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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Patibanda, Rakesh Overdevest, Nathalie Nisal, Shreyas Saini, Aryan Elvitigala, Don Samitha Knibbe, Jarrod van den Hoven, Elise Mueller, Florian Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia School of Electrical Engineering and Computer Science Faculty of Engineering The University of Queensland St LuciaQLD Australia Materialising Memories & Visualisation Institute University of Technology Sydney Sydney Australia Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9798400705830

Traditional games like "Tag" rely on shared control via inter-body interactions (IBIs) – touching, pushing, and pulling – that foster emotional and social connection. Digital games largely limit IBIs, with players using their bodies as input to control virtual avatars instead. Our "Shared Bodily Fusion" approach addresses this by fusing players’ bodies through a mediating computer, creating a shared input and output system. We demonstrate this approach with "Hidden Touch", a game where a novel social electrical muscle stimulation system transforms touch (input) into muscle actuations (output), facilitating IBIs. Through a study (n=27), we identifed three player experience themes. Informed by these fndings and our design process, we mapped their trajectories across our three experiential spaces – threshold, tolerance, and precision – which collectively form our design framework. This framework facilitates the creation of future digital games where IBIs are intrinsic, ultimately promoting the many benefts of social play. © 2024 Copyright held by the owner/author(s).

关键词： Muscle

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Enhanced Multidimensional Harmonic Retrieval in MIMO Wireless Channel Sounding

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IEEE Internet of Things Journal 2025年第11期12卷 16243-16255页

作者： Zhang, Yanming Xu, Wenchao Jin, A-Long Tang, Tianquan Li, Min Ma, Peifeng Jiang, Lijun Gao, Steven The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong Xi'an Jiaotong-Liverpool University Department of Communications and Networking Suzhou China HKU Zhejiang Institute of Research and Innovation Lab for Aerodynamics and Acoustics Hangzhou China Tianjin University School of Microelectronics Tianjin China The Chinese University of Hong Kong Institute of Space and Earth Information Science Department of Geography and Resource Management Hong Kong Hong Kong Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO United States

This paper introduces a recursive parallel dynamic mode decomposition (RPDMD) scheme tailored for multidimensional harmonic retrieval (MHR), specifically applied to MIMO wireless channel sounding. The RPDMD algorithm is devised to address the complexities inherent in multidimensional scenarios, leveraging the dynamic mode decomposition (DMD) framework within a recursive parallel structure. Initially, the observed tensorial multidimensional harmonic data is transformed into a two-dimensional matrix format along the r-th dimension. Subsequently, DMD dissects this matrix data into eigenvalues and their associated modes. The real and imaginary components of the DMD eigenvalues yield damping factors and frequencies in the r-th dimension, respectively. Furthermore, recursive DMD is employed to scrutinize each mode independently for parameter retrieval across the remaining dimensions, enabling parallel analysis. Ultimately, this high-dimensional correlated decomposition scheme delivers paired damping factors and frequencies for all tones. Notably, the proposed approach can ascertain the number of tones in undamped sinusoidal signals, making it particularly suitable for MHR even without prior knowledge of the source count. Numerical experiments demonstrate the accuracy and robustness of the RPDMD scheme, with comparative analysis indicating that RPDMD outperforms similar methods, achieving optimal results with minimal mean square error in high signal-to-noise ratio scenarios. This work presents an effective data-driven solution for the MHR problem in MIMO wireless channel sounding. © 2014 IEEE.

关键词： Eigenvalues and eigenfunctions

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AUTOAL: AUTOMATED ACTIVE LEARNING WITH DIFFERENTIABLE QUERY STRATEGY SEARCH

arXiv

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arXiv 2024年

作者： Wang, Yifeng Zhan, Xueying Huang, Siyu Department of Electrical and Computer Engineering Carnegie Mellon University United States Ray and Stephanie Lane Computational Biology Department Carnegie Mellon University United States Visual Computing Division School of Computing Clemson University United States

As deep learning continues to evolve, the need for data efficiency becomes increasingly important. Considering labeling large datasets is both time-consuming and expensive, active learning (AL) provides a promising solution to this challenge by iteratively selecting the most informative subsets of examples to train deep neural networks, thereby reducing the labeling cost. However, the effectiveness of different AL algorithms can vary significantly across data scenarios, and determining which AL algorithm best fits a given task remains a challenging problem. This work presents the first differentiable AL strategy search method, named AutoAL, which is designed on top of existing AL sampling strategies. AutoAL consists of two neural nets, named SearchNet and FitNet, which are optimized concurrently under a differentiable bi-level optimization framework. For any given task, SearchNet and FitNet are iteratively co-optimized using the labeled data, learning how well a set of candidate AL algorithms perform on that task. With the optimal AL strategies identified, SearchNet selects a small subset from the unlabeled pool for querying their annotations, enabling efficient training of the task model. Experimental results demonstrate that AutoAL consistently achieves superior accuracy compared to all candidate AL algorithms and other selective AL approaches, showcasing its potential for adapting and integrating multiple existing AL methods across diverse tasks and domains. Code will be available at: https://***/haizailache999/AutoAL. © 2024, CC BY.

