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Articles de revues sur le sujet « Algorithmes online »

Auteur : Grafiati

Publié le 25 mai 2024

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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 deal with right-censored samples, a problem that has received little attention in the literature so far. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. Moreover, we adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon. In an extensive experimental evaluation on an adapted version of the ASlib benchmark, we demonstrate that theoretically well-founded methods based on Thompson sampling perform specifically strong and improve in comparison to existing methods.

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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 greedy algorithm is optimal in this setting. Then we design an online algorithm that uses predictions about the input. We show that when predictions are pretty accurate, our algorithm circumvents the above lower bound. We complement our theoretical findings with an empirical evaluation of our algorithms, comparing them with the state-of-the-art scheme. The results confirm that our algorithms exhibit an improved performance for a wide range of input traces.

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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 terminals. These guarantees ensure that algorithms break through the online lower bounds with good predictions and the competitive ratio gracefully degrades as the prediction error grows. We then observe that the theory is predictive of what will occur empirically. We show on graphs where terminals are drawn from a distribution, the new online algorithms have strong performance even with modestly correct predictions.

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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 algorithms, both individually and collectively, and leverage that comprehension to navigate and manipulate algorithms. We discuss how our interviewees constructed literacy of precarious algorithms on their work platforms, suggesting implications for designing algorithmic systems supporting digital work.

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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 algorithmic management of work on OLPs. In the context of both algorithmic matching and algorithmic control, platform workers experience tensions relating to work execution, compensation, and belonging. We show that these tensions trigger market-like and organization-like response behaviors by platform workers. Our research contributes to the emerging literature on OLPs.

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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 based on experimental evaluation, stands in stark 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 and show that in this case, the greedy algorithm is optimal. Then, we design an online algorithm that uses predictions about the input. We show that when predictions are relatively accurate, our algorithm significantly improves over the greedy algorithm. We complement our theoretical findings with an empirical evaluation of our algorithms, comparing them with the state-of-the-art scheme. The results confirm that our algorithms exhibit an improved performance for a wide range of input traces.

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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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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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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 iterates are restricted to a bounded domain or the loss function is strongly convex, OPERA is associated with a non-strongly convex objective function and learns the target function in an unconstrained RKHS. Specifically, we establish a general theorem that guarantees the almost sure convergence for the last iterate of OPERA without any assumptions on the underlying distribution. Explicit convergence rates are derived under the condition of polynomially decaying step sizes. We also establish an interesting property for a family of widely used kernels in the setting of pairwise learning and illustrate the convergence results using such kernels. Our methodology mainly depends on the characterization of RKHSs using its associated integral operators and probability inequalities for random variables with values in a Hilbert space.

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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 supplement to Foucauldian thinking around biopolitics and biopower, of what I call soft biopower and soft biopolitics. These new conceptual devices allow us to better understand the workings of biopower at the level of the category, of using computer code, statistics and surveillance to construct categories within populations according to users’ surveilled internet history. Finally, the article will think through the nuanced ways that algorithmic inference works as a mode of control, of processes of identification that structure and regulate our lives online within the context of online marketing and algorithmic categorization.

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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 aversion.

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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 online paging (a.k.a. caching), but introduces a novel challenge. Our main contribution is an online algorithm which is O(b)- competitive; we also prove that this is asymptotically optimal. We complement our theoretical results with extensive trace-driven simulations, based on real-world datacenter workloads as well as synthetic traffic traces.

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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 power amassed by algorithms, O’Neil sheds much needed light into the seemingly omnipotent world of destructive algorithms. Keywords: algorithms, algorithmic transparency, algorithmic bias, communications, public relations, ethics, data, predictive models

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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 be influenced by personal data that algorithms know about them, and whether users change their behavior based on this understanding. Analyzing 83 in-depth interviews from five countries about people's experiences with researching and searching for products and services online, we show how being aware of personal data collection helps people understand algorithmic processes. However, this does not necessarily enable users to influence algorithmic output, because currently, options that help users control the level of customization they encounter online are limited. Besides the empirical contributions, we discuss research design implications based on the diversity of the sample and our findings for studying algorithm skills.

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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 with the state-of-the-art online classification algorithms.

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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 (vs. men). In addition, it was found that people who had no specific sources to inform themselves about algorithms, and those relying on their friends/family for information, were more likely to have algorithmic misconceptions. Conversely, media channels, school, and having one’s own (online) experiences were found to be sources associated with having fewer algorithmic misconceptions. Theoretical implications are formulated in the context of algorithmic awareness and the digital divide. Finally, societal implications are discussed, such as the need for algorithmic literacy initiatives.

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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 conditions of the educational process. Conclusions were made regarding the feasibility of implementation.

