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2024 International Conference on Advances in Data Engineering and Intelligent Computing systems, ADICS 2024

作者： Mohandas, R. Prasanna, D.S.John Deva Baiju, B.V. Nathiya, S. Rajkumar, T. Dhiliphan Sivashankar, C. Srm Institiute Of Science And Technology Department Of Computational Intelligence Chennai Kattankulathur India Srm Institiute Of Science And Technology Department Of Datascience And Business Systems Chennai Kattankulathur India Vit University TamilNadu Vellore India Prince Shri Venkateshwara Padmavathi Engineering College Department Of Electronics And Communication Engineering Ponmar Chennai India Kalasalingam Academy Of Research And Education Dept. Of Computer Science And Engineering Tamilnadu Krishnankovil India Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks;therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities. © 2024 IEEE.

关键词： Internet of things

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Multiband Microstrip Patch Antenna Operating at Five Distinct 5G mm-Wave Bands

Multiband Microstrip Patch Antenna Operating at Five Distinc...

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2021 International Conference of Technology, science and Administration, ICTSA 2021

作者： Saeed, Osama Y. A. Saeed, Ahmed A. A. Gaid, Abdulguddoos S. A. Aoun, Amjad M. H. Sallam, Amer A. Taiz University Faculty of Engineering and IT Dept. of Communication and Computer Engineering Taiz Yemen Taiz University Faculty of Engineering and IT Dept. O F Computer Networks and Distributed Systems Taiz Yemen

ISBN: (纸本)9781665428620

A novel mm-Wave multiband microstrip patch antenna for 5G wireless communication is designed and presented in this paper. The proposed design is an H-shaped with inset feed and a rectangular slit in the upper side of the radiating patch. The 5G mm-Wave antenna resonates at frequencies of 23.2, 40.3, 59.3, 86.9 and 104.3 GHz with a maximum bandwidth of 1.1, 2.8, 18.1, 7.7 and 10.7 GHz respectively. The antenna has sufficient realized gains of 6.4882, 7.1198, 7.9062, 8.1804 and 9.8156 dBi at the resonant frequencies respectively. Furthermore, the antenna covers the most interest 5G mm-Wave spectrum with a total bandwidth of 40.4 GHz. The proposed multiband antenna is designed using microstrip technology with features such as light weight, low cost, low profile, high gain and efficiency. The 5G antenna is designed using a high frequency structure simulator software (HFSS). It has a small form factor of 7.5 × 8.5 × 0.55 mm3. The antenna is compact enough to incorporate into smart devices and used for various 5G applications. © 2021 IEEE.

关键词： Bandwidth

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Building Collaborative Learning: Exploring Social Annotation in Introductory Programming

arXiv

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arXiv 2024年

作者： de Oliveira Neto, Francisco Gomes Dobslaw, Felix Chalmers The University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden Mid Sweden University Dept. of Quality Mngmt Communication and Inf. Systems Östersund Sweden

The increasing demand for software engineering education presents learning challenges in courses due to the diverse range of topics that require practical applications, such as programming or software design, all of which are supported by group work and interaction. Social Annotation (SA) is an approach to teaching that can enhance collaborative learning among students. In SA, both students and teachers utilize platforms like Feedback Fruits, Perusall, and Diigo to collaboratively annotate and discuss course materials. This approach encourages students to share their thoughts and answers with their peers, fostering a more interactive learning environment. We share our experience of implementing social annotation via Perusall as a preparatory tool for lectures in an introductory programming course aimed at undergraduate students in Software Engineering. We report the impact of Perusall on the examination results of 112 students. Our results show that 81% of students engaged in meaningful social annotation successfully passed the course. Notably, the proportion of students passing the exam tends to rise as they complete more Perusall assignments. In contrast, only 56% of students who did not participate in Perusall discussions managed to pass the exam. We did not enforce mandatory Perusall participation in the course. Yet, the feedback from our course evaluation questionnaire reveals that most students ranked Perusall among their favorite components of the course and that their interest in the subject has increased. © 2024, CC BY.

关键词： Students

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Building Collaborative Learning: Exploring Social Annotation in Introductory Programming

Building Collaborative Learning: Exploring Social Annotation...

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Software Engineering: Software Engineering Education and Training (ICSE-SEET), IEEE/ACM International Conference on

作者： Francisco Gomes De Oliveira Neto Felix Dobslaw Dept. of Computer Science and Engineering Chalmers and the University of Gothenburg Gothenburg Sweden Dept. of Quality Mngmt Communication and Inf. Systems Mid Sweden University Östersund Sweden

ISBN: (数字)9798400704987

ISBN: (纸本)9798350352115

关键词： Training Software design Annotations Federated learning Buildings Programming profession Software engineering

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Unveiling Disparities: NLP Analysis of Software Industry and Vocational Education Gaps

Unveiling Disparities: NLP Analysis of Software Industry and...

