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2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Efraim, Hanoch Basri, Ronen Weizmann Institute of Science Dept. of Computer Science and Applied Mathematics Rehovot Israel

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this work, we present a new approach for designing an autonomous bicycle robot. It is well known that bicycles can be laterally stable at certain velocities. This means that if the steering bar remains free to rotate, a bicycle can be designed so that it naturally steers in a way that corrects for deviations, keeping the bicycle upright. However, to the best of our knowledge this desirable property is not taken advantage of in previous works, since connecting a standard actuator to the steering bar, which is required in order to guide the bicycle to a desired direction, does not allow the steerer to be free to rotate. In this work we propose a unique actuation mechanism that allows guidance while keeping the steerer free to rotate, thus preserving the natural stability of the bicycle. Since the bicycle is mechanically stable there is no need for a low level controller, and thus, no need for sensors and sensor fusion algorithms. We demonstrate the concept with experiments performed on a 3D printed bicycle powered by a small electric motor and guided remotely. We provide all the CAD files and software in hope that it will be used by additional researchers in the field, and advance the frontiers of autonomous systems. © 2022 IEEE.

关键词： Bicycles

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Time-Consistency in Dynamic Cooperative Games with Multiple Trajectories 1

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8th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2024

作者： Li, Zekun Li, Yin Qiao, Wenwen Cheng, Wantong Department of Applied Mathematics and Process Control Saint Petersburg State University Saint Petersburg Russia School of Mathematics Harbin Institute of Technology Harbin China School of Mathematics and Computer Science Yanan University Yanan China

ISBN: (数字)9783031776885

ISBN: (纸本)9783031776878

This paper considers the time consistency of multistage cooperative games in which there are multiple optimal trajectories with complete information. In dynamic cooperative games, an important condition for the distribution of players’ payoffs is that individual rationality needs to be satisfied. If the players’ payoffs do not satisfy individual rationality as the game progresses along the cooperative trajectory, some players will deviate from the cooperative trajectory. If there are multiple optimal trajectories in the multistage game, the situation is further complicated by the fact that the players’ payoffs always satisfy individual rationality on some of the optimal trajectories, but not on others. Alternatively, players’ individual rationality conditions are quite different along different optimal trajectories. Therefore, four different scenarios of multi-stage cooperative games with multiple optimal trajectories are set up in the study and analysed for each of the four different situations. Four different Payoff Distribution Procedure (PDP) formulations are proposed to modify the players’ payoff distributions so that the game satisfies time-consistency on different optimal trajectories. Several theorems are given, and proofs are provided, with corresponding examples. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Multiple optimal trajectories Multistage cooperative games Time consistency

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Categorizing methods for integrating machine learning with executable specifications

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science China(Information sciences) 2024年第1期67卷 5-19页

作者： David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF Department of Computer Science and Applied Mathematics Weizmann Institute of Science Department of Software and Information Systems Engineering Ben-Gurion University of the Negev

Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite *** promise is already delivering extremely impressive results in a variety of ***,while DL-enabled systems achieve excellent performance,they are far from *** has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto ***,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human *** the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system *** this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered *** then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with *** conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.

关键词： machine learning artificial intelligence grey box learning domain knowledge rules behavioral programming deep reinforcement learning survey

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Intelligent Solution System for Cloud Security Based on Equity Distribution:Model and Algorithms

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computers, Materials & Continua 2024年第1期78卷 1461-1479页

作者： Sarah Mustafa Eljack Mahdi Jemmali Mohsen Denden Mutasim Al Sadig Abdullah M.Algashami Sadok Turki Department of Computer Science and Information College of Science at ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Computing and InformaticsUniversity of SharjahSharjahUnited Arab Emirates Higher Institute of Computer Science and Mathematics University of MonastirMonastir5000Tunisia Mars Laboratory University of SousseSousseTunisia Department of Logistic and Maintenance UFR MIM at MetzUniversity of LorraineMetzFrance Department of Computer and Information Technologies College of Telecommunicationand Information Riyadh CTITechnical and Vocational Training Corporation TVTCRiyadh12464Saudi Arabia Department of Computer Science Higher Institute of Applied Sciences of SousseSousse UniversitySousse4000Tunisia

