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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Ali, Muhammad Cheema, Sehrish Munawar Aslam, Zaheer Naz, Ammerha Ayub, Nasir The Superior University Lahore Department of Software Engineering Lahore Pakistan University of Management and Technology Department of Computer Science Sialkot Pakistan University of Sialkot Department of Computer Science Sialkot Pakistan

ISBN: (纸本)9798350335316

Distributed agile development comes with a lot of challenges in particular as it has to do with agile teams working together from different geological locations on the same project. Most probably it happens due to a lack of visibility in the complex development and deployment process, poor communication, and unavailability of the development team and corresponding customer in the same place. These factors affect the performance of the team and increase the overall cost of development. To mitigate all these aspects, we proposed a cloud computing-based Infrastructure which is a combination of both agile as well as cloud computing technology named 'CBAI'. The proposed Infrastructure assists the team members to work efficiently even if they are different geo-locations without burdening the cost. It provides the basic structure for global agile development and is also efficient in reducing the technical liability, and the need for project backlog. © 2023 IEEE.

关键词： Cloud computing

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Imitation Learning from Purified Demonstrations 41

Imitation Learning from Purified Demonstrations

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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RGB-T object tracking with adaptive decision fusion 2

RGB-T object tracking with adaptive decision fusion

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2nd International Conference on Algorithms, Microchips, and Network Applications, AMNA 2023

作者： Bai, Yida Yang, Ming Department of Software Engineering College of Computer & Information Science Southwest University Chongqing400715 China Department of Computer Science College of Computer & Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9781510664791

Visual object tracking is a traditional task in computer vision, which has developed with several decades. With the development of machine learning, correlation filter (CF) has been proposed with satisfying performance and very high framerate. Though the CF framework has numerous strengths in this task, the tracker is fragile to miss the target in several scenes, including extreme illumination, target occlusion and deformation. Recently, thermal modality, which detects the target’s temperature, is robust to the night scenes and can provide a precise target contour. In this paper, we propose a CF based tracker with decision fusion strategy for visible-thermal (RGB-T) tracking. First, we introduce multi-modal KCF trackers as our baseline. Then, we design a decision fusion method considering the Peak-to-Side Rate (PSR) of the score maps, thereby achieving an adaptive fusing those modalities and avoiding model’s heterogeneity. In the experiments, our tracker has validated on the public dataset, namely GTOT. Compared with two uni-modality trackers, the proposed tracker with real-time speed has shown superior results on both target localization and scale estimation. © 2023 SPIE.

关键词： Target tracking

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An enhanced agile-V model for system engineering in complex medical device development 2

An enhanced agile-V model for system engineering in complex ...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Khan, Aliya Ashraf Akram, Muhammad Usman Salam, Anum Abdul Butt, Wasi Haider National University of Sciences and Technology Department of Computer & Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665498197

With the advancement in technology, the scope of medical devices extends from simple hardware machines to integrated hardwares with support of software systems which are themselves used either as medical device or to provide support. Regulatory bodies to ensure safety of both the hardware and software devised the rules to which these medical devices needed to adhere. To meet the constraints, the development of these medical devices either through Water fall model or through agile model is not very feasible. We proposed enhanced Agile V-model for the development of the complex medical devices and applied its recommendation in the development of our wave therapeutic device. © 2022 IEEE.

关键词： Systems engineering

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A Real-Time Carboy Tracking and Counting System Based on YOLOv8 and ByteTrack

A Real-Time Carboy Tracking and Counting System Based on YOL...

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2024 International Conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： Akar, Ferhat Berber, Sezer Koca, Oğuzhan Bayılmış, Cüneyt R&D AYGAZ A.Ş. İstanbul Turkey Software Department Rise Technology Mersin Turkey Deparment of Computer Engineering Sakarya University Sakarya Turkey

ISBN: (纸本)9798350353488

In industrial processes, the accurate detection, counting, and classification of objects are crucial for the efficiency of production lines. However, when these processes are performed manually, human errors and time loss can occur. In this study, a YOLOv8-based system was developed to separate carboys based on their expiration dates, count solid carboys, and detect carboys containing foreign matter at the Pürsu Water Factory. The system, which processes real-time video streams, integrates YOLOv8 for object detection, tracking, and counting. Additionally, the performances of the DeepSORT and ByteTrack algorithms were compared for object tracking. The results show that ByteTrack performed faster and more stably than DeepSORT. The YOLOv8-based model achieved 98% accuracy, and ByteTrack worked at a higher FPS than DeepSORT, thereby improving the efficiency of the production line. This study presents an in-depth analysis of the system's accuracy, speed, and overall performance. © 2024 IEEE.

关键词： Object detection

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Effort-aware cross-project just-in-time defect prediction framework for mobile apps

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Frontiers of computer Science 2022年第6期16卷 15-29页

作者： Tian CHENG Kunsong ZHAO Song SUN Muhammad MATEEN Junhao WEN School of Big Data and Software Engineering Chongqing UniversityChongqing401331China School of Computer Science Wuhan UniversityWuhan430072China Department of Computer Science Air University Multan CampusMultan60000Pakistan

As the boom of mobile devices,Android mobile apps play an irreplaceable roles in people’s daily life,which have the characteristics of frequent updates involving in many code commits to meet new ***-in-Time(JIT)defect prediction aims to identify whether the commit instances will bring defects into the new release of apps and provides immediate feedback to developers,which is more suitable to mobile *** the within-app defect prediction needs sufficient historical data to label the commit instances,which is inadequate in practice,one alternative method is to use the cross-project *** this work,we propose a novel method,called KAL,for cross-project JIT defect prediction task in the context of Android mobile *** specifically,KAL first transforms the commit instances into a high-dimensional feature space using kernel-based principal component analysis technique to obtain the representative ***,the adversarial learning technique is used to extract the common feature embedding for the model *** conduct experiments on 14 Android mobile apps and employ four effort-aware indicators for performance *** results on 182 cross-project pairs demonstrate that our proposed KAL method obtains better performance than 20 comparative methods.

