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LIVING-LANG: Living digital entities by human language technologies

LIVING-LANG: Living digital entities by human language techn...

2022 Annual Conference of the Spanish Association for Natural Language Processing: Projects and Demonstrations, SEPLN-PD 2022

作者： Ureña-López, L. Alfonso Saquete, Estela Martín-Valdivia, María-Teresa Barco, Patricio Martínez Computer Science Department SINAI CEATIC Universidad de Jaén Campus Las Lagunillas Jaén23071 Spain Department of Software and Computing Systems University of Alicante Spain

This project pursues the dynamic modeling at a spatial-temporal level of digital entities in social media for predicting their behavior. Firstly, digital entities are modelled by identifying the characteristics of individuals through their language and footprint on the network. Then, the extraction of relationships between digital entities is one of the nuclear challenges of the project. The proposal pursues this objective on a semantic level, structuring the information into representations of knowledge suitable for logical processing. Considering the heterogeneous nature of the sources to be dealt with, filtering of information is fundamental, using metrics and quality criteria. This spatial-temporal characterization, together with screening processes, will allow us to study high-performance predictive strategies in the evolution of digital entities. This project is coordinated by the SINAI and GPLSI research groups. © 2022 Copyright for this paper by its authors.

关键词： Sentiment analysis

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Detecting Duplicate Contributions in Pull-Based Model CombiningTextual and Change Similarities

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Journal of Computer Science & Technology 2021年第1期36卷 191-206页

作者： Zhi-Xing Li Yue Yu Tao Wang Gang Yin Xin-Jun Mao Huai-Min Wang Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense Technology Changsha 410073China Laboratory of Software Engineering for Complex Systems College of ComputerNational University of Defense TechnologyChangsha 410073China

Communication and coordination between OSS developers who do not work physically in the same location have always been the challenging *** pull-based development model,as the state-of-art collaborative development mechanism,provides high openness and transparency to improve the visibility of contributors'***,duplicate contributions may still be submitted by more than one contributors to solve the same problem due to the parallel and uncoordinated nature of this *** not detected in time,duplicate pull-requests can cause contributors and reviewers to waste time and energy on redundant *** this paper,we propose an approach combining textual and change similarities to automatically detect duplicate contributions in pull-based model at submission *** a new-arriving contribution,we first compute textual similarity and change similarity between it and other existing *** then our method returns a list of candidate duplicate contributions that are most similar with the new contribution in terms of the combined textual and change *** evaluation shows that 83.4%of the duplicates can be found in average when we use the combined textual and change similarity compared to 54.8%using only textual similarity and 78.2%using only change similarity.

关键词： Pull-request Duplicate detection textual similarity change similarity

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Performance Evaluation of Clustering Algorithms for Enhancing Test Case Prioritization in Regression Testing

Performance Evaluation of Clustering Algorithms for Enhancin...

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Advanced Research in computing (ICARC), International Conference on

作者： S. Prabath Puviskar W.V.S.K. Wasalthilaka B.T.G.S Kumara Department of Computing & Information Systems Sabaragamuwa University of Sri Lanka Sri Lanka Department of Software Engineering Sabaragamuwa University of Sri Lanka Sri Lanka

Regression testing plays a crucial role in maintain software quality as applications evolving and getting more complex. Selection of a regression testing technique significantly influences overall software quality. In initial stages, this involves picking relevant test cases and remove unnecessary redundancies during minimization phase. Finally, prioritization of test cases phases most important ones is executed first which continue process iteratively or whole testing carried out which effect time and cost intensively. Test Case Prioritization method effective way to address the issue. In this study, we proposed approach to enhance Test Case Prioritization by integrating fault based methods and time of execution analysis. The study also evaluates the efficiency in proposed methodology through, employing APFD metric. The study aims to improve the precision and effectiveness of test case prioritization methods, advancing overall quality and reliability in dynamic software development environments. Notably, AHC outperforms in comparisons, showcasing its efficacy in improving outcomes.

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Data and Ensemble Machine Learning Fusion Based Intelligent software Defect Prediction System

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Computers, Materials & Continua 2023年第6期75卷 6083-6100页

作者： Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam Al Hamadi Chan Yeob Yeun School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKMBangiSelangor43600Malaysia College of Engineering and IT University of Dubai14143UAE EECS Department Center for Cyber Physical SystemsKhalifa UniversityAbu Dhabi127788UAE

The software engineering field has long focused on creating high-quality software despite limited *** defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the *** approach can enhance the quality of the final product while lowering development *** defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development *** process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and *** study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high *** the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty *** second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and ***,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning *** experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse *** NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and *** to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

关键词： Ensemble machine learning fusion software defect prediction fuzzy logic

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Fuzzy Logic Expert System for Analyzing Student Performance

Fuzzy Logic Expert System for Analyzing Student Performance

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P Aruna Lokhesh R J Shajith Rooban G G Shakthivel K Tarun S R Department of Computing Coimbatore Institute of Technology Coimbatore India MSc Software Systems Coimbatore Institute of Technology Coimbatore India

Assessing the effectiveness of academics at various tiers has grown progressively difficult when employing traditional approaches that predominantly depend on numerical ratings to gauge their teaching and research accomplishments. With the indexing of academic performance in international databases using impact indices at various scales, the demand for advanced computer models has arisen. In this study, we suggest employing expert systems that utilize fuzzy logic to tackle the evaluation of educators, even when confronted with imprecise data and uncertainties. As a novel contribution, this research develops a fuzzy logic model and implements an algorithm using Python and Anaconda, which allows for fuzzy logic rule inference and comprehensive analysis. The implementation of the system is done using Python and leveraging Anaconda's data science environment, providing a seamless and efficient development process. The results of this pilot study involve testing and validating the proposed model through a user-friendly graphical interface, providing comprehensive insights based on minimum criteria along with additional explanations. This approach opens up new possibilities for more accurate assessment of academic performance, particularly when dealing with ambiguous data and uncertain conditions.

