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5th International Conference on Information science, Parallel and Distributed Systems, ISPDS 2024

作者： Zhang, Chunfu Bai, Jing Liang, Yanchun Tavares, Adriano Wang, Lidong Jiang, Jie Zhuhai College of Science and Technology School of Big Data Zhuhai519041 China University of Minho Department of Industrial Electronics Guimaraes4800-058 Portugal Zhuhai College of Science and Technology School of Computer Science Zhuhai519041 China

ISBN: (纸本)9798350352719

Blind signature is a cryptographic technique where a signer signs a message without knowing its content, thereby protecting the privacy of the message. It is widely used in digital currencies, electronic voting systems, anonymous credentials, and other areas. In response to the shortcomings of some existing blind signature schemes in terms of security and efficiency, this article proposes a digital currency scheme based on chaotic hash function and RSA algorithm blind signatures. The chaotic hash function is used to convert digital currencies, improving the shortcomings of previous algorithms. The proposed scheme has anonymous and non-traceable characteristics, can resist various attacks, and has a relatively short processing time, with higher computational efficiency and security performance. © 2024 IEEE.

关键词： Authentication Protocol

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A Drug Box Recognition System Based on Deep Learning and Cloud-Edge Collaboration for Pharmacy Automation 29

A Drug Box Recognition System Based on Deep Learning and Clo...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhu, Honglei Dong, Anming Yu, Jiguo School of Computer Science and Technology Jinan China Big Data Institute Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor's prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it's hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement. © 2023 IEEE.

关键词： Cloud-Edge Collaboration Image Recognition Pharmacy Automation Text Recognition

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Open-Vocabulary Calibration for Fine-tuned CLIP 41

Open-Vocabulary Calibration for Fine-tuned CLIP

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Shuoyuan Wang, Jindong Wang, Guoqing Zhang, Bob Zhou, Kaiyang Wei, Hongxin Department of Statistics and Data Science Southern University of Science and Technology Shenzhen China Department of Computer and Information Science University of Macau Taipa China William & Mary WilliamsburgVA United States School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Hong Kong Baptist University Hong Kong

Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual content generation, and visual chatbots, to name a few. In recent years, considerable efforts and resources have been devoted to adaptation methods for improving the downstream performance of VLMs, particularly on parameter-efficient fine-tuning methods like prompt learning. However, a crucial aspect that has been largely overlooked is the confidence calibration problem in fine-tuned VLMs, which could greatly reduce reliability when deploying such models in the real world. This paper bridges the gap by systematically investigating the confidence calibration problem in the context of prompt learning and reveals that existing calibration methods are insufficient to address the problem, especially in the open-vocabulary setting. To solve the problem, we present a simple and effective approach called Distance-Aware Calibration (DAC), which is based on scaling the temperature using as guidance the distance between predicted text labels and base classes. The experiments with 7 distinct prompt learning methods applied across 11 diverse downstream datasets demonstrate the effectiveness of DAC, which achieves high efficacy without sacrificing the inference speed. Our code is available at https://***/mlstat-Sustech/CLIP Calibration. Copyright 2024 by the author(s)

关键词： Calibration

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Babisha, A. Umarani Srikanth, G. Angel Kiruba, D.R. Sundar, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510299

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing. © 2024 IEEE.

关键词： Audition

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News Text Classification Method for Edge Computing Based on BiLSTM-Attention 6

News Text Classification Method for Edge Computing Based on ...

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Liang, Zhixun Chen, Peng Yi, Yunfei Fan, Yuanyuan Hechi University School of Data and Computer Science He Chi China

ISBN: (纸本)9781665453110

In the coming smart city, the explosive growth of data makes the amount of data contained in news texts more and more, which leads to the decrease in the accuracy of traditional machine learning or deep learning models in the news text classification. Therefore, in this paper, we propose a news text classification model based on BiLSTM-Attention. The data set is selected as 30,000 news texts, and the word segmentation is carried out in turn. The stop words are removed, and the word vector is quantified. Then, the data set is cross-validated according to the ratio of training set to validation set of 8:1. Finally, the experiments with the bilstm model, lstm model and bilstm-short text model show that the BiLSTM-Attention model has the highest accuracy and the lowest loss value. In order to further verify the classification performance of BiLSTMAttention model, the experiment is designed again and Bayes and SVM are added to compare. The experimental results show that the accuracy, recall and F1 value of BiLSTM-Attention model are the highest, which proves that BiLSTM-Attention is more suitable for news text classification. © 2022 IEEE.

