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arXiv 2024年

作者： Kou, Zhiqiang Xuan, Haoyuan Wang, Jing Jia, Yuheng Geng, Xin School of Computer Science and Engineering Southeast University Nanjing211189 China The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China The School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong

Label Distribution Learning (LDL) is a novel machine learning paradigm that addresses the problem of label ambiguity and has found widespread applications. Obtaining complete label distributions in real-world scenarios is challenging, which has led to the emergence of Incomplete Label Distribution Learning (InLDL). However, the existing InLDL methods overlook a crucial aspect of LDL data: the inherent imbalance in label distributions. To address this limitation, we propose Incomplete and Imbalance Label Distribution Learning (I2LDL), a framework that simultaneously handles incomplete labels and imbalanced label distributions. Our method decomposes the label distribution matrix into a low-rank component for frequent labels and a sparse component for rare labels, effectively capturing the structure of both head and tail labels. We optimize the model using the Alternating Direction Method of Multipliers (ADMM) and derive generalization error bounds via Rademacher complexity, providing strong theoretical guarantees. Extensive experiments on 15 real-world datasets demonstrate the effectiveness and robustness of our proposed framework compared to existing InLDL methods. © 2024, CC BY.

关键词： Adversarial machine learning

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Enhancing Older Adults’ Gesture Typing Experience Using the T9 Keyboard on Small Touchscreen Devices

arXiv

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arXiv 2023年

作者： Kuang, Emily Chen, Ruihuan Fan, Mingming Golisano College of Computing and Information Sciences Rochester Institute of Technology United States School of Information Rochester Institute of Technology United States Computational Media and Arts Thrust The Hong Kong University of Science and Technology Guangzhou China Division of Integrative Systems and Design Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

Older adults increasingly adopt small-screen devices, but limited motor dexterity hinders their ability to type effectively. While a 9-key (T9) keyboard allocates larger space to each key, it is shared by multiple consecutive letters. Consequently, users must interrupt their gestures when typing consecutive letters, leading to inefficiencies and poor user experience. Thus, we proposed a novel keyboard that leverages the currently unused key 1 to duplicate letters from the previous key, allowing the entry of consecutive letters without interruptions. A user study with 12 older adults showed that it significantly outperformed the T9 with wiggle gesture in typing speed, KSPC, insertion errors, and deletes per word while achieving comparable performance as the conventional T9. Repeating the typing tasks with 12 young adults found that the advantages of the novel T9 were consistent or enhanced. We also provide error analysis and design considerations for improving gesture typing on T9 for older adults. © 2023, CC BY-NC-ND.

关键词： Touch screens

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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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Over-the-Air Aggregation-Based Federated Learning in Cache-Enabled Wireless Edge Networks

Over-the-Air Aggregation-Based Federated Learning in Cache-E...

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IEEE International Conference on Communications (ICC)

作者： Jun-Pyo Hong Sangjun Park Wan Choi Pukyong National University Busan South Korea School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Electrical and Computer Engineering Seoul National University Seoul South Korea

In conventional federated learning (FL), dataset at the parameter server (PS) is not usually considered but may enhance the performance of FL if available. The benefit of leveraging the dataset at PS can be multiplied in over-the-air aggregation-based FL where combating local gradient update distortion against channel fading is inordinately consequential. In this paper, an over-the-air aggregation framework for communication efficient FL is investigated in cache-enabled wireless edge networks where not only edge devices but also a base station (BS) has its own local dataset. The proposed framework leverages the BS dataset to reduce the number of channel uses necessary for the model convergence and to avoid the overhead incurred by power scale coordination and global channel state information (CSI) acquisition at BS. We present a sufficient condition for convergence to a stationary point without convexity assumption on the objective function. Based on the sufficient condition, a power control method is optimized to facilitate the model convergence without assumptions on power scale coordination and global CSI at BS. Our simulation results validate that BS dataset is beneficial to reduce the number of channel uses for the model convergence and the developed power control method outperforms the conventional method in terms of both convergence rate and converged test accuracy.

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ME-GraphSAGE: Minority Class Feature Enhanced GraphSAGE for Automatic Labeling of Coronary Arteries 18th

ME-GraphSAGE: Minority Class Feature Enhanced GraphSAGE for ...

