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A Novel Insertion Solution for the Travelling Salesman Problem

computers, Materials & Continua 2024年第4期79卷 1581-1597页

作者： Emmanuel Oluwatobi Asani Aderemi Elisha Okeyinka Sunday Adeola Ajagbe Ayodele Ariyo Adebiyi Roseline Oluwaseun Ogundokun Temitope Samson Adekunle Pragasen Mudali Matthew Olusegun Adigun department of computer science Landmark UniversityOmu Aran251103Nigeria SDG 11 Group Landmark UniversityOmu Aran251103Nigeria department of Computing MiVA UniversityAbuja900211Nigeria department of computer science Ibrahim Badamasi Babangida UniversityLapai911101Nigeria department of computer science University of ZululandKwadlangezwa3886South Africa department of computer&Industrial Production Engineering First Technical UniversityIbadan200243Nigeria department of Multimedia Engineering Kaunas University of TechnologyKaunasLT-44249Lithuania department of computer science Colorado State UniversityFort Collins80523USA

The studypresents theHalfMax InsertionHeuristic (HMIH) as a novel approach to solving theTravelling SalesmanProblem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) andNearest Neighbour Heuristic (NNH). The paper discusses the limitations of current construction tour heuristics,focusing particularly on the significant margin of error in FIH. It then proposes HMIH as an alternative thatminimizes the increase in tour distance and includes more nodes. HMIH improves tour quality by starting withan initial tour consisting of a ‘minimum’ polygon and iteratively adding nodes using our novel Half Max *** paper thoroughly examines and compares HMIH with FIH and NNH via rigorous testing on standard TSPbenchmarks. The results indicate that HMIH consistently delivers superior performance, particularly with respectto tour cost and computational efficiency. HMIH’s tours were sometimes 16% shorter than those generated by FIHand NNH, showcasing its potential and value as a novel benchmark for TSP solutions. The study used statisticalmethods, including Friedman’s Non-parametric Test, to validate the performance of HMIH over FIH and *** guarantees that the identified advantages are statistically significant and consistent in various situations. Thiscomprehensive analysis emphasizes the reliability and efficiency of the heuristic, making a compelling case for itsuse in solving TSP issues. The research shows that, in general, HMIH fared better than FIH in all cases studied,except for a few instances (pr439, eil51, and eil101) where FIH either performed equally or slightly better thanHMIH. HMIH’s efficiency is shown by its improvements in error percentage (δ) and goodness values (g) comparedto FIH and NNH. In the att48 instance, HMIH had an error rate of 6.3%, whereas FIH had 14.6% and NNH had20.9%, indicating that HMIH was closer to the optimal solution. HMIH consistently showed superior performanceacross many benchmarks,

关键词： Nearest neighbour heuristic farthest insertion heuristic half max insertion heuristic tour construction travelling salesman problem

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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

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computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of computer science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan department of computer science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan department of computer science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi department of computer science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

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computer Modeling in Engineering & sciences 2024年第6期139卷 2605-2625页

作者： Jungpil Shin Md.Al Mehedi Hasan Abu Saleh Musa Miah Kota Suzuki Koki Hirooka School of computer science and Engineering The University of AizuAizuwakamatsu965-8580Japan department of computer science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

关键词： Japanese Sign Language(JSL) hand gesture recognition geometric feature distance feature angle feature GoogleNet

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A disk I/O optimized system for concurrent graph processing jobs

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Frontiers of computer science 2024年第3期18卷 13-29页

作者： Xianghao XU Fang WANG Hong JIANG Yongli CHENG Dan FENG Peng FANG School of computer science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China department of computer science&Engineering University of Texas at ArlingtonArlingtonTX 76019USA College of computer and Data science Fuzhou UniversityFuzhou 350108China Zhejiang Lab Hangzhou 311121China

In order to analyze and process the large graphs with high cost efficiency,researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity *** the other hand,with the rapidly growing need of analyzing graphs in the real-world,graph processing systems have to efficiently handle massive concurrent graph processing(CGP)***,due to the inherent design for single graph processing job,existing out-of-core graph processing systems usually incur unnecessary data accesses and severe competition of I/O bandwidth when handling the CGP *** this paper,we propose GraphCP,a disk I/O optimized out-of-core graph processing system that efficiently supports the processing of CGP *** proposes a benefit-aware sharing execution model to share the I/O access and processing of graph data among the CGP jobs and adaptively schedule the graph data loading based on the states of vertices,which efficiently overcomes above challenges faced by existing out-of-core graph processing ***,GraphCP adopts a dependency-based future-vertex updating model so as to reduce disk I/Os in the future *** addition,GraphCP organizes the graph data with a Source-Sorted Sub-Block graph representation for better processing capacity and I/O access *** evaluation results show that GraphCP is 20.5×and 8.9×faster than two out-of-core graph processing systems GridGraph and GraphZ,and 3.5×and 1.7×faster than two state-of-art concurrent graph processing systems Seraph and GraphSO.

