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Extended Abstract of Candidate Test Set Reduction for Adaptive Random Testing: An Overheads Reduction Technique

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Extended Abstract of Candidate Test Set Reduction for Adapti...

IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Rubing Huang Haibo Chen Weifeng Sun Dave Towey Faculty of Information Technology Macau University of Science and Technology Taipa Macau China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang Jiangsu China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China

This document 1 is an extended abstract of a science of computer Programming paper, "Candidate Test Set Reduction for Adaptive Random Testing: An Overheads Reduction Technique," presented as a J1C2 (Journal publication first, Conference presentation following) at the 30th IEEE International Conference on software Analysis, Evolution and Reengineering (Saner 2023).The paper presents a candidate set reduction strategy to enhance the Fixed-Sized-Candidate-Set version of Adaptive Random Testing (FSCS-ART). The proposed method reduces the number of randomly-generated candidate test cases by retaining valuable, unused candidates from previous iterations. As the computational costs associated with a stored/retained candidate are less than the costs associated with a randomly-generating one, the overall computational overheads of FSCS-ART are reduced. The reported experimental studies show that the proposed method has a comparable failure-detection effectiveness to FSCS-ART, but less computational overheads.

关键词： Costs Programming software Power capacitors Computational efficiency Testing

Induced Matching below Guarantees: Average Paves the Way for Fixed-Parameter Tractability

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arXiv 2022年

作者： Koana, Tomohiro Technische Universität Berlin Faculty IV Institute of Software Engineering and Theoretical Computer Science Algorithmics and Computational Complexity Germany

In this work, we study the Induced Matching problem: Given an undirected graph G and an integer , is there an induced matching M of size at least ? An edge subset M is an induced matching in G if M is a matching such that there is no edge between two distinct edges of M. Our work looks into the parameterized complexity of Induced Matching with respect to "below guarantee" parameterizations. We consider the parameterization u − for an upper bound u on the size of any induced matching. For instance, any induced matching is of size at most n/2 where n is the number of vertices, which gives us a parameter n/2 − . In fact, there is a straightforward 9n/2− · nO(1)-time algorithm for Induced Matching [Moser and Thilikos, J. Discrete Algorithms]. Motivated by this, we ask: Is Induced Matching FPT for a parameter smaller than n/2 − ? In search for such parameters, we consider MM(G) − and IS(G) − , where MM(G) is the maximum matching size and IS(G) is the maximum independent set size of G. We find that Induced Matching is presumably not FPT when parameterized by MM(G) − or IS(G) − . In contrast to these intractability results, we find that taking the average of the two helps—our main result is a branching algorithm that solves Induced Matching in 49(MM(G)+IS(G))/2− · nO(1) time. Our algorithm makes use of the Gallai–Edmonds decomposition to find a structure to branch on. Copyright © 2022, The Authors. All rights reserved.

关键词： Parameterization

A Deep Learning Framework for Arabic Cyberbullying Detection in Social Networks

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computers, Materials & Continua 2025年第5期83卷 3113-3134页

作者： Yahya Tashtoush Areen Banysalim Majdi Maabreh Shorouq Al-Eidi Ola Karajeh Plamen Zahariev Computer Science Department Jordan University of Science and TechnologyIrbid22110Jordan Department of Information Technology Faculty of Prince AI-Hussein Bin AbdallahⅡFor Information TechnologyThe Hashemite UniversityZarqa13133Jordan Computer Science Department Tafila Technical UniversityTafila6110Jordan Department of Digital Media Software Engineering Ferris State UniversityBig RapidsMI 49503USA Department of Telecommuniations University of Ruse“Angel Kanchev”Ruse7017Bulgaria

Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and ***,alongside its benefits,social media has also given rise to significant challenges,one of the most pressing being *** issue has become a major concern in modern society,particularly due to its profound negative impacts on the mental health and well-being of its *** the Arab world,where social media usage is exceptionblly high,cyberbullying has become increasingly prevalent,necessitating urgent *** detection of harmful online behavior is critical to fostering safer digital environments and mitigating the adverse efcts of *** underscores the importance of developing advanced tools and systems to identify and address such behavior *** paper investigates the development of a robust cyberbullying detection and classifcation system tailored for Arabic comments on *** study explores the efectiveness of various deep learning models,including Bi-LSTM(Bidirectional Long Short Term Memory),LSTM(Long Short-Term Memory),CNN(Convolutional Neural Networks),and a hybrid CNN-LSTM,in classifying Arabic comments into binary classes(bullying or not)and multiclass categories.A comprehensive dataset of 20,000 Arabic YouTube comments was collected,preprocessed,and labeled to support these *** results revealed that the CNN and hybrid CNN-LSTM models achieved the highest accuracy in binary classification,reaching an impressive 91.9%.For multiclass dlassification,the LSTM and Bi-LSTM models outperformed others,achieving an accuracy of 89.5%.These findings highlight the efctiveness of deep learning approaches in the mitigation of cyberbullying within Arabic online communities.

关键词： Arabic text lassification arabic text mining cyberbullying detection neural networks deep learning CNN LSTM YouTube Bi-LSTM

Disentangling the Computational Complexity of Network Untangling

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arXiv 2022年

作者： Froese, Vincent Kunz, Pascal Zschoche, Philipp Technische Universität Berlin Faculty IV Institute of Software Engineering and Theoretical Computer Science Algorithmics and Computational Complexity Germany

We study the network untangling problem introduced by Rozenshtein, Tatti, and Gionis [DMKD 2021], which is a variant of Vertex Cover on temporal graphs-graphs whose edge set changes over discrete time steps. They introduce two problem variants. The goal is to select at most k time intervals for each vertex such that all time-edges are covered and (depending on the problem variant) either the maximum interval length or the total sum of interval lengths is minimized. This problem has data mining applications in finding activity timelines that explain the interactions of entities in complex networks. Both variants of the problem are NP-hard. In this paper, we initiate a multivariate complexity analysis involving the following parameters: number of vertices, lifetime of the temporal graph, number of intervals per vertex, and the interval length bound. For both problem versions, we (almost) completely settle the parameterized complexity for all combinations of those four parameters, thereby delineating the border of fixed-parameter tractability. Copyright © 2022, The Authors. All rights reserved.

关键词： Complex networks

Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data

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Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Junhui Li Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Faculty of Science and Engineering University of Bristol Bristol U.K

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate.

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Vertex Cover and Feedback Vertex Set Above and Below Structural Guarantees

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arXiv 2022年

作者： Kellerhals, Leon Koana, Tomohiro Kunz, Pascal Technische Universität Berlin Faculty IV Institute of Software Engineering and Theoretical Computer Science Algorithmics and Computational Complexity Germany

Vertex Cover parameterized by the solution size k is the quintessential fixed-parameter tractable problem. FPT algorithms are most interesting when the parameter is small. Several lower bounds on k are well-known, such as the maximum size of a matching. This has led to a line of research on parameterizations of Vertex Cover by the difference of the solution size k and a lower bound. The most prominent cases for such lower bounds for which the problem is FPT are the matching number or the optimal fractional LP solution. We investigate parameterizations by the difference between k and other graph parameters including the feedback vertex number, the degeneracy, cluster deletion number, and treewidth with the goal of finding the border of fixed-parameter tractability for said difference parameterizations. We also consider similar parameterizations of the Feedback Vertex Set problem. Copyright © 2022, The Authors. All rights reserved.

