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SSRN 2023年

作者： Fu, Rongrong Wang, Zeyi Wang, Shiwei Mi, Ruifu Wen, Guilin Chen, Junxiang Wang, Yaodong School of Electrical Engineering Yanshan University Qinhuangdao China Jiangxi New Energy Technology Institute Xinyu China Department of Biomedical Informatics The University of Pittsburgh United States School of Computer Science Wuhan University Wuhan China

Capability for dexterous decoding of control intention involving electroencephalography (EEG) is of paramount significance to the research of cognitive perception and movement in computational neuroscience. A reasonable experimental task is a crucial prerequisite for evoking high-quality EEG signals. However, most EEG evoking tasks in computational neuroscience are low demanding sensory-motor tasks, which easily make the subjects fatigue and slack. In this study, a period of high concentration of users can be maintained by a high demanding boundary avoidance task. Meanwhile, instead of decoding control intention from large limbs’ movements, a finer and more challenging control intention decoding from fingers of the same hand was studied under this high demanding task. Specifically, EEG signals during control period of fingers within the same hand are collected under the boundary avoidance task. A novel hybrid model of CNN-Bi-LSTM is proposed for decoding dexterous EEG-based control intention. The experimental results from ten subjects assessed by 5-fold cross-validation method show that analyzing the EEG signals by the proposed model can enable accurate decoding of fingers control intentions within the same hand in this boundary avoidance task. © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

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Parking Occupancy Prediction and Traffic Assignment in a University Environment 10

Parking Occupancy Prediction and Traffic Assignment in a Uni...

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10th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2022

作者： Farag, Mohamed Hilal, Amr El-Tawab, Samy Arab Academy for Science Technology & Maritime Transport Alexandria Egypt Virginia Tech Transportation Institute Virginia Tech BlacksburgVA United States Informatics Lab Virginia Tech BlacksburgVA United States Alexandria University Department of Computer and Systems Engineering Egypt James Madison University College of Integrated Science & Engineering HarrisonburgVA United States

ISBN: (纸本)9781665464635

The fourth industrial revolution has given rise to large-scale data-driven models like smart cities and Intelligent transportation. Within these models, applications like smart parking have been growing rapidly in research and industry. However, different scenarios and environments (e.g., shopping areas, residential places, and business complexes) can require special handling due to the various factors impacting people's schedules and behavior. In this paper, we provide an initial investigation of traffic assignment based on parking prediction for a mid-size university environment where parking is concentrated in three parking garages around the campus. Our initial investigation includes results for parking prediction using a statistical method and plans for an augmenting study using variations of Neural Networks. On top of the parking prediction layer, we propose an application layer that directs and fuses the model predictions to produce the parking options provided to the application user. The presented investigation can help the university administration in their consideration of building additional garages. © 2022 IEEE.

关键词： Forecasting

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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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Development of a Website-Based Financial Transaction Management System Using Agile with the Scrum Framework

Development of a Website-Based Financial Transaction Managem...

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International Conference on ICT For Smart Society (ICISS)

作者： Ayu Latifah Galih Muhammad Ramdan Muhamad Lutfhi Farid Sidik Nadhif Faturrohman Dzikri Nursyaban Agus Ramelan Fachrur Razy Rahman School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung Indonesia Department of Computer Science Institut Teknologi Garut Garut Indonesia Electrical Engineering Departement Universitas Sebelas Maret Solo Indonesia Department of Installation and Electrical Engineering Metal Industry Polytechnic of Morowali Central Sulawesi Indonesia

The process of managing financial transactions, especially in the education sector, is still mostly carried out conventionally, so it has the potential to cause many risks, such as loss or damage to documents related to difficulties in searching for data as needed, transaction processes that take quite a long time, financial records that are not properly recorded. Well, it is prone to discrepancies between the nominal filing and the nominal disbursed conventional representations of financial statements that make the stakeholder evaluation process uncomfortable, and many other things. Therefore this research was conducted to build a website-based information system that can prevent this from happening in the future. The method used in the development of this information system is the Agile method with the Scrum framework. The Scrum framework consists of five stages, starting from Initiate, Plan, and Estimate, Implement, Review and Retrospect, and Release. This website was built using the Javascript programming language using the Vue JS framework for the Front-end. With this website-based application, it is hoped that transaction activities can be carried out better and correctly without being limited by space and time.

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Situating Network Infrastructure with People, Practices, and Beyond: A Community Building Workshop 25

Situating Network Infrastructure with People, Practices, and...

