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Verifying a Carry Look-Ahead Adder with Propositional Projection Temporal Logic

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Chinese Journal of Electronics 2023年第1期22卷 21-24页

作者： Nan Zhang Zhenhua Duan Cong Tian Institute of Computing Theory and Technology Xidian University Xi'an China National Key Laboratory on Integrated Services Network Xidian University Xi'an China

This paper presents a case study for verifying a carry look-ahead adder using the axiom system of Propositional projection temporal logic (PPTL). To this end, the syntax, semantics and axiom system of PPTL are briefly introduced. Further, functions and architectures of the carry look-ahead adder are presented. In addition, four lemmas are proved with the axiom system of PPTL. Finally, the correctness of the adder is verified based on the lemmas and the axiom system of PPTL. The case study shows that the axiom system for PPTL is workable.

关键词： Protocols Semantics Computer architecture Syntactics Hardware Software Intserv networks

Design of a Secure Machine Learning-Based Malware Detection and Analysis Scheme

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Design of a Secure Machine Learning-Based Malware Detection ...

2023 IEEE International Conference on Communications, computing, Cybersecurity and Informatics, CCCI 2023

作者： Kumar, Pankaj Obaidat, Mohammad S. Pandey, Piyush Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan School of Computing Srm University Jordan University of Science and Technology Beijing Beijing100083 China School of Computing Srm University TN Kattankulathur603203 India Algorithmic Research International Institute of Information Technology Center for Security Theory Hyderabad India

ISBN: (纸本)9798350302561

Today's world is rapidly growing towards the advanced concept of technology. technology nowadays is at its peak of development and has great advantages in all fields of work, whether it is in industrial automation, farming, weather forecasting, or disease detection and control. Despite its great advantages, it does have its negative side because a major population is using technology and general-purpose computing devices (i.e., desktops, laptops, smartphones, tablets, etc.). Therefore, most of their personal data is available online. The data is constantly in danger of getting stolen by malicious actors (i.e., hackers), who can find a way to get their hands on this data and then use it for their personal gain (i.e., money-making). The best-known technique to get this information is by using malware exploitation on the machine of the target. Malware is an executable or a binary that is malicious in nature. It may have various devastating effects, and in order to detect and make the system defensive, we need to propose a scheme to detect the malware in the system, analyze the particular file, and then mitigate it. In this paper, we propose a secure machine learning-based malware detection and analysis scheme (in short, SML-MDA). The conducted security analysis and formal security verification of the proposed SMLMDA prove its security against various types of potential attacks. The proposed SML-MDA is then compared with the other similar competing schemes, and its performance has been found to be better in terms of various performance metrics like accuracy and F1-score. © 2023 IEEE.

关键词： Machine learning

Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

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IEEE Open Journal of the Communications Society

IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

AutoSketch: automatic sketch-oriented compiler for query-driven network telemetry 24

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AutoSketch: automatic sketch-oriented compiler for query-dri...

Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation

作者： Haifeng Sun Qun Huang Jinbo Sun Wei Wang Jiaheng Li Fuliang Li Yungang Bao Xin Yao Gong Zhang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Institute of Computing Technology CAS Northeastern University China Huawei Theory Department

ISBN: (纸本)9781939133397

Recent network telemetry witnesses tremendous progress in two directions: query-driven telemetry that targets expressiveness as the primary goal, and sketch-based algorithms that address resource-accuracy trade-offs. In this paper, we propose AutoSketch that aims to integrate the advantages of both classes. In a nutshell, AutoSketch automatically compiles high-level operators into sketch instances that can be readily deployed with low resource usage and incur limited accuracy drop. However, there remains a gap between the expressiveness of high-level operators and the underlying realization of sketch algorithms. AutoSketch bridges this gap in three aspects. First, AutoSketch extends its interface derived from existing query-driven telemetry such that users can specify the desired telemetry accuracy. The specified accuracy intent will be utilized to guide the compiling procedure. Second, AutoSketch leverages various techniques, such as syntax analysis and performance estimation, to construct efficient sketch instances. Finally, AutoSketch automatically searches for the most suitable parameter configurations that fulfill the accuracy intent with minimum resource usage. Our experiments demonstrate that AutoSketch can achieve high expressiveness, high accuracy, and low resource usage compared to state-of-the-art telemetry solutions.

