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Chemistry, Analytical
Lei Chen, Hai-Ning Liang, Jialin Wang, Yuanying Qu, Yong Yue
Summary: The study found that learners with one shared workspace and one single display can achieve better user performance and engagement levels. The back-to-back position with learners sharing their view and control of the workspaces was also the most favorable.
Article
Computer Science, Theory & Methods
Ruitao Xie, Junhong Fang, Junmei Yao, Kai Liu, Xiaohua Jia, Kaishun Wu
Summary: This study leverages emerging edge computing and 5G networks to offload the 3D rendering of interactive multimedia applications onto edge servers. The focus is on optimizing the scheduling algorithm and designing a special utility function to meet each task's preset performance requirements and maximize overall task performance.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2022)
Article
Computer Science, Software Engineering
Peter E. Morse, Anya M. Reading, Tobias Stal
Summary: In this paper, the visualization and researcher interaction of global deep Earth volume datasets are considered. A novel, interactive graphical application suite and workflow are presented, which use an intuitive 2.5D layer compositing approach. The methodology is anticipated to be used in the visualization of multiple datasets representing aspects of the Earth's deep interior and atmosphere.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Computer Science, Artificial Intelligence
Yong Wang, Shouguo Peng, Xiangyang Guan, Jianxin Fan, Zheng Wang, Yong Liu, Haizhong Wang
Summary: Collaboration among logistics companies is crucial for enhancing operational efficiency, with the proposed solution improving synchronization of logistics networks and outperforming other algorithms in terms of cost reduction, waiting time, and vehicle numbers.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Software Engineering
Hanggao Xin, Shaokun Zheng, Kun Xu, Ling-Qi Yan
Summary: This paper presents a novel method to generate single-bounce indirect illumination for dynamic scenes at interactive framerates. The method uses a lightweight neural network to predict screen-space indirect illumination, and achieves high quality and good temporal coherence through bilateral convolution layers and simplified input information.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Computer Science, Software Engineering
Liangsheng Ge, Beibei Wang, Lu Wang, Xiangxu Meng, Nicolas Holzschuch
Summary: Rendering participating media is crucial for creating photorealistic images, and simulating these effects requires tracking a large number of scattering events which is computationally intensive. A new method utilizing a neural network model to represent double and multiple scattering effects in homogeneous participating media is presented, achieving efficient results in rendering.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2021)
Article
Automation & Control Systems
Yuxiang Yang, Yaohui Pan, Xiaojing Zhu, Mingyu Gao, Jing Zhang, Dacheng Tao
Summary: This article proposes a novel dual-forklift collaborative mechanism to address the challenges of large, low-automation, and complex container handling facilities. By utilizing turn lock, hydraulic lifting structure, four-wheel steering structure, and a novel vision system, the system can successfully handle containers in narrow spaces. Experimental results demonstrate that the model outperforms representative models in terms of precision and efficiency, confirming its practical value in industrial applications.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Computer Science, Artificial Intelligence
Zhi-Song Liu, Marie-Paule Cani, Wan-Chi Siu
Summary: See360 is a versatile and efficient framework for 360 degrees panoramic view interpolation using latent space viewpoint estimation, with the proposed Multi-Scale Affine Transformer enabling coarse-to-fine feature rendering. It can be applied to real and synthetic rendering in indoor and outdoor environments, showing real-time performance and versatility.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Software Engineering
Yue Zhao, Jian Zhang, Chi-Wing Fu, Mingliang Xu, Dominik Moritz, Yunhai Wang
Summary: This paper presents a novel method for interactive analysis of large-scale time-series data, which enables fast queries and density field computation using line segment splitting and KD-tree. The interactive system KD-Box is developed to provide rich interactions for visual analysis.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Computer Science, Information Systems
Bashaer Alahmri, Saad Al-Ahmadi, Abdelfettah Belghith
Summary: This article introduces an efficient caching technique named PoolCache, which increases the caching capacity of defined node conglomerates by pooling and managing various caches. The research finds that PoolCache outperforms other caching strategies in terms of content diversity, cache hit ratio, and content retrieval delay.
Article
Computer Science, Artificial Intelligence
Maoguo Gong, Wenfeng Liu, Yu Xie, Zedong Tang, Mingliang Xu
Summary: This paper proposes a fast and scalable multilayer network embedding model HMNE for node classification and link prediction in multilayer networks.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Geochemistry & Geophysics
Yaoting Liu, Jianwen Hu, Xudong Kang, Jing Luo, Shaosheng Fan
Summary: This article proposes a method called Interactformer to enhance the spatial resolution of hyperspectral images (HSIs) by interacting with global and local features extracted by Transformer and 3D convolutional neural network (CNN) branches. A separable self-attention module with linear complexity is designed in the Transformer branch to solve the memory cost problem of traditional self-attention mechanisms. In the 3D CNN branch, the spectral attention module and 3D convolution are applied jointly to protect the spectral correlation and facilitate local feature extraction. Experimental results demonstrate that Interactformer can effectively improve the spatial resolution while preserving the spectral information.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Automation & Control Systems
Jiachen Yang, Meng Xi, Bin Jiang, Jiabao Man, Qinggang Meng, Baihua Li
Summary: This article introduces a fully connected attitude detection network (FADN) for 3D attitude angle estimation, which combines neural network and traditional algorithms to achieve the complete process from single frame image input to 3D attitude angle estimation output. FADN shows high estimation accuracy and fast running speed in performance evaluation experiments, demonstrating its feasibility in real scenarios.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Wenjun Chen, Anwar Haque, Kamran Sedig
Summary: With the increasing size and complexity of communication networks, utilizing interactive visualizations becomes crucial. This paper introduces a system that elegantly visualizes large-scale network data through progressive disclosure and multiple panels, effectively addressing the issue of information overload.
Article
Computer Science, Software Engineering
Piaopiao Yu, Jie Guo, Fan Huang, Zhenyu Chen, Chen Wang, Yan Zhang, Yanwen Guo
Summary: Inserting 3D virtual objects into real-world images is a common practice in photo editing and augmented reality. Generating visually realistic shadows for these objects is a challenge, especially when projecting shadows from real objects onto virtual ones. In this paper, we propose an end-to-end solution called ShadowMover that automatically projects real shadows onto virtual objects for outdoor scenes. Our method uses the Shifted Shadow Map to encode the binary mask of shifted real shadows, and a CNN-based shadow generation model to generate plausible shadows on inserted virtual objects. Extensive experiments demonstrate the effectiveness of our approach.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2023)
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)