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Offshore software Maintenance Outsourcing Process Model Validation:A Case Study Approach

computers, Materials & Continua 2023年第3期74卷 5035-5048页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan

The successful execution and management of Offshore software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

关键词： Offshore outsourcing process model model validation vendor challenges case study

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Stress Analysis in Social Media in Bengali language using BERT-based approaches

Stress Analysis in Social Media in Bengali language using BE...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Khanom, Afroza Akter, Nargis Maisha, Sabrina Jahan Masum, Abdul Kader M. International Islamic University Chittagong Department of Computer Science and Engineering Chattogram Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Recent researches highlight a rapid increase in mental health issues, signaling a concerning rise in stress-related conditions such as high blood pressure, psoriasis, polycystic ovary syndrome (PCOS), etc., and these should have a sophisticated remedy that plays a big role in stress analysis. To proceed with that the ongoing connection between increased stress levels and social media has gained a lot of attention. This study focused on inspecting a complex language, Bengali by extracting comments from YouTube to analyze stress to provide a meaningful solution. We are aware of no research on stress analysis in the Bengali language, although many studies have been done on the same topic in other languages. Thus by paying closer attention to stress analysis in the Bengali language, our work suggested a BERT-based method that has shown good results on a range of NLP tasks. Our approach included Bangla-BERT-base and hybrid models that combine deep and transfer learning, like BERT-LSTM, BERT-GRU, and BERT-CNN-BiLSTM. Our task performed well with an accuracy of 90.36% in stress analysis utilizing the Bengali dataset. Our experiment also helped to get an understanding that hybrid models had an influence in increasing the performance of the BERT-base model. Though our research has made little contribution to the BNLP field considering its vast aspect, expanding the Bengali dataset and conducting further multi-class stress analysis work can have a remarkable contribution. © 2024 IEEE.

关键词： Blood pressure

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Knowledge Graph Driven Inference Testing for Question Answering software 24

Knowledge Graph Driven Inference Testing for Question Answer...

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44th ACM/IEEE International Conference on software engineering, ICSE 2024

作者： Wang, Jun Li, Yanhui Chen, Zhifei Chen, Lin Zhang, Xiaofang Zhou, Yuming Nanjing University State Key Laboratory for Novel Software Technology China School of Computer Science and Engineering Nanjing University of Science and Technology China School of Computer Science and Technology Soochow University China

ISBN: (纸本)9798400702174

In the wake of developments in the field of Natural Language Processing, Question Answering (QA) software has penetrated our daily lives. Due to the data-driven programming paradigm, QA software inevitably contains bugs, i.e., misbehaving in real-world applications. Current testing techniques for testing QA software include two folds, reference-based testing and metamorphic testing. This paper adopts a different angle to achieve testing for QA software: we notice that answers to questions would have inference relations, i.e., the answers to some questions could be logically inferred from the answers to other questions. If these answers on QA software do not satisfy the inference relations, an inference bug is detected. To generate the questions with the inference relations automatically, we propose a novel testing method Knowledge Graph driven Inference Testing (KGIT), which employs facts in the Knowledge Graph (KG) as the seeds to logically construct test cases containing questions and contexts with inference relations. To evaluate the effectiveness of KGIT, we conduct an extensive empirical study with more than 2.8 million test cases generated from the large-scale KG YAGO4 and three QA models based on the state-of-the-art QA model structure. The experimental results show that our method (a) could detect a considerable number of inference bugs in all three studied QA models and (b) is helpful in retraining QA models to improve their inference ability. © 2024 ACM.

关键词： Knowledge graph

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APT Detector: Detect and Identify APT Malware Based on Deep Learning Framework 23

APT Detector: Detect and Identify APT Malware Based on Deep ...

