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SSRN 2023年

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Computer Science Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recognition enable the generation of computer-generated meta data that improve the storage and retrieval of data. We present a semi-automated approach for preserving video footage that combines object identification and ontologies. This significantly cuts down on search time, data scrubbing by providing only the relevant information making for more efficient queries. © 2023, The Authors. All rights reserved.

关键词： Object detection

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software Defect Prediction Using Ensemble Technique

Software Defect Prediction Using Ensemble Technique

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International Conference on Information Management and Technology (ICIMTech)

作者： Pandu Wicaksono Panji Arisaputra Rezky Yunanda Willy Kristian Almuzhidul Mujhid Software Engineering Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

software defect prediction is the methodical process of identifying code segments that are likely to have problems. This is done by analyzing software metrics and using categorization algorithms. This work introduces an alternate approach that utilizes ensemble learning techniques to improve the effectiveness of fault detection models. The models utilized in this study are CodeGPT and CodeBERT, both of which are transformer-based deep learning models that can extract code features from source code. The models are trained using the PROMISE dataset, which consists of Java projects that have been labeled with defects. The test results show that using ensemble learning methods can improve the accuracy, precision, recall, and F1 score of a single model by around 1-3%. This study contributes to the progress of software dependability and quality by utilizing advanced software fault prediction algorithms.

关键词： Codes Source coding software algorithms Predictive models Prediction algorithms Feature extraction Transformers software Ensemble learning software measurement

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Vibrating Particles System Algorithm for Solving Classification Problems

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computer Systems Science & engineering 2022年第12期43卷 1189-1206页

作者： Mohammad Wedyan Omar Elshaweesh Enas Ramadan Ryan Alturki Department of Autonomous Systems Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Software Engineering Information Technology CollegeAL-Hussein bin Talal UniversityMa’an71111Jordan Department of Computer Science Prince Abdullah Bin Ghazi Faculty of Communication and Information TechnologyAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21961Saudi Arabia

Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of *** immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of *** is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial *** probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition *** this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more *** data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was *** suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.

关键词： Vibrating particles system(VPS) probabilistic neural network(PNN) classification problem data mining

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Evolving Assembly Code in an Adversarial Environment

arXiv

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arXiv 2024年

作者： Maliukov, Irina Weiss, Gera Margalit, Oded Elyasaf, Achiya Department of Computer Science Ben-Gurion University of the Negev Be’er Sheva Israel Department of Software and Information System Engineering Ben-Gurion University of the Negev Be’er Sheva Israel

In this work, we evolve Assembly code for the CodeGuru competition. The goal is to create a survivor—an Assembly program that runs the longest in shared memory, by resisting attacks from adversary survivors and finding their weaknesses. For evolving top-notch solvers, we specify a Backus Normal Form (BNF) for the Assembly language and synthesize the code from scratch using Genetic programming (GP). We evaluate the survivors by running CodeGuru games against human-written winning survivors. Our evolved programs found weaknesses in the programs they were trained against and utilized them. To push evolution further, we implemented memetic operators that utilize machine learning to explore the solution space effectively. This work has important applications for cyber-security as we utilize evolution to detect weaknesses in survivors. The Assembly BNF is domain-independent;thus, by modifying the fitness function, it can detect code weaknesses and help fix them. Finally, the CodeGuru competition offers a novel platform for analyzing GP and code evolution in adversarial environments. To support further research in this direction, we provide a thorough qualitative analysis of the evolved survivors and the weaknesses found. © 2024, CC BY.

关键词： Genetic programming

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Orchestrating Sustainable and Service-Differentiable Satellite Networking: A Federated Cross-Orbit Approach

Orchestrating Sustainable and Service-Differentiable Satelli...

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International Workshop on Quality of Service

作者： Yi Ching Chou Long Chen Feng Wang Hengzhi Wang Xiaoqiang Ma Sami Ma Jiangchuan Liu School of Computing Science Simon Fraser University Canada Department of Computer and Information Science The University of Mississippi USA College of Computer Science and Software Engineering Shenzhen University China Department of CSIS Douglas College Canada

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Satellite networks are believed to become an indispensable component in the forthcoming 6G network and beyond. The surging demands attract numerous satellite network operators into this market to compete, yet also cooperate via resource sharing for cost and performance improvement, which is similar to the growth trajectory of how the Internet becomes the network of networks. Hence, we envision a federated network of satellite networks (shortened as federated satellite network) in this paper, where satellite network operators will eventually federate with each other to achieve a win-win situation. However, the yet-to-come federated satellite network faces two unique challenges: sustainability and dynamic topology. As such, we propose a sustainable and service-differentiable framework named Federated Cross-orbit Satellite Network (FCSN). Different from most existing solutions which focused on the Internet or simple cooperation among satellites, the FCSN orchestrates network resources in the dynamic topology to improve sustainability, through service-differentiable offloading in the resource-limited scenario. We formulate the sustainability-oriented federated offloading problem based on the utility and cost models tailored for the FCSN and propose an efficient hardware-budget constrained auction algorithm with a bounded approximation ratio. Finally, we design a truthful and rational payment scheme to motivate the construction of the FCSN. Extensive simulation results based on real-world deployments show that our solution significantly improves sustainability and delay, making it one step further toward the vision of the federated network of satellite networks.

关键词： Satellites Costs Network topology Simulation Approximation algorithms Topology Internet

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Algorithm for finding contours for the purpose of processing symbolic information in conditions of small symbol size and chromatic aberrations 10

Algorithm for finding contours for the purpose of processing...

