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An EFSM-Based Test Data Generation Approach in Model-Based Testing

Computers, Materials & Continua 2022年第6期71卷 4337-4354页

作者： Muhammad Luqman Mohd-Shafie Wan Mohd Nasir Wan Kadir Muhammad Khatibsyarbini Mohd Adham Isa Israr Ghani Husni Ruslai Department of Software Engineering School of ComputingFaculty of EngineeringUniversiti Teknologi Malaysia81310Johor BahruJohorMalaysia GATES IT Solution Sdn Bhd WISMA GATESJalan Perdana 4Taman Sri Pulai Perdana 281300SkudaiJohorMalaysia

Testing is an integral part of software *** fastpaced system developments have rendered traditional testing techniques ***,automated testing techniques are needed to adapt to such system developments ***-based testing(MBT)is a technique that uses system models to generate and execute test cases *** was identified that the test data generation(TDG)in many existing model-based test case generation(MB-TCG)approaches were still *** automatic and effective TDG can further reduce testing cost while detecting more *** study proposes an automated TDG approach in MB-TCG using the extended finite state machine model(EFSM).The proposed approach integrates MBT with combinatorial *** information available in an EFSM model and the boundary value analysis strategy are used to automate the domain input classifications which were done manually by the existing *** results showed that the proposed approach was able to detect 6.62 percent more faults than the conventionalMB-TCG but at the same time generated 43 more *** proposed approach effectively detects faults,but a further treatment to the generated tests such as test case prioritization should be done to increase the effectiveness and efficiency of testing.

关键词： Model-based testing test case generation test data generation combinatorial testing extended finite state machine

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A Secured and Optimized Clustering Approch in Vehicular Ad-hoc Network 4

A Secured and Optimized Clustering Approch in Vehicular Ad-h...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Khan, Imran Khan, Fazlullah Ali, Asad Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Computer Science Department KPK Mardan Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang315104 China University of Engineering and Technology Department of Computer Science KPK Peshawar Pakistan College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

Vehicular ad hoc networks (VANETs) take vital role in intelligent transportation systems, but they face challenges due to dynamic network topology, impacting communication efficiency, especially with increasing active nodes. Security and privacy concerns are paramount, necessitating authentication to ensure only legitimate vehicles access the network. Secure vehicular clustering offers a solution, yet the number of clusters affects network dependability and efficiency. To tackle this, we present a secure clustering mechanism using Bat optimization algorithm, mimicking echolocation behavior to optimize clusters. We introduce an authentication technique for cluster heads to admit approved members, enhancing vehicular authentication. Simulations using MATLAB evaluate our approach, showing improved performance in end-to-end delay, overhead, and cluster head count compared to similar methods. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Knowledge-Guided Cross-Topic Visual Question Generation 30

Knowledge-Guided Cross-Topic Visual Question Generation

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Hongfei Wang, Guohua Xie, Jiayuan Chen, Jiali Fang, Wenhao Cai, Yi School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9782493814104

Visual question generation (VQG) task aims to generate high-quality questions based on the input image. Current methods primarily focus on generating questions containing specified content utilizing answers or question types as constraints. However, these constraints make it challenging to control the topic of generated questions (e.g., conversation or test subject topics) for various applications. Thus, it is necessary to utilize topics as constraints to guide question generation. Considering that there are many topics and it is almost impossible for human annotations to cover them, we propose the cross-topic learning VQG (CTL-VQG) task, which aims to generate questions related to unseen topics in cross-topic scenarios. In this paper, we propose a knowledge-guided cross-topic visual question generation (KC-VQG) model to extract unseen topic-related information for question generation. Specifically, an image-topic feature extractor is introduced in our model to extract topic-related intuitive visual features;an image-topic knowledge extractor is used to extract and select the most appropriate topic-related implicit knowledge from large language models for generating questions. Extensive experiments show that our model outperforms baselines and can effectively generate unseen topic-related questions in cross-topic scenarios. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Data mining

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A Fast Scalable Hardware Priority Queue and Optimizations for Multi-Pushes

A Fast Scalable Hardware Priority Queue and Optimizations fo...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Samuel Collinson Allan Bai Oliver Sinnen Department of Electrical Computer and Software Engineering Parallel and Reconfigurable Computing Lab University of Auckland New Zealand