关键词： kernel-based principal component analysis adversarial learning just-in-time defect prediction cross-project model

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Accurate 3D Grapevine Structure Extraction from High-Resolution Point Clouds

Accurate 3D Grapevine Structure Extraction from High-Resolut...

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Harry Dobbs Casey Peat Oliver Batchelor James Atlas Richard Green Department of Computer Science and Software Engineering University of Canterbury

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Accurate 3D modelling of grapevines is crucial for precision viticulture, particularly for informed pruning decisions and automated management techniques. However, the intricate structure of grapevines poses significant challenges for traditional skeletonization algorithms. This paper presents an adaptation of the Smart-Tree algorithm for 3D grapevine modelling, addressing the unique characteristics of grapevine structures. We introduce a graph-based method for disambiguating skeletonization. Our method delineates individual cane skeletons, which are crucial for precise analysis and management. We validate our approach using annotated real-world grapevine point clouds, demonstrating an improvement of 15.8% in the F1 score compared to the original Smart-Tree algorithm. This research contributes to advancing 3D grapevine modelling techniques, potentially enhancing both the sustainability and profitability of grape production through more precise and automated viticulture practices.

关键词： Point cloud compression Adaptation models Solid modeling Three-dimensional displays Accuracy Profitability Pipelines Skeleton Yield estimation Sustainable development

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Detecting Cross-Domain Ambiguity In Requirements Through Natural Language Processing, A Systematic Literature Review

Detecting Cross-Domain Ambiguity In Requirements Through Nat...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Ibrahim Khalil Israr Ahmad Uzair Rasheed Wasi Haider Butt Zaeem Anwaar Department of Computer and Software Engineering CEME NUST Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

A strong foundation for a project is the first step to measuring or estimating the success parameters of a project. Requirement engineers gather and elicit the requirements of the project in detail after the feasibility study. A project’s success can be measured/estimated if and only if the initially collected requirements are clear, unambiguous, and well-understood. Similarly, ambiguous or not understandable requirements can lead to failure or closure of the project in disastrous form. An initial step in requirement elicitation is usually gathering requirements in natural language. This study analyzes different tools, techniques, and approaches in different research articles used for detecting ambiguities in requirements in the natural language. We identified 33 research articles, published during 2012-24 through a Systematic Literature Review. For automated ambiguities detection in requirements engineering, we identified 17 tools & techniques and 12 NLP approaches. We analyzed the better approach for cross-domain ambiguity detection. SGNS (Skipgram Negative Sampling) variant of Word2Vec for the cross-domain ambiguity detection in the review of the study, resulted to be the most efficient technique implemented with the usage view and easily scalable for multiple requirements and domains.

关键词： Scalability software algorithms Natural language processing software Requirements engineering Systematic literature review

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CLPiDA: A Contrastive Learning Approach for Predicting Potential PiRNA-Disease Associations

CLPiDA: A Contrastive Learning Approach for Predicting Poten...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Xiaowen Zhang, Yuanpeng Zhang, Jiaxuan Deng, Lei Central South University School of Computer Science and Engineering Changsha China Xinjiang University School of Software Urumqi China University of California Department of Electrical and Computer Engineering San DiegoCA United States

ISBN: (纸本)9798350337488

Piwi-interacting RNAs (piRNAs) function as critical regulators, safeguarding genome stability through mechanisms like transposable element repression and gene stability maintenance, while also being associated with various disease pathways. Developing computationally efficient methods to predict piRNA-disease associations is vital for enhancing disease-specific drug discovery while managing costs. In this study, we present CLPiDA, a novel method for predicting potential piRNA-disease associations. CLPiDA begins by computing gaussian kernel similarities for piRNA-piRNA and disease-disease pairs to establish initial embeddings for piRNAs and diseases. Subsequently, it employs a parameter-sharing online and target network, along with data augmentation techniques, to create a contrastive learning framework. This facilitates the generation of embeddings for piRNAs and diseases using piRNA-disease association pairs. Furthermore, CLPiDA employs a cross-prediction approach to determine association scores for specific piRNAs and diseases. Notably, CLPiDA introduces a novel approach by excluding negative samples, thereby avoiding the introduction of false negatives and enhancing its reliability. Comparative experiments validate CLPiDA's superiority in terms of performance over existing methods. Case studies underscore CLPiDA's efficacy as a valuable tool for predicting piRNA-disease associations, providing valuable insights for biological experiments. The data and source code for CLPiDA are available at https://***/altriavin/CLPiDA. © 2023 IEEE.

关键词： contrastive learning graph convolutional network piRNA-disease association

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Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 2085-2128页

作者： Wang, Xiaofei Zhao, Yunfeng Qiu, Chao Hu, Qinghua Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen518000 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system's adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficiency. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research, with the hope of arousing the research community's interest in this emerging and exciting interdisciplinary field. © 1

关键词： Resource allocation

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