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Compressed Sensing by Using Measurement Completion for Robust Image Coding 24

Compressed Sensing by Using Measurement Completion for Robus...

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24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Bo Xiao, Di Bai, Sen Li, Min Communication Nco Academy Army Engineering University Chongqing400035 China College of Computer Science Chongqing University Chongqing400044 China Chongqing Institute of Engineering Chongqing400056 China

ISBN: (纸本)9798350319934

Compressed sensing (CS) has been demonstrated to be an effective method for robust image coding. However, for existing CS-based image coding schemes, recovery performance drops rapidly at high packet loss rates (PLRs) because the received CS measurements are insufficient for stable recovery. To solve this problem, we propose a novel robust image coding scheme by using CS with measurement completion in this paper. By dividing the original image into a lot of down-sampled images with interweaving permutation (IP) and then sampling them with scrambled 2D CS (2DCS), we can obtain the CS measurement vectors of the down-sampled images. Since the CS measurement vectors preserve the correlation of the down-sampled images, they are also highly-correlated with each other. By exploring the correlation among the CS measurement vectors, a measurement completion strategy is proposed, which can recover many lost CS measurements due to packet loss at the decoder side. Simulation results show that the proposed scheme can significantly outperform previous CS-based image coding schemes at high PLRs in terms of rate distortion (R-D) performance. This advantage makes the proposed scheme a good candidate for those image communication systems which need to provide reliable transmission for the image data via channels with high PLRs. © 2022 IEEE.

关键词： Image coding

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DNNEmu: A Lightweight performance Emulator for Distributed DNN Training 22nd

DNNEmu: A Lightweight Performance Emulator for Distributed ...

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22nd International conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Wang, Jian Yu, Enda Dong, Dezun Pang, Zhengbin College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783031226762

Distributed deep learning system usually leverages large-scale GPU clusters to speed up training. Therefore, for organizations lacking GPU resources temporarily, it is difficult to evaluate distributed communication optimization. Specifically, due to the complex interaction between computation and communication, accurately predicting the performance of distributed training is an almost impossible task. In this paper, we propose DNNEmu, a lightweight performance emulator for distributed DNN training to help ML developers design and optimize the communication strategies before target machines are available. Based on our key observation that the computation overhead could be predicted by input features and replaced by simulation programs, we use CPUs to emulate the communication and computation of distributed training. DNNEmu makes layer-wise predictions based on profiling traces collected by the layer-wise profiler, and emulates DNN training by simulating computation. DNNEmu contains three integrated modules: (i) the layer-wise profiler profiling operators with layer names;(ii) the performance predictor predicting the layer-wise operator runtime;and (iii) the computation simulator simulating the actual computation with the predicted runtime. DNNEmu is implemented over MXNet and requires no changes to the developers’ models or inputs. We show that DNNEmu can emulate distributed training with an average estimation error of 7.2 %. © 2023, Springer Nature Switzerland AG.

关键词： Forecasting

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A Noise-Robust Radio Frequency Fingerprint Identification Scheme for Internet of Things Devices

A Noise-Robust Radio Frequency Fingerprint Identification Sc...

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2023 IEEE INFOCOM conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Xing, Yuexiu Chen, Xiaoxing Zhang, Junqing Hu, Aiqun Zhang, Dengyin School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China Jiang Su Yi Tong High-tech Company Limited. Changshu215500 China University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom School of Information Science and Engineering Southeast University Nanjing210096 China

ISBN: (纸本)9781665494274

Radio frequency fingerprint (RFF) identification is a potentially effective technique to address the authentication security of Internet of Things (IoT) devices. Since the complex working environment and limited resources of IoT devices, noise is non-negligible in RFF identification of IoT devices. It is a challenge to suppress the noise without damaging the RFF information. In this paper, we propose a robust RFF identification scheme, which consists of a frequency point selection (FPS) based denoising algorithm, and a convolutional neural network (CNN) classifier. The FPS algorithm performs denoising by filtering out all the frequency components that are independent of the RFF. The CNN is designed with a dynamically decreasing learning rate to accelerate learning and obtain optimal identification performance. Experiments were conducted with 54 ZigBee devices to evaluate the performance of the proposed scheme under three different RFF identification scenarios. The results show that the FPS algorithm brings the highest accuracy improvement of about 25 % when the training signal-to-noise ratio (SNR) is hybrid and the test SNR is 0 dB. © 2023 IEEE.