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25th ACM Conference on computer-Supported Cooperative Work and Social Computing, CSCW 2022

作者： Jang, Esther Han Beol Bidwell, Nicola Liu, Jen Sengers, Phoebe Bagalkot, Naveen Verdezoto, Nervo Densmore, Melissa Vigil-Hayes, Morgan Hasan, Shaddi Paul G. Allen School for Computer Science and Engineering SeattleWA United States International University of Management Windhoek Namibia Cornell University Information Science IthacaNY United States Cornell University Information Science and Science & Technology Studies IthacaNY United States Srishti Manipal Institute of Art Design and Technology Karnataka Bengaluru India Cardiff University School of Computer Science and Informatics Cardiff United Kingdom University of Cape Town Cape Town South Africa Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Virginia Tech Department of Computer Science BlacksburgVA United States

ISBN: (纸本)9781450391900

Our world is now connected and even entangled in unprecedented ways through networked technologies. Yet pockets of unequal connectivity persist, and technical infrastructures for connectivity remain difficult to design and build even for experts. In this workshop we aim to bring together a global community of multi- and inter-disciplinary researchers and implementers working on infrastructure development and connectivity to explore the existing design challenges and opportunities for bringing technical dimensions of networked infrastructures in conversation with human-computer interaction (HCI) and the social science of infrastructure. We will share, assess and define research problems and resources for rethinking networked infrastructures from human-, community-, and society-centered perspectives, understanding them to be embedded with human values and biases. We particularly intend our collaborative work to support real-world connectivity initiatives, which have grown in critical importance over the pandemic years - especially projects in support of Global South communities. Concrete deliverables from the workshop will include: (1) an initial shared bibliography to help formalize the state of knowledge in our area, (2) an agenda of shared goals, challenges, and intentions in our field, (3) a compilation of resources to support future work, and (4) social and organizing infrastructures for continued communication and academic collaboration. © 2022 Owner/Author.

关键词： Human computer interaction

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Dynamic Disaster Management with Real-Time IoT Data Analysis and Response

Dynamic Disaster Management with Real-Time IoT Data Analysis...

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Automation and Computation (AUTOCOM), International Conference on

作者： V Dankan Gowda Avinash Sharma Kdv Prasad Rini Saxena Tarkeshwar Barua Khalid Mohiuddin Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali Punjab India Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Department of Business Informatics College of Business King Khalid University PO Box-3247 Abha Saudi Arabia

As natural and manmade disasters grew in number, as a result the problem of how to quickly and effectively respond to disaster has become fresh. This is precisely the purpose of this research: Using IoT technologies and powerful data analysis techniques, to integrate them into existing disaster management systems. The beginning of the article contains a broadcasted statement to the effect that traditional methods are insufficient, and real-time data and preemptive response systems are needed. With the application of Internet of Things, an integrated system is proposed in which countless types of information such as weather conditions and seismic activity are gathered by sensors and actuators. Advanced machine learning algorithms and predictive modeling are used to analyze the gathered data. This allows us to make real-time decisions. The design and construction of an IoT-based disaster management system is the methodology behind the research. In particular, we will evaluate how effective it is at reducing response times and increasing overall resilience to disasters. The results show a high efficiency in response, which reflects the feasibility of the method. Finally, the paper discusses the problems encountered in implementing IoT and advanced data analysis of disaster risk and suggests future research avenues. There is no doubt that they will change the present disaster management practice forever.

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Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

arXiv

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arXiv 2024年

作者： Li, Haopeng Deng, Andong Liu, Jun Rahmani, Hossein Guo, Yulan Schiele, Bernt Bennamoun, Mohammed Ke, Qiuhong School of Computing and Information Systems University of Melbourne Australia Center for Research in Computer Vision University of Central Florida United States Pillar Singapore University of Technology and Design Singapore School of Computing and Communications Lancaster University United Kingdom School of Electronics and Communication Engineering Sun Yat-sen University China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Germany School of Physics Maths and Computing University of Western Australia Australia Department of Data Science & AI Monash University Australia

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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Automatic Plane Pose Estimation for Cardiac Left Ventricle Coverage Estimation via Deep Adversarial Regression Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4738-4752页

作者： Zhang, Le Bronik, Kevin Piechnik, Stefan K. Lima, Joao A. C. Neubauer, Stefan Petersen, Steffen E. Frangi, Alejandro F. University of Birmingham School of Engineering College of Engineering and Physical Sciences BirminghamB15 2TT United Kingdom University of Oxford Department of Engineering Science OxfordOX1 3PJ United Kingdom Division of Cardiovascular Medicine OxfordOX3 9DU United Kingdom Johns Hopkins University School of Medicine Division of Cardiology BaltimoreMD21287 United States Queen Mary University of London Centre for Cardiovascular Medicine The William Harvey Research Institute LondonE1 1BB United Kingdom Barts Health Nhs Trust Barts Heart Center LondonE1 1BB United Kingdom The University of Manchester Division of Informatics Imaging and Data Science School of Health Sciences The Department of Computer Science School of Engineering ManchesterM13 9PL United Kingdom

