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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md Jobair Hossain Faruk Md Kamrul Siam Alex Theilmann Kristiyan Georgiev Jerry Q. Cheng Hossain Shahriar Department of Computer Science New York Institute of Technology USA Nova School of Business and Economics Portugal R&D Computer Vision and Software Engineering Manager Materials Handling Systems Inc. USA Center for Cybersecurity University of West Florida USA

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Object Detection and Tracking (ODT) represents a critical area within computer vision, with broad applications across various industries, particularly in the automation of logistics operations. However, significant challenges persist, including the logistics-specific datasets and the inefficiency of data annotation, which is crucial for training robust artificial intelligence models for parcel detection and tracking. These challenges are exacerbated when human experts are required to manually assign unique identifiers to objects across frames, hindering the scalability of AI model training. In this research, we present a novel deep learning-based ODT framework, leveraging Convolutional Neural Networks (CNN) integrated with evaluation metrics such as Euclidean Distance and Intersection over Union (IoU) to facilitate robust object detection and tracking. To address the limitations of manual annotation, we develop an automated framework for assigning unique identifiers to bounding boxes across sequential frames. The framework is based on three logistics-related datasets, comprising time-series images of packages on moving conveyor belts. The first two datasets, containing 100 and 1,021 images, are manually annotated, while the third dataset of 5,046 images is automatically annotated using the proposed tool. We also utilize the Hungarian algorithm to optimize identity assignment across frames. The results demonstrate that the framework achieves an accuracy of 99.09% in object detection and tracking with a precision of 99.25% and a recall of 99.10% for the manually annotated dataset. The automated annotation illustrates an accuracy of 98.70%. These findings indicate the robustness and scalability of the proposed framework in automating parcel tracking for logistics operations.

关键词： Training computer vision Accuracy Annotations Scalability Object detection Robustness Convolutional neural networks Object tracking Logistics

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Application Specified Soft-Error Failure Rate Analysis Using Sequential Equivalence Checking Techniques

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Tsinghua Science and Technology 2020年第1期25卷 103-116页

作者： Tun Li Qinhan Yu Hai Wan Sikun Li School of Computer Science National University of Defense TechnologyChangsha 410073China the Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China. School of Software Tsinghua UniversityBeijing 100084China

Soft errors have become a critical challenge as a result of technology scaling. Existing circuit-hardening techniques are commonly associated with prohibitive overhead of performance, area, and power. However,evaluating the influence of soft errors in Flip-Flops(FFs) on the failure of circuit is a difficult verification ***, we proposed a novel flip-flop soft-error failure rate analysis methodology using a formal method with respect to application behaviors. Approach and optimization techniques to implement the proposed methodology based on the given formula using Sequential Equivalence Checking(SEC) are introduced. The proposed method combines the advantage of formal technique-based approaches in completeness and the advantage of application behaviors in accuracy to differentiate vulnerability of components. As a result, the FFs in a circuit are sorted by their failure rates, and designers can use this information to perform optimal hardening of selected sequential components against soft errors. Experimental results of an implementation of a SpaceWire end node and the largest ISCAS’89 benchmark sequential circuits indicate the feasibility and potential scalability of our approach. A case study on an instruction decoder of a practical 32-bit microprocessor demonstrates the applicability of our method.

关键词： soft error failure rate analysis Sequential Equivalence Checking(SEC) application specified

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Enhancing Loan Approval Prediction Through Advanced Machine Learning Models

Enhancing Loan Approval Prediction Through Advanced Machine ...

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Sustainable Computing and Smart systems (ICSCSS), International Conference on

作者： C. Jamunadevi S. Prasath V. E Sathishkumar S. Pandikumar Akshaya J Department of Computer Technology PG Kongu Engineering College Perundurai Erode Department of Computer Science VET Institute of Arts and Science (Co-education) College Thindal School of Engineering and Technology Sunway University Darul Ehsan Malaysia Department of MCA Acharya Institute of Technology Banglore MSc (Software Systems) Kongu Engineering College Perundurai Erode