关键词： Edge computing

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Comments on "A Lightweight Privacy Preserving Authentication Protocol for VANETs" 9

Comments on "A Lightweight Privacy Preserving Authentication...

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9th International Conference on computer and Communications, ICCC 2023

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Kharel, Rupak Chongqing University School of Microelectronics and Communication Engineering Chongqing China University of Central Lancashire School of Psychology and Computer Science Graduate School of Intelligent Data Science Preston United Kingdom National Yunlin University of Science and Technology School of Psychology and Computer Science Graduate School of Intelligent Data Science Yunlin64002 Taiwan University of Central Lancashire School of Psychology and Computer Science Faculty of Electrical and Electronics Engineering Preston United Kingdom Ton Duc Thang University School of Psychology and Computer Science Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350317251

Vehicle use and the concept of a "smart city"are developing quickly. As a result of this progression, the Vehicular Ad-Hoc Network (VANET) is a popular network for inter-vehicular communication. The data gathering about the state of the road, the whereabouts of vehicles, their speeds, and the description of traffic congestion are obtained using the VANET. The public nature of information flow on the VANET creates serious security risks. data security is typically one of the most important duties of VANET. As a result, it becomes one of the researchers' top priorities. Li et al. [1] introduced a Lightweight Privacy Preserving Authentication Protocol for VANETs. They stated that several attacks could be resisted using their secure authentication devised protocol. However, after analyzing we discovered that their authentication protocol is susceptible to roadside impersonation attack and it does not provide vehicle anonymity. In the end, we have provided a few suggestions to cope with Li et al.'s devised protocol current shortcomings. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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NR-Router+: Enhanced Non-Regular Electrode Routing With Optimal Pin Selection for Electrowetting-on-Dielectric Chips

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第9期43卷 2606-2619页

作者： Pan, Youlin Liu, Genggeng Huang, Xing Li, Zipeng Huang, Hsin-Chuan Liang, Chi-Chun Wang, Qining Kim, Chang-Jin Ho, Tsung-Yi Northwestern Polytechnical University School of Computer Science Xi'an710072 China Fuzhou University College of Computer and Data Science Fuzhou350116 China Apple Inc. Silicon Engineering Group CupertinoCA95014 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan University of California at Los Angeles Mechanical and Aerospace Engineering Department Los AngelesCA90095 United States The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

With the advances in microfluidics, electrowetting-on-dielectric (EWOD) chips have widely been applied to various biological and chemical laboratory protocols. Glass-based EWOD chips with nonregular electrodes are proposed, which allow more reliable droplet operations and facilitate the integration of optical sensors for many biochemical applications. Furthermore, nonregular electrode designs are utilized in EWOD chips, e.g., interdigitated electrodes for more reliable droplet manipulation, custom shaped electrodes for specific applications like concentric heating, etc. However, due to the technical challenges of fabricating multilayer interconnection on the glass substrate, e.g., unreliable process and high cost, both control electrodes and wires are fabricated with a single-layer configuration, which poses significant challenges to pin selection for nonregular electrodes. In this article, we propose a minimum-cost flow-based routing algorithm called NR-Router+ that features efficient and robust routing for single-layer EWOD chips with nonregular electrodes. To the best of our knowledge, this is the first work that overcomes the aforementioned challenges. We construct a minimum-cost flow algorithm to generate optimal routing paths followed by a light-weight model to handle flow capacity. A grid reduction strategy is proposed to reduce the computational overhead. Additionally, a flow collocation algorithm based on integer linear programming is presented to efficiently prevent wire overlapping. Experimental results show that NR-Router+ achieves 100% routability while minimizing wirelength with shorter run time. Moreover, NR-Router+ can generate mask files feasible for manufacturing via adjustments of design parameters, thus demonstrating its robustness and efficiency. © 1982-2012 IEEE.

