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7th International Conference on Artificial Intelligence and security, ICAIS 2021

作者： Xu, Zhenggang Zhai, Wenhan Liang, Chen Zhao, Yunlin Huang, Tian Zhou, Minghui Zhou, Libo Yang, Guiyan Hu, Zhiyuan School of Material and Chemical Engineering Hunan City University Yiyang413000 China Country College of Forestry Northwest A & F University Yangling712100 China Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province Central South University of Forestry and Technology Changsha410004 China Engineering Research Center for Internet of Animals Changsha410000 China

ISBN: (纸本)9783030786113

Wild animal tracker, as an important IoT device terminal, can collect animal's position information and movement information in real time. The analysis based on the above big data provides us with a new perspective on recognizing animal behavior. It is useful in scientific research and animal management, while the device is always limited by energy. In order to develop the tracker device and enhance the energy supplying, wild animal tracker module including solar energy module, Maximum Power Point Tracking module, lithium battery, energy module, communication module, Global Positioning System module, data management module, stability controlling module, and ephemeris backup module etc. was designed in the study to optimize energy. Circuits for watchdog, energy management, solar energy protective were also designed in the novel tracker. Solar utilization efficiency and energy conversion efficiency have been enhanced after the energy optimization based on watchdog technology. The novel trackers were applied in many wild animals. Tests showed that the positioning accuracy, energy supplying, data collection all had been improved effectively. The tracker also proper worked in many harsh environments, such as desert and so on. The novel wild animal tracker can not only connect wild animals and the natural environment, but also human activities. There is also a huge market prospect, especially with the development of IoT technology and big data technology. © 2021, Springer Nature Switzerland AG.

关键词： Solar energy

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Promising Bio-Authentication Scheme to Protect Documents for E2E S2S in IoT-Cloud

Promising Bio-Authentication Scheme to Protect Documents for...

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2020 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2020

作者： Sibahee, Mustafa A. Al Lu, Songfeng Abduljabbar, Zaid Ameen Liu, Erasmus Xin Ran, Yanli Al-Ashoor, Ahmed Abdulelah Jasim Hussain, Mohammed Abdulridha Hussien, Zaid Alaa Shenzhen Institute Huazhong University of Science and Technology Shenzhen China Iraq University College Basrah Iraq Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China University of Basrah Basrah Iraq Al-Kinoouze University College Basrah Iraq Neusoft Institute Guangdong Guangdong China Shenzhen University Shenzhen China Southern Technical University Basrah Iraq

ISBN: (数字)9781728172026

ISBN: (纸本)9781728172019

Document integrity and origin for E2E S2S in IoTcloud have recently received considerable attention because of their importance in the real-world fields. Maintaining integrity could protect decisions made based on these message/image documents. Authentication and integrity solutions have been conducted to recognise or protect any modification in the exchange of documents between E2E S2S (smart-to-smart). However, none of the proposed schemes appear to be sufficiently designed as a secure scheme to prevent known attacks or applicable to smart devices. We propose a robust scheme that aims to protect the integrity of documents for each users session by integrating HMAC-SHA-256, handwritten feature extraction using a local binary pattern, one-time random pixel sequence based on RC4 to randomly hide authentication codes using LSB. The proposed scheme can provide users with one-time bio-key, robust message anonymity and a disappearing authentication code that does not draw the attention of eavesdroppers. Thus, the scheme improves the data integrity for a users messages/image documents, phase key agreement, bio-key management and a one-time message/image document code for each users session. The concept of stego-anonymity is also introduced to provide additional security to cover a hashed value. Finally, security analysis and experimental results demonstrate and prove the invulnerability and efficiency of the proposed scheme. © 2020 IEEE.

关键词： Authentication

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Metamorphic Testing for Traffic Light Recognition in Autonomous Driving Systems

Metamorphic Testing for Traffic Light Recognition in Autonom...

