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arXiv 2024年

作者： Hua, Youqing Liu, Shuai Hong, Yiguang Johansson, Karl Henrik Wang, Guangchen School of Control Science and Engineering Shandong University Jinan China Nanyang Technological University Singapore Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper considers the distributed online bandit optimization problem with nonconvex loss functions over a time-varying digraph. This problem can be viewed as a repeated game between a group of online players and an adversary. At each round, each player selects a decision from the constraint set, and then the adversary assigns an arbitrary, possibly nonconvex, loss function to this player. Only the loss value at the current round, rather than the entire loss function or any other information (e.g. gradient), is privately revealed to the player. Players aim to minimize a sequence of global loss functions, which are the sum of local losses. We observe that traditional multi-point bandit algorithms are unsuitable for online optimization, where the data for the loss function are not all a priori, while the one-point bandit algorithms suffer from poor regret guarantees. To address these issues, we propose a novel one-point residual feedback distributed online algorithm. This algorithm estimates the gradient using residuals from two points, effectively reducing the regret bound while maintaining O(1) sampling complexity per iteration. We employ a rigorous metric, dynamic regret, to evaluate the algorithm's performance. By appropriately selecting the step size and smoothing parameters, we demonstrate that the expected dynamic regret of our algorithm is comparable to existing algorithms that use two-point feedback, provided the deviation in the objective function sequence and the path length of the minimization grows sublinearly. Finally, we validate the effectiveness of the proposed algorithm through numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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Uncovering neural scaling laws in molecular representation learning 23

Uncovering neural scaling laws in molecular representation l...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Dingshuo Chen Yanqiao Zhu Jieyu Zhang Yuanqi Du Zhixun Li Qiang Liu Shu Wu Liang Wang Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences Department of Computer Science University of California Los Angeles The Paul G. Allen School of Computer Science and Engineering University of Washington Department of Computer Science Cornell University Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong

Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field. In this paper, we delve into the neural scaling behaviors of MRL from a data-centric viewpoint, examining four key dimensions: (1) data modalities, (2) dataset splitting, (3) the role of pre-training, and (4) model capacity. Our empirical studies confirm a consistent power-law relationship between data volume and MRL performance across these dimensions. Additionally, through detailed analysis, we identify potential avenues for improving learning efficiency. To challenge these scaling laws, we adapt seven popular data pruning strategies to molecular data and benchmark their performance. Our findings underline the importance of data-centric MRL and highlight possible directions for future research.

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AlgoID: A Blockchain Reliant Self-Sovereign Identity Framework on Algorand

AlgoID: A Blockchain Reliant Self-Sovereign Identity Framewo...

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IEEE Symposium on computers and Communications (ISCC)

作者： Andrea De Salve Damiano Di Francesco Maesa Fabio Federico Paolo Mori Laura Ricci Institute of Applied Sciences and Intelligent Systems - National Research Council Lecce Italy Department of Computer Science University of Pisa Pisa Italy Institute of Informatics and Telematics - National Research Council Pisa Italy

The Self-Sovereign Identity (SSI) is a novel paradigm aimed at giving back users sovereignty over their digital identities. Adopting the SSI approach prevents users to have a distinct identity for each service they use, instead, use a unique decentralised identity for all the services they need to access. However, to really benefit from the SSI advantages, an actual decentralised implementation is needed to fit the specific requirements and limits of decentralised architectures, such as blockchain. To this aim, this paper proposes Algorand Identity (AlgoID), a new SSI framework for the Algorand blockchain which differs from the already existing one, because it is fully blockchain based, i.e., it exploits Algorand itself for the storage of the data identity and as the registry location. The proposed framework has been completely implemented and validated through experiments, showing that the time required to execute the framework operations is acceptably low in realistic use cases.

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Exploration of the Role of Cryptoprocessors in Advancing IoT Security

Exploration of the Role of Cryptoprocessors in Advancing IoT...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Elena Kakoulli Eleftherios Zacharioudakis Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Igor Sikorsky Kyiv Polytechnic Institute National Technical University of Ukraine Kyiv Ukraine

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

This paper presents a thorough exploration of Cryptoprocessors in the context of the Internet of Things (IoT). The focus is on hardware support, cryptographic processors, and energy-efficient designs to fortify the security of IoT ecosystems. Leveraging insights from recent research papers, the survey examines various aspects of Cryptoprocessor implementations, ranging from lightweight countermeasures to advanced lattice-based post-quantum cryptography. The proposed paper amalgamates findings from studies on hardware architectures, energy-efficient designs, and innovative cryptographic algorithms to provide a holistic overview of state-of-the-art advancements in IoT security. The synthesis of this survey’s exploration aims to contribute to the ongoing discourse on securing IoT devices through robust hardware-backed cryptographic solutions.