关键词： Electrodes

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Secret Sharing Based Key Agreement Protocol for Body Area Networks 18th

Secret Sharing Based Key Agreement Protocol for Body Area N...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Sheng, Weihong Cai, Bin Hu, Chunqiang Li, Ruinian School of Big Data and Software Engineering Chongqing University Chongqing China Computer Science Department Bowling Green State University Bowling Green United States

ISBN: (纸本)9783031714634

In order to provide more comprehensive medical services and personalized health monitoring according to individual needs, Body Area Networks (BANs) have been extensively studied by many researchers. As BANs involve the transmission of personal private data, the security of the communication is of utmost importance. Unfortunately, existing encryption techniques cannot be directly applied with the limited power or computation ability of the sensors in BANs. An alternative direction is to use physiological signals for key agreement. However, many of the current physiological signal-based key agreement schemes either have high overheads or are vulnerable to certain security issues. In this paper, we proposed a novel secret sharing and Bloom filter based key agreement scheme that balances overhead and security for BANs. Also, we use an approximate hash table to free ourselves from the problem of dependence on the ordering of features, which has been neglected by previous researchers. We validate the security of our scheme with based on real datasets and compare overheads with other solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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A Trajectory Tracking System for Zebrafish Based on Embedded Edge Artificial Intelligence 29

A Trajectory Tracking System for Zebrafish Based on Embedded...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zang, Chuanhao Dong, Anming Yu, Jiguo School of Computer Science and Technology Jinan China Big Data Institute Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Trajectory tracking of zebrafish is an important requirement in studying neurological disorders and developing new psychoactive drug. However, many challenges emerge for stable tracking, since zebrafish are similar in appearance, occlusion, agile, non-linear in moving and easy to swarm, all of which will lead to mistrack for multiple fish. And there is no precedent for tracking zebrafish through embedded edge artificial intelligence device. To overcome these difficulties, we present a tracking system for zebrafish based on RK3588-S. First, We construct an embedded edge AI hardware system consisting of two cameras driven by the RK3588-S, one for the front view and the other for top view of the fish. Then, we develop a 2D tracking algorithm based on YOLOv5 and the Observation-Centric Simple online and real-time tracking (OC-SORT) algorithm, which are transplanted to the RK3588-S for tracking the top and front views of the fish at the edge device. Compared with the previous methods, our method has fewer ID exchanges and highly real-time. Moreover, we apply the MQTT mechanism to establish communication links between the edge and the cloud to reliably transmit data to the cloud. The correspondence between cloud server and embedded AI is 1: N. Finally, we design a multi-view data fusion association algorithm to fuse the data of the two views in the cloud, which are further utilized to build the 3D tracklets of the zebrafish. © 2023 IEEE.

关键词： A Tracking System for Zebrafish Embedded edge artificial intelligence RK3588-S Zebrafish

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From Time Series to Multi-modality: Classifying Multivariate Time Series via Both 1D and 2D Representations 9th

From Time Series to Multi-modality: Classifying Multivariat...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Yang, Chao Wang, Xianzhi Yao, Lina Long, Guodong Xu, Guandong School of Computer Science University of Technology Sydney Ultimo Australia School of Computer Science and Engineering UNSW Kensington Australia Australian AI Institute University of Technology Sydney Ultimo Australia Data Science Institute University of Technology Sydney Ultimo Australia CSIRO Data61 Canberra Australia

ISBN: (纸本)9783031466601

Multivariate time series classification is crucial for various applications such as activity recognition, disease diagnosis, and brain-computer interfaces. Deep learning methods have recently achieved promising performance thanks to their powerful representation learning capacity. However, existing deep learning-based classifiers rely solely on temporal information while disregarding clues from the frequency perspective. In this regard, we propose a novel method for classifying multivariate time series leveraging both temporal and frequency information. We first apply Short-Time Fourier Transform (STFT) to transform time series into spectrograms, which contain a 2D representation of frequency components and their temporal positions. In particular, for each variable, we generate spectrograms with varying frequencies and temporal resolutions under different window sizes. The transformation essentially adds a new modality to 1D time series and converts the multivariate time series classification into a multi-modality data classification task, making it possible to bring powerful backbones from computer vision fields to solve the time series classification problem. We then construct a dual-stream network based on the ResNet architecture that takes in both 1D and 2D representations for accurate multivariate time series classification. Our extensive experiments on 30 public datasets show our method outperforms multiple competitive state-of-the-art baselines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Time series

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