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26th Ibero-American Conference on Software engineering, CIbSE 2023

作者： Robles, Nicolás Potes, Nicolás Garcés, Kelly Izquierdo, Javier Luis Canovas Cabot, Jordi Department of Systems and Computing Engineering Universidad de los Andes Colombia IN3 - UOC ICREA Spain

ISBN: (纸本)9781713855583

The software industry is in continuous evolution, forcing developers to quickly adapt to new requirements to catch up with the latest trends. A clear example is the huge demand for web-based APIs to connect all kinds of services among them. Like any other piece of software, a Web API is continuously changing, and with each change, all client applications must evolve. This adaptation process is critical and essential for software developers. In this paper, we present an exploratory evaluation of the most common changes occurring during the evolution of REST APIs. We define a taxonomy of structural API changes, which we classify according to their impact on client-side software;and propose a repository mining process to identify these changes in real Web APIs. We apply this process to a large set of Azure APIs from APISGURU, a well-known Open Source API repository. Based on the analyzed dataset from APISGURU, we found that breaking changes tend to decrease when a new version of an API from this dataset is released. Other useful findings and insights are discussed throughout the article. © 2023 CIbSE 2023 - XXVI Ibero-American Conference on Software engineering. All rights reserved.

关键词： Application programs

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Secure location-aware geocast routing in Internet of Vehicles

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Multimedia Tools and Applications 2025年第18期84卷 19669-19692页

作者： Kumar, Sunil Dohare, Upasana Kumar, Sushil Department of Computer Science and Information Technology Central University of Haryana Mahendragarh India School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

Efficient message dissemination in Vehicular Ad Hoc Networks (VANETs) relies on robust connectivity between neighboring vehicular nodes, yet it is often compromised by malicious intruders. While recent literature proposes various approaches to enhance geocasting performance, security considerations for connectivity are often overlooked. To address this gap, we propose the Secure Location-Aware Cache Agent based Geocast Routing (SL-CAGR) protocol, emphasizing an Intrusion Prevention Technique (IPT) for message integrity preservation and a Connectivity Reliant Packet Forwarding (CRPF) algorithm for improved VANET connectivity. Our protocol introduces a network model for cache agent-based geocast routing on the Internet of Vehicle (IoV) environment, implements IPT using Diffie-Hellman digital signatures, and presents a secure geocast routing strategy focusing on re-caching and cache agent selection. Empirical simulations demonstrate SL-CAGR's superiority in packet delivery ratio, packet loss, duplication, hop count, end-to-end delay, and network load compared to these protocols. Simulation results demonstrate that SL-CAGR outperforms state-of-the-art techniques like CAG, CTRC, and D-Flooding. SL-CAGR stands out by merging intrusion prevention, secure geocast routing, and cache agent selection, ensuring robust security and efficiency in VANETs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vehicular ad hoc networks

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"Phrasefluent" - An Automated Solution for Children's Speech Disorders Identification and Therapy Treatment 5

"Phrasefluent" - An Automated Solution for Children's Speech...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Karunasekara, Pasan Deshitha, Sheshan De Alwis, Dinuja Karunarathna, Desan Lokuliyana, Shashika Gamage, Narmada Sri Lanka Institute Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

PhraseFluent is a project to develop a complete web-based solution to help people who are suffering from speech disorders. This application is specifically designed for people who are suffering from articulation and stuttering speech disorders. There are few solutions available for speech disorders identification and speech therapies. Those available applications have several drawbacks that lead to the research gap for this research. Those findings do not have any disorder-specific speech detection methods. Those applications generally detect speech disorders not the type of the disorder. Also, the speech therapy exercises suggest applications are practicing words according to the order of the alphabet. Those solutions are not based on the sounds that need to be practiced in order to cure speech disorders. Also, applications that have detection mechanisms are not currently available for wide access. This application mainly contains React-based interactive interfaces and a Python-programmed backend with machine-learning algorithms for functionality. This application can help to automate the process of detecting speech disorders and suggest therapy activities. Also, this application can track the patient's progress with the therapies. Used datasets contain the data with 70% accuracy and the test results gave a 68% percent success rate. This approach will be the first step in automating the complete process of speech disorder diagnosis. © 2023 IEEE.

关键词： Learning algorithms

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Collective Citizen Energy Investment Models for Solar Power: Case Study Croatia

Collective Citizen Energy Investment Models for Solar Power:...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Herenčić, Lin Capuder, Tomislav Kirac, Mislav Čačić, Goran University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia Zagreb Croatia

ISBN: (纸本)9798350332308

Cost-competitiveness of decentralized renewable energy sources (RESs) has opened opportunities for citizens' ownership of RES and for the development of new business models. Citizen energy investment models based on cooperative principles have gained increased attention lately due to promises to support local economic welfare, boost local economy, increase social cohesion, help in grid optimization, and increase citizens' support for new RES projects. In this paper, we provide an overview of collective citizen energy investment concepts and describe the six types of models being developed in Croatia. We provide an overview of the initiatives together with the challenges, threats, and opportunities found when concepts are faced with the real-life practice in Croatia. We show that even though citizens' ownership of RES (mainly individual solar PV systems) is strongly increasing, the current developments of collective (group) citizen energy investment models are predominantly related to sporadic research projects and enthusiasts' initiatives, meaning still no wider-scope investments in RES have been conducted this way. Following the findings, we provide recommendations for removing the barriers and speeding up the development of such projects in the future. © 2023 IEEE.