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10th International Scientific and Practical Conference Information and Measuring Equipment and Technologies, IMET 2020

作者： Konanykhin, A. Yu. Konanykhina, T.N. Department of Computer Science Southwest State University Kursk Russia Department of Software Engineering Southwest State University Kursk Russia

In this article, modern principles of processing and working with images have been investigated and applied for specific tasks of character information recognition in conditions of small character size and image fuzzy due to chromatic aberrations, including non-fuzzy C-means, which have proven themselves with high accuracy and speed. processing of initial data. For ease of perception and a more understandable process, we conditionally divide the algorithm into several stages: image improvement and filtering in order to bring data to the optimal information load of the algorithm by suppressing non-maximum points;correction of intensity values according to the level of grayscale pixels;processing of the region of the image with symbolic information by distributing gray tones. © Published under licence by IOP Publishing Ltd.

关键词： Image enhancement

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Nuclei Instance Segmentation in Colon Histology Images with YOLOv7 2nd

Nuclei Instance Segmentation in Colon Histology Images with...

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2nd International Conference on Advanced engineering, Technology and Applications, ICAETA 2023

作者： Yıldız, Serdar Memiş, Abbas Varlı, Songül Department of Computer Engineering Faculty of Electrical and Electronics Engineering Yıldız Technical University İstanbul Turkey Department of Software Engineering Faculty of Engineering and Natural Sciences İstanbul Health and Technology University İstanbul Turkey Health Institutes of Türkiye İstanbul Turkey BİLGEM TÜBİTAK Kocaeli Turkey

ISBN: (纸本)9783031509193

In histology image analysis, instance-based nuclei segmentation is one of the challenging tasks within the segmentation-guided studies since it is quite troublesome to detect each distinct nuclei instance of each nuclei type in images in contrast to the semantic segmentation in which all the image pixels of a nuclei type are labelled with the same mask ID although the segmented region may comprise of multiple instances. In this paper, an instance-based medical image segmentation task is addressed, and in this context, instances of multiple types of nuclei in colon histology images are aimed to be delineated distinctly. For the instance-based segmentation of the nuclei in colon histology images, the YOLOv7 algorithm and its built-in instance segmentation module are utilized. In the experimental studies performed on Colon Nuclei Identification and Counting (CoNIC) Challenge 2022 colon histology image dataset by using a 5-fold cross-validation performance evaluation strategy, nuclei instances belonging to 6 classes as the neutrophil, epithelial, lymphocyte, plasma, eosinophil and connective were segmented. To calculate the overall system accuracy, the quantification metrics of mean average precision (mAP) and mean panoptic quality (mPQ) were measured. In performance evaluations, quite promising accuracy values were obtained. The mAP values of 0.2885 and 0.2903, and mPQ values of 0.1659 and 0.1704 were observed by using the YOLOv7 algorithm. To the best of our knowledge, this is the first nuclei instance segmentation study with YOLOv7. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Colon histology images CoNIC 2022 dataset Nuclei instance segmentation Nuclei segmentation YOLOv7

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A Real-Time Hand Sign Language Recognition System for Threatening Situations Using Deep Learning

A Real-Time Hand Sign Language Recognition System for Threat...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Zardasht Abdulaziz Abdulkarim Shwany Shayda Khudhur Ismail Karwan Hoshyar Khalid Khoshnaw Twana Saeed Mustafa Murat Karabatak Nashwan Adnan Othman Physiotherapy Department Erbil Polytechnic University Erbil Iraq Network Department Ministry of Higher Education and Scientific Research Erbil Iraq Department of Information and Communication Technology Engineering Erbil Polytechnic University Erbil Iraq Department of Software Firat University Elazig Turkey Department of Computer Science College of Science Knowledge University Erbil Iraq Department of Computer Engineering Knowledge University Erbil Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Hand sign language has been done as physical movements in natural languages that humans have used since ancient times, along with letters, words, and spoken language. This paper presents a real-time method to identify threatening signs by criminals during interrogation, which increases their criminal rank and helps to reach conclusions. The proposed method is to install a camera of appropriate quality in front of the offender, record hand gestures in a specific area of the hand, apply some image processing techniques, such as contrast enhancement techniques, to the image to facilitate recognition as input, and then classify the images using a convolutional neural network (CNN) for a specific problem with high specifications for that topic by and using AlexNet. The accuracy of the presented method is more than 94.2% in real-time testing.

关键词： Deep learning Sign language Image recognition Natural languages Digital forensics Cameras Real-time systems

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Simulation-Based Study on False Alarms in Intrusion Detection Systems for Organizations Facing Dual Phishing and Dos Attacks

Simulation-Based Study on False Alarms in Intrusion Detectio...

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Annual Modeling and Simulation Conference (ANNSIM)

作者： Jeongkeun Shin L. Richard Carley Kathleen M. Carley Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States Software and Societal Systems Department Carnegie Mellon University Pittsburgh PA United States

ISBN: (数字)9781713899310

ISBN: (纸本)9798350350562

Machine learning-based intrusion detection systems (IDS) have attracted considerable attention for their role in proactively identifying intrusion attempts and facilitating swift organizational response. While numerous researchers have conceptually discussed the potential negative impacts of high false alarms in machine learning-based IDS on organizations and have proposed various methods to reduce them, there is a shortage of studies that explore how these false alarms can exacerbate cyberattack damage in different organizational settings and in the face of various cyberattack campaigns. This paper introduces an agent-based modeling and simulation approach to assess false alarm consequences in machine learning-based IDS during dual Denial of Service (DoS) and phishing attacks. The IDS with distinct false positive rates, constructed using the KDD Cup 1999 dataset with diverse machine learning algorithms, were simulated to analyze how these varying false alarm rates affect the extent of damage caused by phishing and DoS attacks.

关键词： Machine learning algorithms Phishing Intrusion detection Organizations Agent-based modeling Cyberattack Faces

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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

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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