检索结果-内蒙古大学图书馆

Enhancing utility in the watchdog privacy mechanism

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zarrabian, Mohammad Amin Ding, Ni Sadeghi, Parastoo Rakotoarivelo, Thierry College of Engineering and Computer Science Australian National University Canberra Australia School of Computing and Information Systems University of Melbourne Melbourne Australia School of Engineering and Information Technology University of New South Wales Canberra Australia Data61 Commonwealth Scientific and Industrial Research Organisation Eveleigh Australia

This paper is concerned with enhancing data utility in the privacy watchdog method for attaining information-theoretic privacy. For a specific privacy constraint, the watchdog method filters out the high-risk data symbols through applying a uniform data regulation scheme, e.g., merging all high-risk symbols together. While this method entirely trades the symbols resolution off for privacy, we show that the data utility can be greatly improved by partitioning the high-risk symbols set and individually privatizing each subset. We further propose an agglomerative merging algorithm that finds a suitable partition of high-risk symbols: it starts with a singleton high-risk symbol, which is iteratively fused with others until the resulting subsets are private. Numerical simulations demonstrate the efficacy of this algorithm in privately achieving higher utilities in the watchdog *** Codes 68P27, © 2021, CC BY.

关键词： Information theory

Sequence Submodular Maximization Meets Streaming 13th

学校读者我要写书评

暂无评论

Sequence Submodular Maximization Meets Streaming

13th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2019

作者： Yang, Ruiqi Xu, Dachuan Guo, Longkun Zhang, Dongmei Department of Operations Research and Scientific Computing Beijing University of Technology Beijing100124 China College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China

ISBN: (纸本)9783030364113

In this paper, we consider the streaming sequence submodular maximization problem, in which the utility function is defined on sequences of element instead of element sets. We encode the values of different sequences by a weighted directed acyclic graph (W-DAG), where the weight of vertex reveals the utility value in selecting single element and the weight of an edge proclaims the additional benefit according to a certain selected order. In addition, the edges are revealed in a streaming fashion that one edge is known in one time slot. The aim is to output a sequence of vertices of length bounded by a given constant k, such that the utility function value is maximized. In this work, we first provide the framework of the sequence submodular maximization under streaming. By utilizing an edge-based thresholding principle, we derive a one pass, -approximation algorithm with memory and update time per edge, where and is the maximum in-degree (out-degree) of the constructed W-DAG. At last, we present a further improved streaming algorithm, which also requires one pass over the stream and attains the same approximation ratio but is with a decreased memory complexity of. © 2019, Springer Nature Switzerland AG.

关键词： Approximation algorithms

5G-Vanet with Edge Technology: Cooperative Data Transmission

学校读者我要写书评

暂无评论

5G-Vanet with Edge Technology: Cooperative Data Transmission

Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Mohammed Al-Farouni Sandeep Kumar Budhani Jatinder Kaur L. Suvonova Manish Kumar Jaishankar Bhatt Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq School of Computing Graphic Era Hill University Uttarakhand Department of Electrical and Communication Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University Tashkent Uzbekistan Scientific Researcher Western Caspian University Baku Azerbaijan Department of Electronics and Communication Engineering GLA University Mathura Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

It is well recognized that linked cars may further enhance in-car entertainment, road safety, and transportation intelligence. 5G-VANET, or 5G-enabled Vehicular Adhoc Networks, may be used to construct software-defined communication with very high accuracy, low latency, and large capacity. Despite being essential for many 5G-VANET enabling programs, mobile data transmission between cars remains a difficult problem due to the massive amount of data required and the high cost of 5G cellular network distribution. This work focuses on cooperative information sharing in 5G-VANET facilitated by edge computing. First, we provide a software-defined cooperation data sharing architecture for 5G-VANET, allowing cellular and targeted short-range communication to operate together effectively. Through cellular connections, OpenFlow-capable cars and the Controller may communicate and exchange contextual data. The data plane (DSRC) makes it easier for nearby cars to share data cooperatively. Secondly, we define the data sharing issue on the created conflict graph as the greatest weighted autonomously set problem, and we provide an effective graph-theoretic solution. In particular, we supply a balanced greedy method that may improve the material distribution balance by taking into consideration ongoing data sharing.