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Tang, Binhui Yang, Jialin Li, Xin Cao, Yang Wang, Jie School of Computer and software Chengdu Jincheng College School of Cyber Science and Engineering Sichuan University China School of Computer and software Chengdu Jincheng College China

ISBN: (纸本)9781450399029

Advanced persistent threat (APT) attacks use sophisticated attack techniques and covert command and control (C&C) channels to conduct long-term sustained cyber attacks on specific targets as unobtrusively as possible. Over the past ten years, APT attacks have become increasingly frequent, gradually taking on a highly organized, nationalized, and militarized nature. Because APT malware is highly covert, has a long latency period, and presents characteristics such as diversification, high frequency, and sophistication. In the face of existing detection methods, it is easy for attackers to bypass detection by customizing malware and adapting and configuring tools to the target network. The continuous improvement of network technologies and tools and the complex and diverse attack process make it very difficult to detect unknown APT malware. To address the problems, this paper takes APT malware as the detection object and collects 6777 malware from 20 types of APT attack actors. Through the improved Convolutional Neural network (CNN) model combined with an attention mechanism to complete automatic extraction and weighting of static malware features, which can detect and discover APT malware. Meanwhile, the recognition effect for APT malware is better than other existing methods, which realize the attribution of APT attack groups. © 2023 Copyright held by the owner/author(s).

关键词： Malware

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LLM-Based Digital Twin Water Conservancy Knowledge Graph Construction 36

LLM-Based Digital Twin Water Conservancy Knowledge Graph Con...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Yang, Yan Ye, Feng Xu, Dong Zhang, Xuejie Xu, Jin College of Computer Science and Software Engineering Hohai University Nanjing China College of Water Conservancy and Hydropower Engineering Hohai University Nanjing China

ISBN: (纸本)9798331527235

In the study, a novel method for extracting domain-specific knowledge in digital twin water conservancy construction is proposed, addressing the challenges of interdisciplinary complexity and the limitations in existing knowledge extraction models' understanding capabilities of domain-specific knowledge. Utilizing a large language model, the novel method incorporates the domain knowledge of digital twin water conservancy into a local model framework. It employs prompt-based fine-tuning and leverages the semantic understanding and generative capabilities of the model to enhance knowledge extraction precision. To optimize entity extraction, innovative heterogeneous entity alignment strategies are introduced. The efficacy of the proposed method is validated through comparative and ablation studies conducted on a water domain corpus. Results indicate a significant improvement over conventional models, with F1 scores for entity and relationship extraction at 88.63% and 84.46%, respectively, and an entity extraction precision rate of 90.11%. Ablation experiments further demonstrate that our method outperforms the baseline large language model, enhancing the F1 scores for entity and relation extraction by 5.5 and 3.2 percentage points, respectively. By adjusting the amount of domain knowledge injected, the influence of the amount of domain text on the knowledge extraction effect is explored. © 2024 IEEE.

关键词： Knowledge graph

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Search and Recommendation Systems with Metadata Extensions 26

Search and Recommendation Systems with Metadata Extensions

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Kim, Woo-Hyeon Kim, Joo-Chang Kyonggi University Division of Ai Computer Science and Computer Engineering Korea Republic of Contents Convergence Software Research Institute Kyonggi University Korea Republic of

ISBN: (纸本)9791188428120

This paper proposes an AI-based video metadata extension model to overcome the limitations of video search and recommendation systems in the multimedia industry. Current video searches and recommendations utilize pre-added metadata. Metadata includes filenames, keywords, tags, genres, etc. This makes it impossible to make direct predictions about the content of a video without pre-added metadata. These platforms also analyze your previous search history, viewing history, etc. to understand your interests in order to serve you personalized videos. This may not reflect the actual content and may raise privacy concerns. In addition, recommendation systems suffer from a cold start problem, which is the lack of an initial target, as well as a bubble effect. Therefore, this study proposes a search and recommendation system by expanding metadata in videos using techniques such as shot boundary detection, speech recognition, and text mining. The proposed method selects the main objects required by the recommendation system based on the object frequency and extracts the corresponding objects from the video frame by frame. In addition, we extract the speech from the video separately, convert the speech to text to extract the script and apply text mining techniques to the extracted script to quantify it. Then, we synchronize the object frequency and the transcript to create a single contextual data. After that, we group videos and clips based on the contextual data and index them. Finally, we utilize Shot Boundary Detection to segment videos based on their content. To ensure that the generated contextual data is appropriate for the video, the proposed model compares the extracted script with the video's subtitle data to check and calibrate its accuracy. The model can then be fine-tuned by tuning and cross-validating the hyperparameter to improve its performance. These models can be incorporated into a variety of content discovery and recommendation platforms. By using expanded

关键词： Speech recognition

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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Generation of hyperspectral point clouds: Mapping, compression and rendering