关键词： Surveys Industries Technological innovation Program processors Ecosystems Hardware Energy efficiency

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A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition

arXiv

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arXiv 2023年

作者： Xiong, Limao Zhou, Jie Zhu, Qunxi Wang, Xiao Wu, Yuanbin Zhang, Qi Gui, Tao Huang, Xuanjing Ma, Jin Shan, Ying School of Computer Science Fudan Univerisity China Research Institute of Intelligent Complex Systems Fudan University China The Department of Computer Science and Technology East China Normal University China Institute of Modern Languages and Linguistics Fudan University China Tencent PCG China

Existing models for named entity recognition (NER) are mainly based on large-scale labeled datasets, which always obtain using crowdsourcing. However, it is hard to obtain a unified and correct label via majority voting from multiple annotators for NER due to the large labeling space and complexity of this task. To address this problem, we aim to utilize the original multi-annotator labels directly. Particularly, we propose a Confidence-based Partial Label Learning (CPLL) method to integrate the prior confidence (given by annotators) and posterior confidences (learned by models) for crowd-annotated NER. This model learns a token- and content-dependent confidence via an Expectation–Maximization (EM) algorithm by minimizing empirical risk. The true posterior estimator and confidence estimator perform iteratively to update the true posterior and confidence respectively. We conduct extensive experimental results on both real-world and synthetic datasets, which show that our model can improve performance effectively compared with strong baselines. © 2023, CC BY.

关键词： Learning systems

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Accelerating Stable Matching between Workers and Spatial-Temporal Tasks for Dynamic MCS: A Stagewise Service Trading Approach

arXiv

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arXiv 2025年

作者： Qi, Houyi Liwang, Minghui Wang, Xianbin Fu, Liqun Hong, Yiguang Li, Li Cheng, Zhipeng School of Informatics Xiamen University Fujian China Department of Control Science and Engineering Canada Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Jiangsu China

Designing proper incentives in mobile crowdsensing (MCS) networks represents a critical mechanism in engaging distributed mobile users (workers) to contribute heterogeneous data for diverse applications (tasks). We develop a novel stagewise trading framework to reach efficient and stable matching between tasks and workers, upon considering the diversity of tasks and the dynamism of MCS networks. This framework integrates futures and spot trading stages, where in the former, we propose futures trading-driven stable matching and pre-path-planning (FT-SMP3) for long-term task-worker assignment and pre-planning of workers’ paths based on historical statistics and risk analysis. While in the latter, we investigate spot trading-driven DQN path planning and onsite worker recruitment (ST-DP2WR) mechanism to enhance workers’ and tasks’ practical utilities by facilitating temporary worker recruitment. We prove that our proposed mechanisms support crucial properties such as stability, individual rationality, competitive equilibrium, and weak Pareto optimality theoretically. Also, comprehensive evaluations confirm the satisfaction of these properties in practical network settings, demonstrating our commendable performance in terms of service quality, running time, and decision-making overheads. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk analysis

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Resilient and Adaptive Replanning for Multi-Robot Target Tracking with Sensing and Communication Danger Zones

arXiv

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arXiv 2024年

作者： Li, Peihan Wu, Yuwei Liu, Jiazhen Sukhatme, Gaurav S. Kumar, Vijay Zhou, Lifeng Department of Electrical and Computer Engineering Drexel University PhiladelphiaPA19104 United States GRASP Lab University of Pennsylvania PhiladelphiaPA19104 United States Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States Department of Computer Science University of Southern California Los AngelesCA90089 United States

Multi-robot collaboration for target tracking presents significant challenges in hazardous environments, including addressing robot failures, dynamic priority changes, and other unpredictable factors. Moreover, these challenges are increased in adversarial settings if the environment is unknown. In this paper, we propose a resilient and adaptive framework for multi-robot, multi-target tracking in environments with unknown sensing and communication danger zones. The damages posed by these zones are temporary, allowing robots to track targets while accepting the risk of entering dangerous areas. We formulate the problem as an optimization with soft chance constraints, enabling real-time adjustments to robot behavior based on varying types of dangers and failures. An adaptive replanning strategy is introduced, featuring different triggers to improve group performance. This approach allows for dynamic prioritization of target tracking and risk aversion or resilience, depending on evolving resources and real-time conditions. To validate the effectiveness of the proposed method, we benchmark and evaluate it across multiple scenarios in simulation and conduct several real-world experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Authentic Learning on DevOps Security with Labware: Git Hooks To Facilitate Automated Security Static Analysis

