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2nd Symposium on Foundations of Responsible Computing, FORC 2021

作者： Ganesh, Arun Zhao, Jiazheng Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Computer Science Department Stanford University CA United States

ISBN: (纸本)9783959771870

We give the first closed-form privacy guarantees for the Generalized Gaussian mechanism (the mechanism that adds noise x to a vector with probability proportional to exp(−(||x||p/σ)p) for some σ, p), in the setting of answering k counting (i.e. sensitivity-1) queries about a database with (ϵ, δ)-differential privacy (in particular, with low ∞-error). Just using Generalized Gaussian noise, we obtain a mechanism such that if the true answers to the queries are the vector d, the mechanism outputs answers d with the ∞-error guarantee: E [||d− d||∞] = O √k log log k log(1/δ)/ϵ This matches the error bound of [18], but using a much simpler mechanism. By composing this mechanism with the sparse vector mechanism (generalizing a technique of [18]), we obtain a mechanism improving the √k log log k dependence on k to √k log log log k, Our main technical contribution is showing that certain powers of Generalized Gaussians, which follow a Generalized Gamma distribution, are sub-gamma. In subsequent work, the optimal ∞-error bound of O(√k log(1/δ)/ϵ) has been achieved by [4] and [9] independently. However, the Generalized Gaussian mechanism has some qualitative advantages over the mechanisms used in these papers which may make it of interest to both practitioners and theoreticians, both in the setting of answering counting queries and more generally. © Arun Ganesh and Jiazheng Zhao;licensed under Creative Commons License CC-BY 4.0 2nd Symposium on Foundations of Responsible Computing (FORC 2021).

关键词： Gaussian distribution

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Spectrum Transformer: Wideband Spectrum Sensing using Multi-Head Self-Attention

Spectrum Transformer: Wideband Spectrum Sensing using Multi-...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Weishan Zhang Yue Wang Xiang Chen Zhi Tian Department of Electrical and Computer Engineering George Mason University Fairfax VA USA Department of Computer Science Georgia State University Atlanta GA USA

Data-driven machine learning techniques have been advocated to detect the existence of target signals in complex wireless environments. However, wideband spectrum sensing has to deal with special challenges, including enlarged data dimensionality, insufficient training data, and implicit inter-band dependencies. Facing these issues, conventional deep models unfortunately suffer from high model complexity, inferior sensing accuracy, and notorious over-fitting issues due to ignorance of domain knowledge on spectrum occupancy patterns given limited training data. All these factors lead to ineffective learning model design in the most current literature. To fill this gap, this paper proposes a novel multi-task learning solution for wideband spectrum sensing. Empowered by the multi-head self-attention mechanism, we design an efficient Spectrum Transformer architecture to effectively learn both the inter-band spectrum occupancy correlations and the inner-band spectrum features of the wideband spectrum. Spectrum Transformer outperforms the existing methods based on convolutional neural networks especially in small-data regimes, by achieving higher sensing accuracy with 89% reduction in model complexity.

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Data-Driven Latent Space Representation for Robust Bipedal Locomotion Learning

Data-Driven Latent Space Representation for Robust Bipedal L...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Guillermo A. Castillo Bowen Weng Wei Zhang Ayonga Hereid Electrical and Computer Engineering Ohio State University Columbus OH USA Department of Computer Science Iowa State University Ames IA USA SUSTech Institute of Robotics Southern University of Science and Technology (SUSTech) China Mechanical and Aerospace Engineering Ohio State University Columbus OH USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a novel framework for learning robust bipedal walking by combining a data-driven state representation with a Reinforcement Learning (RL) based locomotion policy. The framework utilizes an autoencoder to learn a low-dimensional latent space that captures the complex dynamics of bipedal locomotion from existing locomotion data. This reduced dimensional state representation is then used as states for training a robust RL-based gait policy, eliminating the need for heuristic state selections or the use of template models for gait planning. The results demonstrate that the learned latent variables are disentangled and directly correspond to different gaits or speeds, such as moving forward, backward, or walking in place. Compared to traditional template model-based approaches, our framework exhibits superior performance and robustness in simulation. The trained policy effectively tracks a wide range of walking speeds and demonstrates good generalization capabilities to unseen scenarios.

关键词： Legged locomotion Training Manifolds Supervised learning Reinforcement learning Aerospace electronics Robustness

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Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

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computers, Materials & Continua 2021年第9期68卷 2995-3013页

