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31st EACSL Annual Conference on computer science Logic, CSL 2023

作者： Alvarez-Picallo, Mario Ghica, Dan Sprunger, David Zanasi, Fabio Programming Languages Laboratory Huawei Research Centre Cambridge United Kingdom Department of Computer Science University of Birmingham United Kingdom Department of Computer Science University College London United Kingdom

ISBN: (纸本)9783959772648

We formulate a reverse-mode automatic differentiation (RAD) algorithm for (applied) simply typed lambda calculus in the style of Pearlmutter and Siskind [27], using the graphical formalism of string diagrams. Thanks to string diagram rewriting, we are able to formally prove for the first time the soundness of such an algorithm. Our approach requires developing a calculus of string diagrams with hierarchical features in the spirit of functorial boxes, in order to model closed monoidal (and cartesian closed) structure. To give an efficient yet principled implementation of the RAD algorithm, we use foliations of our hierarchical string diagrams. © Mario Alvarez-Picallo, Dan Ghica, David Sprunger, and Fabio Zanasi.

关键词： Differentiation (calculus)

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Comparison of Naïve Bayes with graph based methods for keyphrase extraction in modern standard Arabic language

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International Journal of Speech Technology 2023年第1期26卷 141-150页

作者： Loukam, Mourad Department of Computer Science Faculty of Exact Sciences and Informatics Hassiba Benbouali University of Chlef Chlef Algeria LMA Laboratory Hassiba Benbouali University Chlef Algeria

Keywords and Keyphrases are very important to capture the semantics contained in texts. Their extraction is a topic of particular relevance to a great number of researchers. Keywords and Keyphrases are useful for many applications, like: Indexation, Information Retrieval, Classification, Recommendation, Contextual advertising, etc. Their extraction is a challenging problem. Many approaches have been proposed for Keywords/Keyphrases extraction for several languages, including the Arabic language. However, the question remains to: what is the most performing approach in this field of research?. In this paper, a comparison is performed between two different methods applied to Keyphrase extraction from Arabic Texts: namely, Naïve Bayes and an approach based on a graph model. The experiments and the results obtained attest that the Graph based approach is more performing than Naïve Bayes. For the first approach the F-score is close to 70%;while the second one obtains an F-score of less than 64%. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Extraction

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An Immutable Framework for Smart Healthcare Using Blockchain Technology

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computer Systems science & Engineering 2023年第7期46卷 165-179页

作者： Faneela Muazzam A.Khan Suliman A.Alsuhibany Walid El-Shafai Mujeeb Ur Rehman Jawad Ahmad Department of Computer Science Quaid-i-Azam UniversityIslamabad45320Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Science Security Engineering LaboratoryPrince Sultan UniversityRiyadh11586Saudi Arabia James Watt School of Engineering University of GlasgowG128QQUK School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare *** Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other stakeholders to maintain valuable data and medical *** traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service(DoS)attack can compromise the complete healthcare *** article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things(IoMT)to address the stated *** proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy *** efficacy of the proposed framework is evaluated in the context of service execution time and *** experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.

关键词： Blockchain technology healthcare applications cybersecurity services IoMT DoS EHR

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Survey semantic Arabic language in deep learning based recommendation system 17

Survey semantic Arabic language in deep learning based recom...

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17th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2023

作者： Kadhem, Rasha Mallat, Souheyl Zrigu, Mounir University of ALQadisiyah Department of Computer Science Iraq Research Laboratory in Algebra Numbers Theory and Intelligent Systems Monastir Tunisia University of Monastir Faculty of Sciences of Monastir Department of Computer Science Tunisia

ISBN: (纸本)9798350338904

Due to problems, Arabic-speaking internet users have surged, although nothing is done on it. It is challenging to develop a repliable recognition system (RS) for cursive languages such as Arabic. Variations in text size, fonts, word semantics, user Arabic region, etc. complicate these issues. Deep learning models can model big datasets and handle them. Good features can be selected and learned consecutively by both CNNs and RNNs. In numerous studies, both of these neural networks have proven to be superior than their counterparts. This is true for text recognition, voice recognition, and several NLP tasks (NLP), but they are not qualified to deal with the semantics of the text, so we decided to find the best DL technique for semantic Arabic language from a lot of research to be a survey to other searchers. Our paper compared different algorithms and their accuracy. © 2023 IEEE.

关键词： Semantics

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Real-Time Multi-object Tracking Using YOLOv8 and SORT on a SoC FPGA 21st

Real-Time Multi-object Tracking Using YOLOv8 and SORT on a...