关键词： Internet of things

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high-Dimensional Causal Bayesian Optimization 27

High-Dimensional Causal Bayesian Optimization

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27th European conference on Artificial Intelligence, ECAI 2024

作者： Wu, Yupeng Wang, Weiye Zhang, Yangwenhui Li, Mingjia Liu, Yuanhao Qian, Hong Zhou, Aimin Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University Shanghai200062 China

ISBN: (纸本)9781643685489

Causal global optimization (CGO) aims to complete optimization tasks through causal inference. In the high-dimensional CGO problems, traditional causal Bayesian optimization (CBO) methods struggle with the curse of dimensionality attributed to the number of variables in the causal graph, and scale inconsistency among Gaussian Process (GP) models. These issues limit the application of CBO in domains requiring optimization over large causal graphs. To address these limitations, this paper proposes a high-dimensional causal Bayesian optimization (HCBO) algorithm. To address the curse of dimensionality, HCBO introduces a submodularity indicator for variable subsets through the concept of causal intrinsic dimensionality (CID). It then uses the submodular optimization algorithm to find approximations of CID within polynomial sample complexity. Theoretically, we disclose a sufficient condition for CID’s existence. To address the issue of scale inconsistency among GP models, HCBO introduces a scale-normalized scoring function, ensuring stable identification of the optimal GP model corresponding to CID for intervention. Extensive experiments are conducted on high-dimensional synthetic and real-world tasks, i.e., coral ecology and health. The existence of CID is verified across the datasets of all tasks. HCBO achieves state-of-the-art performance in CGO problems and can handle causal graphs at a scale 10 times larger than that manageable by previous CBO methods. © 2024 The Authors.

关键词： Optimization algorithms

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A Dynamic Algorithm for Submodular Maximization with a Knapsack Constraint 23

A Dynamic Algorithm for Submodular Maximization with a Knaps...

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23rd IEEE International conference on high performance Computing and Communications, 7th IEEE International conference on Data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhu, Tianshuai Han, Kai Zhang, Enpei Wu, Benwei School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781665494571

Submodular maximization is a fundamental problem in computer science theory, as well as occupies a significant place in machine learning and data mining applications, including influence maximization in social networks, recommendation systems, exemplar-based clustering, viral marketing, and data summarization. In this paper, we study this classic problem under a knapsack constraint in a fully dynamic setting. Given a stream of arbitrary interleaved element insertions and deletions, the dynamic setting requires a solution quickly after each update. By adopting repeated sampling, threshold greedy and filtering technique, our algorithm achieves an approximation ratio of 1/3 for monotone submodular objective function, with polylogarithmic amortized update time. We extensively evaluate the performance of our algorithm against the existing work in two applications including maximum coverage and movie recommendation. The experimental results demonstrate the superiority of our algorithm. © 2021 IEEE.

关键词： Machine learning algorithms Social networking (online) Smart cities Filtering Heuristic algorithms Machine learning Approximation algorithms

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Correlation Power Analysis and Protected Implementation on Lightweight Block Cipher FESH 8

Correlation Power Analysis and Protected Implementation on L...

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8th IEEE International conference on Big Data Security on Cloud, 8th IEEE International conference on high performance and Smart Computing, and 8th IEEE International conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Ding, Shijun Wang, An Sun, Shaofei Ding, Yaoling Hou, Xintian Han, Dong Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China Information Engineering University Institute of Cyberspace Security Zhengzhou450001 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Chinese Academy of Sciences Institute of Information Engineering Beijing100093 China Co. Ltd. Tianjin300300 China

ISBN: (纸本)9781665480697

With the development of the Internet of Things (IoT), the demand for lightweight cipher came into being. At the same time, the security of lightweight cipher has attracted more and more attention. FESH algorithm is a lightweight cipher proposed in 2019. Relevant studies have proved that it has strong ability to resist differential attack and linear attack, but its research on resisting side-channel attack is still blank. In this paper, we first introduce a correlation power analysis for FESH algorithm and prove its effectiveness by experiments. Then we propose a mask scheme for FESH algorithm, and prove the security of the mask. According to the experimental results, protected FESH only costs 8.6%, 72.3%, 16.7% of extra time, code and RAM. © 2022 IEEE.

