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A High-Performance Data Verification Mechanism in Blockchain-based IoT Systems 21

A High-Performance Data Verification Mechanism in Blockchain...

引用

21st IEEE International Symposium on parallel and distributed processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Jingyu Zhou, Pian Sun, Yongtao Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

With the rapid development of the distributed network and communication, the Internet of Things (IoT) systems have been applied to all walks of life. Blockchain technology is one of the most popular research fields recently, which can provide a reliable storage solution and solve information security issues for IoT systems. Transaction information is the most critical fundamental data in the blockchain systems, which needs to be verified to avoid being tempered with by malicious nodes in the process of transmission. With the exponential growth of the number of IoT devices, the limitations of the current storage structure put too much pressure on the system nodes. How to improve verification efficiency has become a key challenge. To alleviate this problem, this paper proposes a novel high-performance verification mechanism for data security protection in blockchain-based IoT systems. We design a new storage structure based on Huffman trunk tree (HTT), and conduct the quantitative analysis of transaction weights. Transactions are stored in full-featured devices in the form of Huffman Merkle tree (HMT), and only the content of HTT is saved in lightweight devices. Finally, the performance superiority of our mechanism is proved through theoretical analysis and experimental evaluation. In blockchain-based IoT systems, our mechanism significantly reduces the data transmission cost and computation overhead, effectively improving the efficiency of data verification. © 2023 IEEE.

关键词： Efficiency

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DSUTO: Differential Rate SAC-based UAV-Assisted Task Offloading Algorithm in Collaborative Edge Computing 29

DSUTO: Differential Rate SAC-Based UAV-Assisted Task Offload...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Zhang, Longxin Tan, Runti Ai, Minghui Xiang, Huazheng Peng, Cheng Zeng, Zhihao Hunan University of Technology College of Computer Science Zhuzhou China

ISBN: (纸本)9798350330717

Mobile edge computing effectively enhances service quality and decreases system cost by processing resource-intensive tasks at the network edge. Today, unmanned aerial vehicles (UAVs) are increasingly being utilized for task offloading services in remote areas due to their convenient deployment and flexible mobility. However, the complex task environment when using UAVs brings great challenges to the optimization strategy's capacity to solve and converge in a stable manner. To solve this issue, a differential rate rule (DRR) is proposed in this work with the goal of improving the update stability of the agent in the actor-critic reinforcement learning (RL). Second, a UAV-assisted task offloading algorithm called DSUTO is designed based on DRR and maximum entropy RL. Finally, a UAV-assisted mobile device-edge-cloud collaborative computing model is constructed with time-varying channel obstacles and user movement, thus solving a multi-objective joint optimization problem on the task completion cost (including delay and energy consumption) and UAV endurance under resource constraints. The experiment results demonstrate that DSUTO not only has excellent performance in terms of convergence and stability, but also significantly reduces the total system cost by 21.38% compared with the latest benchmark algorithms under complex environment conditions. © 2023 IEEE.

关键词： device-edge-cloud fusion maximum entropy reinforcement learning mobile edge computing task offloading UAV assistance

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GraphBinMatch: Graph-based Similarity Learning for Cross-Language Binary and Source Code Matching

GraphBinMatch: Graph-based Similarity Learning for Cross-Lan...

引用

1st International conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： TehraniJamsaz, Ali Chen, Hanze Jannesari, Ali Iowa State Univ Ames IA 50011 USA

ISBN: (纸本)9798350364613;9798350364606

Matching binary to source code and vice versa has various applications in different fields, such as computer security, software engineering, and reverse engineering. Even though there exist methods that try to match source code with binary code to accelerate the reverse engineering process, most of them arc designed to focus on one programming language. However, in real life, programs are developed using different programming languages depending on their requirements. Thus, cross-language binary-to-source code matching has recently gained more attention. Nonetheless, the existing approaches still stniggle to have precise predictions due to the inherent difficulties when the problem of matching binary code and source code needs to be addressed across programming languages. In this paper, we address the problem of cross-language binary source code matching. We propose GraphBinMatch, an approach based on a graph neural network that learns the similarity between binary and source codes. We evaluate GraphBinMatch on several tasks, such as cross-language binary-to-source code matching and cross-language source-to-source matching We also evaluate the performance of our approach on single-language binary-to-source code matching. Experimental results show that GraphBinMatch significantly outperforms stale-of-the-art, with improvements as high as 15% over the Fl score.

