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10th International Conference on software and Information engineering, ICSIE 2021

作者： Tuape, Micheal Hasheela-Mufeti, Victoria Iiyambo, Petrus Kayanda, Anna Kasirinen, Jussi Department of Software Engineering Lappeenranta-Lahti University of Technology Finland Department of Computing Mathematical and Statistical Sciences University of Namibia Namibia Information Systems Department College of Business Education Dar-es Salaam Tanzania United Republic of

ISBN: (纸本)9781450384315

Low-quality software products are synonymous with small software companies, yet thousands of process tools and methods are available, although unfortunately, they remain unutilized. The utilization of these process tools are said to be very challenging to small software companies with the difficulty of adaptability tagged to the context in which the companies operate since the process tools are not designed to take care of the operational context of the small software companies. This survey study was undertaken to empirically investigate the implication of the organizational dynamics on using process tools in SSCs. A total of 115 respondents from Tanzania, Namibia, and Ghana were reached through a structured questionnaire. Our findings reveal that the ad-hoc behaviors and attitude breed a culture that inhibits procedural and process-intensive practices synonymous with most software engineering process tools. This finding is interesting because this area has been underestimated while looking at software practice, yet it is significant in understanding practice in relation to software process tools, significantly how small software companies can be helped to adopt the different tools and also provide a mechanism to help the designers of process tools make tools that are usable in the context of the practice of SSCs. © 2021 ACM.

关键词： Surveys

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Training and Serving System of Foundation Models: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Zhou, Jiahang Chen, Yanyu Hong, Zicong Chen, Wuhui Yu, Yue Zhang, Tao Wang, Hui Zhang, Chuanfu Zheng, Zibin School of Systems Science and Engineering Sun Yat-sen University Guangzhou528406 China School of Software Engineering Sun Yat-sen University Guangzhou510275 China Department of Computing The Hong Kong Polytechnic University 999077 Hong Kong Peng Cheng Laboratory Shenzhen518000 China

Foundation models (e.g., ChatGPT, DALL-E, PengCheng Mind, PanGu-Σ) have demonstrated extraordinary performance in key technological areas, such as natural language processing and visual recognition, and have become the mainstream trend of artificial general intelligence. This has led more and more major technology giants to dedicate significant human and financial resources to actively develop their foundation model systems, which drives continuous growth of these models’ parameters. As a result, the training and serving of these models have posed significant challenges, including substantial computing power, memory consumption, bandwidth demands, etc. Therefore, employing efficient training and serving strategies becomes particularly crucial. Many researchers have actively explored and proposed effective methods. So, a comprehensive survey of them is essential for system developers and researchers. This paper extensively explores the methods employed in training and serving foundation models from various perspectives. It provides a detailed categorization of these state-of-the-art methods, including finer aspects such as network, computing, and storage. Additionally, the paper summarizes the challenges and presents a perspective on the future development direction of foundation model systems. Through comprehensive discussion and analysis, it hopes to provide a solid theoretical basis and practical guidance for future research and applications, promoting continuous innovation and development in foundation model systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

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Texture and Orientation-based Feature Extraction for Robust Facial Expression Recognition

Texture and Orientation-based Feature Extraction for Robust ...

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International Conference on software engineering Research, Management and Applications (SERA)

作者： Sajid Ali Khan Mohammed Bin Abdulrahman Alawairdhi Mousa AL-Akhras Department of Software Engineering Foundation University Islamabad Islamabad Pakistan College of Computing and Informatics Saudi Electronic University Saudi Arabia Computer Information Systems Department King Abdullah II School for Information Technology The University of Jordan Amman

Facial expressions are the most effective way to characterize people’s motives, emotions, and feelings. Several new methods are proposed each year; however, the accuracy of facial expression recognition still needs to be improved especially in uncontrolled conditions. In this paper, we propose a hybrid facial expression model that considers both texture and orientation features to classify expressions. Two types of descriptors namely Local binary pattern and Weber local descriptor are used to preserve the local intensity information and orientation of edges. In the next step, computing the Histograms of oriented gradients (HOG) features from the Local binary pattern and Weber local descriptor images to capture micro-expressions. Then, the AdaBoost feature selection algorithm is utilized to choose the best features from the combined HOG features. The results of the experiments demonstrate that the method proposed in this study performs better than existing methods.

