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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Akber, Syed Muhammad Abrar Kazmi, Sadia Nishat Szczesna, Agnieszka Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

In the modern digital world, the use of animated applications has increased significantly and such applications have quietly become an integral part of life. Capturing human motions is a fundamental aspect of these applications which is performed by motion-capturing (MoCap) systems. Due to the heterogeneity of MoCap systems and marker configurations, the captured skeletons may vary in their structure, number of joints, and bone lengths. The need for motion retargeting arises to transfer motion captured from one articulated actor to another. Although motion retargeting is a long-standing issue, current methods cannot automatically execute retargeting between skeletons. This paper presents the retraining of a popular motion retargeting network with a new large-scale motion dataset. The retraining aims to demonstrate the capabilities of the motion retargeting network to handle the problem of motion retargeting between skeletons that may have different structures. The paper first, presents the motion retargeting network that employs an encoder-decoder architecture. Afterward, we re-train the motion retargeting network with a new MoCap dataset. To validate the effectiveness of the network, we visually compare the generated results with the existing retargeting model. The results show that the motion retargeting network effectively retargets the different motions. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Decoder Deep neural network Encoder Motion retargeting

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Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies

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Machine Intelligence Research 2024年第5期21卷 993-1010页

作者： Hui Guan Guorong Cai Hang Xu Department of Computer Science and Technology Shenyang University of Chemical TechnologyShenyang110142China Key Laboratory of Industrial Intelligence Technology on Chemical Process Shenyang110142China

Since requirement dependency extraction is a cognitively challenging and error-prone task,this paper proposes an automatic requirement dependency extraction method based on integrated active learning *** this paper,the coefficient of variation method was used to determine the corresponding weight of the impact factors from three different angles:uncertainty probability,text similarity difference degree and active learning variant prediction divergence *** combining the three factors with the proposed calculation formula to measure the information value of dependency pairs,the top K dependency pairs with the highest comprehensive evaluation value are selected as the optimal *** the optimal samples are continuously added into the initial training set,the performance of the active learning model using different dependency features for requirement dependency extraction is rapidly ***,compared with other active learning strategies,a higher evaluation measure of requirement dependency extraction can be achieved by using the same number of ***,the proposed method using the PV-DM dependency feature improves the weight-F1 by 2.71%,the weight-recall by 2.45%,and the weight-precision by 2.64%in comparison with other strategies,saving approximately 46%of the labelled data compared with the machine learning approach.

关键词： Requirement dependency dependency extraction dependency features integrated active learning strategies coefficient of variation

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Performance Analysis of RouteSegmentation Algorithm in Identifying Perimeter Area Around Travel Route 16

Performance Analysis of RouteSegmentation Algorithm in Ident...

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16th International Conference on Information Technology and Electrical Engineering, ICITEE 2024

作者： Bachmid, Muhammad Avied Anshari Haq, Muhammad Yasir Mumtaz, Muhammad Rafly Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Media Game and Mobile Laboratory Department of Informatics Engineering Malang Indonesia Universitas Brawijaya Faculty of Computer Science Malang Indonesia Universitas Brawijaya Faculty of Computer Science Media Game and Mobile Laboratory Department of Information System Malang Indonesia

ISBN: (纸本)9798350375817

Prior study has developed the RouteSegmentation algorithm to identify the perimeter area surrounding a route. In this study, a comparative experiment was carried out to investigate the performance of the RouteSegmentation algorithm implementation in contrast to the RouteBoxer algorithm. Both algorithms play a crucial role in geographical information systems (GIS), particularly in enhancing navigational tools by identifying points of interest along specified routes. Through systematic experimentation using various perimeter distance values and types of route, the trade-offs between processing efficiency and the granularity of the area identified by these algorithms were evaluated. The results demonstrate that RouteSegmentation maintained relatively consistent performance across different perimeter distance values and statistically significantly faster processing time than RouteBoxer. Furthermore, while the RouteBoxer algorithm benefits from larger perimeter distance values by reducing processing time, it compromises the coverage results of the algorithm and potentially undermines its usefulness in practical scenarios. This study not only provides insights into the optimal use of perimeter distance values for each algorithm but also guides users in selecting the appropriate algorithm based on their specific application needs, balancing detailed geographical analysis and runtime efficiency. © 2024 IEEE.

关键词： Transportation routes

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Classification with ensembles and case study on functional magnetic resonance imaging

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Digital Communications and Networks 2022年第1期8卷 80-86页

作者： Adnan OM.Abuassba Zhang Dezheng Hazrat Ali Fan Zhang Khan Ali Department of Computer Science Arab Open University-PalestineRamallahPalestine Department of Computer Science and Communication Engineering University of Science and Technology BeijingChina and Beijing Key Laboratory of Knowledge Engineering for Materials ScienceBeijing100083China Department of Electrical and Computer Engineering COMSATS University IslamabadAbbottabad CampusAbbottabadPakistan

The ensemble is a technique that strategically combines basic models to achieve better accuracy ***,combination methods,and selection topology are the main factors determining ensemble ***,it is a challenging task to design an efficient ensemble *** though numerous paradigms have been proposed to classify ensemble schemes,there is still much room for *** paper proposes a general framework for creating ensembles in the context of ***,the ensemble framework consists of four stages:objectives,data preparing,model training,and model *** is comprehensive to design diverse *** proposed ensemble approach can be used for a wide variety of machine learning *** validate our approach on real-world *** experimental results show the efficiency of the proposed approach.