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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 his five similarity algorithms, including TF-IDF and LSA. The data was a collection of correct answers with a total of 371 texts. The similarity algorithm's performance was compared with human correction results. Evaluation was performed using Root Mean Square Error (RMSE). This study shows that his TF-IDF algorithm like Jaccard has the lowest his RMSE compared to human judgement. However, the LSA algorithm tended better to follow human rating patterns for descriptive tests..

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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 POMDP methods, analyze their properties and discuss their advantages and disadvantages; and to thoroughly evaluate these online approaches in different environments under various metrics (return, error bound reduction, lower bound improvement). Our experimental results indicate that state-of-the-art online heuristic search methods can handle large POMDP domains efficiently.

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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 unique subgame perfect safety-level equilibrium and we describe the associated scheduling algorithm (which is not FCFS). The uniqueness result holds for both randomized and deterministic algorithms, with a different equilibrium algorithm in each case. When the goal of the servers is to minimize competitive ratio, we prove that it is an equilibrium for each server to apply FCFS: each server obtains the optimal competitive ratio of 2.

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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 give an online algorithm with competitive ratio 4⌊ log h⌋ + 6; if h is not known in advance, we give an online algorithm with competitive ratio 2⋅h log -1/2 h + 8⋅h3 log -1/2 h.

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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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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 response. With the mechanical tasks, algorithmic and human-made decisions were perceived as equally fair and trustworthy and evoked similar emotions; however, human managers' fairness and trustworthiness were attributed to the manager's authority, whereas algorithms' fairness and trustworthiness were attributed to their perceived efficiency and objectivity. Human decisions evoked some positive emotion due to the possibility of social recognition, whereas algorithmic decisions generated a more mixed response – algorithms were seen as helpful tools but also possible tracking mechanisms. With the human tasks, algorithmic decisions were perceived as less fair and trustworthy and evoked more negative emotion than human decisions. Algorithms' perceived lack of intuition and subjective judgment capabilities contributed to the lower fairness and trustworthiness judgments. Positive emotion from human decisions was attributed to social recognition, while negative emotion from algorithmic decisions was attributed to the dehumanizing experience of being evaluated by machines. This work reveals people's lay concepts of algorithmic versus human decisions in a management context and suggests that task characteristics matter in understanding people's experiences with algorithmic technologies.

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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 by many students, students and teachers of Mathematics in the process of graphing functions and graphs in an intuitive and detailed way, thereby developing develop students' thinking.

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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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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 individual imperfect crowdsourcing methods. We will specifically show how relatively unstructured crowdsourcing can yield surprisingly accurate predictions of movie box-office revenue, celebrity mortality, and retail pizza topping sales.

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Cotter, Kelley. "Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram." New Media & Society 21, no. 4 (2018): 895–913. http://dx.doi.org/10.1177/1461444818815684.

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Algorithms are said to affect social realities, often in unseen ways. This article explores conscious, instrumental interactions with algorithms, as a window into the complexities and extent of algorithmic power. Through a thematic analysis of online discussions among Instagram influencers, I observed that influencers’ pursuit of influence resembles a game constructed around “rules” encoded in algorithms. Within the “visibility game,” influencers’ interpretations of Instagram’s algorithmic architecture—and the “game” more broadly—act as a lens through which to view and mechanize the rules of the game. Illustrating this point, this article describes two prominent interpretations, which combine information influencers glean about Instagram’s algorithms with preexisting discourses within influencer communities on authenticity and entrepreneurship. This article shows how directing inquiries toward the visibility game makes present the interdependency between users, algorithms, and platform owners and demonstrates how algorithms structure, but do not unilaterally determine user behavior.

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Barnes, Naomi, and Samuel Hames. "Centralising Qualitative Research in Big Data Methods Through Algorithmic Ethnography." Journal of Digital Social Research 5, no. 1 (2023): 90–108. http://dx.doi.org/10.33621/jdsr.v5i1.129.

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Responding to the challenge for qualitative researchers to claim a central place in conversations about big data, analytics, datafication, data mining and the role of algorithms, this article describes a mixed-method research partnership focused on algorithmic ethnography. In the debates about the opacity of online algorithms, qualitative researchers typically advocate for access to code. This standard discourse centralises the technical aspects of big data and networked ethnographies. Instead, this article outlines a research methodology that analyses algorithmic discourses by working alongside the technical expertise of data scientists and utilizes the affordability of big data methods to do qualitative work. The potential for qualitative research skills to investigate the underlying technical processes that frame online social interactions is proposed as a way to place how people understand the world at the centre of big data research.

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Wijermars, Mariëlle, and Mykola Makhortykh. "Sociotechnical imaginaries of algorithmic governance in EU policy on online disinformation and FinTech." New Media & Society 24, no. 4 (2022): 942–63. http://dx.doi.org/10.1177/14614448221079033.