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BlockHDFS:Blockchain-integrated Hadoop distributed file system for secure provenance traceability

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Blockchain(Research and Applications) 2021年第4期2卷 30-36页

作者： Viraaji Mothukuri Sai S.Cheerla Reza M.Parizi Qi Zhang Kim-Kwang Raymond Choo College of Computing and Software Engineering Kennesaw State UniversityKennesawGA30144USA IBM Thomas J.Research Center Yorktown HeightsNY10598USA Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Hadoop Distributed File System(HDFS)is one of the widely used distributed file systems in big data analysis for frameworks such as *** allows one to manage large volumes of data using low-cost commodity ***,vulnerabilities in HDFS can be exploited for nefarious *** reinforces the importance of ensuring robust security to facilitate file sharing in Hadoop as well as having a trusted mechanism to check the authenticity of shared *** is the focus of this paper,where we aim to improve the security of HDFS using a blockchain-enabled approach(hereafter referred to as BlockHDFS).Specifically,the proposed BlockHDFS uses the enterprise-level Hyperledger Fabric platform to capitalize on files'metadata for building trusted data security and traceability in HDFS.

关键词： Big data Hadoop Blockchain Hyperledger fabric Hadoop distributed file system(HDFS) Traceability Security Privacy

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GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation

GLoT: A Novel Gated-Logarithmic Transformer for Efficient Si...

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IEEE Future Networks World Forum (FNWF)

作者： Nada Shahin Leila Ismail Department of Computer Science and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of Information Technology UAE University Al-Ain United Arab Emirates Emirates Center for Mobility Research UAE University Al-Ain United Arab Emirates

ISBN: (数字)9798350379495

ISBN: (纸本)9798350379501

Machine Translation has played a critical role in reducing language barriers, but its adaptation for Sign Language Machine Translation (SLMT) has been less explored. Existing works on SLMT mostly use the Transformer neural network which exhibits low performance due to the dynamic nature of the sign language. In this paper, we propose a novel Gated-Logarithmic Transformer (GLoT) that captures the long-term temporal dependencies of the sign language as a time-series data. We perform a comprehensive evaluation of GloT with the transformer and transformer-fusion models as a baseline, for Sign-to-Gloss-to-Text translation. Our results demonstrate that GLoT consistently outperforms the other models across all metrics. These findings underscore its potential to address the communication challenges faced by the Deaf and Hard of Hearing community.

关键词： Sign language Adaptation models Translation Accuracy Terminology Neural networks Semantics Logic gates Transformers Testing

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Improved Traffic Filtering for IoT Security 5

Improved Traffic Filtering for IoT Security

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5th IEEE International Conference on Electro-computing Technologies for Humanity, NIGERCON 2024

作者： Ovili, Henry Peter Okpor, Margaret Nwanze, N.E. Olayinka, Tosin Ojei, Emmanuel Nwankwo, Wilson Delta State University of Science and Technology Department of Information Systems & Technology Ozoro Nigeria Delta State University of Science and Technology Department of Cyber Security Ozoro Nigeria Delta State University of Science and Technology Faculty of Engineering Marine Engineering Department Ozoro Nigeria Wellspring University Department of Computing Benin City Nigeria Delta State University of Science and Technology Department of Software Engineering Ozoro Nigeria

ISBN: (纸本)9798331542559

This research presents an advanced traffic filtering technique using a Support Vector Machine (SVM) model to enhance IoT network security, achieving 85% accuracy, with high precision (87%) and recall (96%) in identifying legitimate traffic. However, the model struggles with neutral traffic, highlighting the need for further refinement, and offers a scalable solution for real-time dynamic analysis, with future work focusing on improving neutral traffic classification and integrating additional features to enhance performance. © 2024 IEEE.

关键词： Anomaly detection

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Goal-Modeling Privacy-by-Design Patterns for Supporting GDPR Compliance

Goal-Modeling Privacy-by-Design Patterns for Supporting GDPR...

