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Synthesis Lectures on Visualization 2021年第3期8卷 1-145页

作者： Cafaro, Francesco Roberts, Jessica School of Informatics and Computing Indiana University-Purdue University Indianapolis United States School of Interactive Computing Georgia Institute of Technology United States

When you picture human-data interactions (HDI), what comes to mind? The datafication of modern life, along with open data initiatives advocating for transparency and access to current and historical datasets, has fundamentally transformed when, where, and how people encounter data. People now rely on data to make decisions, understand current events, and interpret the world. We frequently employ graphs, maps, and other spatialized forms to aid data interpretation, yet the familiarity of these displays causes us to forget that even basic representations are complex, challenging inscriptions and are not neutral;they are based on representational choices that impact how and what they communicate. This book draws on frameworks from the learning sciences, visualization, and human-computer interaction to explore embodied HDI. This exciting sub-field of interaction design is based on the premise that every day we produce and have access to quintillions of bytes of data, the exploration and analysis of which are no longer confined within the walls of research laboratories. This volume examines how humans interact with these data in informal (not work or school) environments, paritcularly in museums. The first half of the book provides an overview of the multi-disciplinary, theoretical foundations of HDI (in particular, embodied cognition, conceptual metaphor theory, embodied interaction, and embodied learning) and reviews socio-technical theories relevant for designing HDI installations to support informal learning. The second half of the book describes strategies for engaging museum visitors with interactive data visualizations, presents methodologies that can inform the design of hand gestures and body movements for embodied installations, and discusses how HDI can facilitate people's sensemaking about data. This cross-disciplinary book is intended as a resource for students and early-career researchers in human-computer interaction and the learning sciences, as well as f

关键词： Visualization

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Improving Student Performance Prediction Using a PCA-based Cuckoo Search Neural Network Algorithm

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Procedia Computer Science 2023年 225卷 4598-4610页

作者： Maria Ali Muhammad daniyal liaquat Muhammad Nouman Atta Abdullah Khan Saima Anwar Lashari Dzati Athiar Ramli Institute of Computer Science and Information Technology the University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh KSA Intelligent Biometrics Group (IBG) School of Electrical and Electronic Engineering USM Engineering Campus University Sains Malaysia 14300 Nibong Tebal Pulau Pinang

The ANN is a commonly used network for pattern recognition, and has been trained for various tasks such as prediction, classification, and engineering. However, this model faces challenges such as local minima and slow convergence, which have been addressed through different strategies such as combining the artificial neural network (ANN) with optimised models like the cuckoo search (CS) algorithm. However, for large datasets, the hybrid ANN-based CS algorithm can lead to overfitting. To overcome this issue, the authors propose a new algorithm called Principal Component Analysis with Cuckoo Search Neural Network (PCACSNN). The performance of this algorithm is compared to other commonly used algorithms such as ANN, backpropagation neural network (BPNN), and cuckoo search backpropagation (CSBP), using the Mean Square Error (MSE) and accuracy on classification problems. The simulations were performed on the Student Performance dataset taken from the UCIMLR. The results show that the proposed model performs better than the other models, achieving high accuracy and low MSE for both mathematics and Portuguese student datasets. For the mathematics students, the suggested model attained an accuracy of (99.32%) with MSE of 2.77E-07 for 70% training data and an accuracy of 98.52% with MSE of 2.50E-04 for 30% training data. Similarly, for the Portuguese student dataset, the proposed model obtained (99.38%) accuracy with MSE of 1.09E-08 for 70% training data and 98.72% accuracy with MSE of 1.01E-04 for 30% training data.

关键词： Artificial Neural Network (ANN) Cuckoo Search (CS) Algorithm Portuguese Mathematics Accuracy Mean Square Error (MSE) Local Minima Slow Convergence Back Propagation

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Sharding:A Scalability Solutions for Blockchain Networks

Sharding:A Scalability Solutions for Blockchain Networks

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Dattatray Raghunath Kale Amolkumar N Jadhav Kruishna Prasad K Somanath Janardan Salunkhe Sumit Hirve Chandrashekhar Goswami Department of Computer Science & Engg MIT School of Computing MIT Art Design and Technology University Pune India Department of Computer Science & Engg Annasaheb Dange College of Engineering and Technology Ashta India Department of Computer Science & Engg Shrinivas University Mangalore India Department of Computer Science & Engg D Y Patil Agriculture and Technical University India Faculty of Computing and Informatics Sir Padampat Singhania University Udaipur Rajasthan

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks.