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Natural Language-Based Software Engineering (NLBSE), IEEE/ACM International Workshop on

作者： Emil Bäckstrand Rasmus Djupedal Lena-Maria Öberg Francisco Gomes De Oliveira Neto Dept. of Quality Management Communication and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers|University of Gothenburg Gothenburg Sweden

ISBN: (数字)9798400705762

ISBN: (纸本)9798350352559

The rapid growth of software industry highlights the importance of the education system in producing competent professionals to meet industry demands. Previous research has identified a gap between industry needs and the content of educational programs. This study presents Vocational Education and Labour Market Analyser (VELMA), a tool designed to extract information from job ads and educational curricula (both from Sweden), utilising topic modelling to identify the diverse technologies and skills in demand within the industry and those covered by professional education. Particularly, we use Latent Dirichlet Allocation (LDA) to categorise keywords into cohesive themes for document frequency analysis. Our findings highlight industry demand for skills in cloud and embedded technologies, security engineering, and software architecture. In contrast, the Higher Vocational Education (HVE) curricula emphasise the education of web developers and general object-oriented programming languages. CCS CONCEPTS • Applied computing $\rightarrow$ Document management and text processing; Education; $\cdot$ Software and its engineering $\rightarrow$ Designing software.

关键词： Industries Analytical models Text analysis Object oriented modeling Education Software Natural language processing

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Automated Boundary Identification for Machine Learning Classifiers

Automated Boundary Identification for Machine Learning Class...

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Search-Based and Fuzz Testing (SBFT), IEEE/ACM International Workshop on

作者： Felix Dobslaw Robert Feldt Dept. of Quality Mngmt Communication and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

ISBN: (数字)9798400705625

ISBN: (纸本)9798350376012

AI and Machine Learning (ML) models are increasingly used as (critical) components in software systems, even safety-critical ones. This puts new demands on the degree to which we need to test them and requires new and expanded testing methods. Recent boundary-value identification methods have been developed and shown to automatically find boundary candidates for traditional, non-ML software: pairs of nearby inputs that result in (highly) differing outputs. These can be shown to developers and testers, who can judge if the boundary is where it is supposed to ***, we explore how this method can identify decision boundaries of ML classification models. The resulting ML Boundary Spanning Algorithm (ML-BSA) is a search-based method extending previous work in two main ways. We empirically evaluate ML-BSA on seven ML datasets and show that it better spans and thus better identifies the entire classification boundary(ies). The diversity objective helps spread out the boundary pairs more broadly and evenly. This, we argue, can help testers and developers better judge where a classification boundary actually is, compare to expectations, and then focus further testing, validation, and even further training and model refinement on parts of the boundary where behaviour is not ideal.

关键词： Training Conferences Machine learning Fuzzing Software systems Classification algorithms Testing

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Reliability Analysis of a Monitoring System for Extraterrestrial Habitats

Reliability Analysis of a Monitoring System for Extraterrest...

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IEEE International Conference on Wireless and Mobile Computing, Networking And communications (WiMob)

作者： Saurabh Band Florian Stechmann Malavika Unnikrishnan Shadi Attarha Christiane Heinicke Andreas Willig Anna Foerster dept. Sustainable Communication Networks University of Bremen Germany Center of Applied Space Technology and Microgravity University of Bremen Germany Dept. of Computer Science and Software Engineering University of Canterbury New Zealand

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

This paper presents a robust and dependable monitoring system designed for space missions where reliability is critical. Theoretically, we use the Markov chain model to evaluate the availability of the system, considering multiple instances of backup devices. The results indicate a substantial improvement in system availability with the inclusion of backup devices. The findings highlight the significance of redundancy in enhancing system dependability while considering practical constraints. Based on this insight, we design an algorithm to deploy backup devices in the system that would improve the system's availability without putting strain on the resources (e.g., channel congestion). It uses backup devices at the sensor node and gateway levels. The algorithm involves backup devices that actively monitor and take over in case of failures, enhancing the system's ability to recover lost or faulty data. We test the system in three failure scenarios: hard failures, sensor failures, and gateway failures. We perform the experiments on our hardware prototypes. Experimental results demonstrate that using backup devices significantly improves the Packet Reception Ratio (PRR) in failure scenarios. We observed an improvement of 30 - 34% in packet reception for different failure scenarios.