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IEEE International Conference on Software Quality, Reliability and security Companion (QRS-C)

作者： Tongtong Bai Yong Fan Ya Pan Mingshuang Qing School of Computer Science and Technology Southwest University of Science and Technology Mianyang China Sichuan Big Data and Intelligent System Engineering Technology Research Center Mianyang China

ISBN: (纸本)9781665478373

Traffic light recognition is significant for environ-ment perception and behavior planning in autonomous driving systems. Erroneous traffic light recognition results can lead to serious traffic accidents. Thus, it is necessary to ensure the correctness of traffic light recognition in autonomous driving systems. However, the existing approaches to testing traffic light recognition need manual collection and labeling test data which become prohibitively expensive as the number of test situations increases. In this paper, we propose an approach that uses metamorphic testing to test traffic light recognition. The approach does not need labels to verify traffic light recognition results and can automatically generate traffic lights of different colors of the same scene. In the final experimental stage, we implement our approach to test traffic light recognition models and end-to-end autonomous driving systems. The result of the experiment demonstrates that our approach verifies traffic light recognition results independent of labels and can automatically generate natural traffic lights. Moreover, our approach also discovers traffic light recognition errors.

关键词： Image color analysis Conferences Software quality Manuals Software reliability Planning security

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LCCG: A Locality-Centric Hardware Accelerator for High Throughput of Concurrent Graph Processing

LCCG: A Locality-Centric Hardware Accelerator for High Throu...

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Supercomputing Conference

作者： Jin Zhao Yu Zhang Xiaofei Liao Liaang He Binzsheng He Haikun Liu Hai Jin Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China University of Warwick United Kingdom National University of Singapore Singapore

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

In modern data centers, massive concurrent graph processing jobs are being processed on large graphs. However, existing hardware/-software solutions suffer from irregular graph traversal and intense resource contention. In this paper, we propose LCCG, a Locality-entric programmable accelerator that augments the many-core processor for achieving higher throughput of Concurrent Graph processing jobs. Specifically, we develop a novel topology-aware execution approach into the accelerator design to regularize the graph traversals for multiple jobs on-the-fly according to the graph topology, which is able to fully consolidate the graph data accesses from concurrent jobs. By reusing the same graph data among more jobs and coalescing the accesses of the vertices' states for these jobs, LCCG can improve the core utilization. We conduct extensive experiments on a simulated 64-core processor. The results show that LCCG improves the throughput of the cutting-edge software system by 11.3~23.9 times with only 0.5% additional area cost. More-over, LCCG gains the speedups of 4.7~10.3, 5.5~13.2, and 3.8~8.4 times over state-of-the-art hardware graph processing accelerators (namely, HATS, Minnow, and PHI, respectively).

关键词： data centers Costs High performance computing Throughput Software systems Topology Hardware acceleration

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DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

arXiv

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arXiv 2023年

作者： Yue, Shengbin Chen, Wei Wang, Siyuan Li, Bingxuan Shen, Chenchen Liu, Shujun Zhou, Yuxuan Xiao, Yao Yun, Song Huang, Xuanjing Wei, Zhongyu Research Institute of Intelligent Complex Systems Fudan University China Shanghai Center for Mathematical Sciences Fudan University China School of Software Engineering Huazhong University of Science and Technology China School of Data Science Fudan University China School of Computer Science Fudan University China Rule of Law Institute Northwest University of Political and Law China New York University Shanghai China

We propose DISC-LawLLM, an intelligent legal system utilizing large language models (LLMs) to provide a wide range of legal services. We adopt legal syllogism prompting strategies to construct supervised fine-tuning datasets in the Chinese Judicial domain and fine-tune LLMs with legal reasoning capability. We augment LLMs with a retrieval module to enhance models' ability to access and utilize external legal knowledge. A comprehensive legal benchmark, DISC-Law-Eval, is presented to evaluate intelligent legal systems from both objective and subjective dimensions. Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of our system in serving various users across diverse legal scenarios. The detailed resources are available at https://***/FudanDISC/DISC-LawLLM. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition

arXiv

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arXiv 2023年

作者： Shou, Yuntao Liu, Huan Cao, Xiangyong Meng, Deyu Dong, Bo School of Computer Science and Technology The Ministry of Education Key Laboratory for Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong Shaanxi Xi’an710049 China School of Computer Science and Technology Xi’an Jiaotong Shaanxi Xi’an710049 China The BigKE Joint Innovation Center SHMEEA Shanghai China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China