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Loans generate the majority of revenue forbanks and other financial institutions. Loan approval is a critical process in banking organizations because they can only lend to specific people or organizations due to restricted resources or credit. In order to forecastif a specific individual is eligible for a loan or not, a variety of machine learning techniques are combined with algorithms such as bagging classifiers. These algorithms include logistic regression classifiers, support vector classifiers (SVC), decision trees, and random forest. This system aims to enhance the accuracy and robustness of bank loan approvalpredictions through the implementation of an ensemble machine learning model with 97% accuracy. Leveraging the strengths of Random Forest (RF) and XGBoost (eXtreme Gradient Boosting), this ensemble approach seeks to mitigate the limitations of individual models, providing a more reliable and precise decision-making system for loan approval processes. However, these systems encounter challenges like managing imbalanced datasets and ensuring model interpretability. Furthermore, integrating diverse data sources while maintaining data quality and consistency presents significant difficulties.

关键词： Radio frequency Accuracy Machine learning algorithms Decision making Organizations Predictive models Robustness

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Information and Intelligent Forecasting systems Based on the Methods of Neural Network Theory

Information and Intelligent Forecasting Systems Based on the...

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Smart Information systems and Technologies (SIST), IEEE International Conference on

作者： Yuliia Kostiuk Olena Kryvoruchko Alona Desyatko Yuliia Samoilenko Kateryna Stepashkina Rostislav Zakharov Department of Software Engineering and Cybersecurity State University of Trade and Economics Kyiv Ukraine Department of automation and computer technologies of control systems National University of Food Technologies Kyiv Ukraine

This paper is dedicated to the problem of the research of the information and intelligent forecasting systems which solve the problems of regression analysis. This class of the systems includes the systems that use different mathematical apparatus for forecasting, for the implementation of which a wide range of methods and algorithms is used. The paper also deals with the problems of regression analysis using elements of neural network theory. Developing a set of functions to form an approximating function allows you to create models that work efficiently. In existing information and intelligence systems, either implemented functions adapted to solve specific forecasting tasks, or, as in the case of neuropackages, it is considered that the proposed functions can be used to solve a wide range of tasks, adaptation to the conditions of the task occurs during the design of the neural network architecture. To create an approximating function, it is possible to implement linear and non-linear functions, which have proven themselves well in neural network analysis for solving a wide range of tasks and are obtained within the framework of deterministic chaos theory. To implement the function that approximates the dependence of the predicted value on the forecast arguments, the use of a two-layer heterogeneous, artificial neural network without feedback is proposed.

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Success Factors of Requirement Elicitation in the Field of software engineering

Success Factors of Requirement Elicitation in the Field of S...

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Advancements in Computing (ICAC), International Conference on

作者： Buddhima Attanayaka Dasuni Nawinna Kalpani Manathunga Pradeep K.W. Abeygunawardhana Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Systems & Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398106

Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors.

关键词： Resistance Industries software Stakeholders Task analysis Interviews Business

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Why do people think liberals drink lattes? How social media afforded self-presentation can shape subjective social sorting

arXiv

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arXiv 2024年

作者： Phillips, Samantha C. Carley, Kathleen M. Joseph, Kenneth Software and Societal Systems Carnegie Mellon University 4665 Forbes Ave PittsburghPA15213 United States Computer Science and Engineering University at Buffalo 12 Capen Hall BuffaloNY14260 United States

Social sorting, the alignment of social identities, affiliations, and/or preferences with partisan groups, can increase in-party attachment and decrease out-party tolerance. We propose that self-presentation afforded by social media profiles fosters subjective social sorting by shaping perceptions of alignments between non-political and political identifiers. Unlike previous work, we evaluate social sorting of naturally occurring, public-facing identifiers in social media profiles selected using a bottom-up approach. Using a sample of 50 million X users collected five times between 2016 and 2018, we identify users who define themselves politically and generate networks representing simultaneous co-occurrence of identifiers in profiles. We then systematically measure the alignment of nonpolitical identifiers along political dimensions, revealing alignments that reinforce existing associations, reveal unexpected relationships, and reflect online and offline events. We find that while most identifiers bridge political divides, social sorting of identifiers along political lines is occurring to some degree in X profiles. Our results have implications for understanding the role of social media in facilitating (the perception of) polarization and polarization mitigation strategies such as bridging interventions and algorithms. © 2024, CC BY-NC-ND.