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18th Chinese Conference on Image and Graphics technology and Application Conference, IGTA 2023

作者： Ding, Yang Fu, Tianyu Chen, Sigeng Xiao, Deqiang Fan, Jingfan Song, Hong Yu, Yang Yang, Jian Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photoics Beijing Institute of Technology Beijing China School of Medical Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Cardiac Surgery Beijing Anzhen Hospital Capital Medical University Beijing China

ISBN: (纸本)9789819975488

Automatic labeling of coronary artery segments improves efficiency in the diagnosis and treatment of coronary artery disease, but faces challenges due to the class imbalance between main and side branches. State-of-the-art methods primarily focus on position-direction and pixel features, which leads to suboptimal performance when dealing with bifurcated segments. In this paper, we propose a minority class feature enhanced GraphSAGE (ME-GraphSAGE), which alleviates class imbalance by generating minority class nodes. We extract bifurcation features from Digital Subtraction Angiography (DSA) images taken from four commonly observed views of coronary arteries. These features, along with other relevant ones, are fed into ME-GraphSAGE to enhance the accuracy of segment labeling in bifurcated regions. By combining the results from the four views, a higher-level sixteen-segment-based coronary labeling is obtained. Our method is evaluated on a dataset of 205 coronary DSA sequences. The experimental results show that ME-GraphSAGE significantly outperforms state-of-the-art methods in labeling coronary artery branches. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Bifurcation (mathematics)

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MAA: Modulation-adaptive Acoustic Gesture Recognition

MAA: Modulation-adaptive Acoustic Gesture Recognition

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Yingxin Shan Peng Liao Xuyu Wang Lingling An Shiwen Mao Guangzhou Institute of Technology Xidian University Xi’an China Knight Foundation School of Computing & Information Sciences Florida International University Miami FL USA School of Computer Science and Technology Xidian University Xi’an China Department of Electrical and Computer Engineering Auburn University Auburn AL USA

In this paper, we propose a modulation-adaptive acoustic gesture recognition system with smartphones (termed, MAA), which can achieve a high recognition accuracy under various modulation schemes and quickly adapt to a new modulation at low cost. Specifically, MAA creates an acoustic channel model to capture temporal and spatial features, and leverages a domain adversarial network to eliminate the difference among modulation schemes when performing the same gesture. The proposed framework includes a data collection module, a signal preprocessing module, a channel construction module, and a domain adaptation module. For data collection, we determine the appropriate signal length and bandwidth for transmitted acoustic signals. In the signal preprocessing module, channel estimation and background noise removal are incorporated. Then, we develop a tensor reconstruction network and a feature mapping network in the channel construction module to directly map features to specific gestures. For domain adaptation, we train the above two networks in the source domain, and use an adversarial network to adapt to the target domain. Experimental results show that the proposed MAA achieves a good performance on gesture recognition with different modulation schemes, with better adaptation to new modulation schemes than several state-of-the-art baselines.

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Effective Location-based Recommendation Systems for Holiday using RBM Machine Learning Approach

Effective Location-based Recommendation Systems for Holiday ...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： Ramkumar Jayaraman Karthikeyan C A. Sahaya Anselin Nisha K. Somasundaram N. Naga Saranya Vijendra Babu D Department of Computing Technology SRM Institue of Science and Technology Kattankulathur Tamil Nadu India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Saveetha School of Engineering SIMATS Institute of Information Technology Chennai Tamil Nadu India Departement of MCA Meenakshi College of Engineering Chennai Tamil Nadu India School of Electronics Engineering Vellore Institute of Technology Vellore Tamil Nadu India

Application developers and researchers took many steps in finding out proper tourism recommendations for various seasons. With the faster development in the travel department through modern technologies, it has gotten fundamental to present headways and upgrades in the administrations given to the sightseers, to ensure their ease of travel and satisfaction. Over the years, there has been no optimal system providing all the necessities required by a tourist. Based on the holidata recommendation, proposed system makes to decrease the time spent on the planning period and it helps to increase the deployment of process to be more effective. Regarding the customer preferences, the customer details and their location are shared and there are some other information available based on the users are, their destination trip, dates for the travel, budget and there are other user attractive aspects as it helps to have the effective trip. Based on the above things, the travel can be planned for the trip entirely. This system uses RBM to predict the user ratings and recommend the best attraction. An attempt has been made to reduce the MAE in RBM prediction.