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Priority queues (PQ) are an essential data structure for many important algorithms. Hardware implementations of priority queues can accelerate such algorithms significantly. Usually a choice has to be made between very fast (fixed-size) hardware PQs or scalable PQs. Fast hardware queues can provide single cycle push and pop operations, but are limited in size. Scalable hardware queues use embedded memory to achieve scalability, but thereby compromise on speed. This paper proposes a fast and scalable hardware priority queue that can be used for general purpose. It is based on a modular and flexible hybrid design, using a shift register queue combined with a heap-based queue. In the best case, i.e. when only the first queue is needed, push and pop operations only take a single cycle. In an experimental evaluation on a Xilinx FPGA we demonstrate that performance of the hybrid queue maintains a good balance between performance and scalability even for applications where the size of the working set of data can have large variance. We further propose an optimisation of the heap-based queue to support workloads that repeat several consecutive push operations followed by a single pop operation, which is the workload, for instance, in state space search or ray-tracing.

关键词： Distributed processing Scalability Sociology Ray tracing Shift registers Data structures Hardware

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Alignment efficient image-sentence retrieval considering transferable cross-modal representation learning

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Frontiers of Computer Science 2024年第1期18卷 77-91页

作者： Yang YANG Jinyi GUO Guangyu LI Lanyu LI Wenjie LI Jian YANG School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Computing Hong Kong Polytechnic UniversityHong Kong 100872China State Key Lab.for Novel Software Technology Nanjing UniversityNanjing 210094China 14th Research Institute of China Electronics Technology Group Corporation Nanjing 210094China guangyu.li2017@***

Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent representations of heterogeneous modalities,thereby to search similar instances in one modality according to the query from another modality in *** basic assumption behind these methods is that parallel multi-modal data(i.e.,different modalities of the same example are aligned)can be obtained in *** other words,the image-sentence cross-modal retrieval task is a supervised task with the alignments as ***,in many real-world applications,it is difficult to realign a large amount of parallel data for new scenarios due to the substantial labor costs,leading the non-parallel multi-modal data and existing methods cannot be used *** the other hand,there actually exists auxiliary parallel multi-modal data with similar semantics,which can assist the non-parallel data to learn the consistent ***,in this paper,we aim at“Alignment Efficient Image-Sentence Retrieval”(AEIR),which recurs to the auxiliary parallel image-sentence data as the source domain data,and takes the non-parallel data as the target domain *** single-modal transfer learning,AEIR learns consistent image-sentence cross-modal representations of target domain by transferring the alignments of existing parallel ***,AEIR learns the image-sentence consistent representations in source domain with parallel data,while transferring the alignment knowledge across domains by jointly optimizing a novel designed cross-domain cross-modal metric learning based constraint with intra-modal domain adversarial ***,we can effectively learn the consistent representations for target domain considering both the structure and semantic ***,extensive experiments on different transfer scenarios validate that AEIR can achieve better retrieval results comparing with the baselines.

关键词： image-sentence retrieval transfer learning semantic transfer structure transfer

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Machine Learning Algorithms for Gold Price Prediction

Machine Learning Algorithms for Gold Price Prediction

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作者： Nguyen, Duyen Mai Thi Debnath, Narayan C. Quach, Luyl-Da Nguyen, Vinh Dinh School of Computing and Information Technology Eastern International University Thủ Dầu Một Viet Nam Department of Software Engineering FPT University Can Tho City Viet Nam

Due to its potential for reliably anticipating gold prices, machine learning for gold price prediction has become a hot study topic in recent years, which is essential for investors and policymakers. This research did a comprehensive survey and experiments on various machine learning algorithms, such as linear regression, support vector machines, decision trees, gaussian process regression, random forest regression, polynomial features, lasso regression, k-nearest neighbor, ridge regression and neural networks, to gold price prediction. These models have been able to provide accurate predictions of gold prices, often outperforming traditional statistical methods. However, the quality of the input data and the model selection procedure affect how accurate these models are. Comprehensive experimental results showed that the support vector machine algorithm achieved the most accurate predictions, with the lowest error rates and the best R2 score. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Wireless and Service Allocation for Mobile Computation Offloading with Task Deadlines

arXiv

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arXiv 2023年

作者： Chen, Hong Todd, Terence D. Zhao, Dongmei Karakostas, George Department of Electrical and Computer Engineering Department of Computing and Software McMaster University HamiltonON Canada