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Faster Algorithms for Structured Linear and Kernel Support Vector Machines

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arXiv 2023年

作者： Gu, Yuzhou Song, Zhao Zhang, Lichen New York University United States Simons Institute for the Theory of Computing UC Berkeley United States Massachusetts Institute of Technology United States

Quadratic programming is a ubiquitous prototype in convex programming. Many machine learning problems can be formulated as quadratic programming, including the famous Support Vector Machines (SVMs). Linear and kernel SVMs have been among the most popular models in machine learning over the past three decades, prior to the deep learning era. Generally, a quadratic program has an input size of Θ(n2), where n is the number of variables. Assuming the Strong Exponential Time Hypothesis (SETH), it is known that no O(n2−o(1)) time algorithm exists when the quadratic objective matrix is positive semidefinite (Backurs, Indyk, and Schmidt, NeurIPS’17). However, problems such as SVMs usually admit much smaller input sizes: one is given n data points, each of dimension d, and d is oftentimes much smaller than n. Furthermore, the SVM program has only O(1) equality linear constraints. This suggests that faster algorithms are feasible, provided the program exhibits certain structures. In this work, we design the first nearly-linear time algorithm for solving quadratic programs whenever the quadratic objective admits a low-rank factorization, and the number of linear constraints is small. Consequently, we obtain results for SVMs: • For linear SVM when the input data is d-dimensional, our algorithm runs in time Õ(nd(ω+1)/2 log(1/ϵ)) where ω ≈ 2.37 is the fast matrix multiplication exponent;• For Gaussian kernel SVM, when the data dimension d = O(log n) and the squared dataset radius is sub-logarithmic in n, our algorithm runs in time O(n1+o(1) log(1/ϵ)). We also prove that when the squared dataset radius is at least Ω(log2 n), then Ω(n2−o(1)) time is required. This improves upon the prior best lower bound in both the dimension d and the squared dataset radius. © 2023, CC BY-NC-SA.

关键词： Support vector machines

Dynamical-invariant-based holonomic quantum gates:theory and experiment

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Fundamental Research 2023年第2期3卷 229-236页

作者： Yingcheng Li Tao Xin Chudan Qiu Keren Li Gangqin Liu Jun Li Yidun Wan Dawei Lu State Key Laboratory of Surface Physics Department of PhysicsCenter for Field Theory and Particle Physicsand Institute for Nanoelectronic devices and Quantum computingFudan UniversityShanghai 200433China Shanghai Qi Zhi Institute Shanghai 200030China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China d Guangdong Provincial Key Laboratory of Quantum Science and Engineering Shenzhen 518055GuangdongChina Center for Quantum Computing Peng Cheng LaboratoryShenzhen 518055China Institute of Physics Chinese Academy of SciencesBeijing 100190China

Among existing approaches to holonomic quantum computing,the adiabatic holonomic quantum gates(HQGs)suffer errors due to decoherence,while the non-adiabatic HQGs either require additional Hilbert spaces or are difficult to ***,we report a systematic,scalable approach based on dynamical invariants to realize HQGs without using additional Hilbert *** presenting the theoretical framework of our approach,we design and experimentally evaluate single-qubit and two-qubits HQGs for the nuclear magnetic resonance *** single-qubit gates acquire average fidelity 0.9972 by randomized benchmarking,and the controlled-NOT gate acquires fidelity 0.9782 by quantum process *** approach is also platform-independent,and thus may open a way to large-scale holonomic quantum computation.