Accurate segmentation of the ventricles plays a crucial role in determining cardiac functional parameters such as ventricular volume, ventricular mass, and ejection fraction. However, poor image quality, such as inadequate coverage of the left ventricle (LV) and right ventricle (RV) in cardiac magnetic resonance (CMR) image sequences, can significantly affect the assessment of cardiac function. This study investigates issues related to missing or corrupted imaging planes, which often lead to incomplete ventricle coverage. To address the challenge of estimating ventricle coverage in CMR images regardless of variations in imaging parameters such as device type, magnetic field strength, and protocol execution, we introduce a novel convolutional neural network (CNN) based on adversarial learning. Additionally, we integrate supplementary information (e.g., cross-view image data) as privileged information to enhance the interpretability of our model's predictions and identify potential biases or inaccuracies. This research represents the first attempt to automatically estimate ventricular coverage by identifying missing slices and plane orientations in CMR images using a dataset-agnostic approach. The effectiveness of the proposed model is demonstrated through the evaluation of datasets from three diverse and sizable image acquisition cohorts, demonstrating superior performance compared to existing methods. © 2020 IEEE.

关键词： Image quality

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Promoting sustainable logistics in the electronics industry: Circular intuitionistic fuzzy framework for evaluating smart robotics technologies

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Expert Systems with Applications 2025年 287卷

作者： Athar Farid, Hafiz Muhammad Dabic-Miletic, Svetlana Jameel, Toqeer Simic, Vladimir Riaz, Muhammad Pamucar, Dragan Department of Computer Science University of Huddersfield Huddersfield United Kingdom University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 Belgrade11010 Serbia Department of Mathematics SIMATS Engineering Saveetha Institute of Mechanical and Technical Sciences Tamilnadu Chennai India Department of Mathematics University of the Punjab Lahore54590 Pakistan Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan İstinye University Faculty of Engineering and Natural Sciences Industrial Engineering Department Sarıyer İstanbul34396 Turkey

The integration of robotic smart technologies into manufacturing is essential for enhancing efficiency, reducing errors, and improving overall production quality. However, decision-making in this domain is often challenged by uncertainty and imprecise information. This study utilises circular intuitionistic fuzzy sets (CIFS) to tackle the challenge of ambiguity in expert evaluations. CIFS can capture both the membership and non-membership degrees of an evaluated element, while ensuring that these values remain within a circular boundary. The evaluation process initiates with the circular intuitionistic fuzzy (CIF) logarithmic percentage change-driven objective weighting (LOPCOW) method, which utilises logarithmic transforms to performance scores to determine the significance of each criterion. The framework proposes the CIF ranking comparison (RANCOM) method to ascertain subjective criterion weights. The CIF measurement of alternatives and ranking according to compromise solution (MARCOS) method is then suggested to rank the robotic smart technology alternatives based on multiple performance criteria. A case study is conducted to demonstrate the effectiveness of the proposed framework. The case study demonstrates that robotic smart technologies play a pivotal role in enhancing sustainability and efficiency in the electronics industry, but also present challenges such as high costs as well as potential ecology and social concerns. Sensitivity analysis and comparative evaluations confirm the stability and efficiency of the model, reinforcing its applicability in intelligent manufacturing. The findings help managers choose the best sustainable robotic alternative based on smart technology engagement. Fundamental guidelines for determining the most acceptable alternative for smart production and logistics activities are provided. © 2025 Elsevier Ltd

关键词： Industrial electronics

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Chinese E-Romance: Analyzing and Visualizing 7.92 Million Alibaba Valentine's Day Purchases

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Data and Information Management 2021年第4期5卷 363-371页

作者： Wang, Yongzhen Liu, Xiaozhong Ju, Yingnan Börner, Katy Lin, Jun Sun, Changlong Si, Luo WISE Lab Institute of Science of Science and S&T Management Faculty of Humanities and Social Sciences Dalian University of Technology Dalian China Computer Science Department Worcester Polytechnic Institute WorcesterMA United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Alibaba DAMO Academy Hangzhou China

The days that precede Valentine's Day are characterized by extensive gift shopping activities all across the globe. In China, where much shopping takes place online, there has been an explosive growth in e-commerce sales during Valentine's Day over the recent years. This exploratory study investigates the extent to which each product category and each shopper group can exhibit romantic love within China's e-market throughout the 2 weeks leading up to 2019 Valentine's Day. Massive data from Alibaba, the biggest e-commerce retailer worldwide, are utilized to formulate an innovative romance index (RI) to quantitatively measure e-romantic values for products and shoppers. On this basis, millions of shoppers, along with their millions of products purchased around Valentine's Day, are analyzed as a case study to demonstrate their love consumption and romantic gift-giving. The results of the analysis are then illustrated to help understand Chinese e-romance based on the perspectives of different product categories and shopper groups. This empirical information visualization also contributes to improving the segmentation, targeting, and positioning of China's e-market for Valentine's Day. © 2021 Yongzhen Wang et al., published by Sciendo

关键词： Sales

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