关键词： Industries Sharding Scalability Memory Throughput Blockchains Security

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Training Learning Weights of Elman Neural Network Using Salp Swarm Optimization Algorithm

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Procedia Computer Science 2023年 225卷 1974-1986页

作者： Qaedah Ali Musaeed Naji Mahdi Maria Ali Muhammad Nouman Atta Abdullah Khan Saima Anwar Lashari Dzati Athiar Ramli Institute of Computer Science and Information Technology the University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh KSA Intelligent Biometrics Group (IBG) School of Electrical and Electronic Engineering USM Engineering Campus University Sains Malaysia 14300 Nibong Tebal Pulau Pinang

Elman Neural Network (ENN) is one of the most common type of recurrent neural network (RNN). It is frequently applied to numerous applications such as classification and predication. However, the ENN model, suffers from serious flaws like network stagnancy and delayed convergence during training. To enhance the effectiveness of the ENN and address the mention problems, numerous studies have been conducted to speed up the learning process of ENN. Many optimization techniques are being used and each algorithm has its own characteristics in terms of efficiency. Some of the algorithms inspire from nature, while others come from the swarm's combined behaviour. Different numbers of optimization methods, including Particle Swarm Optimization (PSO), Bat Algorithm, and Cuckoo Search (CS) are applied to improve the ENN's learning process. As a result, this study suggested using Salp Swarm algorithm (SSA) to address these issues and speed up the ENN algorithm's learning process. The SSA, which is based on the typical behaviour of Salp swarms, is proposed in this research as a novel optimization technique. Due to its effectiveness and Salp intelligent behaviour, a novel Salp algorithm improves the learning process of the artificial neural network (ANN), and ENN models. Therefore this study proposed Salp swarm artificial neural network (SSANN) and Salp swarm Elman neural network (SSElmanNN). Performance of the suggested models is evaluated against ANN, back propagation neural network (BPNN), along with ENN in term of accuracy and Mean Square Error (MSE). Two datasets such as IRIS and Credit Card are used for simulation. The simulation results demonstrate that the suggested models outperform the other algorithms used in this study in terms of MSE and accuracy.

关键词： ANN ENN BPNN SSA MSE Iris and Credit Card

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MVC-VPR: Mutual Learning of Viewpoint Classification and Visual Place Recognition

arXiv

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arXiv 2024年

作者： Gu, Qiwen Wang, Xufei Zhang, Fenglin Zhao, Junqiao Tao, Siyue Ye, Chen Feng, Tiantian Jiang, Changjun Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Shanghai Research Institute for Intelligent Autonomous System Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Visual Place Recognition (VPR) aims to robustly identify locations by leveraging image retrieval based on descriptors encoded from environmental images. However, drastic appearance changes of images captured from different viewpoints at the same location pose incoherent supervision signals for descriptor learning, which severely hinder the performance of VPR. Previous work proposes classifying images based on manually defined rules or ground truth labels for viewpoints, followed by descriptor training based on the classification results. However, not all datasets have ground truth labels of viewpoints and manually defined rules may be suboptimal, leading to degraded descriptor performance. To address these challenges, we introduce the mutual learning of viewpoint self-classification and VPR. Starting from coarse classification based on geographical coordinates, we progress to finer classification of viewpoints using simple clustering techniques. The dataset is partitioned in an unsupervised manner while simultaneously training a descriptor extractor for place recognition. Experimental results show that this approach almost perfectly partitions the dataset based on viewpoints, thus achieving mutually reinforcing effects. Our method even excels state-of-the-art (SOTA) methods that partition datasets using ground truth labels. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Single-cell omics: experimental workflow, data analyses and applications

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Science China(Life Sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Motion Simulation for Fatigue Reduction and Actual Measurement Using EMG

Motion Simulation for Fatigue Reduction and Actual Measureme...

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International Symposium on Soft computing and Intelligent Systems (SCIS)

作者： Miki Eto Masahiro Migita Masashi Toda Kazuaki Kondo Qi An Junichi Akita Yuichi Nakamura Graduate School of Science and Technology Kumamoto University Kumamoto Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University Kumamoto Japan Academic Center for Computing and Media Studies Kyoto University Kyoto Japan Graduate School of Frontier Sciences Tokyo University Tokyo Japan Transdisciplinary Sciences for Innovation Kanazawa University Ishikawa Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Muscle fatigue is an important phenomenon in exercise. Muscle fatigue affects the duration of an activity, so delaying muscle fatigue can lengthen the duration of exercise. Furthermore, Muscle fatigue is one of the risk factors for musculoskeletal disorders (MSDs). Repetitive tasks, in particular, have been identified as a primary risk factor for MSDs. Force application with less muscle fatigue is essential for maintaining and improving exercise performance and preventing disease. In this study, we focus on generating movements that cause less muscle fatigue. The purpose of this study is to identify force application with less muscle fatigue and to actually measure these movements. Simulation and actual measurement will be conducted for the “back-and-forth rubbing motion with the arm”. The “back-and-forth rubbing motion with the arm” was set as one of the repetitive movements. Based on the simulation results, we will verify whether fatigue can actually be suppressed by actual measurement.