关键词： cross-language code similarity binary-source matching

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Traceable Ring Signature Scheme in Internet of Vehicles based on Lattice Cryptography 21

Traceable Ring Signature Scheme in Internet of Vehicles Base...

引用

作者： Liao, Wei Han, Lansheng Chen, Peng Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350329223

Lattice cryptography, as a recognized Cryptosystem that can resist quantum computation, has great potential for development. Lattice based signature scheme is currently a research focus. In this paper, the traceable ring signature scheme based on lattice cryptography in the Internet of Vehicles (IoV) environment is studied. Firstly, users' public and private keys are generated on the lattice, which effectively reduces the key size and time cost. Secondly, the scheme also achieves traceability by adding additional information. The scheme satisfies strong unforgeability, traceability, and anonymity. In addition, the traceable ring signature scheme based on lattice cryptography is applied in the IoV environment to effectively protect user privacy. Model communication can not only protect user privacy, but also trace the signer when necessary, achieving a balance between security and privacy. The simulation results show that the average delay of IoV of our scheme is better than that of similar schemes. The research in this paper improves the security of data transmission in the IoV environment, enables it to adapt to the complex and changeable new network environment, and effectively guarantees the information security of vehicles and owners. © 2023 IEEE.

关键词： Cryptography

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Adaptive Load Balancing based on Machine Learning for Iterative parallel Applications 28

Adaptive Load Balancing based on Machine Learning for Iterat...

引用

28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Oikawa, Anna Victoria C. R. Freitas, Vinicius Castro, Marcio Pilla, Laercio L. Fed Univ Santa Catarina UFSC Florianopolis SC Brazil Univ Paris Saclay CNRS LRI Orsay France

ISBN: (纸本)9781728165820

The performance of irregular scientific applications can be easily affected by an uneven distribution of work among the computing resources. In this context, Load Balancing (LB) stands as one of the most important solutions to improve resource utilization. However, choosing the best-performing load balancing algorithm for a given application is not a trivial task. For instance, manually and statically choosing an LB algorithm does not work in situations where applications have a dynamic or unknown behavior. In this context, we propose a Machine Learning-based Adaptive Load Balancer (ADAPTIVELB) to automate the load balancing algorithm decision at run time. This approach monitors and collects information about the application dynamically, and according to the analyzed data, it makes a decision of invoking the most suitable LB algorithm. Our experiments show that ADAPTIVELB can select a good load balancing algorithm in most of the cases, leading to performance improvements over statically chosen LB algorithms and over the absence of a load balancer.

关键词： dynamic load balancing machine learning performance

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Design and Implementation of Student Behavior Big Data Analysis and Prediction System based on Deep Learning

Design and Implementation of Student Behavior Big Data Analy...

引用

2023 International conference on Information Education and Artificial Intelligence, ICIEAI 2023

作者： Liu, Yani Chongqing College of Architecture and Technology Chongqing China

ISBN: (纸本)9798400716157

With the continuous acceleration of digital campus construction, students have accumulated a lot of data and information in their daily study and life, which provides an important basis for student management. In this regard, based on the current situation and shortcomings in the practical application of colleges and universities, this paper will put forward a set of construction scheme of college students' behavior analysis and prediction system, aiming at constructing the corresponding student behavior analysis model and providing decision-making reference for multi-dimensional, multi-perspective and diversified educational programs. The whole system is designed with B/S architecture, the front end is an interactive interface, and the back end is based on Spark, Hadoop and Tensorflow to complete the construction of the server, which reduces the difficulty of use and improves the work efficiency. Practice has proved that the system supports the distributed parallel processing framework to optimize the convolutional neural network model, so as to improve the training and running speed of various analysis models, and can support users to use the student behavior analysis model to complete the analysis and processing of massive data, so as to make the school teaching and student management more accurate and scientific, and then make a useful attempt to promote the high-quality development of colleges and universities. © 2023 ACM.

