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Proceedings of the 56th ACM Technical Symposium on computer Science Education V. 2

作者： Alicia Nicki Washington Cecilé Sadler Shaundra Daily Computer Science Duke University Durham NC USA Media Lab Massachusetts Institute of Technology Cambridge MA USA Electrical and Computer Engineering Duke University Durham NC USA

ISBN: (纸本)9798400705328

This Birds of a Feather (BoF) session brings together computing educators, administrators, and graduate students to explore innovative approaches for creating supportive learning environments in computer science education. Building on the success of the Cultural Competence in Computing (3C) Fellows program, which has cultivated a community of over 250 computing professionals, this session provides a structured framework for addressing current challenges in computing education and developing effective teaching strategies. Participants will engage in dynamic discussions and collaborative activities examining successful pedagogical approaches, sharing implementation experiences, and exploring evidence-based practices for improving student outcomes. The session format includes focused breakout groups organized by geographical region, allowing participants to network with colleagues facing similar institutional challenges. Each group will include experienced educators who have successfully implemented innovative teaching practices, facilitating knowledge transfer and mentorship opportunities. Through guided discussions, participants will identify key obstacles in their educational environments and develop practical solutions based on peer experiences and proven methodologies. The BoF concludes with a collaborative planning session where participants will develop actionable steps for implementing new teaching approaches at their institutions. This session emphasizes building sustainable professional networks and communities of practice, enabling ongoing collaboration and resource sharing. Attendees will gain practical insights into educational innovation, forge valuable professional connections, and develop specific strategies to advance the work at their home institutions. This session particularly benefits educators seeking to implement research-based improvements in computing education.

关键词： broadening participation

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Learning repeatable speech embeddings using an intra-class correlation regularizer 23

Learning repeatable speech embeddings using an intra-class c...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jianwei Zhang Suren Jayasuriya Visar Berisha School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ School of Arts Media and Engineering School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ College of Health Solutions School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ

A good supervised embedding for a specific machine learning task is only sensitive to changes in the label of interest and is invariant to other confounding factors. We leverage the concept of repeatability from measurement theory to describe this property and propose to use the intra-class correlation coefficient (ICC) to evaluate the repeatability of embeddings. We then propose a novel regularizer, the ICC regularizer, as a complementary component for contrastive losses to guide deep neural networks to produce embeddings with higher repeatability. We use simulated data to explain why the ICC regularizer works better on minimizing the intra-class variance than the contrastive loss alone. We implement the ICC regularizer and apply it to three speech tasks: speaker verification, voice style conversion, and a clinical application for detecting dysphonic voice. The experimental results demonstrate that adding an ICC regularizer can improve the repeatability of learned embeddings compared to only using the contrastive loss; further, these embeddings lead to improved performance in these downstream tasks.

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Design of Digital Film and Television Projection System Based On Blockchain technology

Design of Digital Film and Television Projection System Base...

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2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Liao, Xiaowen Mao, Pandi Xu, Daolian School of Arts and Media Chongqing Metropolitan College of Science and Technology Chongqing402167 China School of Electrical Engineering and Intelligent Manufacturing Chongqing Metropolitan College of Science and Technology Chongqing402167 China College of Optoelectronic Engineering Chongqing University Chongqing40030 China

ISBN: (纸本)9781665456371

Digital Rights Management (DRM) is widely used to protect digital content and has made a major contribution to protecting digital content. However, traditional DRM technology has shortcomings, such as centralized and opaque copyright and transaction information. In order to solve the security problem of centralized management of digital movies, a digital movie distribution system architecture based on blockchain technology is proposed. Provide authenticity and traceability of data and business logic in film trading, license certification, projection control and other fields. It is developed through the decentralized consensus mechanism and decentralized IPFS storage mechanism using smart contract scripts in the blockchain. Create efficient and accurate implementation and reduce manual intervention and authorization to ensure traffic control throughout the working day. Our mechanism allows rights holders to set prices for different content usage rules according to their own wishes. Customers have the flexibility to choose the usage rules they want to purchase. We have also developed a licensing framework based on blockchain, which is close to the current DRM standard and easy to promote. © 2022 IEEE.