关键词： Time-frequency analysis Force measurement Simulation Force Shoulder Muscles Fatigue Market research Frequency measurement Motion measurement

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Texture and Orientation-based Feature Extraction for Robust Facial Expression Recognition

Texture and Orientation-based Feature Extraction for Robust ...

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International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Sajid Ali Khan Mohammed Bin Abdulrahman Alawairdhi Mousa AL-Akhras Department of Software Engineering Foundation University Islamabad Islamabad Pakistan College of Computing and Informatics Saudi Electronic University Saudi Arabia Computer Information Systems Department King Abdullah II School for Information Technology The University of Jordan Amman

Facial expressions are the most effective way to characterize people’s motives, emotions, and feelings. Several new methods are proposed each year; however, the accuracy of facial expression recognition still needs to be improved especially in uncontrolled conditions. In this paper, we propose a hybrid facial expression model that considers both texture and orientation features to classify expressions. Two types of descriptors namely Local binary pattern and Weber local descriptor are used to preserve the local intensity information and orientation of edges. In the next step, computing the Histograms of oriented gradients (HOG) features from the Local binary pattern and Weber local descriptor images to capture micro-expressions. Then, the AdaBoost feature selection algorithm is utilized to choose the best features from the combined HOG features. The results of the experiments demonstrate that the method proposed in this study performs better than existing methods.

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Effect of Camera Simulator Application for Basic Photography Subjects at Vocational school: One-Group Pre-test and Post-test Design

Effect of Camera Simulator Application for Basic Photography...

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International Conference on Education and technology (ICET)

作者： Qori Qonita Novian Anggis Suwastika Maslin Masrom Broadcast and Film Areas of Expertise Sekolah Menengah Kejuruan Negeri (SMKN) 10 Bandung Bandung Indonesia Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia School of Computing Telkom University Bandung Indonesia

This study examines the impact of using the camera simulator application in basic photography subjects for students cognitive skills majoring in broadcasting and film in vocational high schools in Indonesia. Camera simulators have the advantage of providing opportunities for students to practice more freely and independently. In this study, the CamSim Pro application was employed as the camera simulator and was installed on desktop systems. Eighty eligible students from Public Vocational High school (PVHS) 10 Bandung participated in using the camera simulator as an intervention technology in two topics in basic photography subjects: aperture priority mode and shutter priority. This study adopts a quasi-experimental research method with a one-group pre-test and post-test design, examining the impact of camera simulator intervention. This research uses five stages, (1) designing the pre-test and its evaluation, (2) selecting the group/population to be tested, (3) preparing and providing a camera simulator intervention, (4) designing the post-test and evaluating it, and (5) conducting data analysis referring to the results of the pre-test and the post-test. The data normality test uses the Kolgomorov-Smirnov method and the Shapiro-Wilk method. Data analysis to measure the impact of technological interventions uses the Wilcoxon Signed Ranks method and the significance of the technological interventions using the Z statistic and asymptotic significance. Analysis of the collected data unveiled a compelling narrative. The Z statistic exhibited a notable value of −4.897, paired with an asymptotic significance value of < 0.001. These findings support the hypothesis that integrating a camera simulator to facilitate learning significantly enhances students’ cognitive skills in fundamental photography subjects.

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Energy and Latency-Aware Resource Management for UAV-Assisted Mobile Edge computing Against Jamming

Energy and Latency-Aware Resource Management for UAV-Assiste...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ziling Shao Helin Yang Liang Xiao Wei Su Zehui Xiong Institute of Artificial Intelligence Xiamen University Xiamen China School of Informatics Xiamen University Xiamen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University Xiamen China Pillar of Information System Technology & Design Singapore University of Technology and Design Singapore

Unmanned aerial vehicles (UAVs) have been increasingly employed as aerial servers in mobile edge computing (MEC) systems, providing essential computing, communication, and storage services for edge users. This UAV-assisted MEC paradigm shows great promise in enhancing both computing and communication performances. However, the presence of malicious jammers poses significant challenges to the system's reliability and efficiency. In this study, we explore the resource management problem in a multi-UAV-assisted MEC scenario under the influence of multiple malicious jammers. To mitigate the impact of jamming attacks, we propose a resource management approach with the primary objective of minimizing system energy consumption and latency while adhering to UAV energy constraints. Due to the dynamic and time-varying nature of the communication environment, we present a deep reinforcement learning (DRL)-based algorithm that dynamically adjusts the CPU frequency and communication bandwidth of the UAV to optimize the system performance even under jamming attacks. Through simulations, we demonstrate the effectiveness of the proposed algorithm in significantly reducing the overall system latency (both computational and communication latency) as well as minimizing energy consumption.

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