In mobile computation offloading (MCO), mobile devices (MDs) can choose to either execute tasks locally or to have them executed on a remote edge server (ES). This paper addresses the problem of assigning both the wireless communication bandwidth needed, along with the ES capacity that is used for the task execution, so that task completion time constraints are satisfied. The objective is to obtain these allocations so that the average power consumption of the mobile devices is minimized, subject to a cost budget constraint. The paper includes contributions for both soft and hard task completion deadline constraints. The problems are first formulated as mixed integer nonlinear programs (MINLPs). Approximate solutions are then obtained by decomposing the problems into a collection of convex subproblems that can be efficiently solved. Results are presented that demonstrate the quality of the proposed solutions, which can achieve near optimum performance over a wide range of system parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Computation offloading

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SeerahGPT: Retrieval Augmented Generation based Large Language Model

SeerahGPT: Retrieval Augmented Generation based Large Langua...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Surayya Obaid Narmeen Zakaria Bawany Department of Computer Science and Software Engineering Jinnah Univesity for Women Center for Computing Research Karachi Pakistan

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

Large Language Models(LLMs) have become widely recognized in recent years for their exceptional performance in language generation capabilities. As a result, an unprecedented rise is seen in its use cases in various domains specifically involving Natural Language Processing(NLP). These models however perform suboptimally when exploited in the field of studies where authenticity of the generated content is a critical aspect. One such domain is usage of LLM for exploration of the life of Prophet Muhammad S.A.W(commonly referred to as Seerah). It is of utmost significance to ensure the authenticity and reliability in the sources used and reported by the LLM due to the sensitive nature of the domain. The contemporary LLMs, however, lack the explainability in their response due to their inherent black-box nature. In our study, we have presented a novel LLM named SeerahGPT that addresses this challenge with the help of retrieval-augmented generation (RAG). This technique enables the model to utilize both parametric and nonparametric memories for generating response of queries. Our model, built on the Llama-2-7b architecture, employs Sentence Transformer embedding to effectively retrieve relevant information. The model's capabilities are augmented by integrating it with a corpus having Islamic texts such as the Quranic translation and Hadith collections, and historical accounts. The model's performance is benchmarked against its base model using both quantitative and qualitative metrics. The comparative analysis with Llama-2-7b revealed that SeerahGPT incorporation with external knowledge sources, provided more authentic and verifiable responses, despite the others exhibiting greater fluency. Performance metrics such as BLEU, ROUGE, and METEOR indicated SeerahGPT's better accuracy and contextual handling. This study paves way for analysis of such sensitive domains in more efficient way that can be utilized in other complex domains such as Islamic theology and Fiqh or legal

关键词： Measurement Accuracy Translation Law Large language models Retrieval augmented generation Transformers Reliability Meteors Context modeling

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Design of a Secured and Trusted E-Voting System Using Blockchain Technology

Design of a Secured and Trusted E-Voting System Using Blockc...

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SmartBlock4Africa, IEEE

作者： Elegba Jeremiah. O. Agbelusi O. Olutayo Victor A. Akintola Kolawole G. Department of Software Engineering School of Computing The Federal University of Technology Akure Nigeria Department of Computer Science School of Computing The Federal University of Technology Akure Nigeria

ISBN: (数字)9798350376838

ISBN: (纸本)9798350376845

The state of election in Nigeria is worrisome. Electoral malpractices have been a major challenge to the Nigerian government in recent times. Government has made frantic efforts to tackle these electoral challenges but the recent event in year 2023 presidential election is worrisome suggests that the solution is not near. Recently, the percentage of eligible voters who vote on election days is declining. Voters turnout went up from 52.3% in 1999 - the first general election since 1993 - to 69% in 2003. But, it has been on the decline nearly ever since − 57.5% in 2007, 53.7% in 2011, 43.7% in 2015 and 34.8% in 2019. In 2023, it is 28.63%. With the way things are going, if urgent action is not taken, this could signal the end of democracy in Nigeria. This research presents the design and implementation of a trusted and secured e-voting based on blockchain technology that will increase voters confidence and trust. The system utilizes *** for the frontend and Solidity on the Polygon blockchain for the backend, ensuring transparency, tamper-resistance, and heightened security. This innovative approach addresses trust and reliability concerns in traditional voting systems, offering a modernized and trustworthy electoral process.

关键词： Smart contracts Authentication Blockchains Reliability Security Electronic voting

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