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21st International Symposium on Applied Reconfigurable Computing, ARC 2025

作者： Danilowicz, Michal Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031879944

Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation on low-power and real-time embedded platforms is highly desirable. Modern MOT algorithms should be able to track objects of a given class (e.g. people or vehicles). In addition, the number of objects to be tracked is not known in advance, and they may appear and disappear at any time, as well as be obscured. For these reasons, the most popular and successful approaches have recently been based on the tracking paradigm. Therefore, the presence of a high quality object detector is essential, which in practice accounts for the vast majority of the computational and memory complexity of the whole MOT system. In this paper, we propose an FPGA (Field-Programmable Gate Array) implementation of an embedded MOT system based on a quantized YOLOv8 detector and the SORT (Simple Online Realtime Tracker) tracker. We use a modified version of the FINN framework to utilize external memory for model parameters and to support operations necessary required by YOLOv8. We discuss the evaluation of detection and tracking performance using the COCO and MOT15 datasets, where we achieve 0.21 mAP and 38.9 MOTA respectively. As the computational platform, we use an MPSoC system (Zynq UltraScale+ device from AMD/Xilinx) where the detector is deployed in reprogrammable logic and the tracking algorithm is implemented in the processor system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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Effective short text classification via the fusion of hybrid features for IoT social data

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Digital Communications and Networks 2022年第6期8卷 942-954页

作者： Xiong Luo Zhijian Yu Zhigang Zhao Wenbing Zhao Jenq-Haur Wang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovation School University of Science and Technology BeijingFoshan528399China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Department of Electrical Engineering and Computer Science Cleveland State UniversityClevelandOH44115USA Department of Computer Science and Information Engineering National Taipei University of TechnologyTaipei10608China

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

关键词： Information fusion Short text classi fication BERT Bidirectional encoder representations fr 0om transformers Deep learning Social data

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CA-DTS:A Distributed and Collaborative Task Scheduling Algorithm for Edge Computing Enabled Intelligent Road Network

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Journal of computer science & Technology 2023年第5期38卷 1113-1131页

作者：胡世红罗渠元李光辉施巍松叶保留 Key Laboratory of Water Big Data Technology of Ministry of Water Resources Houhai UniversityNanjing 210098China School of Computer and Information Houhai UniversityNanjing 210098China School of Information Science and Technology Southwest Jiaotong UniversityChengdu 611756China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuci 214122China Department of Computer and Information Sciences University of DelawareNewarkDE 19716U.S.A National Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210093China

Edge computing enabled Intelligent Road Network(EC-IRN)provides powerful and convenient computing services for vehicles and roadside sensing *** continuous emergence of transportation applications has caused a huge burden on roadside units(RSUs)equipped with edge servers in the Intelligent Road Network(IRN).Collaborative task scheduling among RSUs is an effective way to solve this ***,it is challenging to achieve collaborative scheduling among different RSUs in a completely decentralized *** this paper,we first model the interactions involved in task scheduling among distributed RSUs as a Markov *** that multi-agent deep reinforcement learning(MADRL)is a promising approach for the Markov game in decision optimization,we propose a collaborative task scheduling algorithm based on MADRL for EC-IRN,named CA-DTS,aiming to minimize the long-term average delay of *** reduce the training costs caused by trial-and-error,CA-DTS specially designs a reward function and utilizes the distributed deployment and collective training architecture of counterfactual multi-agent policy gradient(COMA).To improve the stability of performance in large-scale environments,CA-DTS takes advantage of the action semantics network(ASN)to facilitate cooperation among multiple *** evaluation results of both the testbed and simulation demonstrate the effectiveness of our proposed *** with the baselines,CA-DTS can achieve convergence about 35%faster,and obtain average task delay that is lower by approximately 9.4%,9.8%,and 6.7%,in different scenarios with varying numbers of RSUs,service types,and task arrival rates,respectively.

