iteration algorithm, the second algorithm is presented. The convergence of the proposed algorithms to the optimal solution in online training is investigated. Simulation results are p

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Banerjee, Siddhartha, Vasilis Gkatzelis, Safwan Hossain, Billy Jin, Evi Micha, and Nisarg Shah. "Proportionally Fair Online Allocation of Public Goods with Predictions." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/3.

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We design online algorithms for fair allocation of public goods to a set of N agents over a sequence of T rounds and focus on improving their performance using predictions. In the basic model, a public good arrives in each round, and every agent reveals their value for it upon arrival. The algorithm must irrevocably decide the investment in this good without exceeding a total budget of B across all rounds. The algorithm can utilize (potentially noisy) predictions of each agent’s total value for all remaining goods. The algorithm's performance is measured using a proportional fairness objective

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Hao, Shuji, Peilin Zhao, Yong Liu, Steven C. H. Hoi, and Chunyan Miao. "Online Multitask Relative Similarity Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/253.

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Relative similarity learning~(RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real-world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first

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Yang, Zhengchen, and Jiping Zheng. "Online Submodular Maximization via Adaptive Thresholds." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/781.

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Submodular function maximization has been studied extensively in recent years due to its numerous applications in machine learning and artificial intelligence. We study a natural online variant of this problem on massive streaming data in which elements arrive one-by-one and the algorithm has to maintain a solution under cardinality constraint, i.e., k. Upon arrival of an element, the algorithm to maximize a monotone submodular function has to decide whether to accept the element and may replace a previously chosen element. Existing algorithms cannot simultaneously achieve optimal performance

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Yang, Peng, Peilin Zhao, and Xin Gao. "Bandit Online Learning on Graphs via Adaptive Optimization." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/415.

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Traditional online learning on graphs adapts graph Laplacian into ridge regression, which may not guarantee reasonable accuracy when the data are adversarially generated. To solve this issue, we exploit an adaptive optimization framework for online classification on graphs. The derived model can achieve a min-max regret under an adversarial mechanism of data generation. To take advantage of the informative labels, we propose an adaptive large-margin update rule, which enjoys a lower regret than the algorithms using error-driven update rules. However, this algorithm assumes that the full inform

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Lintzmayer, Carla Negri, Flávio Keidi Miyazawa, and Eduardo Candido Xavier. "Online Circle and Sphere Packing∗." In III Encontro de Teoria da Computação. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/etc.2018.3158.

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In the Online Circle Packing in Squares, circles arrive one at a time and we need to pack them into the minimum number of unit square bins. We improve the previous best-known competitive ratio for the bounded space version from 2.439 to 2.3536 and we also give an unbounded space algorithm. Our algorithms also apply to the Online Circle Packing in Isosceles Right Triangles and Online Sphere Packing in Cubes, for which no previous results were known.

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Banas, Ryan, Andrew McDonald, and Tegwyn Perkins. "NOVEL METHODOLOGY FOR AUTOMATION OF BAD WELL LOG DATA IDENTIFICATION AND REPAIR." In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0070.

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Subsurface analysis-driven field development requires quality data as input into analysis, modelling, and planning. In the case of many conventional reservoirs, pay intervals are often well consolidated and maintain integrity under drilling and geological stresses providing an ideal logging environment. Consequently, editing well logs is often overlooked or dismissed entirely. Petrophysical analysis however is not always constrained to conventional pay intervals. When developing an unconventional reservoir, pay sections may be comprised of shales. The requirement for edited and quality checked

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Sviridov, Mikhail, Anton Mosin, Sergey Lebedev, and Ron Thompson. "VENDOR-NEUTRAL STOCHASTIC INVERSION OF LWD DEEP AZIMUTHAL RESISTIVITY DATA AS A STEP TOWARD EFFICIENCY STANDARDIZATION OF GEOSTEERING SERVICES." In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0103.

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While proactive geosteering, special inversion algorithms are used to process the readings of logging-while-drilling resistivity tools in real-time and provide oil field operators with formation models to make informed steering decisions. Currently, there is no industry standard for inversion deliverables and corresponding quality indicators because major tool vendors develop their own device-specific algorithms and use them internally. This paper presents the first implementation of vendor-neutral inversion approach applicable for any induction resistivity tool and enabling operators to stand

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Rapports d'organisations sur le sujet "Algorithmes online"

Ur, Shmuel. Analysis of Online Algorithms for Organ Allocation. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada249361.

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Santesson, S., and P. Hallam-Baker. Online Certificate Status Protocol Algorithm Agility. RFC Editor, 2011. http://dx.doi.org/10.17487/rfc6277.

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Harriss, Lydia, and Katie Raymer. Online Information and Fake News. Parliamentary Office of Science and Technology, 2017. http://dx.doi.org/10.58248/pn559.

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Internet search engines and social media platforms are an increasingly popular way of accessing news and information. In 2017, the proportion of UK adults consuming news online exceeded those who watched news on TV (74% versus 69%). This note considers how people access news online, how algorithms (sequences of instructions) and social networks influence the content that users see, and options for mitigating any negative impact.

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Labrindis, Alexandros, and Nick Roussopoulos. A Performance Evaluation of Online Warehouse Update Algorithms. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada441038.

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Streeter, Matthew, and Daniel Golovin. An Online Algorithm for Maximizing Submodular Functions. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada476748.

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Balman, Mehmet, and Tevfik Kosar. An Online Scheduling Algorithm with Advance Reservation for Large-Scale Data Transfers. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1050437.

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Sadoune, Igor, Marcelin Joanis, and Andrea Lodi. Algorithmic collusion and the minimum price Markov game. CIRANO, 2025. https://doi.org/10.54932/mmfd7174.

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This paper introduces the Minimum Price Markov Game (MPMG), a theoretical model that reasonably approximates real-world first-price markets following the minimum price rule, such as public auctions. The goal is to provide researchers and practitioners with a framework to study market fairness and regulation in both digitized and non-digitized public procurement processes, amid growing concerns about algorithmic collusion in online markets. Using multi-agent reinforcement learningdriven artificial agents, we demonstrate that (i) the MPMG is a reliable model for first-price market dynamics, (ii)

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Mathew, Jijo K., Christopher M. Day, Howell Li, and Darcy M. Bullock. Curating Automatic Vehicle Location Data to Compare the Performance of Outlier Filtering Methods. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317435.

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Agencies use a variety of technologies and data providers to obtain travel time information. The best quality data can be obtained from second-by-second tracking of vehicles, but that data presents many challenges in terms of privacy, storage requirements and analysis. More frequently agencies collect or purchase segment travel time based upon some type of matching of vehicles between two spatially distributed points. Typical methods for that data collection involve license plate re-identification, Bluetooth, Wi-Fi, or some type of rolling DSRC identifier. One of the challenges in each of thes

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automati

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Danylchuk, Hanna B., and Serhiy O. Semerikov. Advances in machine learning for the innovation economy: in the shadow of war. Криворізький державний педагогічний університет, 2023. http://dx.doi.org/10.31812/123456789/7732.

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This preface introduces the selected and revised papers presented at the 10th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2022), held online in Ukraine, on November 17-18, 2022. The conference aimed to bring together researchers, practitioners, and students from various fields to exchange ideas, share experiences, and discuss challenges and opportunities in applying computational intelligence and data science for the innovation economy. The innovation economy is a term that describes the emerging paradigm of economic development that is driven by

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