关键词： Classification Ensemble learning Extreme learning machine

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Prediction of three-dimensional ocean temperature in the South China Sea based on time series gridded data and a dynamic spatiotemporal graph neural network

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Acta Oceanologica Sinica 2024年第7期43卷 26-39页

作者： Feng Nan Zhuolin Li Jie Yu Suixiang Shi Xinrong Wu Lingyu Xu Department of Computer Engineering and Science Shanghai UniversityShanghai 200444China Key Laboratory of Digital Ocean National Marine Data and Information ServiceTianjin 300171China

Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related ***,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean *** graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among *** this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior *** and spatial dependencies in the time series were then captured using temporal and graph *** also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid *** this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea *** compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.

关键词： dynamic associations three-dimensional ocean temperature prediction graph neural network time series gridded data

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A NOVEL DISCRETE RAT SWARM OPTIMIZATION (DRSO) ALGORITHM FOR SOLVING THE TRAVELING SALESMAN PROBLEM

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and Engineering 2022年第2期5卷 287-299页

作者： Mzili, Toufik Riffi, Mohammed Essaid Mzili, Ilyass Dhiman, Gaurav Department of Computer Science Laboratory LAROSERI Faculty of Science Chouaib Doukkali University Morocco Department of Computer Science Government Bikram College of Commerce Punjab India

Metaheuristics are often used to find solutions to real and complex problems. These algorithms can solve optimization problems and provide solutions close to the global optimum in an acceptable and reasonable time. In this paper, we will present a new bio-inspired metaheuristic based on the natural chasing and attacking behaviors of rats in nature, called a Rat swarm optimizer. Which has given good results in solving several continuous optimization problems, and adapted it to solve a discrete, NP-hard, and classical optimization problem that is the traveling salesman problem (TSP) while respecting the natural behavior of rats. To test the efficiency of the adaptation of our proposal, we applied the adapted rat swarm optimization (RSO) algorithm to some reference instances of TSPLIB. The obtained results show the performance of the proposed method in solving the traveling salesman problem (TSP). © 2022 by the authors.

关键词： Combinatorial optimization

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OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning 41

OLLIE: Imitation Learning from Offline Pretraining to Online...

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41st International Conference on Machine Learning, ICML 2024

作者： Yue, Sheng Hua, Xingyuan Ren, Ju Lin, Sen Zhang, Junshan Zhang, Yaoxue Department of Computer Science and Technology Tsinghua University Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science University of Houston Texas United States Department of Electrical and Computer Engineering University of California Davis United States

In this paper, we study offline-to-online Imitation Learning (IL) that pretrains an imitation policy from static demonstration data, followed by fast finetuning with minimal environmental interaction. We find the naïve combination of existing offline IL and online IL methods tends to behave poorly in this context, because the initial discriminator (often used in online IL) operates randomly and discordantly against the policy initialization, leading to misguided policy optimization and unlearning of pretraining knowledge. To overcome this challenge, we propose a principled offline-to-online IL method, named OLLIE, that simultaneously learns a near-expert policy initialization along with an aligned discriminator initialization, which can be seamlessly integrated into online IL, achieving smooth and fast finetuning. Empirically, OLLIE consistently and significantly outperforms the baseline methods in 20 challenging tasks, from continuous control to vision-based domains, in terms of performance, demonstration efficiency, and convergence speed. This work may serve as a foundation for further exploration of pretraining and finetuning in the context of IL. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

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Chinese Journal of Electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of Software Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

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DemogCF model of personalized recommendations based on demographic characteristics for overcoming data sparsity and cold start problems

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International Journal of Information Technology (Singapore) 2025年第1期17卷 169-177页

作者： Fkih, Fethi Rhouma, Delel Omri, Mohamed Nazih Department of Computer Science College of Computer Qassim University Buraydah Saudi Arabia MARS Research Laboratory LR17ES05 University of Sousse Sousse Tunisia

With the rapid expansion of the internet, the size of e-commerce websites expanded with a huge number of products that need to be processed. This makes internet users confused with a number of choices that caused difficulty to find products suited to their preferences. Recommendation Systems (RS) are information filtering tools that help the user to find the appropriate products by generating personalized recommendations. The collaborative Filtering (CF) approach is suffering from data sparsity and cold start problems due to the absence or the insufficiency of user ratings. In order to resolve these problems, we propose in this paper a novel model, named DemogCF, that used the demographic information age, gender and occupation to generate ratings for unrated items. New ratings will be combined with the existing ratings by the Recommender System for items recommendation. In fact, filling all gaps in the ratings matrix will, necessarily, fix the data sparsity and cold start problems. Our model is compared with 2 other models on 2 standards dataset. The proposed RS provided good results in terms of standards evaluation metrics. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cold start Collaborative filtering Data sparsity Demographic information Recommender systems

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Challenges of Distance Learning During the Coronavirus (COVID-19) Pandemic A Case Study of the University of Petra 2

Challenges of Distance Learning During the Coronavirus (COVI...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Omar, Khalil Saleh, Yaser Issa, Ghassan University Of Petra Computer Science Department Amman Jordan Princes Sumaya University For Technology Department Of Computer Graphics And Animation Amman Jordan School Of Information Technology Skyline University College Sharjah United Arab Emirates

ISBN: (纸本)9798350394962

The Coronavirus (COVID-19) pandemic has affected all aspects of our daily life and imposed social distancing among people. In higher education, the shift from traditional learning (TL) to the distance learning (DL) model has become inevitable to continue academic life. However, this unexpected shift is complex and poses new challenges and obstacles for the stakeholders of the DL system. In this research, the DL system's stakeholders have been classified into students, instructors, technical administrators, and educational administrators. This research study aims to gather the perceptions of and satisfaction level of these stakeholders with DL and explore the challenges encountered by these stakeholders during the shift to DL during the COVID-19 pandemic. Therefore, four questionnaires were developed and distributed to the identified stakeholders at the University of Petra, which has been adopted as a case study for this research. © 2024 IEEE.

关键词： Coronavirus

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