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SSRN 2022年

作者： Wang, Huafeng Li, Hanlin Liu, Wanquan Gu, Xianfeng School of Information Technology North China University of Technology Beijing China School of Software Beihang University Beijing China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China Department of Computer Science Stony BrookNY11794-2424 United States

A large number of existing methods have demonstrated the significance of motion information for action recognition in video. In the literature, most of the previous methods rely heavily on the temporal differences of features extracted by convolutional networks (CNN) to represent the motion. However, this type of motion representation approach may have two potential drawbacks: 1) The difference operation may cause the incompleteness of the extracted moving target contour;2) Treating all the extracted motion features equally may lead to situations where some motion features will not contribute to the classification or even produce negative incentives. Inspired by the fact that the human eye is often a cumulative and selective process of visual attention when observing a video sequence, we propose a motion accumulation and selection network (MAS-Net) based on a novel embedded 2D CNN. According to the experimental results on typical video datasets such as Something-Something V1&V2 and Kinetics-400, it is shown that MAS-Net has achieved the state-of-the-arts on Something-Something V1&V2 and the competitive results on Kinetics-400, while the computational load is kept at a relatively low level. © 2022, The Authors. All rights reserved.

关键词： Motion estimation

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Allied Power Constraint Optimization and Optimal Beam Tracking Scheme for Mobile mmWave Massive MIMO Communications

TechRxiv

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TechRxiv 2021年

作者： Ghosh, Joydev Ra, In-Ho Singh, Saurabh Haci, Hüseyin Al-Utaibi, Khaled A. Sait, Sadiq M. School of Computer Science and Robotics Tomsk Polytechnic University Tomsk Russia School of Computer Information and Communication Engineering Kunsan National University Gunsan Korea Republic of Department of Industrial & Systems Engineering Dongguk University Korea Republic of Department of Electrical and Electronics Engineering Near East University Mersin 10 Turkey Computer Science and Software Engineering Department University of Ha’il Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

The fifth generation (5G) networks and internet of things (IoT) promise to transform our lives by enabling various new applications from driver-less cars to smart cities. These applications will introduce enormous amount of data traffic and number of connected devices in addition to the current wireless networks. Thus 5G networks require many researches to develop novel telecommunication technologies to accommodate these increase in data traffic and connected devices. In this paper, novel power constraint optimization and optimal beam tracking schemes are proposed for mobile mmWave massive MIMO communications. A recently published novel channel model that is different from other widely used ones is considered. The channel model considers the number of clusters and number of rays within each cluster as varying due to user mobility. The proposed power constraint optimization scheme harmonizes conventional total power constraint (TPC) and uniform power constraint (UPC) schemes into a new one called allied power constraint (APC) that can significantly improve the system performance in 5G networks while achieving fairness among users. TPC and UPC have major drawbacks with respect to fairness and achieving quality-of-service (QoS) for users in dense networks. Thus APC aims to harmonize TPC and UPC by adjusting each antenna element’s constraint to adapt for some power resilience to a specific antenna element, hence proposing an intermediate solution between the two extreme case power constraint optimization schemes. Three optimal beam tracking schemes: (i) conventional exhaustive search (CES), (ii) multiobjective joint optimization codebook (MJOC), and (iii) linear hybrid combiner (LHS) scheme, have been provided for the mobile mmWave massive MIMO system with the proposed APC scheme. For the proposed APC scheme a comprehensive performance analysis is provided and compared with TPC and UPC. Spectral efficiency (SE), bit-error-rate (BER), Jain’s fairness index, channel occup

关键词： Channel state information

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Optimization of wear properties on AA7075/Sic/Mos2hybrid metal matrix composite by response surface methodology

Optimization of wear properties on AA7075/Sic/Mos2hybrid met...

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2020 International Conference on Materials, Manufacturing and Mechanical engineering for Sustainable Developments, ICMSD 2020

作者： Umanath, K. Palanikumar, K. Sankaradass, Veeramalai Uma, K. Deaprtment of Mechanical Engineering Acadamy of Maritime Education and Training Chennai603112 India Sri SairamInstitute of Technology Chennai600044 India Department of Computer Science and Engineering ISL Engineering College Hyderabad Telangana500005 India Department of Software and Systems Engineering School of Information Technology and Engineering VIT University Vellore632014 India