关键词： Copyrights

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An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

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computers, Materials & Continua 2022年第1期70卷 1315-1334页

作者： Naglaa F.Soliman Naglaa S.Ali Mahmoud I.Aly Abeer D.Algarni Walid El-Shafai Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt International Academy for Engineering and Media Science October CityEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Breast cancer is themost common type of cancer,and it is the reason for cancer death toll in women in recent *** diagnosis is essential to handle breast cancer patients for treatment at the right *** with mammography is the preferred examination for breast cancer,as it is available worldwide and ***-Aided Detection(CAD)systems are used to analyze medical images to detect breast cancer,*** death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after *** of mammogram images has four main steps:pre-processing,segmentation of the region of interest,feature extraction and classification of the images into normal or abnormal *** paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect *** pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter,removal of artifacts using morphological operations,and contrast enhancement using a fuzzy enhancement *** proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure(EME)and local contrast *** comparison shows an outstanding performance of the proposed technique from the visual and numerical *** segmentation process is performed using Otsu’smultiple thresholding *** method segments the image regions into five classes with variable intensities using four *** effectiveness is measured based on visual quality of the segmentation output,as it gives details about the image and positions of *** performance of the proposed framework is measured using Dice coefficient,Hausdorff,and Peak Signal-to-Noise Ratio(PSNR)*** segmented tumor region with the proposed segmentation method is 81%of the ground truth region provided by an ***,the proposed framew

关键词： Breast cancer mammogram images CAD contrast enhancement fuzzy logic segmentation

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Adaptive Medical Image Segmentation Using Deep Convolutional Neural Networks

Adaptive Medical Image Segmentation Using Deep Convolutional...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Babu, Sangita Pandey, Sakshi Palle, Kowstubha Prasad, P. Venkata Mallala, Balasubbareddy Pund, Sachin Computer Science King Khalid University College of Science and Arts in Rijal Alma Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Hyderabad India Chaitanya Bharathi Institute of Technology Department of Eee Hyderabad India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Nagpur India

ISBN: (纸本)9798350329773

Recent developments in deep learning have made it possible to apply it to medical image segmentation. Deep convolutional neural networks (DCNN s) are a type of deep learning model used for segmenting medical images. This approach has been found to be effective in accurately segmenting medical images, and better results in faster segmentation times than traditional methods. DCNN s use a combination of convolutional layers, pooling layers, and activation functions to detect patterns in the input images and learn a set of features from it. DCNN s have the advantage of having fewer parameters compared to other methods. This makes them easily adaptable and portable across different applications. By using the latest advances in CNN s, more accurate segmentation can be achieved in a shorter time. This makes DCNNs a powerful tool for medical image segmentation. © 2023 IEEE.

关键词： Convolutional neural networks

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Cloud Computing Based POC Diagnostic Device: Rapid Infectious Disease Testing by Data Analytics 1

Cloud Computing Based POC Diagnostic Device: Rapid Infectiou...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Basha, H. Anwer Ramalingam, L. Hasan, Md Ashfaqul Ananda, M.H. Velmurugan, S. Maniraj, S.P. Saveetha College of Liberal Arts & Sciences Simats University Department of Computer Science Tamil Nadu Chennai India Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Reva University School of Electrical and Electronics Engineering Karnataka Bangalore India Tjs Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350330915

To meet the growing need for affordable, efficient, and accurate diagnostic tools, the system has developed a Raspberry Pi-based Point-of-Care (POC) gadget for speedy and reliable detection of infectious illnesses. This groundbreaking gadget, built on the flexible and powerful Raspberry Pi computer, ushers in a new era of state-of-the-art POC diagnostics by combining molecular and image analysis methods. Complex testing for pathogens like COVID-19 and malaria may be carried out using this state-of-the-art system, which integrates a camera module and custom biosensors. The device's software parses sensor data, improves diagnostic precision using machine learning techniques, and presents a straightforward interface for clinicians and patients alike. This study takes a holistic approach by considering all relevant factors, including adherence to regulations, user education, upkeep, and data management. Our POC diagnostic gadget driven by a Raspberry Pi provides a low-cost, high-throughput method for diagnosing illness, which may have far-reaching implications for the future of healthcare delivery in both under-resourced and well-endowed regions. © 2023 IEEE.

关键词： Healthcare Infectious Disease Internet of Things, Cloud Computing Point-of-Care Diagnostics

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A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

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computers, Materials & Continua 2022年第1期70卷 1491-1507页

作者： Faiz Ali Shah Muhammad Attique Khan Muhammad Sharif Usman Tariq Aimal Khan Seifedine Kadry Orawit Thinnukool Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia National University of Science and Technology Islamabad IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s *** manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an ***,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf *** to the literature,many automated methods have been developed for the recognition of fruit diseases at the early ***,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant *** processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these ***,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based *** proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final *** experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.

