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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Ridho Hendra Yoga Perdana Toan-Van Nguyen Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea School of Computer Science and Engineering International University - Vietnam National University HCM City Vietnam Dept. of Software and Communications Engineering Hongik University Republic of Korea

In this paper, we investigate a deep learning-based spectral efficiency maximization in multiple users multiple simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) networks, where multiple STAR-RISs are deployed to assist a base station (BS) in communicating with multiple near and far users simultaneously. To improve spectral efficiency (SE), we formulate a joint optimization of the precoding matrix, the phase shift of reflection and transmission of the STAR-RIS problem subject to the individual quality of services (QoS) of each user, maximum power budget at BS and the discrete phase shift. The formulated problem belongs to the class of mixed-integer non-convex optimization problem, which is difficult to solve optimally. We first relax the formulated problem and then transform it into an equivalent non-convex one but with a more tractable form. We then apply the inner approximation method to approximate the non-convex parts iteratively. Towards real-time optimization, we propose a novel deep learning framework with a convolutional neural network (CNN) model to predict the downlink SE based on users’ position information and channel gains. Numerical results show that the proposed approach improves the SE compared to the massive MIMO systems with conventional RIS. Besides, the DL framework for the proposed scheme predicts the optimal SE within a short time compared to the conventional approach.

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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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Respect the Difference: Reinforcement Learning for Heterogeneous FPGA Placement

Respect the Difference: Reinforcement Learning for Heterogen...

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IEEE International Conference on Field-Programmable Technology (FPT)

作者： Fatemehsadat Mahmoudi Mohamed A. Elgammal Soheil Gholami Shahrouz Kevin E. Murray Vaughn Betz Dept. of Electrical & Computer Engineering University of Toronto Toronto Canada Cerebras Systems Toronto Canada

Simulated Annealing (SA) is widely used for FPGA placement due to its ability to adapt to different architectures and optimization goals. However, as FPGA capacity has grown and the devices have become more heterogeneous, the solution space has become very large, making efficient approaches to explore the space critical. We present RLPlace 2.0, an FPGA placer based on simulated annealing that utilizes Reinforcement Learning (RL) and better-than-random initial placement for efficient solution space exploration. The RL agent decides not only on the move types to use at each point in the anneal but also the type of block to move. The RL agent that controls the process is trained entirely online, enabling automatic adaptation to the architecture and design. For traditional FPGA benchmarks, RLPlace 2.0 outperforms both VPR 8 and RLPlace 1.0 in wirelength (WL) and critical path delay (CPD) at all runtime points. For example, on average over the Koios benchmark suite, RLPlace 2.0 reduces CPD by 3% and WL by 1.8% while cutting placement time by 54% at the default run time point vs. VPR 8. On more complex and heterogeneous FPGAs that incorporate a hard Network on Chip (NoC), RLPlace 2.0 enables more efficient co-optimization of NoC and programmable routing metrics by learning when and how to move the NoC routers throughout the anneal. On average for benchmarks containing NoC routers, RLPlace 2.0 achieves the same WL and CPD, 41% reduced NoC latency, and 6% improved NoC bandwidth usage in 50% less time compared to RLPlace 1.0.

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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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Investigating Principle Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health Informatics Application

Investigating Principle Component Analysis Impact on the Per...

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Information Technology (ACIT)

作者： Ahmed BaniMustafa Sattam Almatarneh Olla Bulkrock Raed Alazaidah Hossam Almahasneh Ghassan Samara Computer Information Science Dept. Higher Colleges of Technology Abu Dhabi UAE Data Science & Artificial Intelligence Dept. Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah KSA Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Science Dept. Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. The dataset used in this study consists of thousands of images that represent four types of blood cells. These images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. The study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. The performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. The recorded performance is then compared to drawing a decisive conclusion.

关键词： Training Logistic regression Cells (biology) Nearest neighbor methods Vectors Numerical models Classification algorithms Random forests Principal component analysis Biomedical imaging

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IEEE 802.15.6 Relaying Protocol for MBANs 1

IEEE 802.15.6 Relaying Protocol for MBANs

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Waheed, Tabassum Aqeel-Ur-Rehman Shaikh, Faisal Karim Hamdard University Dept. of Computer Engineering Karachi Pakistan Sir Syed University of Engineering Technology Dept. of Software Engineering Karachi Pakistan Mehran University of Engieneering Technology Dept. of Telecommmunication Engineering Jamshoro Pakistan

ISBN: (纸本)9781665424134

During the recent COVID-19 pandemic, Medical Body area networks (MBANs) emerged as an essential tool for the early diagnosis and treatment of infected patients. MBANs played an instrumental role in the remote treatment of COVID patients through early diagnosis using machine learning and IoT (Internet of Things). MBANs have paved their way in the health care industry and are being used in remote monitoring, emergency handling, and elderly assistance. The availability of the IEEE 802.15.6 standard has opened doors for new wearable consumer devices and IoMT (Internet of Medical Things) enabled applications to facilitate disease management. This standard was developed for wireless communication at a short-range, around the human body with low power requirements. This standard allows 2hop star topology within an intra-MBAN so that when direct communication is not possible, alternate routes are available through relays. This paper proposes a protocol for an intra-MBAN topology, which is a novel T-Relay mechanism that implements the 2-hop star topology. To validate the proposed protocol, it was tested on OMNeT ++ framework with Castalia Simulator for various scenarios. The simulation results show that the T-Relay mechanism enhances the PDR when the channel has variations. © 2021 IEEE.

关键词： Wireless local area networks (WLAN)

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Enhancing Cybersecurity Through Advanced Fraud and Anomaly Detection Techniques: A Systematic Review

Enhancing Cybersecurity Through Advanced Fraud and Anomaly D...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Oluwambo Tolulope Olowe Ayodele A. Adebiyi Adebiyi O. Marion Olowe Mofiyinfoluwa Tobi Deborah Olaniyan Julius Olaniyan Aderemi Emmanuel Kayode Akindeji Dept of Computer Science Landmark University Omuaran Nigeria Dept. of Industrial & Systems Engineering University of Pretoria Pretoria South Africa Dept. of Power Electric Engineering Durban University of Technology Durban South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Fraud and anomaly detection in cybersecurity have become critical areas of research, prompting a comprehensive review of recent developments and model effectiveness. This study investigates prevalent trends and challenges in fraud and anomaly detection, focusing on data science approaches within e-commerce and smart cities. Through the analysis, key factors influencing detection efficacy, such as deep learning models and hybrid approaches, are examined. Findings highlight the multidimensional nature of cyber threats across various domains and underscore the importance of proactive security measures, including robust authentication, network fortification, and employee training. This paper contributes to enhancing awareness and understanding of evolving cyber threats while offering actionable insights for bolstering detection capabilities and mitigating risks.

关键词： Deep learning Training Systematics Authentication Data science Market research software

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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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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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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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