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Datafication and the use of algorithmic systems increasingly blur distinctions between policy fields. In the financial sector, for example, algorithms are used in credit scoring, money has become transactional data sought after by large data-driven companies, while financial technologies (FinTech) are emerging as a locus of information warfare. To grasp the context specificity of algorithmic governance and the assumptions on which its evaluation within different domains is based, we comparatively study the sociotechnical imaginaries of algorithmic governance in European Union (EU) policy on online disinformation and FinTech. We find that sociotechnical imaginaries prevalent in EU policy documents on disinformation and FinTech are highly divergent. While the first can be characterized as an algorithm-facilitated attempt to return to the presupposed status quo (absence of manipulation) without a defined future imaginary, the latter places technological innovation at the centre of realizing a globally competitive Digital Single Market.

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Yavich, Roman, Sergey Malev, and Vladimir Rotkin. "Triangle Generator for Online Mathematical E-learning." Higher Education Studies 10, no. 3 (2020): 72. http://dx.doi.org/10.5539/hes.v10n3p72.

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What constitutes learning in the 21st century is the capacity for e-learning. Integral components of e-education are training content management systems. Existing content generators more transform content than create original content. Creating a methodology and technology for generating original content is important and relevant. In order to form adequate methods for generating content, primarily for mathematical and related disciplines, the problem of generating a triangle, together with its many attributes, is considered as the simplest object of elementary mathematics. We create a simulation model that describes the properties of the object and apply modified methods of optimization and corresponding algorithms to it. The current algorithmic layout demonstrates the performance of the developed system. It generates triangles in any configurations matching the given parameters and demonstrates their properties.

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Pan Liangze, 潘良泽, 刘诚 Liu Cheng та 朱健强 Zhu Jianqiang. "基于时域剪切的纳秒脉冲在线测量算法". Chinese Journal of Lasers 48, № 24 (2021): 2404004. http://dx.doi.org/10.3788/cjl202148.2404004.

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Mao Zinian, 茆子念, 周志峰 Zhou Zhifeng, 沈亦纯 Shen Yichun та 王立端 Wang Liduan. "高鲁棒性Camera-IMU外参在线标定算法". Laser & Optoelectronics Progress 61, № 4 (2024): 0411005. http://dx.doi.org/10.3788/lop231200.

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Liu, Zhicheng, Ling Chen, Hong Chang, Donglei Du, and Xiaoyan Zhang. "Online algorithms for BP functions maximization." Theoretical Computer Science 858 (February 2021): 114–21. http://dx.doi.org/10.1016/j.tcs.2021.01.020.

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Höhne, Felix, Sören Schmitt, and Rob van Stee. "SIGACT News Online Algorithms Column 36." ACM SIGACT News 51, no. 4 (2020): 89–107. http://dx.doi.org/10.1145/3444815.3444830.

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In this column, we will discuss some papers in online algorithms that appeared in 2020. As usual, we make no claim at complete coverage here, and have instead made a selection. If we have unaccountably missed your favorite paper and you would like to write about it or about any other topic in online algorithms, please don't hesitate to contact us!

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Thiessen, Ernest, and Peter Holt. "Online Collaboration Algorithms for Small Claims." International Journal of Online Dispute Resolution 6, no. 2 (2019): 209–11. http://dx.doi.org/10.5553/ijodr/235250022019006002013.

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van Stee, Rob. "SIGACT News Online Algorithms Column 39." ACM SIGACT News 53, no. 2 (2022): 83. http://dx.doi.org/10.1145/3544979.3544992.

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For this issue, I am pleased to present an extensive survey by Debasis Dwibedy and Rakesh Mohanty on online makespan scheduling. This is a topic which continues to inspire new research and it is great that these authors have provided an updated survey.

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Yang, Tianbao, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, and Steven Hoi. "Online Kernel Selection: Algorithms and Evaluations." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1197–203. http://dx.doi.org/10.1609/aaai.v26i1.8298.

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Kernel methods have been successfully applied to many machine learning problems. Nevertheless, since the performance of kernel methods depends heavily on the type of kernels being used, identifying good kernels among a set of given kernels is important to the success of kernel methods. A straightforward approach to address this problem is cross-validation by training a separate classifier for each kernel and choosing the best kernel classifier out of them. Another approach is Multiple Kernel Learning (MKL), which aims to learn a single kernel classifier from an optimal combination of multiple kernels. However, both approaches suffer from a high computational cost in computing the full kernel matrices and in training, especially when the number of kernels or the number of training examples is very large. In this paper, we tackle this problem by proposing an efficient online kernel selection algorithm. It incrementally learns a weight for each kernel classifier. The weight for each kernel classifier can help us to select a good kernel among a set of given kernels. The proposed approach is efficient in that (i) it is an online approach and therefore avoids computing all the full kernel matrices before training; (ii) it only updates a single kernel classifier each time by a sampling technique and therefore saves time on updating kernel classifiers with poor performance; (iii) it has a theoretically guaranteed performance compared to the best kernel predictor. Empirical studies on image classification tasks demonstrate the effectiveness of the proposed approach for selecting a good kernel among a set of kernels.

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