关键词： Fruits diseases data augmentation deep learning features fusion feature selection

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An Enhanced Review Analysis of Security Issues Involved in Drones used with IOT technology and its Measures to Avoid Attacks 4

An Enhanced Review Analysis of Security Issues Involved in D...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Chabra, Rishu Dixit, Krishna Kant Singh, Devendra Kishore, G.Uday Al-Attabi, Kassem Thiyagarajan, G. Chitkara University Centre of Research Impact and Outreach Punjab Rajpura140401 India Gla University Department of Electrical Engineering Mathura India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Dehradun248007 India Cmr College of Engineering & Technology Department of Cse India The Islamic University Najaf Iraq S.A.College of Arts & Science School of Management-MBA Tamilnadu Chennai India

ISBN: (纸本)9798350360165

In the evolving landscape of the Internet of Drones (IoD), securing touchy and assignment-vital facts is paramount because of the wider assault floor and stringent electricity constraints specific to IoD networks. This paper introduces 'IoD-Crypt,' a completely specific cryptographic framework engineered to address those traumatic situations by offering a strong, strength-inexperienced answer tailored for battery-confined IoD packages. Leveraging specialized precomputation strategies and self-certified cryptographic primitives, IoD-Crypt achieves notable computation and conversation efficiencies. By circumventing the rules of latest public key cryptography suites, the framework guarantees a immoderate stage of protection inside the random oracle model. Through complete implementations on everyday drone processors, which consist of eight-bit AVR and 32-bit ARM, IoD-Crypt demonstrates an remarkable reduce rate in energy consumption, as lots as forty eight times lower than conventional techniques. This massive development paves the manner for at ease, sustainable IoD operations, emphasizing the framework's capability for big adoption and trying out. © 2024 IEEE.

关键词： Drones

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Transmission Line Fault Detection and Classification Using Feature Extraction Based Self-Attention Convolutional Neural Network with Time Series Image 3

Transmission Line Fault Detection and Classification Using F...

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3rd IEEE International Conference on Distributed Computing and electrical Circuits and Electronics, ICDCECE 2024

作者： Almusawi, Muntather Babu, D. Raja Mazumder, Debarshi Bala Dhandayuthapani, V. Saranya, N. Naga College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India College Of Computing And Information Sciences University Of Technology And Applied Sciences Department Of It Shinas campus Oman Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

The transmission lines continuously experience the number of shunt faults and its effective in practical system increases the instability, load damages and line restoration cost. This work implements an advanced self-attention convolutional neural network (SAT-CNN) method for fault detection and classification (FDC) of high voltage transmission lines experiencing a constant number of shunt faults. Faults cause load damage in real-time applications, increase instability, and increase the cost of line restoration. Therefore, a precise model is needs to identify and categorize the flaws to quickly restore he problematic phases. In this research, implemented SAT-CNN feature extraction model with imaging-based on time series that can accurately detect and classify faults. By employing a number of input signals, including, current, voltage, and combined current and voltage signals, at different sampling frequencies, the efficacy of implemented SAT-CNN model is evaluated. Implemented SAT-CNN method obtains high performance when compared to existing methods including weight-sharing network (WSCN), Truncated singular value decomposition and Human urbanization algorithm based Recurrent Perceptron Neural Network (TSVD-HUARPNN), and Graph convolutional neural network (GCN), and result achieved 99.90% accuracy value. © 2024 IEEE.

关键词： Convolutional neural networks

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Task Offloading in Fog Computing with Deep Reinforcement Learning: Future Research Directions Based on Security and Efficiency Enhancements

arXiv

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arXiv 2024年

作者： Pakmehr, Amir Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Electrical Engineering Media and Computer Science Ostbayerische Technische Hochschule Amberg-Weiden Amberg Germany

The surge in Internet of Things (IoT) devices and data generation highlights the limitations of traditional cloud computing in meeting demands for immediacy, Quality of Service, and location-aware services. Fog computing emerges as a solution, bringing computation, storage, and networking closer to data sources. This study explores the role of Deep Reinforcement Learning in enhancing fog computing's task offloading, aiming for operational efficiency and robust security. By reviewing current strategies and proposing future research directions, the paper shows the potential of Deep Reinforcement Learning in optimizing resource use, speeding up responses, and securing against vulnerabilities. It suggests advancing Deep Reinforcement Learning for fog computing, exploring blockchain for better security, and seeking energy-efficient models to improve the Internet of Things ecosystem. Incorporating artificial intelligence, Our results indicate potential improvements in key metrics, such as task completion time, energy consumption, and security incident reduction. These findings provide a concrete foundation for future research and practical applications in optimizing fog computing architectures. © 2024, CC BY-NC-ND.

关键词： Deep reinforcement learning

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