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computers and Graphics (Pergamon) 2022年 106卷 267-276页

作者： López, Alfonso Jurado, Juan M. Jiménez-Pérez, J. Roberto Feito, Francisco R. Department of Computer Science University of Jaén Spain Department of Software Engineering University of Granada Spain

Hyperspectral data are being increasingly used for the characterization and understanding of real-world scenarios. In this field, UAV-based sensors bring the opportunity to collect multiple samples from different viewpoints. Thus, light-material interactions of real objects may be observed in outdoor scenarios with a significant spatial resolution (5 cm/pixel). Nevertheless, the generation of hyperspectral 3D data still poses challenges in post-processing due to the high geometric deformation of images. Most of the current solutions use both LiDAR (Light Detection and Ranging) and hyperspectral sensors, which are integrated into the same acquisition system. However, these present several limitations due to errors derived from inertial measurements for data fusion and the spatial resolution according to the LiDAR capabilities. In this work, a method is proposed for the generation of hyperspectral point clouds. Input data are formed by push-broom hyperspectral images and 3D point clouds. On the one hand, the point clouds may be obtained by applying a typical photogrammetric workflow or LiDAR technology. Then, hyperspectral images are geometrically corrected and aligned with the RGB orthomosaic. Accordingly, hyperspectral data are ready to be mapped on the 3D point cloud. This procedure is implemented on the GPU by testing which points are visible for each pixel of the hyperspectral imagery. This work also provides a novel solution to generate, compress and render 3D hyperspectral point clouds, enabling the study of geometry and the hyperspectral response of natural and artificial materials in the real world. © 2022 Elsevier Ltd

关键词： Data fusion

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A Multi-Metric Ranking with Label Correlations Approach for Library Migration Recommendations 31

A Multi-Metric Ranking with Label Correlations Approach for ...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Zhang, Jiancheng Luo, Qin Wu, Peng School of Computer Science and Software Engineering SouthWest Petroleum University Chengdu China School of Information and Engineering Sichuan Tourism University Chengdu China

ISBN: (纸本)9798350330663

While third-party libraries provide benefit to software systems, they also bring unique challenges. It often happens that developers need to replace some already-used libraries with other functionality-equivalent libraries. However, it is not easy to find a relevant candidate from overwhelming libraries. Despite several approaches have been proposed to mine library migrations from historical data, the study of library recommendation from the perspective of both open-source projects and third-party libraries is lacking. Therefore, conducting such research may assist developer better select suitable third-party libraries. In this paper, we propose a multi-metric ranking with label correlations (MMRLC) algorithm, which can recommend libraries holistically from the both perspectives. Not only does it mine library migrations from existing software data, MMRLC further leverages label correlations of libraries in Maven Central Repository to make recommendations. To demonstrate the usefulness, three popular algorithms were conducted on a benchmark dataset for comparison. The results show that our approach can recommend libraries with precision @ 1 of 0.8454 and recall @20 of 0.9301. Moreover, to demonstrate the generality, we select 366 libraries and resort to TagWiki to generate the related library labels, and the results show that our approach still has comparable performance. © 2024 IEEE.

关键词： Libraries

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HSM:Hand Surface Menu Solution for Interaction in VR 10

HSM:Hand Surface Menu Solution for Interaction in VR

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10th International Conference on Virtual Reality, ICVR 2024

作者： Zhang, Hailong Li, Lin Xue, Chang Wei, XiaoCai Zhou, Yin Hefei University of Technology School of Computer Science and Information Engineering Hefei China Hefei University of Technology School of The Software Engineering Hefei China

ISBN: (纸本)9798350364231

The menu interaction methods in VR, such as floating menus, are still considered unnatural. A solution is proposed in this paper where menus are tightly attached to the user's palm. Firstly, use UV mapping technology to attach the menu to the palm. Secondly, optimal layout parameters for the hand surface menu were determined through user experiments. Finally, a dataset was built to train the interaction error detection model, aiming to improve interaction success rates under various hand poses in VR. Experimental results demonstrate that compared to other VR menu methods, HSM reduces the average interaction error rate by 30.1% and shortens the average completion time by 12.4% under the optimal hand pose. © 2024 IEEE.

关键词： Virtual environments

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