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Split Learning of Multi-Modal Medical Image Classification

Split Learning of Multi-Modal Medical Image Classification

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Artificial Intelligence (CAI), IEEE Conference on

作者： Bishwamittra Ghosh Yuan Wang Huazhu Fu Qingsong Wei Yong Liu Rick Siow Mong Goh Agency for Science Technology and Research (A*STAR) Institute of High Performance Computing (IHPC) Singapore Max Planck Institute for Software Systems Saarbrücken Germany

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

In the past decades, machine learning (ML) has made significant progress in medical image classification. The success can be attributed to two factors: (i) unique patient data collected and processed by clinics/hospitals and (ii) corresponding ML models solving the underlying classification task. In practice, patient data may contain sensitive information unique to patients’ demography; and ML models often require higher computational resources beyond the affordability of an individual *** practical concerns, we explore a collaborative ML approach in which the data provider, referred to as the client, aims to leverage the computational resources of a server in jointly training a unified ML model without the need to share any raw data. Specifically, we focus on the skin lesion classification problem using a real-world dataset containing multi-modal image inputs and multi-label ground *** enable collaborative yet privacy-preserving skin lesion classification, we develop a learning framework called SplitFusionNet based on u-shape split learning. The key idea of SplitFusionNet is to split the ML model into a (client, server) partition of deep neural network layers: the client layers process multimodal input data and multi-labels, while server layers perform computationally extensive mid-layer computations. Additionally, we apply lossless compression and decompression to improve the communication cost between the client and the server. Experimentally, SplitFusionNet requires less training pipeline time than non-split centralized training while achieving equal predictive performance.

关键词： Training Image coding Computational modeling Pipelines Collaboration Machine learning Data models

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： Computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

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Unveiling Public Perception of ChatGPT Through Twitter Data and Machine Learning 4

Unveiling Public Perception of ChatGPT Through Twitter Data ...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

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

ISBN: (纸本)9798350369786

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. © 2023 IEEE.

关键词： Tweets

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A NEW HOPE: Contextual Privacy Policies for Mobile Applications and An Approach Toward Automated Generation

arXiv

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arXiv 2024年

作者： Pan, Shidong Tao, Zhen Hoang, Thong Zhang, Dawen Li, Tianshi Xing, Zhenchang Xu, Xiwei Staples, Mark Rakotoarivelo, Thierry Lo, David School of Computing Australian National University Australia Software and Computational Systems Research Program CSIRO’s Data61 Australia Khoury College of Computer Sciences Northeastern University United States School of Computing and Information Systems Singapore Management University Singapore

Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application’s graphical user interfaces (GUIs). In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SEEPRIVACY, specifically designed to automatically generate CPPs for mobile applications. This method uniquely integrates vision-based GUI understanding with privacy policy analysis, achieving 0.88 precision and 0.90 recall to detect contexts, as well as 0.98 precision and 0.96 recall in extracting corresponding policy segments. A human evaluation shows that 77% of the extracted privacy policy segments were perceived as well-aligned with the detected contexts. These findings suggest that SEEPRIVACY could serve as a significant tool for bolstering user interaction with, and understanding of, privacy policies. Furthermore, our solution has the potential to make privacy notices more accessible and inclusive, thus appealing to a broader demographic. A demonstration of our work can be accessed at https://***/SeePrivacy/ © 2024, CC BY.

关键词： Graphical user interfaces

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Deep reinforcement learning-based algorithms selectors for the resource scheduling in hierarchical Cloud computing

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Journal of Network and Computer Applications 2022年第0期208卷

作者： Zhou, Guangyao Wen, Ruiming Tian, Wenhong Buyya, Rajkumar School of Information and Software Engineering University of Electronic Science and Technology of China China Cloud Computing and Distributed Systems Lab School of Computing and Information Systems The University of Melbourne Australia

Cloud computing environment is becoming increasingly complex due to its large-scale information growth and increasing heterogeneity of computing resources. Hierarchical Cloud computing dividing the system into multi-levels with multiple subsystems to support the adaptability to abundant requests from users has been widely applied and brings great challenges to resource scheduling. It is critical to find an effective way to address the complex scheduling problems in hierarchical Cloud computing, whose scenarios and optimization objectives often change with the types of subsystems. In this paper, we propose a scheduling framework to select the scheduling algorithms (SFSSA) for different scheduling scenarios considering no algorithm well suitable to all scenarios. To concretize SFSSA, we propose deep learning-based algorithms selectors (DLS) trained by labeled data and deep reinforcement learning-based algorithms selectors (DRLS) trained by feedback from dynamic scenarios to complete the algorithms selection regarding the scheduling algorithms as selectable tools. Then, we apply strategies including pre-trained model, long experience reply and joint training to improve the performance of DRLS. To enable the quantitative comparison of selectors, we introduce a weighted cost model for the trade-off between solution and complexity. Through multiple sets of experiments in hierarchical Cloud computing with multi subsystems for five types of scheduling problems and varying weights of cost, we demonstrate DLS and DRLS outperform baseline strategies. Compared with random selector, greedy selector, round-robin selector, single best selector, virtual best selector and single fast selector, DLS reduces the cost by 47.4%, 46.1%, 33.9%, 47.9%, 19.3%, 18.8% under stable parameter ranges, and DRLS reduces the cost by 41.1%, 40.6%, 11.7%, 42.3%, 11.5%, 12.5% in dynamic scenarios respectively. In experiments, we also validate DRLS has stronger adaptability than DLS in dynamic schedulin

关键词： Reinforcement learning

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