In the cloud environment,ensuring a high level of data security is in high *** planning storage optimization is part of the whole security process in the cloud *** enables data security by avoiding the risk of data loss and data *** development of data flow scheduling approaches in the cloud environment taking security parameters into account is *** our work,we propose a data scheduling model for the cloud *** is made up of three parts that together help dispatch user data flow to the appropriate *** first component is the Collector Agent whichmust periodically collect information on the state of the network *** second one is the monitoring agent which must then analyze,classify,and make a decision on the state of the link and finally transmit this information to the *** third one is the scheduler who must consider previous information to transfer user data,including fair distribution and reliable *** should be noted that each part of the proposedmodel requires the development of its *** this article,we are interested in the development of data transfer algorithms,including fairness distribution with the consideration of a stable link *** algorithms are based on the grouping of transmitted files and the iterative *** proposed algorithms showthe performances to obtain an approximate solution to the studied problem which is an NP-hard(Non-Polynomial solution)*** experimental results show that the best algorithm is the half-grouped minimum excluding(HME),with a percentage of 91.3%,an average deviation of 0.042,and an execution time of 0.001 s.

关键词： Cyber-security cloud computing cloud security algorithms heuristics

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On the Practical Power of Automata in Pattern Matching

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SN computer science 2024年第4期5卷 400页

作者： Amir, Ora Amir, Amihood Fraenkel, Aviezri Sarne, David Department of Computer Science Bar-Ilan University Ramat-Gan 52900 Israel Camtek Migdal HaEmek Israel College of Computing Georgia Tech Atlanta 30332 GA United States Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot 76100 Israel

Many papers in the intersection of theoretical and applied algorithms show that the simple, asymptotically less efficient algorithm, performs better than the bestcomplex theoretical algorithms on random data or in specialized “real world” applications. This paper considers the Knuth–Morris–Pratt automaton, and shows a counter-intuitive practical result. The classical pattern matching paradigm is that of seeking occurrences of one string—the pattern, in another—the text, where both strings are drawn from an alphabet set Σ. Assuming the text length is n and the pattern length is m, this problem can naively be solved in time O(nm). In Knuth, Morris and Pratt’s seminal paper of 1977, an automaton, was developed that allows solving this problem in time O(n) for any alphabet. This automaton, which we will refer to as the KMP-automaton, has proven useful in solving many other problems. A notable example is the parameterized pattern matching model. In this model, a consistent renaming of symbols from Σ is allowed in a match. The parameterized matching paradigm has proven useful in problems in software engineering, computer vision, and other applications. It has long been believed that for texts where the symbols are uniformly random, the naive algorithm will perform as well as the KMP algorithm. In this paper, we examine the practical efficiency of the KMP algorithm versus the naive algorithm on a randomly generated text. We analyze the time under various parameters, such as alphabet size, pattern length, and the distribution of pattern occurrences in the text. We do this for both the original exact matching problem and parameterized matching. While the folklore wisdom is vindicated by these findings for the exact matching case, surprisingly, the KMP algorithm always works significantly faster than the naive in the parameterized matching case. We check this hypothesis for DNA texts and image data and observe a similar behavior as in the random text. We also show a very stru

关键词： Knuth–Morris–Pratt automaton Parameterized matching Simple algorithms Uniformly random text

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Consensus Learning with Deep Sets for Essential Matrix Estimation

arXiv

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arXiv 2024年

作者： Moran, Dror Margalit, Yuval Trostianetsky, Guy Khatib, Fadi Galun, Meirav Basri, Ronen Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Robust estimation of the essential matrix, which encodes the relative position and orientation of two cameras, is a fundamental step in structure from motion pipelines. Recent deep-based methods achieved accurate estimation by using complex network architectures that involve graphs, attention layers, and hard pruning steps. Here, we propose a simpler network architecture based on Deep Sets. Given a collection of point matches extracted from two images, our method identifies outlier point matches and models the displacement noise in inlier matches. A weighted DLT module uses these predictions to regress the essential matrix. Our network achieves accurate recovery that is superior to existing networks with significantly more complex architectures. Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix algebra

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The Wisdom of a Crowd of Brains: A Universal Brain Encoder

arXiv

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arXiv 2024年

作者： Beliy, Roman Wasserman, Navve Zalcher, Amit Irani, Michal Department of Computer Science and Applied Mathematics The Weizmann Institute of Science