Nowadays, composites are profoundly thriving as the replacement of ferrous alloys. The Aluminum manifested into a great substitute due to its properties like lightweight, low density, high strength, and corrosion resistance. The process of fusing the non-ceramics reinforcement like Silicon carbide, Boron carbide, Aluminum oxide, Titanium diboride with a non-metal like Graphite, Molybdenum disulfide or with other non-ceramic to form a hybrid metal matrix composite. In the present paper, an investigation carried out on Aluminum 7075 material by the process of stir casting where the Silicon Carbide (SiC) with three different weight % (i.e., 3%, 6%, 9%) and Molybdenum Disulphide (MoS2) with 1% constant weight % added to the base material. The tensile test and hardness test were then done on a UTM and Rockwell Hardness testing machine, respectively, for all three prepared materials. As the percentage of SiC increases in the material, the hardness value and tensile strength are increasing. The Al7075+9%SiC+1%MoS2is the highest of all. © 2020 Elsevier Ltd. All rights reserved.

关键词： Sulfur compounds

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Using an Expert Panel to Validate the Malaysian SMEs-software Process Improvement Model (MSME-SPI) 4th

Using an Expert Panel to Validate the Malaysian SMEs-Softwar...

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4th Computational Methods in systems and software, CoMeSySo 2020

作者： Almomani, Malek A. Basri, Shuib Almomani, Omar Capretz, Luiz Fernando Balogun, Abdullateef Husni, Moath Gilal, Abdul Rehman Department of Software Engineering The World Islamic Sciences and Education University Amman11947 Jordan Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak32610 Malaysia Department of Computer Network and Information Systems The World Islamic Sciences and Education University Amman11947 Jordan Department of Electrical and Computer Engineering Western University LondonON Canada Department of Computer Science University of Ilorin PMB 1515 Ilorin Nigeria Department of Computer Science Sukkur IBA University Sukkur65200 Pakistan

ISBN: (纸本)9783030633219

This paper presents the components of a newly developed Malaysian SMEs - software Process Improvement model (MSME-SPI) that can assess SMEs software development industry in managing and improving their software processes capability. The MSME-SPI is developed in response to practitioner needs that were highlighted in an empirical study with the Malaysian SME software development industry. After the model development, there is a need for independent feedback to show that the model meets its objectives. Consequently, the validation phase is performed by involving a group of software process improvement experts in examining the MSME-SPI model components. Besides, the effectiveness of the MSME-SPI model is validated using an expert panel. Three criteria were used to evaluate the effectiveness of the model namely: usefulness, verifiability, and structure. The results show the model effective to be used by SMEs with minor modifications. The validation phase contributes towards a better understanding and use of the MSME-SPI model by the practitioners in the field. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： software design

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Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging

arXiv

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arXiv 2025年

作者： Ayllón, Elena Mulero Mantegna, Massimiliano Shen, Linlin Soda, Paolo Guarrasi, Valerio Tortora, Matteo Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden Department of Naval Electrical Electronics and Telecommunications Engineering University of Genoa Genoa Italy

Accurate lung tumor segmentation is crucial for improving diagnosis, treatment planning, and patient outcomes in oncology. However, the complexity of tumor morphology, size, and location poses significant challenges for automated segmentation. This study presents a comprehensive benchmarking analysis of deep learning-based segmentation models, comparing traditional architectures such as U-Net and DeepLabV3, self-configuring models like nnUNet, and foundation models like MedSAM, and MedSAM 2. Evaluating performance across two lung tumor segmentation datasets, we assess segmentation accuracy and computational efficiency under various learning paradigms, including few-shot learning and fine-tuning. The results reveal that while traditional models struggle with tumor delineation, foundation models, particularly MedSAM 2, outperform them in both accuracy and computational efficiency. These findings underscore the potential of foundation models for lung tumor segmentation, highlighting their applicability in improving clinical workflows and patient outcomes. © 2025, CC BY-NC-SA.

关键词： Image segmentation

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TIMED AUTOMATA ROBUSTNESS ANALYSIS VIA MODEL CHECKING

arXiv

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arXiv 2021年

作者： Bendík, Jaroslav Sencan, Ahmet Gol, Ebru Aydin Černá, Ivana Max Planck Institute for Software Systems Kaiserslautern Germany Department of Computer Engineering Middle East Technical University Ankara Turkey Faculty of Informatics Masaryk University Brno Czech Republic

Timed automata (TA) have been widely adopted as a suitable formalism to model time-critical systems. Furthermore, contemporary model-checking tools allow the designer to check whether a TA complies with a system specification. However, the exact timing constants are often uncertain during the design phase. Consequently, the designer is often able to build a TA with a correct structure, however, the timing constants need to be tuned to satisfy the specification. Moreover, even if the TA initially satisfies the specification, it can be the case that just a slight perturbation during the implementation causes a violation of the specification. Unfortunately, model-checking tools are usually not able to provide any reasonable guidance on how to fix the model in such situations. In this paper, we propose several concepts and techniques to cope with the above mentioned design phase issues when dealing with reachability and safety specifications. © 2021, CC BY.