Conversational emotion recognition (CER) is an important research topic in human-computer interactions. Although recent advancements in transformer-based cross-modal fusion methods have shown promise in CER tasks, they tend to overlook the crucial intra-modal and inter-modal emotional interaction or suffer from high computational complexity. To address this, we introduce a novel and lightweight cross-modal feature fusion method called Low-Rank Matching Attention Method (LMAM). LMAM effectively captures contextual emotional semantic information in conversations while mitigating the quadratic complexity issue caused by the self-attention mechanism. Specifically, by setting a matching weight and calculating inter-modal features attention scores row by row, LMAM requires only one-third of the parameters of self-attention methods. We also employ the low-rank decomposition method on the weights to further reduce the number of parameters in LMAM. As a result, LMAM offers a lightweight model while avoiding overfitting problems caused by a large number of parameters. Moreover, LMAM is able to fully exploit the intra-modal emotional contextual information within each modality and integrates complementary emotional semantic information across modalities by computing and fusing similarities of intra-modal and inter-modal features simultaneously. Experimental results verify the superiority of LMAM compared with other popular cross-modal fusion methods on the premise of being more lightweight. Also, LMAM can be embedded into any existing state-of-the-art CER methods in a plug-and-play manner, and can be applied to other multi-modal recognition tasks, e.g., session recommendation and humour detection, demonstrating its remarkable generalization ability. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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FedAG:A Federated Learning Method Based on data Importance Weighted Aggregation

FedAG:A Federated Learning Method Based on Data Importance W...

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IEEE International Conference on Communications in China (ICCC)

作者： Mengchu Xu Yan Zeng Meiting Xue Ji-Lin Zhang Jian Wan Mingyao Zhou Yilin Wen Yukun Shi School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China Zhejiang Engineering Research Center of Data Security Governance Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Huawei Enterprise Communication Technology Co Hangzhou China

Federated learning is a distributed machine learning approach that trains models with multiple clients and data locally. However, the existing methods ignore the differences between local models caused by the data heterogeneity, and give a high aggregation weight for the low-quality local model, which leads to low accuracy of the global model. For this problem, we propose a federated learning method based on data importance weighted aggregation, FedAG. It constructs an evaluation model of data importance based on the data distribution skewness, data volume, and data quality of clients, and aggregates local models with different weights computed by the evaluation model of data importance into a global model. We make experiments on five datasets including EMNIST, MNIST, and CIFAR-10. The results show that the accuracy of FedAG is respectively improved by 6.5%, 1.8%, 1.3% and 2.1%, compared with FedAvg, FedProx, FedVar, and Scaffold algorithms.

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Discrete unified gas-kinetic scheme for the conservative Allen-Cahn equation

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Physical Review E 2022年第4期105卷 045317-045317页

作者： Chunhua Zhang Hong Liang Zhaoli Guo Lian-Ping Wang Guangdong Provincial Key Laboratory of Turbulence Research and Applications Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 Guangdong China Department of Physics Hangzhou Dianzi University Hangzhou 310018 China State Key Laboratory of Coal Combustion Huazhong University of Science and Technology Wuhan 430074 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen 518055 Guangdong China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Shenzhen 518055 Guangdong China

In this paper, two discrete unified gas-kinetic scheme (DUGKS) methods with piecewise-parabolic flux reconstruction are presented for the conservative Allen-Cahn equation (CACE). One includes a temporal derivative of the order parameter in the force term while the other does not include temporal derivative in the force term but results in a modified CACE with additional terms. In the context of DUGKS, the continuum equations recovered from the piecewise-linear and piecewise-parabolic reconstructions for the fluxes at cell faces are subsequently derived. It is proved that the resulting equation with the piecewise-linear reconstruction is a first-order approximation to the discrete velocity kinetic equation due to the presence of the force term and the nonconservation property of the momentum of the collision model. To guarantee second-order accuracy of DUGKS, the piecewise-parabolic reconstruction for numerical flux is proposed. To validate the accuracy of the present DUGKS with the proposed flux evaluation, several benchmark problems, including the diagonal translation of a circular interface, the rotation of a Zalesak disk and the deformation of a circular interface, have been simulated. Numerical results show that the accuracy of both proposed DUGKS methods is almost comparable and improved compared with the DUGKS with linear flux reconstruction scheme.