关键词： Solar energy

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"Hand Model" - A Static Sinhala Sign Language Translation Using Media-Pipe and SVM Compared with Hybrid Model of KNN, SVM and Random Forest Algorithms 5

"Hand Model" - A Static Sinhala Sign Language Translation Us...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Gedaragoda, G.H.M.S.C. Wijesinghe, W.R.S. Abeywickrama, T.R. Lokuliyana, Shashika Mahaadikara, Hansika Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study of sign language recognition has been a thriving area of research for nearly twenty years. SSL translation using computer vision relies on extensive training using large number of images or video sequences. Yet, it poses a challenge for languages with limited resources, like Sinhala Sign Language. Due to the absence of accessible datasets for such investigations and lack of SSL recognition systems for real-time scenarios, the authors of this research have developed a completed system that consist of three components which are, static sign recognition, dynamic sign recognition and natural language processing to output a meaningful sentence. This paper describes the models and the results which are developed for static sign recognition. The final model for the Sinhala Static recognition has developed using media-pipe and SVM algorithm. And to compare the results and performance authors have used CNN, SVM, KNN and Random-forest algorithms to compare the results and performance. In this research, authors have made a valuable contribution by introducing a new dataset and developing a system for translating static SSL that gives 99.67% of accuracy and best performance when compared to the other two models and algorithms described in this paper. © 2023 IEEE.

关键词： Support vector machines

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Probabilistic Distributed Intrusion Detection For Zero-Trust Multi-Access Edge computing 36

Probabilistic Distributed Intrusion Detection For Zero-Trust...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Sharma, Rahul Chan, Chien Aun Leckie, Christopher The University of Melbourne School of Computing and Information Systems Australia The University of Melbourne Department of Electrical and Electronic Engineering Australia

ISBN: (纸本)9781665477161

With the advent of Multi-Access Edge computing (MEC) in 5G, there is a shift in the core processing and deployment of applications to the edge of the network. This enables applications requiring ultra low latency response times and high bandwidth but also exposes them to new cyber attacks. One of the proposed ways to strengthen application security in MEC is to use Zero Trust Strategies (ZTS). However, despite its strong authentication and authorisation capabilities, it is still vulnerable to attacks and requires further mechanisms to detect anomalies, especially across multiple clusters. Collaborative Intrusion Detection systems (CIDS) have been proposed for detecting sophisticated coordinated attacks but often fall short in detecting focused stealthy attacks. In this paper, we present a novel tree-based Probabilistic Distributed CIDS to detect stealthy and volumetric anomalies in service-to-service interactions deployed across multiple edge clusters with a Zero Trust Network setup. We perform our evaluation based on detection accuracy and network overhead and outline its performance improvement against a traditional Distributed CIDS and a previously proposed Hybrid CIDS. Based on an evaluation using telemetry data from real-world application clusters, our novel CIDS resulted in improvements in detection accuracy of 99.4% and network overhead of up to 51.81% when detecting anomalies in service path interactions, HTTP methods, service endpoint interactions, and unusual variance in numerical values comprising request size, response size, and traffic processing duration. © 2023 IEEE.

关键词： Intrusion detection

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Signs to Speech 2

Signs to Speech

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vardhan, Harsh Harsavardhan, S. Lakshmi, M. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

This research introduces an innovative Sign Language to Speech Conversion Model using Convolutional Neural Networks (CNNs) to address communication barriers for the people who are deaf and unable to hear properly. The model employs deep learning for the automatic extraction of spatial patterns from sign language images. A diverse sign language dataset is carefully curated, undergoing Pre-processing for improved quality. The CNN architecture, designed for adaptability, captures local and global features, enabling accurate *** the training phase, the model learns to map sign language gestures to spoken language representations. Performance evaluation methods which include accuracy, recall, precision and F1 score, demonstrate the effectiveness of the model. Results underscore the significance for the deaf community. The discussion explores broader implications in education, healthcare, and social interactions, acknowledging limitations and proposing future research *** conclusion, the Sign Language to Speech Conversion Model contributes to sign language recognition, offering an advanced solution for communication barriers and promoting inclusivity in diverse social settings. © 2024 IEEE.

关键词： Convolution

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm 6

Web Application to predict Skin Disease: An Approach Using D...

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6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract) 39

A Survey on Modern Deep Neural Network for Traffic Predictio...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Tedjopurnomo, David Alexander Bao, Zhifeng Zheng, Baihua Choudhury, Farhana Murtaza Qin, A.K. Rmit University School of Computing Technologies Australia Singapore Management University School of Computing and Information Systems Singapore The University of Melbourne School of Computing and Information Systems Australia Swinburne University of Technology Department of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350322279

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. This research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare;making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field. © 2023 IEEE.

关键词： Deep neural networks

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Violence Detection System using MobileNetV2 3

Violence Detection System using MobileNetV2

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3rd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Kumar, Rahul Gupta, Aniket Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350375190

The number of cases of violence and fights has been increasing around the world. With the use of CCTV, such incidents can be recorded but the detection of Violence is a major issue around the globe, and it plays a vital role in the safety and security of the area. It helps in effective surveillance and law enforcement. The effectiveness of any violence detection system depends on the speed and accuracy with which it detects the events and how it performs over different types of videos of varied sizes and formats. In the proposed algorithm, the dataset used contains videos of different types and scenarios to help the model understand and train better. To improve the overall effectiveness of the model, Lightweight CNN MobileNetV2 has been used along with that Bidirectional LSTM (BiLSTM) has been used. The accuracy rate of the proposed model is 98%. © 2024 IEEE.

关键词： Long short-term memory

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