关键词： Greedy algorithms Costs 5G mobile communication Simulation Information sharing Transportation computer architecture Telecommunication computing Automobiles Edge computing

Anomalous platelet transport & fat-tailed distributions

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Kotsalos, Christos Boudjeltia, Karim Zouaoui Dutta, Ritabrata Latt, Jonas Chopard, Bastien University of Geneva Computer Science Department Scientific and Parallel Computing Group Faculty of Medicine Université Libre de Bruxelles CHU de Charleroi University of Warwick Department of Statistics

The transport of platelets in blood is commonly assumed to obey an advection-diffusion equation. Here we propose a disruptive view, by showing that the random part of their velocity is governed by a fat-tailed probability distribution, usually referred to as a Lévy flight. Although for small spatio-temporal scales, it is hard to distinguish it from the generally accepted "red blood cell enhanced" Brownian motion, for larger systems this effect is dramatic as the standard approach may underestimate the flux of platelets by several orders of magnitude, compromising in particular the validity of current platelet function tests. Copyright © 2020, The Authors. All rights reserved.

关键词： Platelets

Unified Graph Neural Network Force-field for the Periodic Table

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Choudhary, Kamal DeCost, Brian Major, Lily Butler, Keith Thiyagalingam, Jeyan Tavazza, Francesca Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Theiss Research La Jolla CA92037 United States Materials Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Computer Science Aberystwyth University SY23 3DB United Kingdom Scientific Computing Department Rutherford Appleton Laboratory Science and Technology Facilities Council Harwell Campus DidcotOX11 0QX United Kingdom

Classical force fields (FF) based on machine learning (ML) methods show great potential for large scale simulations of materials. MLFFs have hitherto largely been designed and fitted for specific systems and are not usually transferable to chemistries beyond the specific training set. We develop a unified atomisitic line graph neural network-based FF (ALIGNN-FF) that can model both structurally and chemically diverse materials with any combination of 89 elements from the periodic table. To train the ALIGNN-FF model, we use the JARVIS-DFT dataset which contains around 75000 materials and 4 million energy-force entries, out of which 307113 are used in the training. We demonstrate the applicability of this method for fast optimization of atomic structures in the crystallography open database and by predicting accurate crystal structures using genetic algorithm for alloys. © 2022, CC BY.

关键词： Genetic algorithms

Reduced basis methods for numerical room acoustic simulations with parametrized boundaries

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Llopis, Hermes Sampedro Engsig-Karup, Allan P. Jeong, Cheol-Ho Pind, Finnur Hesthaven, Jan S. Rambøll Denmark Copenhagen Denmark Acoustic Technology Group Department of Electrical Engineering Technical University of Denmark Kongens Lyngby Denmark Scientific Computing Section Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Treble Technologies Reykjavík Iceland Computational Mathematics and Simulation Science Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

The use of model-based numerical simulation of wave propagation in rooms for engineering applications requires that acoustic conditions for multiple parameters are evaluated iteratively and this is computationally expensive. We present a reduced basis methods (RBM) to achieve a computational cost reduction relative to a traditional full order model (FOM), for wave-based room acoustic simulations with parametrized boundary conditions. In this study, the FOM solver is based on the spectral element method, however other numerical methods could be applied. The RBM reduces the computational burden by solving the problem in a low-dimensional subspace for parametrized frequency-independent and frequency-dependent boundary conditions. The problem is formulated and solved in the Laplace domain, which ensures the stability of the reduced order model based on the RBM approach. We study the potential of the proposed RBM framework in terms of computational efficiency, accuracy and storage requirements and we show that the RBM leads to 100-fold speed-ups for a 2D case with an upper frequency of 2kHz and around 1000-fold speed-ups for an analogous 3D case with an upper frequency of 1kHz. While the FOM simulations needed to construct the ROM are expensive, we demonstrate that despite this cost, the ROM has a potential of three orders of magnitude faster than the FOM when four different boundary conditions are simulated per room surface. Moreover, results show that the storage model for the ROM is relatively high but affordable for the presented 2D and 3D cases. © 2021, CC BY-NC-ND.