关键词： Parameterization

Heterogeneous Hadoop Cluster-Based Image Processing Workload Distribution Framework between CPU and GPU

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Scientific Programming 2023年第1期2023卷

作者： Naz, Najia Zada, Islam Malik, Abdul Haseeb Nadeem, Muhammad Ali, Sikandar Department of Computer Science Bacha Khan University Charsadda Khyber Pakhtunkhwa Peshawar24420 Pakistan Department of Computer Science and Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Department of Computer Science University of Peshawar Khyber Pakhtunkhwa Peshawar25120 Pakistan Department of Information Technology The University of Haripur Khyber Pakhtunkhwa Haripur22620 Pakistan

Due to the rapid development of image data and the necessity to analyze it to extract meaningful information, heterogeneous systems have gained prominence. One of the most critical aspects of distributed systems is load balancing. When it comes to the distribution of workload in a balanced manner in a cluster, some heterogeneous systems are used for image processing. When workloads are allocated in these systems, the computational power of the processors is not considered. As a result, in these heterogeneous systems for image processing applications, an uneven workload distribution issue is found. A workload distribution programming framework is presented and discussed in this paper. The proposed strategy consists of two parts. As a first step, image data is split into optimal split sizes and distributed across nodes, then the image data is distributed across CPU and GPU in a second step for processing. A heterogeneous environment is created by combining the CPU and GPU. The OpenCL Java bindings are used to set up both the CPU and GPU to run the program. To assess the performance of the suggested technique, certain tests are carried out and compared to current platforms. For image processing applications in heterogeneous clusters, the proposed workload distribution approach distributed image data efficiently. The results of the proposed solution (Hadoop + GPU) show that using an effective workload allocation mechanism in heterogeneous systems reduces average execution time while improving overall application performance. © 2023 Najia Naz et al.

关键词： Graphics processing unit

A Novel Gooseneck Barnacle Optimizer Based Machine Learning Approach for Rainfall Prediction

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A Novel Gooseneck Barnacle Optimizer Based Machine Learning ...

Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Marzia Ahmed Mohd Herwan Sulaiman Mostafijur Rahman Ahmad Johari Mohamad Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pahang Malaysia Department of Software Engineering Daffodil International university Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh

In dynamic meteorological prediction, accurate rainfall forecasting is a mystery. In a complex and dynamic natural environment with unpredictable sky movements, we propose an innovative methodology that forecasts weekly average rainfall patterns using the Gooseneck Barnacle Optimizer and the Least Squares Support Vector Machine (LSSVM). The significance of rainfall prediction is shown by its widespread implications, including public health. However, conventional single-model techniques and machine-learning methods must represent rainfall pattern changes, limiting our preparation. With a 2014–2018 dataset from reliable meteorological libraries, our research explores the possibility of this novel combination. The Gooseneck Barnacle Optimizer (GBO) serves as the bedrock of our methodology, introducing a novel evolutionary algorithm inspired by the intricate mating behaviors of gooseneck barnacles. GBO aptly captures the dynamic interplay of factors such as navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement during mating, culminating in two crucial optimization stages: exploration and exploitation. In contrast to previous algorithms like the Barnacle Mating Optimizer (BMO), GBO stands out by more accurately emulating the unique mating behaviors of gooseneck barnacles. The prediction job is performed by integrating the Least Squares Support Vector Machines (LSSVM) algorithm with GBO as an objective function, using the optimized hyper-parameter values. The results suggest that the GBO algorithm exhibits superior performance compared to existing methodologies. This is accomplished by efficiently augmenting the original random population for a particular problem, resulting in a convergence towards the global optimum and producing significantly enhanced optimization outcomes.

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Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation

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arXiv 2025年

作者： Naeem, Muhammad Ahtsam Saleem, Muhammad Asim Sharif, Muhammad Imran Akber, Shahzad Saleem, Sajjad Akhtar, Zahid Siddique, Kamran School of Information and Software Engineering University of Electronic Science and Technology of China China Faculty of Computing Riphah International University Faisalabad Campus Pakistan Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Department of Information and Technology Washington University of Science and Technology AlexandriaVA United States Department of Network and Computer Security State University of New York Polytechnic Institute United States Department of Computer Science and Engineering University of Alaska Anchorage Anchorage United States

The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image;(2) Feature extraction is performed using a Deep CNN model, then these extracted features are concatenated;(3) Feature selection is performed using wrapper-based feature selection;(4) Classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features. © 2025, CC BY.

关键词： Deep learning