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Critical success factors for client proposal selection: A vendor perspective in offshore software maintenance outsourcing

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AIP Conference Proceedings 2023年第1期2484卷

作者： Atif Ikram b) Masita Abdul Jalil Amir Bin Ngah Ahmad Salman Khan Tahir Iqbal 1School of Informatics and Applied Mathematics Universiti Malaysia Terengganu Malaysia 2Department of Computer Science & Information Technology The University of Lahore Pakistan 3Department of Software Engineering The University of Lahore Pakistan 4Department of Computer Science Bahria University Lahore Campus Pakistan

Offshore software Maintenance Outsourcing (OSMO) is increasing by leaps and bounds. In offshore outsourcing, most of the highly-paid countries outsource software development and maintenance projects to such developing countries where skilled human resources are available at economical packages. The OSMO clients get quality-oriented services and OSMO vendors earn valuable foreign exchange if the OSMO process is executed successfully. But the existing literature advocates that all the OSMO business deals do not end successfully. The right combination of vendor and client plays a vital role. For a vendor, it is important to find a more suitable client among multiple options. The scope of the current paper is to find out such critical success factors which can help out OSMO vendor to select a more suitable OSMO client among many options. This practice will provide ease to OSMO vendors in the OSMO client selection and decision-making process to select a suitable client. The method used to conduct this study is the Literature Review (LR). The literature review found different critical success factors related to the research question. Some of these factors include the client's contract, the client's required team size, the domain of the required project, the size of the software maintenance project, etc. OSMO vendor organizations can use this information in the decision-making process to select more suitable OSMO clients for successful offshore business. This study also recommends the implementation of any suitable machine learning technique on the critical success factors (founded in the current study) in the decision-making process.

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Task-Oriented Dynamic Knowledge Distillation for Continuous Few-Shot Relation Extraction

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Knowledge-Based Systems 2025年 325卷

作者： Hexing Yang Xuan Zhang Chen Gao Weiyi Shang Kunpeng Du Wei Cai Danyang Wang Tong Li School of Software Yunnan University Yunnan 650091 China Yunnan Key Laboratory of Software Engineering Yunnan 650091 China School of Information Science and Engineering Yunnan University Yunnan 650091 China Department of Electrical and Computer Engineering University of Waterloo Ontario N2L 3G1 Canada School of Big Data Yunnan Agricultural University Kunming 65020l China The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province Yunnan Agricultural University Kunming 65020l China

Continuous Few-Shot Relation Extraction (CFRE) is a task in continual learning that closely mirrors real-world scenarios. This task emphasizes the acquisition of new relations from a limited set of training samples while maintaining the retention of knowledge related to previously learned relations. The main challenges of CFRE include catastrophic forgetting and balancing between stability and adaptability. In existing research, knowledge distillation methods are commonly used to alleviate catastrophic forgetting. However, the knowledge distillation methods currently applied to CFRE tasks, whether single-teacher or multi-teacher models, do not perform data partitioning, which impacts the learning of new relations and limits the recognition performance of old relations. To tackle these challenges, we propose a CFRE model based on Task-Oriented Dynamic Knowledge Distillation (TODKD). Specifically, we designed a dual-level task space separation knowledge distillation method that utilizes different teacher models at various stages to guide the sample training of relations belonging to different task stages, thereby alleviating catastrophic forgetting. To alleviate sample sparsity in few-shot scenarios, we use memory samples as an example and design prompt templates to assist LLMs in producing diverse data samples. Additionally, we designed dynamic weight loss to balance the model’s stability and adaptability effectively, resulting in a more uniform distribution of relation features across different tasks. Comprehensive experiments on the FewRel and TACRED datasets demonstrate that our TODKD model outperforms multiple competitive baseline approaches. Importantly, our model shows a marked enhancement compared to prior state-of-the-art techniques in the final task.

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Robust Bayesian optimization with reinforcement learned acquisition functions

arXiv

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arXiv 2022年

作者： Liu, Zijing Qu, Xiyao Liu, Xuejun Lyu, Hongqiang Ministry of Industry and Information Technology Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China Key Laboratory of Aerodynamic Noise Control State Key Laboratory of Aerodynamics Mianyang621000 China

In Bayesian optimization (BO) for expensive black-box optimization tasks, acquisition function (AF) guides sequential sampling and plays a pivotal role for efficient convergence to better optima. Prevailing AFs usually rely on artificial experiences in terms of preferences for exploration or exploitation, which runs a risk of a computational waste or traps in local optima and resultant re-optimization. To address the crux, the idea of data-driven AF selection is proposed, and the sequential AF selection task is further formalized as a Markov decision process (MDP) and resort to powerful reinforcement learning (RL) technologies. Appropriate selection policy for AFs is learned from superior BO trajectories to balance between exploration and exploitation in real time, which is called reinforcement-learning-assisted Bayesian optimization (RLABO). Competitive and robust BO evaluations on five benchmark problems demonstrate RL's recognition of the implicit AF selection pattern and imply the proposal's potential practicality for intelligent AF selection as well as efficient optimization in expensive black-box problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Mining important nodes in complex software network based on ripple effects of probability 19