关键词： graph processing disk I/O concurrent jobs

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On building automation system security

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High-Confidence Computing 2024年第3期4卷 103-122页

作者： Christopher Morales-Gonzalez Matthew Harper Michael Cash Lan Luo Zhen Ling Qun Z.Sun Xinwen Fu department of computer science University of Massachusetts LowellLowell 01854USA department of Electrical and computer Engineering University of Central FloridaOrlando 32816USA School of computer science and Technology Southeast UniversityMa’anshan 243032China School of computer science and Engineering Anhui University of TechnologyNanjing 211189China

Building Automation Systems(BASs)are seeing increased usage in modern society due to the plethora of benefits they provide such as automation for climate control,HVAC systems,entry systems,and lighting *** BASs in use are outdated and suffer from numerous vulnerabilities that stem from the design of the underlying BAS *** this paper,we provide a comprehensive,up-to-date survey on BASs and attacks against seven BAS protocols including BACnet,EnOcean,KNX,LonWorks,Modbus,ZigBee,and *** studies of secure BAS protocols are also presented,covering BACnet Secure Connect,KNX Data Secure,KNX/IP Secure,ModBus/TCP Security,EnOcean High Security and Z-Wave *** and ZigBee do not have security *** point out how these security protocols improve the security of the BAS and what issues remain.A case study is provided which describes a real-world BAS and showcases its vulnerabilities as well as recommendations for improving the security of *** seek to raise awareness to those in academia and industry as well as highlight open problems within BAS security.

关键词： Building automation system BAS protocols Security Attack

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A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 965-981页

作者： Yong-Chao Li Rui-Sheng Jia Ying-Xiang Hu Hong-Mei Sun the College of computer science and Engineering Shandong University of Science and TechnologyQingdao 266590 the Faculty of Information science and Engineering Ocean University of ChinaQingdao 266000China the College of computer science and Engineering Shandong University of Science and TechnologyQingdao 266590China the College of computer science and Engineering Nanjing University of Science and TechnologyNanjing 210000China

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

关键词： Crowd density estimation linear feature calibration vision transformer weakly-supervision learning

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar department of computer science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan department of computer science National University of PakistanRawalpindiPunjab46000Pakistan department of computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan department of computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia department of computer science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK department of computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Research on General-Purpose Brain-Inspired Computing Systems

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Journal of computer science & Technology 2024年第1期39卷 4-21页

作者：渠鹏纪兴龙陈嘉杰庞猛李宇晨刘晓义张悠慧 department of computer science and Technology Tsinghua UniversityBeijing 100084China department of Precision Instrument Tsinghua UniversityBeijing 100084China

Brain-inspired computing is a new technology that draws on the principles of brain science and is oriented to the efficient development of artificial general intelligence(AGI),and a brain-inspired computing system is a hierarchical system composed of neuromorphic chips,basic software and hardware,and algorithms/applications that embody this *** the system is developing rapidly,it faces various challenges and opportunities brought by interdisciplinary research,including the issue of software and hardware *** paper analyzes the status quo of brain-inspired computing *** by some design principle and methodology of general-purpose computers,it is proposed to construct"general-purpose"brain-inspired computing systems.A general-purpose brain-inspired computing system refers to a brain-inspired computing hierarchy constructed based on the design philosophy of decoupling software and hardware,which can flexibly support various brain-inspired computing applications and neuromorphic chips with different ***,this paper introduces our recent work in these aspects,including the ANN(artificial neural network)/SNN(spiking neural network)development tools,the hardware agnostic compilation infrastructure,and the chip micro-archi-tecture with high flexibility of programming and high performance;these studies show that the"general-purpose"system can remarkably improve the efficiency of application development and enhance the productivity of basic software,thereby being conductive to accelerating the advancement of various brain-inspired algorithms and *** believe that this is the key to the collaborative research and development,and the evolution of applications,basic software and chips in this field,and conducive to building a favorable software/hardware ecosystem of brain-inspired computing.

关键词： brain-inspired computing neuromorphic chip compiler spiking neural network

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Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

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computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer Nouf Abdullah Almujally Saud S.Alotaibi Abdulwahab Alazeb Jeongmin Park department of computer science Air UniversityIslamabad44000Pakistan department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information System department Umm Al-Qura UniversityMakkahSaudi Arabia department of computer science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia department of computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

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SWG: an architecture for sparse weight gradient computation

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science China(Information sciences) 2024年第2期67卷 302-321页

作者： Weiwei WU Fengbin TU Xiangyu LI Shaojun WEI Shouyi YIN School of Integrated Circuits Tsinghua University department of Electronic and computer Engineering The Hong Kong University of Science and Technology

On-device training for deep neural networks(DNN) has become a trend due to various user preferences and scenarios. The DNN training process consists of three phases, feedforward(FF), backpropagation(BP), and weight gradient(WG) update. WG takes about one-third of the computation in the whole training process. Current training accelerators usually ignore the special computation property of WG and process it in a way similar to FF/BP. Besides, the extensive data sparsity existing in WG, which brings opportunities to save computation, is not well explored. Nevertheless, exploiting the optimization opportunities would meet three underutilization problems, which are caused by(1) the mismatch between WG data dimensions and hardware parallelism,(2) the full sparsity, i.e., the sparsity of feature map(Fmap),error map(Emap), and gradient, and(3) the workload imbalance resulting from irregular sparsity. In this paper, we propose a specific architecture for sparse weight gradient(SWG) computation. The architecture is designed based on hierarchical unrolling and sparsity-aware(HUSA) dataflow to exploit the optimization opportunities of the special computation property and full data sparsity. In HUSA dataflow, the data dimensions are unrolled hierarchically on the hardware architecture. A valid-data trace(VDT) mechanism is embedded in the dataflow to avoid the underutilization caused by the two-sided input sparsity. The gradient is unrolled in PE to alleviate the underutilization induced by output sparsity while maintaining the data reuse opportunities. Besides, we design an intra-and inter-column balancer(IIBLC) to dynamically tackle the workload imbalance problem resulting from the irregular sparsity. Experimental results show that with HUSA dataflow exploiting the full sparsity, SWG achieves a speedup of 12.23× over state-of-the-art gradient computation architecture, Train Ware. SWG helps to improve the energy efficiency of the state-of-the-art training accelerator LNPU from

关键词： CNN training gradient computation sparsity architecture

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