关键词： Polarization

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A GNN-Based Learning Approach for Energy Optimization in Relay-Assisted IoT Networks

A GNN-Based Learning Approach for Energy Optimization in Rel...

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IEEE Conference on Wireless Communications and Networking

作者： Huapeng Yang Han Ji Zhangqin Huang Xiping Wu Beijing Engineering Research Center for IoT Software and Systems College of Computer Science Beijing University of Technology Beijing China School of Electrical and Electronic Engineering University College Dublin Dublin Ireland The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Minimizing energy consumption is critical for the long-range (LoRa) Internet of Things (IoT) networks, to extend the battery lifetime of end devices (EDs) while reducing the maintenance cost. To overcome the excessive computational complexity required by the traditional optimization methods, learning approaches such as deep neural network (DNN) and reinforcement learning (RL) have been researched in wireless networks, where star topologies are widely employed. In contrast, LoRa usually deploys relays to assist the connection between the EDs and the gateway (GW), leading to a much more complex network topology. Consequently, DNN and RL would become less effective in LoRa, since those methods are difficult to learn the complex network topology constructed by LoRa. In this paper, we propose a learning method based on graph neural network (GNN), which is known for its prominent ability to capture and represent intricate graph-structural dependencies, to tackle the energy optimization problem for relay-assisted LoRa. Specifically, the multi-hop LoRa network is modeled as a directed graph, with channel state information (CSI) defined as node features, while the spreading factor and transmission power are deemed labels. A hierarchical message aggregation mechanism is proposed to effectively capture the multi-hop structural dependencies, followed by the process of inductive learning. Results show that against conventional optimization algorithms, the proposed method can achieve near-optimal energy consumption with a gap of 10% to 16%, while reducing the runtime by about six orders of magnitude. Compared to DNN, the GNN-based model can provide an energy saving of up to 32%, at a similar level of inference time.

关键词： Energy consumption Runtime Wireless networks LoRa Artificial neural networks Spread spectrum communication Complex networks Graph neural networks Topology Internet of Things

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Effort Allocation for Deadline-Aware Task and Motion Planning: A Metareasoning Approach

arXiv

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arXiv 2024年

作者： Sung, Yoonchang Shperberg, Shahaf S. Wang, Qi Stone, Peter The Department of Computer Science The University of Texas at Austin AustinTX78712 United States The Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Be’er Sheva Israel Sony AI United States

In robot planning, tasks can often be achieved through multiple options, each consisting of several actions. This work specifically addresses deadline constraints in task and motion planning, aiming to find a plan that can be executed within the deadline despite uncertain planning and execution times. We propose an effort allocation problem, formulated as a Markov decision process (MDP), to find such a plan by leveraging metareasoning perspectives to allocate computational resources among the given options. We formally prove the NP-hardness of the problem by reducing it from the knapsack problem. Both a model-based approach, where transition models are learned from past experience, and a model-free approach, which overcomes the unavailability of prior data acquisition through reinforcement learning, are explored. For the model-based approach, we investigate Monte Carlo tree search (MCTS) to approximately solve the proposed MDP and further design heuristic schemes to tackle NP-hardness, leading to the approximate yet efficient algorithm called DP_Rerun. In experiments, DP_Rerun demonstrates promising performance comparable to MCTS while requiring negligible computation time. © 2024, CC BY.

关键词： Motion planning

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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Research and Development of Kubernetes Operator for Lightweight Multi-Access Edge Computing Platform Simulator

Research and Development of Kubernetes Operator for Lightwei...

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systems of Signals Generating and Processing in the Field of on Board Communications

作者： Ivan Filianin Nikita Borisov Vladimir Bogatyrev Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia Department of Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation St. Petersburg Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweight MultiAccess Edge Computing Platform Simulator (LWMECPS) deployment. Special attention is given to the operator Kubernetes architecture and how to use Operator Lifecycle Manager (OLM) to continuously deploy LWMECPS and machine learning models for it.

关键词： computer languages Multi-access edge computing Codes Computational modeling Machine learning computer architecture Complex systems Research and development

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