作者： M.S.Maharajan T.Abirami Irina V.Pustokhina Denis A.Pustokhin K.Shankar Department of Computer Science and Engineering GRT Institute of Engineering and TechnologyTiruttani631209India Department of Information Technology Kongu Engineering CollegeErode638060India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Wireless Sensor Networks(WSN)started gaining attention due to its wide application in the fields of data collection and information *** recent advancements in multimedia sensors demand the Quality of Service(QoS)be maintained up to certain *** restrictions and requirements in QoS management completely depend upon the nature of target *** of the major QoS parameters in WSN are energy efficiency,network lifetime,delay and *** this scenario,clustering and routing are considered as the most effective techniques to meet the demands of *** they are treated as NP(Non-deterministic Polynomial-time)hard problem,Swarm Intelligence(SI)techniques can be *** current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence(QoSCRSI)*** proposed QoSCRSI technique performs two-level clustering and proficient ***,the fuzzy is hybridized with Glowworm Swarm Optimization(GSO)-based clustering(HFGSOC)technique for optimal selection of Cluster Heads(CHs).Here,Quantum Salp Swarm optimization Algorithm(QSSA)-based routing technique(QSSAR)is utilized to select the possible routes in the *** order to evaluate the performance of the proposed QoSCRSI technique,the authors conducted extensive simulation analysis with varying node *** experimental outcomes,obtained from the proposed QoSCRSI technique,apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency,network lifetime,overhead,throughput,and delay.

关键词： Quality of service clustering routing energy efficiency wireless sensor networks swarm intelligence

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Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities

arXiv

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arXiv 2024年

作者： Xie, Hong Mo, Jinyu Lian, Defu Wang, Jie Chen, Enhong School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Department of Electronic Engineering and Information Science University of Science and Technology of China China College of Computer Science Chongqing University China

Motivated by distributed selection problems, we formulate a new variant of multi-player multi-armed bandit (MAB) model, which captures stochastic arrival of requests to each arm, as well as the policy of allocating requests to players. The challenge is how to design a distributed learning algorithm such that players select arms according to the optimal arm pulling profile (an arm pulling profile prescribes the number of players at each arm) without communicating to each other. We first design a greedy algorithm, which locates one of the optimal arm pulling profiles with a polynomial computational complexity. We also design an iterative distributed algorithm for players to commit to an optimal arm pulling profile with a constant number of rounds in expectation. We apply the explore then commit (ETC) framework to address the online setting when model parameters are unknown. We design an exploration strategy for players to estimate the optimal arm pulling profile. Since such estimates can be different across different players, it is challenging for players to commit. We then design an iterative distributed algorithm, which guarantees that players can arrive at a consensus on the optimal arm pulling profile in only M rounds. We conduct experiments to validate our algorithm. © 2024, CC BY.

关键词： Stochastic systems

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Quantum versus randomized communication complexity, with efficient players 12

Quantum versus randomized communication complexity, with eff...

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12th Innovations in Theoretical computer science Conference, ITCS 2021

作者： Girish, Uma Raz, Ran Tal, Avishay Department of Computer Science Princeton University NJ United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States

ISBN: (纸本)9783959771771

We study a new type of separations between quantum and classical communication complexity, separations that are obtained using quantum protocols where all parties are efficient, in the sense that they can be implemented by small quantum circuits, with oracle access to their inputs. Our main result qualitatively matches the strongest known separation between quantum and classical communication complexity [8] and is obtained using a quantum protocol where all parties are efficient. More precisely, we give an explicit partial Boolean function f over inputs of length N, such that: (1) f can be computed by a simultaneous-message quantum protocol with communication complexity polylog(N) (where at the beginning of the protocol Alice and Bob also have polylog(N) entangled EPR pairs). (2) Any classical randomized protocol for f, with any number of rounds, has communication complexity at least Ω~ (N1/4). (3) All parties in the quantum protocol of Item (1) (Alice, Bob and the referee) can be implemented by quantum circuits of size polylog(N) (where Alice and Bob have oracle access to their inputs). Items (1), (2) qualitatively match the strongest known separation between quantum and classical communication complexity, proved by Gavinsky [8]. Item (3) is new. (Our result is incomparable to the one of Gavinsky. While he obtained a quantitatively better lower bound of Ω (N1/2) in the classical case, the referee in his quantum protocol is inefficient). Exponential separations of quantum and classical communication complexity have been studied in numerous previous works, but to the best of our knowledge the efficiency of the parties in the quantum protocol has not been addressed, and in most previous separations the quantum parties seem to be inefficient. The only separations that we know of that have efficient quantum parties are the recent separations that are based on lifting [10, 5]. However, these separations seem to require quantum protocols with at least two rounds of commun

关键词： Separation

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Extended Abstract: Enhancing Accessibility to MODTRAN Documentation: A Chatbot Framework Using Retrieval-Augmented Generation (RAG)

Extended Abstract: Enhancing Accessibility to MODTRAN Docume...

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IEEE Southeastcon

作者： Zarin T. Shejuti Debzani Deb Emily R. Dunkel Department of Computer Science Winston-Salem State University Winston-Salem USA Jet Propulsion Laboratory California Institute of Technology Pasadena USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

This work presents the development of a chatbot using Retrieval-Augmented Generation (RAG) to streamline access to MODerate resolution atmospheric TRANsmission (MODTRAN)'s extensive documentation. By combining document retrieval with a Large Language Model (LLM), the chatbot delivers accurate, context-aware answers to user queries. Key resources, including the MODTRAN6 User Manual, Algorithm Theoretic Basis Document (ATBD), and the frequently asked questions section from the MODTRAN website, were processed into a searchable vector database. The evaluation showed that the chatbot effectively provides satisfactory and accurate responses, though occasional extraneous information highlights the need for refinement.