关键词： Geometric gates Dynamical invariant Nuclear magnetic resonance Holonomic gates Platform-independent

A Tighter Complexity Analysis of SparseGPT

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arXiv 2024年

作者： Li, Xiaoyu Liang, Yingyu Shi, Zhenmei Song, Zhao Stevens Institute of Technology United States The University of Hong Kong Hong Kong University of Wisconsin-Madison United States The Simons Institute the Theory of Computing The University of California Berkeley United States

In this work, we improved the analysis of the running time of SparseGPT [Frantar, Alistarh ICML 2023] from O(d3) to O(dω + d2+a+o(1) + d1+ω(1,1,a)−a) for any a ∈ [0, 1], where ω is the exponent of matrix multiplication. In particular, for the current ω ≈ 2.371 [Alman, Duan, Williams, Xu, Xu, Zhou 2024], our running time boils down to O(d2.53). This running time is due to the analysis of the lazy update behavior in iterative maintenance problems such as [Deng, Song, Weinstein 2022;Brand, Song, Zhou ICML 2024]. © 2024, CC BY.

关键词： Matrix algebra

Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk

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arXiv 2024年

作者： Gu, Yuzhou Kuang, Nikki Lijing Ma, Yi-An Song, Zhao Zhang, Lichen New York University United States University of California San Diego United States Simons Institute for the Theory of Computing UC Berkeley United States Massachusetts Institute of Technology United States

We consider the problem of sampling from a d-dimensional log-concave distribution π(θ) ∝ exp(−f (θ)) for L-Lipschitz f, constrained to a convex body with an efficiently computable self-concordant barrier function, contained in a ball of radius R with a w-warm start. We propose a robust sampling framework that computes spectral approximations to the Hessian of the barrier functions in each iteration. We prove that for polytopes that are described by n hyperplanes, sampling with the Lee-Sidford barrier function mixes within Oe((d2 + dL2R2) log(w/δ)) steps with a per step cost of Oe(ndω−1), where ω ≈ 2.37 is the fast matrix multiplication exponent. Compared to the prior work of Mangoubi and Vishnoi, our approach gives faster mixing time as we are able to design a generalized soft-threshold Dikin walk beyond log-barrier. We further extend our result to show how to sample from a d-dimensional spectrahedron, the constrained set of a semidefinite program, specified by the set {x ∈ Rd : Pdi=1 xiAi ≽ C} where A1, . . ., Ad, C are n × n real symmetric matrices. We design a walk that mixes in Oe((nd + dL2R2) log(w/δ)) steps with a per iteration cost of Oe(nω + n2d3ω−5). We improve the mixing time bound of prior best Dikin walk due to Narayanan and Rakhlin that mixes in Oe((n2d3 + n2dL2R2) log(w/δ)) steps. © 2024, CC BY-NC-SA.

关键词： C (programming language)

Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study

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arXiv 2024年

作者： Ke, Yekun Li, Xiaoyu Liang, Yingyu Shi, Zhenmei Song, Zhao Stevens Institute of Technology United States The University of Hong Kong Hong Kong University of Wisconsin-Madison United States The Simons Institute for the Theory of Computing University of California Berkeley United States

Recent empirical studies have identified fixed point iteration phenomena in deep neural networks, where the hidden state tends to stabilize after several layers, showing minimal change in subsequent layers. This observation has spurred the development of practical methodologies, such as accelerating inference by bypassing certain layers once the hidden state stabilizes, selectively fine-tuning layers to modify the iteration process, and implementing loops of specific layers to maintain fixed point iterations. Despite these advancements, the understanding of fixed point iterations remains superficial, particularly in high-dimensional spaces, due to the inadequacy of current analytical tools. In this study, we conduct a detailed analysis of fixed point iterations in a vector-valued function modeled by neural networks. We establish a sufficient condition for the existence of multiple fixed points of looped neural networks based on varying input regions. Additionally, we expand our examination to include a robust version of fixed point iterations. To demonstrate the effectiveness and insights provided by our approach, we provide case studies that looped neural networks may exist 2d number of robust fixed points under exponentiation or polynomial activation functions, where d is the feature dimension. Furthermore, our preliminary empirical results support our theoretical findings. Our methodology enriches the toolkit available for analyzing fixed point iterations of deep neural networks and may enhance our comprehension of neural network mechanisms. © 2024, CC BY.

关键词： Deep neural networks