Authentic Learning on DevOps Security with Labware: Git Hook...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Md Mostafizur Rahman MD Abdul Barek Mst Shapna Akter Abm Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Information Technology University of West Florida Florida USA Department of Intelligent Systems and Robotics University of West Florida Florida USA Center for Cyber Security University of West Florida Florida USA Dept. of Computer Science and Software Engineering Auburn University USA Tuskegee University Alabama USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This paper presents an innovative approach to DevOps security education, addressing the dynamic landscape of cybersecurity threats. We propose a student-centered learning methodology by developing comprehensive hands-on learning modules. Specifically, we introduce labware modules designed to automate static security analysis, empowering learners to identify known vulnerabilities efficiently. These modules offer a structured learning experience with pre-lab, hands-on, and post-lab sections, guiding students through DevOps concepts and security challenges. In this paper, we introduce hands-on learning modules that familiarize students with recognizing known security flaws through the application of Git Hooks. Through prac-tical exercises with real-world code examples containing security flaws, students gain proficiency in detecting vulnerabilities using relevant tools. Initial evaluations conducted across educational institutions indicate that these hands-on modules foster student interest in software security and cybersecurity and equip them with practical skills to address DevOps security vulnerabilities.

关键词： Learning systems DevOps Codes Education Static analysis Dynamic scheduling Software

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Signal propagation in transformers: theoretical perspectives and the role of rank collapse 22

Signal propagation in transformers: theoretical perspectives...

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Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Lorenzo Noci Sotiris Anagnostidis Luca Biggio Antonio Orvieto Sidak Pal Singh Aurelien Lucchi Dept of Computer Science ETH Zürich Dept of Computer Science ETH Zürich and Robotics & ML CSEM SA Alpnach Switzerland Dept of Computer Science ETH Zürich and MPI for Intelligent Systems Tübingen Department of Mathematics and Computer Science University of Basel

ISBN: (纸本)9781713871088

Transformers have achieved remarkable success in several domains, ranging from natural language processing to computer vision. Nevertheless, it has been recently shown that stacking self-attention layers — the distinctive architectural component of Transformers — can result in rank collapse of the tokens' representations at initialization. The question of if and how rank collapse affects training is still largely unanswered, and its investigation is necessary for a more comprehensive understanding of this architecture. In this work, we shed new light on the causes and the effects of this phenomenon. First, we show that rank collapse of the tokens' representations hinders training by causing the gradients of the queries and keys to vanish at initialization. Furthermore, we provide a thorough description of the origin of rank collapse and discuss how to prevent it via an appropriate depth-dependent scaling of the residual branches. Finally, our analysis unveils that specific architectural hyperparameters affect the gradients of queries and values differently, leading to disproportionate gradient norms. This suggests an explanation for the widespread use of adaptive methods for Transformers' optimization.

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Dependable and Resilient Emergency MAGDM Approach for the Selection of HADCs

Dependable and Resilient Emergency MAGDM Approach for the Se...

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IEEE International Conference on Dependable systems, Services and Technologies (DESSERT)

作者： Gia Sirbiladze Janusz Kacprzyk Bidzina Midodashvili Bezhan Ghvaberidze Jose Antonio Lozano Tinatin Davitashvili Department of Computer Sciences Iv. Javakhishvili Tbilsi State University Tbilisi Georgia Intelligent Systems Laboratory Systems Research Institute Polish Academy of Sciences Warsawa Poland Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain

Today, much attention is paid to the engineering of dependable and resilient intelligent systems and models. This direction includes multi-attribute group decision making (MAGDM) models and systems in emergency situations. As a rule, they are characterized by insufficient objective data and lack of response time to the solution of the problem. We consider in this direction a new MAGDM of facilities locations selection problem (FLSP) based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. Expert’s evaluations of each candidate of humanitarian aid distribution center (HADC) relative to uncertain attributes in q-rung orthopair fuzzy numbers (q-ROFNs) are presented. The offered model is examined with a numerical example.

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