Mining important nodes in complex software network based on ...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Ren, Jiadong Huang, Guoyan Wang, Qian He, Haitao Liu, Xinqian Zhao, Xiaolin Computer Virtual Technology and System Integration Laboratory of Hebei Province College of Information Science and Engineering Yanshan University Qinhuangdao Hebei China Beijing Key Laboratory of Software Security Engineering Technology School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781450371582

The complexity of software directly leads to an increasing cost in software testing and maintenance. Finding the important nodes with significant vulnerability is helpful for fault discovery and further reduces the damage to the software system. In this paper, a new algorithm named MIN-REP (Mining the Important Nodes based on Ripple Effects of Probability) is proposed to find out the paths with greater possibility for fault propagation, and then the important nodes are mined. To build a model of directed unweighted software network, functions are taken as the nodes and the dependencies between the functions are regarded as the edges. Fault propagation tendency paths are discovered based on the function execution paths and minimum probability threshold. The frequency of each directed edge in the set of fault propagation tendency path is taken as the weight of the corresponding edge. Then some metrics related to ripple effects of probability are calculated. Finally, the nodes with the metric at top-k are taken as the important nodes. The experiment verifies the accuracy and efficiency of the algorithm MIN-REP. © 2019 Association for Computing Machinery.

关键词： Probability

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A Survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications

arXiv

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arXiv 2021年

作者： Zhou, Xinlei Liu, Han Pourpanah, Farhad Zeng, Tieyong Wang, Xizhao College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University 518060 China College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Department of Mathematics Chinese University of Hong Kong Hong Kong

Quantifying the uncertainty of supervised learning models plays an important role in making more reliable predictions. Epistemic uncertainty, which usually is due to insufficient knowledge about the model, can be reduced by collecting more data or refining the learning models. Over the last few years, scholars have proposed many epistemic uncertainty handling techniques which can be roughly grouped into two categories, i.e., Bayesian and ensemble. This paper provides a comprehensive review of epistemic uncertainty learning techniques in supervised learning over the last five years. As such, we, first, decompose the epistemic uncertainty into bias and variance terms. Then, a hierarchical categorization of epistemic uncertainty learning techniques along with their representative models is introduced. In addition, several applications such as computer vision (CV) and natural language processing (NLP) are presented, followed by a discussion on research gaps and possible future research directions. Copyright © 2021, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Robustness-oriented k Edge Server Placement

Robustness-oriented k Edge Server Placement

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Guangming Cui Qiang He Xiaoyu Xia Feifei Chen Hai Jin Yun Yang School of Software and Electrical Engineering Swinburne University of Technology Australia School of Information Technology Deakin University Australia School of Computer Science and Technology HuaZhong University of Science and Technology China

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

Mobile Edge Computing (MEC) is an emerging and prospective computing paradigm that supports low-latency content delivery. In a MEC environment, edge servers are attached to base stations or access points in closer proximity to endusers to reduce the end-to-end latency in their access to online content. From an edge infrastructure provider's perspective, a cost-effective k edge server placement (kESP) places k edge servers within a particular geographic area to maximize their coverage. However, in the distributed MEC environment, edge servers are often subject to failures due to various reasons, e.g., software exceptions, hardware faults, cyberattacks, etc. End-users connected to a failed edge server have to access online content from the remote cloud if they are not covered by any other edge servers. This significantly jeopardizes endusers' quality of experience. Thus, the robustness of an edge server network must be considered in edge server placement. In this paper, we formally model this Robustness-oriented k Edge Server Placement (RkESP) problem, and prove that finding the optimal solution to this problem is .,Vl'-hard. Thus, we firstly propose an integer programming based optimal approach, namely Opt, to find optimal solutions to small-scale RkESP problems. Then, we propose an approximate approach, namely Approx, for solving large-scale RkESP problems efficiently with an O(k)-approximation ratio. Finally, the performance of the two approaches is experimentally evaluated against five state-of-the-art approaches on a real-world dataset and a large-scale synthesized dataset.

关键词： Servers Base stations Robustness Mobile handsets Cloud computing software Quality of experience

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An Optical Sensing Method for Lung Deposition Surface Area Concentration Measurement

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Deng, Tian Zheng, Xiang Bian, Ang Nienkotter, Andreas Lv, Xiaole Zeng, Jin Xu, Wenbo Huazhong University of Science and Technology School of Electronic Information and Communications Hubei Wuhan 430074 China Huazhong University of Science and Technology School of Electronic Information and Communication Hubei Wuhan 430074 China Xihua University school of computer and software engineering Sichuan Chengdu 610039 China Sichuan University Institute for Disaster Management and Reconstruction Sichuan Chengdu 610065 China Hubei University of Technology School of Computer Science Hubei Wuhan 430074 China Wuhan University of Technology School of Navigation Hubei Wuhan 430074 China