关键词： Boundary conditions

Quantum noise protects quantum classifiers against adversaries

学校读者我要写书评

暂无评论

Physical Review Research 2021年第2期3卷 023153-023153页

作者： Yuxuan Du Min-Hsiu Hsieh Tongliang Liu Dacheng Tao Nana Liu UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering University of Sydney Australia Hon Hai Quantum Computing Research Center No. 32 Jihu Road Neihu District Taipei 114 Taiwan Institute of Natural Sciences Shanghai Jiao Tong University Shanghai 200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai 200240 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai 200240 China

Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies. However, noise has often played beneficial roles, from enhancing weak signals in stochastic resonance to protecting the privacy of data in differential privacy. It is then natural to ask: Can we harness the power of quantum noise that is beneficial to quantum computing? An important current direction for quantum computing is its application to machine learning, such as classification problems. One outstanding problem in machine learning for classification is its sensitivity to adversarial examples. These are small, undetectable perturbations from the original data where the perturbed data is completely misclassified in otherwise extremely accurate classifiers. They can also be considered as worst-case perturbations by unknown noise sources. We show that by taking advantage of depolarization noise in quantum circuits for classification, a robustness bound against adversaries can be derived where the robustness improves with increasing noise. This robustness property is intimately connected with an important security concept called differential privacy, which can be extended to quantum differential privacy. For the protection of quantum data, this quantum protocol can be used against the most general adversaries. Furthermore, we show how the robustness in the classical case can be sensitive to the details of the classification model, but in the quantum case the details of the classification model are absent, thus also providing a potential quantum advantage for classical data. This opens the opportunity to explore other ways in which quantum noise can be used in our favor, as well as identifying other ways quantum algorithms can be helpful in a way which is distinct from quantum speedups.

关键词： Machine learning Quantum algorithms Quantum information processing Quantum protocols

Beware of "Explanations" of AI

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Martens, David Shmueli, Galit Evgeniou, Theodoros Bauer, Kevin Janiesch, Christian Feuerriegel, Stefan Gabel, Sebastian Goethals, Sofie Greene, Travis Klein, Nadja Kraus, Mathias Kühl, Niklas Perlich, Claudia Verbeke, Wouter Zharova, Alona Zschech, Patrick Provost, Foster University of Antwerp Department of Engineering Management Antwerp2000 Belgium National Tsing Hua University Institute of Service Science Hsinchu30013 Taiwan INSEAD Technology and Business Fontainebleau77300 France Goethe University Frankfurt Department of Information Systems Frankfurt60629 Germany TU Dortmund University Department of Computer Science Dortmund44227 Germany LMU Munich Munich Center for Machine Learning Munich80539 Germany Erasmus University Rotterdam School of Management Rotterdam3062 Netherlands Copenhagen Business School Department of Digitalization Copenhagen2000 Denmark Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany University of Regensburg Faculty of Informatics and Data Science Regensburg93053 Germany University of Bayreuth Faculty of Law Business and Economics Bayreuth95440 Germany New York University Department of Technology Operations and Statistics New YorkNY10012 United States KU Leuven Faculty of Economics and Business Leuven3000 Belgium Humboldt-Universität zu Berlin School of Business and Economics Berlin10099 Germany Leipzig University Faculty of Economics and Management Science Leipzig04109 Germany

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations. © 2025, CC BY.

关键词：

OMPRacer: A Scalable and Precise Static Race Detector for OpenMP Programs 20

学校读者我要写书评

暂无评论

OMPRacer: A Scalable and Precise Static Race Detector for Op...

Supercomputing Conference

作者： Bradley Swain Yanze Li Peiming Liu Ignacio Laguna Giorgis Georgakoudis Jeff Huang Computer Science and Engineering Texas A & M University College Station TX Coderrect Inc College Station TX Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

We present OMPRACER, a static tool that uses flow-sensitive, interprocedural analysis to detect data races in OpenMP programs. OMPRACER is fast, scalable, has high code coverage, and supports the most common OpenMP features by combining state-of-the-art pointer analysis, novel value-flow analysis, happens-before tracking, and generalized modelling of OpenMP APIs. Unlike dynamic tools that currently dominate data race detection, OMPRACER achieves almost 100% code coverage using static analysis to detect a broader category of races without running the program or relying on specific input or runtime behaviour. OMPRACER has competitive precision with dynamic tools like Archer and ROMP: passing 105/116 cases in DataRaceBench with a total accuracy of 91%. OMPRACER has been used to analyze several Exascale computing Project proxy applications containing over 2 million lines of code in under 10 minutes. OMPRACER has revealed previously unknown races in an ECP proxy app and a production simulation for COVID19.

关键词： Runtime Exascale computing Static analysis Production Tools Hardware Performance analysis