关键词： Side channel attack

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StackSight: Unveiling WebAssembly through Large Language Models and Neurosymbolic Chain-of-Thought Decompilation 41

StackSight: Unveiling WebAssembly through Large Language Mod...

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41st International conference on Machine Learning, ICML 2024

作者： Fang, Weike Zhou, Zhejian He, Junzhou Wang, Weihang Department of Computer Science University of Southern California Los AngelesCA United States

WebAssembly enables near-native execution in web applications and is increasingly adopted for tasks that demand high performance and robust security. However, its assembly-like syntax, implicit stack machine, and low-level data types make it extremely difficult for human developers to understand, spurring the need for effective WebAssembly reverse engineering techniques. In this paper, we propose StackSight, a novel neurosymbolic approach that combines Large Language Models (LLMs) with advanced program analysis to decompile complex WebAssembly code into readable C++ snippets. StackSight visualizes and tracks virtual stack alterations via a static analysis algorithm and then applies chain-of-thought prompting to harness LLM's complex reasoning capabilities. Evaluation results show that StackSight significantly improves WebAssembly decompilation. Our user study also demonstrates that code snippets generated by StackSight have significantly higher win rates and enable a better grasp of code semantics. Copyright 2024 by the author(s)

关键词： Semantics

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PancakeFS: A Write Efficiently and Read Optimized Filesystem

PancakeFS: A Write Efficiently and Read Optimized Filesystem

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE International conference on

作者： Yongkang Wang Yang Liu Zhiguang Chen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of High-Performance Computing Institute for Quantum Information College of Computer National University of Defense Technology Changsha China

With the development of emerging technologies such as cloud computing and large AI models (such as LLM), many applications have placed higher demands on the intensive read and write processing of massive data. Addressing the limitations of traditional file systems, we propose PancakeFS, an LSM-Tree-based file system. Leveraging the write-friendly characteristics of LSM-Tree, PancakeFS exhibits efficient write performance. We improve read performance through the design of an inode allocation algorithm based on subtree partitioning and optimization of the disk layout for hot data reconstruction. Our experimental results demonstrate that PancakeFS outperforms Ext4 and TableFS in terms of both read and write performance.

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Few-shot malicious traffic classification based on Siamese Neural Network 23

Few-shot malicious traffic classification based on Siamese N...

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作者： Wu, Kailin Wang, Pan Wang, ZiXuan School of Modern Posts Nanjing University of Posts & Telecommunications Nanjing China

ISBN: (纸本)9781665494571

The process of learning good features for machine learning applications can be computationally expensive, and can prove difficult when there is little data available. A typical example is few-shot learning. In this case, we must make predictions correctly and only give a few examples of each class. In this article, we propose a method based on a one-dimensional convolutional siamese neural network, which uses a unique structure to naturally rank the similarities between inputs. Once the network is adjusted, we can use powerful discriminative features to extend the network's predictive capabilities to new data, and to new categories from unknown distributions. Using the convolutional architecture, we can achieve powerful results that exceed other deep learning models, and have close to the most advanced performance on few-shot classification tasks. The results show that this method can obtain higher recognition accuracy than the traditional methods of smote and GAN generated data training. © 2021 IEEE.

关键词： Training Deep learning Neural networks computer architecture Generative adversarial networks Task analysis

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Effective Integration of Text Diffusion and Pre-Trained Language Models with Linguistic Easy-First Schedule 30

Effective Integration of Text Diffusion and Pre-Trained Lang...

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Joint 30th International conference on Computational Linguistics and 14th International conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ou, Yimin Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

Diffusion models have become a powerful generative modeling paradigm, achieving great success in continuous data patterns. However, the discrete nature of text data results in compatibility issues between continuous diffusion models (CDMs) and pre-trained language models (PLMs). That is, the performance of diffusion models even degrades when combined with PLMs. To alleviate this issue, we propose to utilize a pre-trained decoder to convert the denoised embedding vectors into natural language instead of using the widely used rounding operation. In this way, CDMs can be more effectively combined with PLMs. Additionally, considering that existing noise schedules in text diffusion models do not take into account the linguistic differences among tokens, which violates the easy-first policy for text generation, we propose a linguistic easy-first schedule that incorporates the measure of word importance, conforming to easy-first-generation linguistic features and bringing about improved generation quality. Experiment results on the E2E dataset and five controllable tasks show that our approach can combine the merits of CDMs and PLMs, significantly outperforming other diffusion-based models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Diffusion

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