关键词： Specifications

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An Exploration of Robotic Process Automation in all Spans of Corporate Considerations 7

An Exploration of Robotic Process Automation in all Spans of...

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7th International Conference on Advanced Computing and Communication systems, ICACCS 2021

作者： Balakrishnan, S. Hameed, M.Syed Shahul Venkatesan, K. Aswin, G. Sri Krishna College of Engg. and Technology Dept. of Computer Science and Business Systems Tamilnadu Coimbatore India University of Technology and Applied Sciences - sur PO BOX: 484 PC 411 SUR Oman St. Josephs College of Engineering Department of Ece Full Stack Software Developer at Zealbots in Chadura Tech Tamilnadu Tiruchirapalli India

ISBN: (纸本)9781665405201

In advance, the change of digital transformation persistently operates the function of Robotic Process Automation (RPA) and draws out all forms of corporate considerations. The RPA raises a certain kind of business measure mechanization innovation that assists thought the software programming framework which enhances the system robots or man-made artificial intelligence (AI). While RPA is a famous subject in the corporate world and this holds scholarly exploration on research work which does not have a hypothetical and theoretical investigation over RPA. They portray the trait of RPA in a given direction which furnishes the concentration for additional exploration on advancement research which includes a software solution based on routine works, organized structured information and predetermined results. This helps in investigating the reports of emerging advantages in obtaining the efficiency of RPA with its cost and speed that decreases all the possible errors. © 2021 IEEE.

关键词： Robot programming

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PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery

arXiv

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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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Data distribution aware clustering for parallel split learning in healthcare applications

引用

Future Generation computer systems 2026年 174卷

作者： Md. Tanvir Arafat Md. Abdur Razzaque Abdulhameed Alelaiwi Md. Zia Uddin Mohammad Mehedi Hassan Department of Computer Science and Engineering University of Dhaka Dhaka 1000 Bangladesh Department of Software Engineering King Saud University Riyadh Saudi Arabia SINTEF Digital Oslo Norway Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Split learning, a promising approach in privacy-preserving machine learning, decentralizes model training by dividing it among client devices and a central server. However, split learning has exhibited a certain level of slowness in its vanilla approach, mainly due to the serial processing of devices. Recent research endeavors have addressed this challenge by introducing parallelism and thus accelerating the split learning process. However, the existing split learning methodologies often overlook the critical aspect of data distribution among client devices. This paper introduces a Data Distribution Aware Clustering-based Parallel Split Learning (DCSL), a scheme purposefully crafted to address the complexities stemming from non-identically and non-independently distributed (non-IID) data among client devices engaged in the split learning paradigm. In healthcare applications, comprehending the intricacies of data distribution is imperative, particularly given the non-IID nature of medical datasets, to ensure accurate analysis and decision-making. The DCSL leverages a novel clustering technique to create clusters of medical client devices, considering the data distributions of their local datasets, and employs parallel model training within the device clusters. It enhances model convergence and reduces training latency by optimizing the cluster formation. Extensive experiments demonstrate that DCSL outperforms traditional split learning approaches, significantly improving accuracy and reducing training latency across various applications.

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Chances and Challenges of ChatGPT and Similar Models for Education in M&S

Chances and Challenges of ChatGPT and Similar Models for Edu...

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Simulation Winter Conference

作者： Andreas Tolk Philip Barry Margaret L. Loper Ghaith Rabadi William T. Scherer Levent Yilmaz Modeling and Analysis Innovation Center The MITRE Corporation Charlottesville VA USA Mission Analysis Group L3Harris Corporation Herndon VA USA Georgia Tech Research Institute Georgia Institute of Technology Atlanta GA USA University of Central Florida School of Modeling Simulation and Training Orlando FL USA Department of Systems and Information Engineering University of Virgina Charlottesville VA USA Samuel Ginn College of Engineering Computer Science and Software Engineering Auburn University Auburn AL USA

This position paper summarizes the inputs of a group of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within Modeling and Simulation education. The experts also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. Generally, the use of ChatGPT is encouraged, but it needs to be embedded into an updated curriculum with more emphasis on validity constraints, systems thinking, and ethics.

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