关键词： Forecasting

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GAF-GAN: Gated Attention Feature Fusion Image Inpainting network based on Generative Adversarial network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

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TLB-based Block-Grain Classification of Private Data 28

TLB-based Block-Grain Classification of Private Data

引用

28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Upadhyay, Bhargavi R. Ros, Alberto Murty, N. S. Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Bengaluru India Univ Murcia Dept Ingn & Tecnol Comp Murcia Spain Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Commun Engn Bengaluru India

ISBN: (纸本)9781728165820

Sequential and parallel applications use most of the data as private in a multicore system. Recent proposals made use of this observation to reduce the area of the coherence directories or the memory access latency. The driving force of these proposals is the classification of private/shared memory data. The effectiveness of these proposals depends on the number of detected private data. The existing proposals perform the private/shared classification at page granularity, leading to a noticeable amount of miss-classified memory blocks. We propose a mechanism that works on block granularity using the translation lookaside buffer (TLB) to make accurate detection of private data, which increases the effectiveness of proposals relying on a private/shared classification. Simulation results show that the block-grain approach obtains 17.0% more accessed private miss data than the page-grain approach, which translates to an improvement in system performance by 6.02% compared to a page-grain approach.

关键词： directory-based cache coherence private/shared classification system performance

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META: Motion Excitation With Temporal Attention for Compressed Video Action Recognition 29

META: Motion Excitation With Temporal Attention for Compress...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Li, Shaojie Li, Jiaqi Guo, Jinxin Ma, Ming Inner Mongolia University Huhhot China

ISBN: (纸本)9798350330717

Compressed video action recognition has gained significant attention recently due to its ability to replace the raw video with I-frames and compressed motion clues, such as motion vectors and residuals. This results in substantial reductions in storage and computation costs. However, this task suffers from coarse and a lack of structures that can capture long-range spatiotemporal dependencies. To address these issues, this paper proposes a novel module called Motion Excitation with Temporal Attention (META) and utilizes network structures that can capture long-range dependencies. The META module stimulates motion information between I-frames and enhances the motion representation of the motion vectors. It first assigns different weights to feature-level frames, and then calculates the feature-level temporal differences from spatiotemporal features. Finally, it utilizes these differences to excite the motion-sensitive channels of the features. In addition, for compressed video action recognition, we have introduced a new network structure that combines CNN and Transformer. It can seamlessly integrate the merits of convolution and self-attention in a concise transformer format. The proposed method is evaluated on the challenging HMDB-51 and UCF-101 datasets. The extensive comparison results and ablation studies demonstrate the effectiveness and strength of the proposed method. © 2023 IEEE.

关键词： compressed video action recognition multi-stream network temporal modeling

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Exploiting CXL-based Memory for distributed Deep Learning 51

Exploiting CXL-based Memory for Distributed Deep Learning

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51st International conference on parallel processing (ICPP)

作者： Arif, Moiz Assogba, Kevin Rafique, M. Mustafa Vazhkudai, Sudharshan Rochester Inst Technol Rochester NY 14623 USA Micron Technol Inc Austin TX USA

ISBN: (纸本)9781450397339

Deep learning (DL) is being widely used to solve complex problems in scientific applications from diverse domains, such as weather forecasting, medical diagnostics, and fluid dynamics simulation. DL applications consume a large amount of data using large-scale high-performance computing (HPC) systems to train a given model. These workloads have large memory and storage requirements that typically go beyond the limited amount of main memory available on an HPC server. This significantly increases the overall training time as the input training data and model parameters are frequently swapped to slower storage tiers during the training process. In this paper, we use the latest advancements in the memory subsystem, specifically Compute Express Link (CXL), to provide additional memory and fast scratch space for DL workloads to reduce the overall training time while enabling DL jobs to efficiently train models using data that is much larger than the installed system memory. We propose a framework, called DeepMemoryDL, that manages the allocation of additional CXL-based memory, introduces a fast intermediate storage tier, and provides intelligent prefetching and caching mechanisms for DL workloads. We implement and integrate DeepMemoryDL with a popular DL platform, TensorFlow, to show that our approach reduces read and write latencies, improves the overall I/O throughput, and reduces the training time. Our evaluation shows a performance improvement of up to 34% and 27% compared to the default TensorFlow platform and CXL-based memory expansion approaches, respectively.

关键词： Deep Learning Prefetching Caching TensorFlow Data Pipeline

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