关键词： Wireless communication Wireless sensor networks Space missions Redundancy Prototypes Logic gates Reliability theory Reliability engineering Monitoring Strain

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meQ: Selecting MEC Resources by Considering Service communication Requirements

meQ: Selecting MEC Resources by Considering Service Communic...

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IEEE International Conference on Cloud Computing Technology and science (CloudCom)

作者： Matthias Frei Reinhard German Anatoli Djanatliev Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany Dept. of Computer Science Computer Networks and Communication Systems University of Erlangen-Nürnberg Germany

ISBN: (纸本)9781665463683

The Multi-Access Edge Computing (MEC) paradigm enables services that have strict network requirements like a maximum tolerable latency or a minimum bandwidth requirement. This consequently leads to a tight coupling of networks and computing, since the service can only function correctly if its communication requirements are met. Numerous existing strategies for MEC service deployment or resource selection put the characteristics of computing demands correlated with the free capacity of nodes or inter-service dependencies in their focus. In contrast, our work is a complementary attempt to enable especially the consideration of the strict communication needs of MEC services. It shows how edge features, i.e., knowledge of edge network conditions and service requirements, can be used to support resource selection and orchestration strategies. We consider and discuss how our model meQ can be applied in real communication systems as well as in heterogeneous networks, where clients may have multiple uplink connections. To enable communication-optimized selection strategies, meQ consists of two major components. A sharable, graph-based network representation and online searches to determine resources. The model benefits from existing work and limits the search space with the use of edge features. A performed simulation study demonstrates the benefits of meQ. The results show that it is possible to significantly reduce the search space, leading to lower search efforts also in large network graphs. An additional system-level simulation proves integration and applicability under realistic conditions.

关键词： Knowledge engineering Couplings computers Cloud computing Multi-access edge computing communication systems Computational modeling

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Tasks Scheduling Techniques in Cloud Computing

Tasks Scheduling Techniques in Cloud Computing

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Information Technology To Enhance e-learning and Other Application (IT-ELA)

作者： Waleed K. Awad Eman T. Mahdi Dept. of computer science College of computer sceince and IT university of anbar Rmadi Iraq Dept. of computer networks systems College of computer sceince and IT University of anbar Rmadi Iraq

Cloud Computing considers one of the most significant innovations that has aroused the curiosity of technologists all around the world. It has many advantages, but it also has a large number of challenges that reduce the efficiency of this technology, including scheduling tasks, which is crucial for organizing user needs and achieving numerous objectives. Because cloud computing serves millions of users at once, it must be able to meet all user needs with excellent performance and a guarantee of the quality of service. Therefore, to fairly and effectively fulfill these requirements, a suitable tasks scheduling system is required. The scheduling solutions should be chosen to satisfy the varied user needs while minimizing the make-span and expense. Task scheduling is employed to assign specific tasks to specific resources at one particular time. The issues with task scheduling have been addressed using a wide variety of ways. Task scheduling's fundamental tenet is to prioritize performance and minimize time lost between activities. In this paper, an overview of the different scheduling approaches used in the cloud is proposed. This paper also discusses the scheduling parameters used in these methods. As well as this paper suggest scenario of scheduling framework that addresses the significant challenges of scheduling tasks in the cloud by dividing the incoming tasks into three groups based on critical tasks (insensitive task, sensitive task and more sensitive task) and other requirements such as minimum execution time or minimum of cost and prioritization.

关键词： Cloud computing Technological innovation Costs Sensitivity Processor scheduling Scheduling algorithms Quality of service

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Neural Network Coding in Data Compression systems in communication Channels 6

Neural Network Coding in Data Compression Systems in Communi...

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6th International Scientific Conference on Information, Control, and communication Technologies, ICCT 2022

作者： Berezkin, Aleksandr Kukunin, Dmitry Kirichek, Ruslan State University of Telecommunications Bonch-Bruevich Saint-Petersburg Dept. of Software Engineering and Computer Science St. Petersburg Russia Bonch-Bruevich Saint-Petersburg State University of Telecommunications Dept. of Communication Networks and Data Transmission St. Petersburg Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

The article describes the use of a neural network implementation of BCH codes encoding and decoding devices for error correction in on-the-fly data compression systems. The user payload vector is encoded in the BCH code with additional redundancy, which provides linear data separability and better convergence when training the autoencoder. On the receiving side, the decoding device operates in the error correction mode, which makes it possible not to impose strict requirements on the accuracy of data recovery when training the autoencoder. Examples of such devices are described in the article. © 2022 IEEE.

关键词： Data compression

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