关键词： Lattice-Boltzmann methods

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Robust and Fast Measure of Information via Low-rank Representation

arXiv

引用

arXiv 2022年

作者： Dong, Yuxin Gong, Tieliang Yu, Shujian Chen, Hong Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China Machine Learning Group UiT - The Arctic University of Norway Norway College of Science Huazhong Agriculture University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China

The matrix-based Rényi’s entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This intriguing property makes it widely applied in statistical inference and machine learning tasks. However, this information theoretical quantity is not robust against noise in the data, and is computationally prohibitive in large-scale applications. To address these issues, we propose a novel measure of information, termed low-rank matrix-based Rényi’s entropy, based on low-rank representations of infinitely divisible kernel matrices. The proposed entropy functional inherits the specialty of of the original definition to directly quantify information from data, but enjoys additional advantages including robustness and effective calculation. Specifically, our low-rank variant is more sensitive to informative perturbations induced by changes in underlying distributions, while being insensitive to uninformative ones caused by noises. Moreover, low-rank Rényi’s entropy can be efficiently approximated by random projection and Lanczos iteration techniques, reducing the overall complexity from O(n3) to O(n2s) or even O(ns2), where n is the number of data samples and s n. We conduct large-scale experiments to evaluate the effectiveness of this new information measure, demonstrating superior results compared to matrix-based Rényi’s entropy in terms of both performance and computational efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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research on mechanism of improving the cut-off frequency of terahertz Schottky diodes in air-bridge structures by structure optimization

Research on mechanism of improving the cut-off frequency of ...

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International Conference on (ICEPT) Electronic Packaging technology

作者： Yuebo Liu Ke Zhang QiuLing Qiu Xi Luo Xiaoting Chen Ruihan Liu Honghui Liu Reliability Physics and Application Technology of Electronic Component Key Laboratory China Electronic Product Reliability and Environmental Testing Research Institute (CEPREI) Guangzhou China Reliability Research and Analysis Centre China Electronic Product Reliability and Environmental Testing Research Institute (CEPREI) Guangzhou China Components and materials sector data center China Electronic Product Reliability and Environmental Testing Research Institute (CEPREI) Guangzhou China State Key Laboratory of High Power Semiconductor Lasers Changchun University of Science and Technology Changchun China School of Materials Science and Engineering Xiamen University of Technology Xiamen China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China Testing technology Research Center China Electronic Product Reliability and Environmental Testing Research Institute (CEPREI) Guangzhou China

ISBN: (数字)9798350353808

ISBN: (纸本)9798350353815

In the realm of air bridge structures for Schottky barrier diodes operating in the millimetre wave or terahertz band, cut-off frequency serves as a critical metric for gauging device performance. This research holds significance in guiding device design and enhancing performance by refining the device's architecture, thereby reducing capacitance values and elevating cut-off frequency without adding complexity to the manufacturing process. In this study, we investigate how the shape and spacing of mesas or pads impact device capacitance through electrostatic field simulations, leveraging Maxwell's capacitance matrix and mutual capacitance matrix. Simulation analysis and experimental validation reveal that devices with circular Schottky contact electrode pads and minimal pad area exhibit lower capacitance values. Furthermore, we observe that the capacitance of the device initially decreases and then increases with the length of the air bridge. An optimal air bridge length exists that minimizes the capacitance of the device. This research offers valuable insights for refining these devices.

关键词： Electrodes Performance evaluation Schottky diodes Shape Atmospheric modeling Cutoff frequency Schottky barriers

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