关键词： Active learning

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Socio-technical Approaches to Solid Waste Management in Rural India: A Case Study of Smart Pipe Composting in Raichur District, Karnataka 8th

Socio-technical Approaches to Solid Waste Management in Rura...

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Chethan, R.K. Jambha, Aniketh V. Pathania, Chirag Siddhartha, Mutyala Sai Sri Sanjay Arifuzzaman Darshan, K. Cornet, Souresh Jayaprakash, Sajithkumar K. Department of Commerce and Management School of Arts Humanities and Commerce Amrita Vishwa Vidyapeetham Mysuru India Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Artificial Intelligence Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus Karnataka Mysuru India Department of Visual Communication School of Arts Humanities and Commerce Amrita Vishwa Vidyapeetham Mysuru India Amrita School for Sustainable Futures Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819994885

Rural areas in developing countries are plagued with complex, intricate challenges. In India, rural communities are seriously lagging regarding Sanitation and Waste management facilities. The majority of communities lack the resources to address those issues. Government programs have partly been able to address those issues, but much still needs to be done to solve the challenges completely. This study aimed to understand better the challenges associated with Solid Waste Management in rural India. It specifically explores how technology can help to complete the last mile of sustainability through a user-centered and participatory problem-solving approach. Taking the case of a community in Raichur district, Karnataka, a smart pipe composting design is proposed to address the Solid Waste Management challenges in the community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Waste management

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Exploring transfer learning for Deep NLP systems on rarely annotated languages

arXiv

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arXiv 2024年

作者： Yadav, Dipendra Strauß, Tobias Yordanova, Kristina University of Rostock Department of Computer Science and Electrical Engineering Institute of Visual & Analytic Computing Mobile Multimedia Information Systems Albert-Einstein-Str. 22 Rostock18059 Germany

Natural language processing has seen exponential growth since deep learning has been used for various tasks. Deep learning has replaced the old rule-based methods by providing better performance on almost all the tasks in NLP, as deep learning basically looks into the hidden patterns and structures in the data which were sometimes not detected and at other-times overlooked by the rule-based systems. The research and development in NLP have been however focused on some handful languages of the world given their financial importance and a huge number of speakers. This has left out most of the other languages unexplored. The other reason for the lack of research in these languages is the unavailability of properly annotated and the required amount of datasets which is very important for training deep learning neural networks. The situation looks grim. When we look at the world language tree, thankfully we see that most of the unexplored languages have other well-explored languages nearby which basically means that both the languages are kind of similar. So if we could somehow adapt research and development from one language to another, it could be really helpful for both the languages. The goal of this thesis work is to explore transfer learning for the task of Part-of-speech(POS) tagging between Hindi and Nepali languages which are part of the Indo-Aryan language family with very high similarity. We have tried to explore the possibility of jointly training a model for the task of POS tagging in both Hindi and Nepali language and see if it helps in improving the performance of the model. We also try to explore if multitask learning in the Hindi language can be helpful for the task of POS tagging with auxiliary tasks of the gender tagging and singularity/plurality tagging. The deep learning architecture used for this work is BLSTM-CNN-CRF. The model is trained with monolingual word embeddings, vector mapped embeddings, and also with jointly trained Hindi-Nepali word emb

关键词： Embeddings

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Psscl: A Progressive Sample Selection Framework with Contrastive Loss Designed for Noisy labels

SSRN

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SSRN 2024年

作者： Zhang, Qian Zhu, Yi Cordeiro, Filipe R. Chen, Qiu School of Information Technology Jiangsu Open University Nanjing210036 China Department of Electrical Engineering and Electronics Graduate School of Engineering Kogakuin University Tokyo163-8677 Japan Visual Computing Lab Department of Computing Universidade Federal Rural de Pernambuco Brazil