关键词： edge computing deep reinforcement learning task scheduling vehicular edge computing

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A Class Alignment Multi-Source Domain Adaptation for Partial Discharge Condition Assessment With Unknown Faults in GIS

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IEEE Internet of Things Journal 2025年第12期12卷 19955-19971页

作者： Wang, Yanxin Yan, Jing Yang, Zhou Zhang, Wenjie Wang, Jianhua Geng, Yingsan Srinivasan, Dipti Xi'an Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipment Department of Electrical Engineering Xi'an710049 China National University of Singapore Energy Management and Microgrid Laboratory Singapore117581 Singapore Xi'an Jiaotong University Department of Computer Science Xi'an710049 China

Recently, domain adaptation has emerged as a powerful technique for on-site partial discharge (PD) condition assessment in gas-insulated switchgear (GIS). However, most existing methods face three major challenges: 1) Relying on a single source domain for model development poses difficulties in effectively utilizing source domain samples with distribution differences. 2) Limited condition assessment for unknown fault samples on-site, which faces distributional differences between multiple source domains. 3) Handling only a single task, which makes it challenging to generalize to multiple tasks simultaneously. To address these concerns, we propose a class alignment multi-source domain adaptation network (CLMSDAN) for GIS PD condition assessment with unknown faults. First, a diversity feature extractor is developed to extract diverse features while addressing the negative transfer issue caused by knowledge differences by mining both inter-domain and intra-domain features, thus enabling the transfer of rich knowledge at multiple levels. Second, a novel multi-source domain adaptation approach is employed from multiple perspectives to align distribution and distinguish between shared and unknown classes. Finally, a multi-classifier complementary strategy is introduced to recognize unknown faults, which automatically filters out source domain irrelevant class samples while distinguishing the contributions of different source domains to the target task. Experimental results show that CLMSDAN achieves 94.86% accuracy in diagnosis and 93.38% in severity assessment, outperforming baseline methods by over 10% in both tasks. This highlights its superior generalization and robustness across varying conditions and noise levels. © 2014 IEEE.

关键词： Electric switchgear

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Fourier-domain mode-locked lasers on a hybrid-integrated silicon photonic chip

Fourier-domain mode-locked lasers on a hybrid-integrated sil...

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Smart Photonic and Optoelectronic Integrated Circuits 2025

作者： Kwan, Ching Chi Poon, Andrew W. Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (纸本)9781510684881

We are in the process of developing a hybrid-integrated compact frequency-comb Fourier-domain mode-locked (FDML) laser source for swept-source optical coherence tomography (SS-OCT) applications. Our approach incorporates silicon photonic components, including electro-optic (EO) and thermo-optic (TO)-tunable silicon microring-based Vernier filters as the tunable filter to achieve a high sweep rate in multi-MHz and a high-quality-factor silicon microring frequency comb to enhance the imaging depth. We aim to use an external semiconductor optical amplifier with these components to demonstrate the feasibility of high-performance hybrid-integrated chip-based laser source for SS-OCT. © 2025 SPIE.

关键词： Semiconductor optical amplifiers

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A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting

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IEEE Transactions on Knowledge and Data Engineering 2023年第11期35卷 10967-10980页

作者： Li, Guanyao Zhong, Shuhan Deng, Xingdong Xiang, Letian Gary Chan, S.-H. Li, Ruiyuan Liu, Yang Zhang, Ming Hung, Chih-Chieh Peng, Wen-Chih Hong Kong University of Science and Technology Clear Water Bay Hong Kong Hong Kong Guangdong Enterprise Key Laboratory for Urban Sensing Monitoring and Early Warning Guangzhou Urban Planning and Design Survey Research Institute Guangzhou Guangdong510060 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Clear Water Bay Hong Kong Hong Kong Chongqing University College of Computer Science Chongqing400044 China National Chung Hsing University Department of Computer Science and Engineering Taichung402 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan

We study the forecasting problem for traffic with dynamic, possibly periodical, and joint spatial-temporal dependency between regions. Given the aggregated inflow and outflow traffic of regions in a city from time slots 0 to t-1, we predict the traffic at time t for any region. Prior arts in the area often considered the spatial and temporal dependencies in a decoupled manner, or were rather computationally intensive in training with a large number of hyper-parameters which needed tuning. We propose ST-TIS, a novel, lightweight and accurate Spatial-Temporal Transformer with information fusion and region sampling for traffic forecasting. ST-TIS extends the canonical Transformer with information fusion and region sampling. The information fusion module captures the complex spatial-temporal dependency between regions. The region sampling module is to improve the efficiency and prediction accuracy, cutting the computation complexity for dependency learning from O(n2) to O(nn), where n is the number of regions. With far fewer parameters than state-of-the-art deep learning models, ST-TIS's offline training is significantly faster in terms of tuning and computation (with a reduction of up to 90% on training time and network parameters). Notwithstanding such training efficiency, extensive experiments show that ST-TIS is substantially more accurate in online prediction than state-of-the-art approaches (with an average improvement of 9.5% on RMSE, and 12.4% on MAPE compared to STDN and DSAN). © 1989-2012 IEEE.

关键词： Forecasting

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