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18th International Conference on software Technologies, ICSOFT 2023

作者： Al-Obeidallah, Mohammed Ghazi Piras, Luca Iloanugo, Onyinye Mouratidis, Haralambos Alkubaisy, Duaa Dellagiacoma, Daniele Department of Software Engineering Al Ain University Abu Dhabi United Arab Emirates School of Computing Middlesex University London United Kingdom School of Computing Robert Gordon University Aberdeen United Kingdom Institute for Analytics and Data Science University of Essex Colchester United Kingdom College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Centre for Secure Intelligent and Usable Systems University of Brighton Brighton United Kingdom

ISBN: (纸本)9789897586651

The introduction of the European General Data Protection Regulation (GDPR) has imposed obligations on organisations collecting data in the EU. This has been beneficial to citizens due to rights reinforcement achieved as data subjects. However, obligations heavily affected organisations, and their privacy requirements analysts, having issues with interpreting and implementing GDPR principles. This paper proposes visual GDPR Patterns supporting analysts through Privacy-by- Design (PbD) and GDPR compliance analysis. In order to achieve that, we extended a requirements modeling tool, SecTro, which is used to assist analysts in creating visual requirements models. Specifically, we extended SecTro with novel visual GDPR patterns representing GDPR principles. We evaluated the patterns in a healthcare case study. The evaluation results suggest that the GDPR patterns can help analysts in PbD modeling analysis, by representing GDPR principles and considering relevant ready-to-use alternatives, towards achieving GDPR compliance. © 2023 by SCITEPRESS-Science and Technology Publications, Lda.

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Unveiling Public Perception of ChatGPT Through Twitter Data and Machine Learning

Unveiling Public Perception of ChatGPT Through Twitter Data ...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Vadivel Abishethvarman Kuhaneswaran Banujan Ashansa Wijeratne Ravikumar Nirubikaa Samantha Kumara Senthan Prasanth Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Software Engineering Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Physical Sciences and Technology Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

The study comprehensively analysed public sentiment towards ChatGPT, a language processing AI tool, using approximately 135,000 tweets from an initial dataset of 236,275 entries. After cleaning and pre-processing the data using basic NLP techniques, Machine Learning (ML) models—Long Short-Term Memory (LSTM), Decision Tree (DT), and Support Vector Machine (SVM)—were applied to classify the tweets into positive, negative, and neutral categories. The LSTM model demonstrated superior performance across all evaluation metrics—accuracy, precision, recall, and F1 score—for all tweet categories. Word clouds generated for each category revealed that the most frequent words in positive tweets included ‘student,’ 'education,’ 'research,’ and ‘teacher,’ indicating a strong association of ChatGPT with educational and research contexts. Similarly, ‘AI,’ ‘student,’ 'research,’ ‘course,’ and ‘education’ dominated neutral and negative tweets, highlighting prevalent discussion topics. However, the study had limitations, including dependency on a single dataset, potential loss of contextual information during pre-processing, and a focus on English tweets only. Despite these limitations, the study provides valuable insights into the public’s perception of ChatGPT, underscoring its significant association with education and research and the effectiveness of the LSTM model in classifying tweets. The findings suggest that future research should incorporate diverse data sources and languages better to understand global sentiment towards ChatGPT and similar AI tools.

关键词： Support vector machines Social networking (online) Blogs Education Machine learning Chatbots Tag clouds

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Efficient Training Approaches for Performance Anomaly Detection Models in Edge computing Environments

arXiv

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arXiv 2024年

作者： Fernando, Duneesha Rodriguez, Maria A. Arroba, Patricia Ismail, Leila Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne Parkville Australia The CCS Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain Lab Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates

Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL's model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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From Rule-Based Models to Deep Learning Transformers Architectures for Natural Language Processing and Sign Language Translation systems: Survey, Taxonomy and Performance Evaluation

arXiv

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arXiv 2024年

作者： Shahin, Nada Ismail, Leila Intelligent Distributed Computing and Systems Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University Abu Dhabi United Arab Emirates

With the growing Deaf and Hard of Hearing population worldwide and the persistent shortage of certified sign language interpreters, there is a pressing need for an efficient, signs-driven, integrated end-to-end translation system, from sign to gloss to text and vice-versa. There has been a wealth of research on machine translations and related reviews. However, there are few works on sign language machine translation considering the particularity of the language being continuous and dynamic. This paper aims to address this void, providing a retrospective analysis of the temporal evolution of sign language machine translation algorithms and a taxonomy of the Transformers architectures, the most used approach in language translation. We also present the requirements of a real-time Quality-of-Service sign language machine translation system underpinned by accurate deep learning algorithms. We propose future research directions for sign language translation *** Codes I.2, I.2.7, I.4, I.4.9 © 2024, CC BY.

关键词： Neural machine translation

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