Image-to-fMRI encoding is important for both neuroscience research and practical applications. However, such "Brain-Encoders" have been typically trained per-subject and per fMRI-dataset, thus restricted to very limited training data. In this paper we propose a Universal Brain-Encoder, which can be trained jointly on data from many different subjects/datasets/machines. What makes this possible is our new voxel-centric Encoder architecture, which learns a unique "voxel-embedding" per brain-voxel. Our Encoder trains to predict the response of each brain-voxel on every image, by directly computing the cross-attention between the brain-voxel embedding and multi-level deep image features. This voxel-centric architecture allows the functional role of each brain-voxel to naturally emerge from the voxel-image cross-attention. We show the power of this approach to: (i) combine data from multiple different subjects (a "Crowd of Brains") to improve each individual brain-encoding, (ii) quick & effective Transfer-Learning across subjects, datasets, and machines (e.g., 3-Tesla, 7-Tesla), with few training examples, and (iii) we show the potential power of the learned voxel-embeddings to explore brain functionality (e.g., what is encoded where in the brain). © 2024, CC BY-NC-SA.

关键词： Image enhancement

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Towards new realities: Implications of personalized online layers in our daily lives

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i-com 2024年第2期23卷 221-229页

作者： Herder, Eelco Stojko, Laura Strecker, Jannis Neumayr, Thomas Yigitbas, Enes Augstein, Mirjam Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Science University of the Bundeswehr Munich Neubiberg Germany Institute of Computer Science University of St. Gallen St. Gallen Switzerland Research Group Personalized Environments and Collaborative Systems University of Applied Sciences Upper Austria Hagenberg Austria Faculty of Electrical Engineering Computer Science and Mathematics Paderborn University Paderborn Germany

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world. © 2024 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Human computer interaction

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ROAR: A Benchmark for NFT Rarity Meters 6

ROAR: A Benchmark for NFT Rarity Meters

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6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Belousov, Dmitry Shuklin, Maksim Stepin, Alexander Yanovich, Yury Moscow Institute of Physics and Technology Phystech School of Applied Mathematics and Informatics Moscow Russia Hse University Faculty of Computer Science Moscow Russia Skolkovo Institute of Science and Technology Wireless Center Moscow Russia

ISBN: (纸本)9798350316742

Rarity meters are incorporated by industry and discursive by academia. Rarity, as an intuitive term, attracted numerous researchers to present their own view of it. While there is existing literature on comparing rarity meters, it requires access to NFT collection data, which can be challenging for researchers without a background in blockchain technology. This has created a demand for an easily accessible rarity meter benchmark. In this paper, we introduce the Rating over all Rarities (ROAR) benchmark, which includes data from one hundred popular NFT collections from the Ethereum blockchain, implemented a weighted correlation-based performance measurement function, as well as four state-of-the-art rarity meters (***, Kramer, OpenRarity, and NFTGo), along with a new rarity meter called ROAR. Our experiments show that the ROAR rarity meter, an ensemble of the other four meters, outperforms its competitors, with *** and Kramer as runner-ups. The ROAR benchmark is a tool for examination and testing of rarity meter ideas, and we challenge readers to develop models that can outperform the ROAR rarity meter. © 2024 IEEE.

关键词： Blockchain

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First-Fit Coloring of Forests in Random Arrival Model 49

First-Fit Coloring of Forests in Random Arrival Model

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49th International Symposium on Mathematical Foundations of computer science, MFCS 2024

作者： Bosek, Bartlomiej Gutowski, Grzegorz Lasoń, Michal Przybylo, Jakub Institute of Theoretical Computer Science Faculty of Mathematics and Computer Science Jagiellonian University Kraków Poland Institute of Mathematics The Polish Academy of Sciences ul. Śniadeckich 8 Warszawa00-656 Poland AGH University of Krakow Faculty of Applied Mathematics al. A. Mickiewicza 30 Kraków30-059 Poland

ISBN: (纸本)9783959773355

We consider a graph coloring algorithm that processes vertices in order taken uniformly at random and assigns colors to them using First-Fit strategy. We show that this algorithm uses, in expectation, at most (1 + o(1)) · ln n / ln ln n different colors to color any forest with n vertices. We also construct a family of forests that shows that this bound is best possible. © Bartlomiej Bosek, Grzegorz Gutowski, Michal Lasoń, and Jakub Przybylo.

关键词： Graph algorithms

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