关键词： Accuracy Databases Large language models Atmospheric modeling Retrieval augmented generation Refining Documentation Manuals Chatbots Vectors

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Modeling Flow Boiling Utilizing Machine Learning Vision Data

Modeling Flow Boiling Utilizing Machine Learning Vision Data

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Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITHERM)

作者： Cho-Ning Huang Sanghyeon Chang Youngjoon Suh Yoonjin Won Chirag R. Kharangate Department of Mechanical and Aerospace Engineering Case Western Reserve University Cleveland OH USA Department of Mechanical and Aerospace Engineering University of California Irvine CA USA Department of Electrical Engineering and Computer Science University of California Irvine CA USA

ISBN: (数字)9798350364330

ISBN: (纸本)9798350364347

Flow boiling is a very efficient configuration for meeting the high heat dissipation demands of thermal management systems. However, the lack of a clear understanding of physics affecting two-phase flow has limited its wide implementation across thermal systems. Recently, novel machine learning vision tools have been developed to capture physical feature information during subcooled flow boiling in a rectangular channel with single-sided heating. These features include local and averaged, as well as transient and steady-state statistical data on void fraction, vapor-liquid voids, interfacial behaviors, and liquidsolid wall wetting front areas. In this study, this data is used to model performance parameters in flow boiling. The statistical information relating to void fraction, bubble behaviors, interfacial waviness, and wetting fronts is analyzed and correlated with heat transfer coefficients, and critical heat flux. The data is used in combination with traditional control-volume-based theoretical modeling techniques to capture the relationship between the parameter of interest and the input parameters. The data on channel void fraction and wetting front areas are used to improve theoretical models predicting heat transfer coefficient. The data on interfacial behaviors and wetting front areas is to improve mechanistic model predicting critical heat flux. With this work, a new approach to utilizing machine vision data is proposed and validated.

关键词： Heating systems Correlation Wavelength measurement Thermomechanical processes Machine learning Predictive models Thermal management Data models Transient analysis Heat transfer

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Green Cloud Based Efficient Security Mechanism for Aadhaar Voting System 2

Green Cloud Based Efficient Security Mechanism for Aadhaar V...

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2nd International Conference on Technological Advancements in Computational sciences, ICTACS 2022

作者： Therese, M. Julie Dharanyadevi, P. Ann George, Jissy Ananth, Christo Ananth Kumar, T. Sri Manakula Vinayagar Engineering College Department of Electronics and Communication Engineering Puducherry India Pondicherry Technological University Department of Computer Science and Engineering Puducherry India College of Ict and Graphic Design British University of Bahrain P.O. Box 30303 Bahrain Samarkand State University Department of Natural and Exact Sciences Uzbekistan Ifet College of Engineering Department of Computer Science & Engineering Villupuram India

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

Elections are crucial in forming governments in countries like India. The value of democracy lies in the 'Votes' flung by people, through which they elect their representatives for forming the government. At present Electronic Voting Machine (EVM) is used which has certain disadvantages with respect to security aspects. The main goal of the paper is to design a reliable voting machine with improved security and ease of accessibility while also addressing the shortcomings of the present system. Here, electronic components such as fingerprint sensors, microcontroller, LCD display and Global System for Mobile (GSM) modules are used. The entire process will take place in a secured green cloud domain, thereby reducing the security risks and utilize the computer infrastructure more efficiently with curtailed energy consumption. © 2022 IEEE.

关键词： Voting machines

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New Algorithms for Distributed Fair k-Center Clustering: Almost Accurate as Sequential Algorithms 24

New Algorithms for Distributed Fair k-Center Clustering: Alm...

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

作者： Xiaoliang Wu Qilong Feng Ziyun Huang Jinhui Xu Jianxin Wang School of Computer Science and Engineering Central South University & Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie PA USA Department of Computer Science and Engineering State University of New York at Buffalo Buffalo NY USA Hunan Provincial Key Lab on Bioinformatics Central South University & Xiangjiang Laboratory Changsha China

ISBN: (纸本)9798400704864

Fair clustering problems have been paid lots of attention recently. In this paper, we study the k-Center problem under the group fairness and data summarization fairness constraints, denoted as Group Fair k-Center (GFkC) and Data Summarization Fair k-Center (DSFkC), respectively, in the massively parallel computational (MPC) distributed model. The previous best results for the above two problems in the MPC model are a 9-approximation with violation 7 (WWW 2022) and a (17+ε)-approximation without fairness violation (ICML 2020), respectively. In this paper, we obtain a (3+ε)-approximation with violation 1 for the GFkC problem in the MPC model, which is almost as accurate as the best known approximation ratio 3 with violation 1 for the sequential algorithm of the GFkC problem. Moreover, for the DSFkC problem in the MPC model, we obtain a (4+ε)-approximation without fairness violation, which is very close to the best known approximation ratio 3 for the sequential algorithm of the DSFkC problem. Empirical experiments show that our distributed algorithms perform better than existing state-of-the-art distributed methods for the above two problems.

关键词： clustering fairness machine learning

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