关键词： Machine learning algorithms Machine learning Approximation algorithms Prediction algorithms Planning Unsupervised learning Recommender systems

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System for Predicting Heart Disease using Sophisticated Machine Learning Techniques

System for Predicting Heart Disease using Sophisticated Mach...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Trilok Nath Pandey Bibhuti Bhusan Dash Lalbihari Barik Utpal Chandra De Abinash Tripathy Sudhansu Shekhar Patra School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India School of Computer Applications KIIT Deemed to be University Bhubaneswar India Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Kingdom of Saudi Arabia School of Computer Applications KIIT Deemed to be University Bhubaneswar Odisha India

Cardiovascular illness stands as a significant global health issue, and timely identification of this condition holds paramount importance to ensure effective intervention. In this paper, we introduce a fused model for anticipating heart ailments. This amalgamation incorporates four distinct algorithms: LightGBM, XGBoost, Multilayer Perceptron, and AdaBoost. Through an assessment on the Cleveland dataset, AdaBoost emerged as the most accurate with a precision of 0.985. Subsequently, XGBoost followed closely with an accuracy of 0.980, succeeded by LightGBM at 0.956, and Multilayer Perceptron at 0.936. To harness the collective power of these models, we employed a weighted bagging technique that accords weights to predictions based on their individual accuracy rates. The resultant composite model attained an accuracy of 0.96. This outcome is slightly lower than the model with the highest accuracy, as it involves a weighted mean calculation across model predictions. This study indicates the potential utility of the proposed methodology in cardiac disease prediction, leveraging the ensemble approach to potentially outperform standalone models.

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Tomo-NeRL: tomographic neural representation learning for implicit CT image reconstruction

Tomo-NeRL: tomographic neural representation learning for im...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoqin Tang Boheng Tan Yuanhao Guo Xianping Yu Jake Kendrick Mark Reynolds College of Computer and Information Science Southwest University Chongqing China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Research Institute of Highway Ministry of Transport Beijing China School of Physics Mathematics and Computing The University of Western Australia Perth Australia Centre for Advanced Technologies in Cancer Research (CATCR) Perth Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

CT image reconstruction is a challenging inverse problem of estimating image intensities from sensor signals. While deep learning-based methods hold promise, they are typically classified into fully-supervised, self-supervised, and case-by-case approaches, each offering distinct advantages and limitations. Among these, case-by-case reconstruction techniques, exemplified by models like NeRP, provide the advantage of flexible data-sampling and image sizes, making them well-suited for the complex CT inverse problem. However, existing case-by-case models encounter difficulties in achieving precise reconstructions, particularly in sparse-view scenarios. To address this limitation, we propose tomo-NeRL, a novel implicit reconstruction model that incorporates tomographic information of individual pixels within the reconstruction network. By harnessing this tomo-graphic data for implicit inverse learning, tomo-NeRL enhances reconstruction quality while effectively mitigating artifacts. The core innovation of tomo-NeRL lies in defining and extracting tomographic insights, such as visual characteristics and spatial variations specific to tomographic imaging, from the sinogram data for individual pixels. Moreover, our work introduces an adaptive inversion framework that amalgamates tomographic information from diverse projection angles, constructing a latent feature space that enhances the reconstruction process. Extensive experiments unequivocally showcase tomo-NeRL’s outstanding reconstruction performance, excelling in artifact suppression and detail preservation, especially in sparse-view scenarios.

关键词： Representation learning Visualization Technological innovation Inverse problems Computed tomography Training data Tomography Reconstruction algorithms Data mining Image reconstruction

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A differential public PUF design for lightweight authentication 16

A differential public PUF design for lightweight authenticat...

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2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Duan, Shengyu Sai, Gaole School of Computer Engineering and Science Shanghai University Shanghai200444 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9783800755899

Physical Unclonable Functions (PUFs) have emerged as a promising primitive to provide a hardware keyless security mechanism for integrated circuit applications. Public PUFs (PPUFs) address the crucial PUF vulnerability of requiring databases to store the secrete reference information. This paper investigates one of the PPUF families, differential PPUF (dPPUF), which exploits complexly interacted logic to create time gap between actual execution and simulation, and to prevent simulation attacks. Conventional dPPUFs have high costs. We observe the repressers have large impact on simulation time, but take a great proportion in current dPPUF structure. We thereby present a represser network with specially designed interconnection and shared logic, to maintain a high circuit complexity and to reduce the area cost. We show the proposed dPPUF has similar resilience over simulation attacks as the conventional design. Area cost can be reduced by up to 44%, and thus the proposed design can be used for lightweight authentication. © VDE VERLAG GMBH.

关键词： Authentication

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