The concentration of lung deposition surface area (LDSA) is a crucial metric for assessing potential health impacts associated with particle size distribution. Traditional LDSA sensors, which rely on particle charging, are limited to measuring particles up to 300 nm in size. However, aerosols in the submicron to micron range are also significant for evaluating exposure risks. In this study, we introduce an optical method for LDSA sensing that leverages light-scattering angular spectrum and deep learning techniques. Our approach utilizes a compact, lens-free sensor to sample angular spectrum signals, which are then processed by a ResNet-like neural network to adjust the conversion factor between LDSA concentration and scattering light intensity. Experimental results demonstrate that our prototype achieves a maximum relative error of 14.78% in measuring the angular spectrum. Additionally, the neural network compensates for variations in particle size distribution and refractive index, refining the conversion factor. When compared to reference LDSA concentrations provided by laboratory instruments, our prototype maintains relative errors within 17.66%. With its simple mechanical design, high accuracy, and cost-effectiveness, this method presents a promising solution for online and field measurements of LDSA concentration. © 1963-2012 IEEE.

关键词： Deep learning Exposure hazard assessment Light-scattering angular spectrum Lung deposition surface area

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Dynamic Task Offloading with Minority Game for Internet of Vehicles in Cloud-Edge Computing

Dynamic Task Offloading with Minority Game for Internet of V...

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IEEE International Conference on Web Services (ICWS)

作者： Bowen Shen Xiaolong Xu Fei Dai Lianyong Qi Xuyun Zhang Wanchun Dou School of Computer and Software Nanjing University of Information Science and Technology Nanjing China School of Big Data and Intelligence Engineering Southwest Forestry University Kunming China School of Information Science and Engineering Qufu Normal University China Macquaire University Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

With the advent of the Internet of Vehicles (IoV), drivers are now provided with diverse time-sensitive vehicular services that usually require a large scale of computation. As civilian vehicles are generally insufficient in computational resources, their service requests are offloaded to cloud data centers and edge computing devices (ECDs) with ample computational resources to enhance the quality of service (QoS). However, ECDs are often overloaded with excessive service requests. In addition, as the network conditions and service compositions are complicated and dynamic, the centralized control of ECDs is hard to achieve. To tackle these challenges, a dynamic task offloading method with minority game (MG) in cloud-edge computing, named DOM, is proposed in this paper. Technically, MG is an effective tool with a distributed mechanism which can minimize the dependency on centralized control in resource allocation. In the MG, reinforcement learning (RL) is applied to optimize the distributed decision-making of participants. Finally, with a real-world dataset of IoV services, the effectiveness and adaptability of DOM are evaluated.

关键词： Cloud computing Games Quality of service Reinforcement learning Space exploration Task analysis Vehicle dynamics

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In-situ data scheduling optimization based on rainbow DQN for IIoT

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Future Generation computer Systems 2026年 174卷

作者： Peiying Zhang Siyi Li Lizhuang Tan Neeraj Kumar Jian Wang Kai Liu Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala 147004 India Department of Computer Science University of Economics and Human Sciences Warszawa 01-043 Poland Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia College of Science China University of Petroleum (East China) Qingdao 266580 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Space Network and Communications Tsinghua University Beijing 100084 China

In industrial systems, in-situ computing refers to a mode of local or proximate data processing within the cloud–edge-terminal architecture. In the Industrial Internet of Things (IIoT) environment, servers are typically deployed near terminal devices to coordinate and manage the scheduling of in-situ data, forming a comprehensive in-situ server system. With the rapid growth of IIoT terminal devices, challenges such as high processing delays and severe demand congestion arise in the in-situ data scheduling process. To address this challenge, this paper proposes a scheduling optimization scheme based on Rainbow DQN. The scheme designs a scheduling buffer and classification strategy to distinguish in-situ data based on storage demand characteristics, thereby optimizing the scheduling process. At the same time, a multi-level storage structure is introduced to store in-situ data classified according to the scheduling strategy. Additionally, the Rainbow DQN algorithm is utilized to assist in the scheduling decision-making of in-situ data retrieval, effectively alleviating system congestion while ensuring certain throughput performance. Extensive simulation experiments show that the Rainbow DQN algorithm achieves a reduction of 60.82% in average waiting delay, 62.94% in average unprocessed demand, and a 58.27% reduction in average completion delay.

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A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique

arXiv

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arXiv 2022年

作者： Rehman, Abdur Abbas, Sagheer Khan, M.A. Ghazal, Taher M. Adnan, Khan Muhammad Mosavi, Amir School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Riphah School of Computing and Innovation Faculty of Computing Riphah International university Pakistan School of Information Technology Skyline University College University City Sharjah Sharjah1797 United Arab Emirates Selangor Bangi43600 Malaysia Department of Software Gachon University Seongnam13120 Korea Republic of Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava Bratislava81107 Slovakia John von Neumann Faculty of Informatics Obuda University Budapest1034 Hungary Faculty of Civil Engineering TU-Dresden Dresden01062 Germany

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the e

关键词： Blockchain

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