Large-scale image datasets frequently contain unavoidable noisy labels, resulting in overfitting in deep neural networks and declining performance. Most existing methods for learning from noisy labels operate as one-stage frameworks, where training data division and semi-supervised learning (SSL) are intertwined for optimization. Accordingly, their effectiveness is significantly influenced by the precision of the separated clean set, prior knowledge of noise, and the robustness of SSL. In this paper, we propose a progressive sample selection framework with contrastive loss for noisy labels named PSSCL. This framework operates in two stages, using robust and contrastive losses to augment the robustness of the model. The first stage focuses on identifying a small clean set through a long-term confidence detection strategy, while the second stage aims to enhance performance by expanding this clean set. PSSCL demonstrates significant improvement across various benchmarks when compared with state-of-the-art methods. The code will be available at https://***/LanXiaoPang613/PSSCL. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks

arXiv

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arXiv 2023年

作者： Xiong, Ziliang Jonnarth, Arvi Eldesokey, Abdelrahman Johnander, Joakim Wandt, Bastian Forssén, Per-Erik Computer Vision Laboratory Department of Electrical Engineering LiU Sweden Visual Computing Center KAUST Saudi Arabia Husqvarna Group Huskvarna Sweden Zenseact Sweden

computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it is common to detect uncertainty by predicting probability distributions. In this context, we investigate the regression-by-classification paradigm which can represent multimodal distributions, without a prior assumption on the number of modes. Through experiments on a specifically designed synthetic dataset, we demonstrate that traditional loss functions lead to poor probability distribution estimates and severe overconfidence, in the absence of full ground truth distributions. In order to alleviate these issues, we propose hinge-Wasserstein – a simple improvement of the Wasserstein loss that reduces the penalty for weak secondary modes during training. This enables prediction of complex distributions with multiple modes, and allows training on datasets where full ground truth distributions are not available. In extensive experiments, we show that the proposed loss leads to substantially better uncertainty estimation on two challenging computer vision tasks: horizon line detection and stereo disparity estimation. The code is available at:https://***/XZLeo/ hinge-Wasserstein Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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Psscl: A Progressive Sample Selection Framework with Contrastive Loss Designed for Noisy labels

SSRN

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SSRN 2024年

关键词： Deep neural networks

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Multi-Level Speaker Representation for Target Speaker Extraction

Multi-Level Speaker Representation for Target Speaker Extrac...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Ke Li, Junjie Wang, Shuai Wei, Yangjie Wang, Yi Wang, Yannan Li, Haizhou Key Laboratory of Intelligent Computing in Medical Image Northeastern University China SDS SRIBD The Chinese University of Hong Kong Shenzhen China The Hong Kong Polytechnic University Hong Kong Tencent Ethereal Audio Lab Tencent Shenzhen China University of Bremen Germany Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)9798350368741

Target speaker extraction (TSE) relies on a reference cue of the target to extract the target speech from a speech mixture. While a speaker embedding is commonly used as the reference cue, such embedding pre-trained with a large number of speakers may suffer from confusion of speaker identity. In this work, we propose a multi-level speaker representation approach, from raw features to neural embeddings, to serve as the speaker reference cue. We generate a spectral-level representation from the enrollment magnitude spectrogram as a raw, low-level feature, which significantly improves the model's generalization capability. Additionally, we propose a contextual embedding feature based on cross-attention mechanisms that integrate frame-level embeddings from a pre-trained speaker encoder. By incorporating speaker features across multiple levels, we significantly enhance the performance of the TSE model. Our approach achieves a 2.74 dB improvement and a 4.94% increase in extraction accuracy on Libri2mix test set over the baseline. © 2025 IEEE.

关键词： Cocktail party problem selective auditory attention speaker confusion speaker feature target speaker extraction

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Advancing Proactive Producer Mobility Management in Named Data Networking: A Conceptual Model 7

Advancing Proactive Producer Mobility Management in Named Da...

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7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Zukri, Nurul Hidayah Ahmad Arif, Ahmad Suki Che Mohamed Alsamman, Mohammed Abrar, Ahmad Fazea, Yousef Universiti Teknologi MARA Cawangan Perlis Kampus Arau Perlis Malaysia School of Computing Universiti Utara Malaysia Kedah Malaysia InterNetWorks Research Lab Universiti Utara Malaysia Kedah Malaysia Department of Computer Science and Electrical Engineering Marshall University One John Marshall Dr. HuntingtonWV25755 United States

ISBN: (纸本)9798331519513

In recent years, the Named Data Networking (NDN) paradigm has emerged as a promising candidate for future Internet architecture. Underpinned by the notion of a data-centric forwarding philosophy, NDN is purported to offer seamless network communication for mobile users. However, while it provides inherent support for consumer mobility, viable solutions for producer mobility remain insufficient. Therefore, this study aims to highlight the mobility issues in NDN, particularly concerning high latency, Interest packet loss, and Interest retransmission, with a focus on producer mobility. In this paper, we critically review previous works based on proactive and reactive approaches. Furthermore, we propose a mobility management model that adopts a proactive approach, estimating the producer's handoff time from the connected access point. The proposed model leverages this information to notify consumers about the moving producer, aiming to prevent consumers from sending Interest to the mobile producer during handoff and thereby overcome the associated challenges. © 2024 IEEE.

关键词： Named Data Network Proactive Producer Mobility

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