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arXiv 2022年

作者： Wang, Chunzhuo Kumar, T. Sunil De Raedt, Walter Camps, Guido Hallez, Hans Vanrumste, Bart e-Media Research Lab Belgium ESAT-STADIUS Division KU Leuven Leuven3000 Belgium Vellore Institute of Technology Chennai India Life Science Department IMEC Heverlee3001 Belgium Division of Human Nutrition and Health Department of Agrotechnology and Food Sciences Wageningen University and Research Wageningen6700EA Netherlands One-Planet Research Center Wageningen6708WE Netherlands M-Group DistriNet Department of Computer Science KU Leuven Sint-Michiels8200 Belgium

Unhealthy dietary habits are considered as the primary cause of various chronic diseases, including obesity and diabetes. The automatic food intake monitoring system has the potential to improve the quality of life (QoL) of people with diet-related diseases through dietary assessment. In this work, we propose a novel contactless radar-based approach for food intake monitoring. Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is employed to recognize fine-grained eating and drinking gestures. The fine-grained eating/drinking gesture contains a series of movements from raising the hand to the mouth until putting away the hand from the mouth. A 3D temporal convolutional network with self-attention (3D-TCN-Att) is developed to detect and segment eating and drinking gestures in meal sessions by processing the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this work collects data in continuous meal sessions (more realistic scenarios). We create a public dataset comprising 70 meal sessions (4,132 eating gestures and 893 drinking gestures) from 70 participants with a total duration of 1,155 minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are included in this dataset. To validate the performance of the proposed approach, seven-fold cross-validation method is applied. The 3D-TCN-Att model achieves a segmental F1-score of 0.896 and 0.868 for eating and drinking gestures, respectively. The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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QA-KGNet: A Language Model-Driven Knowledge Graph Question-Answering Model

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Ruan Jian Xue Bao/Journal of Software 2023年第10期34卷

作者： Qiao, Shao-Jie Yang, Guo-Ping Yu, Yong Han, Nan Qin, Xiao Qu, Lu-Lu Ran, Li-Qiong Li, He School of Software Engineering Chengdu University of Information Technology Chengdu610225 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China School of Management Chengdu University of Information Technology Chengdu610225 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China School of Computer Science and Technology XiDian University Xi’an710071 China

The question-answering system based on knowledge graphs can analyze user questions, and has become an effective way to retrieve relevant knowledge and automatically answer the given questions. The knowledge graph-based question-answering system usually uses a neural program induction model to convert natural language question into a logical form, and the answer can be obtained by executing the logical form on the knowledge graph. However, the knowledge question-answering system by using pre-trained language models and knowledge graphs involves two challenges: 1) given the QA (Question-answering) context, relevant knowledge needs to be identified from a large KG (Knowledge Graph);2) we need to perform the joint reasoning on QA context and KG. Based on these challenges, a language model-driven knowledge graph question-answering model is proposed, which connects the QA context and KG to form a joint graph, and uses a language model to calculate the relevance of the given QA context nodes and KG nodes, and a multi-head graph attention network is employed to update the node representation. Extensive experiments on the CommonsenseQA, OpenBookQA and MedQA-USMLE real datasets are conducted to evaluate the performance of QA-KGNet and the experimental results show that QA-KGNet outperforms existing benchmark models and exhibits excellent structured reasoning capability. © 2023 Chinese Academy of sciences. All rights reserved.

关键词： Knowledge graph

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Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation 9th

Attention Based Hierarchical Aggregation Network for 3D Left...

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9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and computer-Assisted Intervention, MICCAI 2018

作者： Li, Caizi Tong, Qianqian Liao, Xiangyun Si, Weixin Sun, Yinzi Wang, Qiong Heng, Pheng-Ann School of Computer Science Wuhan University Wuhan430072 China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518000 China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)9783030120283

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. The atrial segmentation is essential for the understanding of the human atria structure which is vital to the AF treatment. In this paper, we propose a novel three-dimensional (3D) segmentation network combining hierarchical aggregation and attention mechanism for 3D left atrial segmentation, named attention based hierarchical aggregation network (HAANet). In our network, the shallow and deep feature fusion capability of encoder-decoder convolutional neural networks is enhanced through hierarchical aggregation. Besides, attention mechanism is adopted to promote the ability of extracting efficient features. Experimental results demonstrate the HAANet can produce good results for 3D left atrial segmentation and the dice score of our HAANet reaches 92.30. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

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Toward whole-genome inference of polygenic scores with fast and memory-efficient algorithms

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American journal of human genetics 2025年

作者： Shadi Zabad Chirayu Anant Haryan Simon Gravel Sanchit Misra Yue Li School of Computer Science McGill University Montreal QC Canada. Parallel Computing Lab Intel Labs Bangalore Karnataka India. Department of Human Genetics McGill University Montreal QC Canada. Electronic address: simon.gravel@mcgill.ca. School of Computer Science McGill University Montreal QC Canada. Electronic address: yueli@cs.mcgill.ca.

With improved whole-genome sequencing and variant imputation techniques, modern genome-wide association studies (GWASs) have enriched our understanding of the landscape of genetic associations for thousands of disease phenotypes. However, translating the marginal associations for millions of genetic variants to integrated polygenic risk scores (PRSs) that capture their joint effects on the phenotype remains a major challenge. Due to technical and statistical constraints, commonly used PRS methods in this setting either perform heuristic pruning and thresholding or overlook most genetic association signals by restricting inference to small variant sets, such as HapMap3. Here, we present a set of algorithmic improvements and compact data structures that enable scaling summary-statistics-based PRS inference to tens of millions of variants while avoiding numerical instabilities common in such high-dimensional settings. These enhancements consist of highly compressed linkage-disequilibrium (LD) matrix format, which integrates with streamlined and parallel coordinate-ascent updating schemes. When incorporated into our existing PRS method (VIPRS), the proposed algorithms yield over 50-fold reductions in storage requirements and lead to orders-of-magnitude improvements in runtime and memory efficiency. The updated VIPRS software can now perform variational Bayesian regression over 1.1 million HapMap3 variants in under a minute. Using this scalable implementation, we applied VIPRS to 75 of the most heritable, continuous phenotypes in the UK Biobank, leveraging marginal associations for up to 18 million bi-allelic variants. These experiments demonstrated that VIPRS is 1-2 orders of magnitude more efficient than popular baselines while being competitive with the best-performing methods in terms of prediction accuracy.

关键词： Bayesian inference GWAS LD algorithms inference linkage disequilibrium polygenic prediction polygenic risk score whole-genome analysis

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Text compression-aided transformer encoding

arXiv

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arXiv 2021年

作者： Li, Zuchao Zhang, Zhuosheng Zhao, Hai Wang, Rui Chen, Kehai Utiyama, Masao Sumita, Eiichiro Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Kyoto619-0289 Japan

Text encoding is one of the most important steps in Natural Language Processing (NLP). It has been done well by the self-attention mechanism in the current state-of-the-art Transformer encoder, which has brought about significant improvements in the performance of many NLP tasks. Though the Transformer encoder may effectively capture general information in its resulting representations, the backbone information, meaning the gist of the input text, is not specifically focused on. In this paper, we propose explicit and implicit text compression approaches to enhance the Transformer encoding and evaluate models using this approach on several typical downstream tasks that rely on the encoding heavily. Our explicit text compression approaches use dedicated models to compress text, while our implicit text compression approach simply adds an additional module to the main model to handle text compression. We propose three ways of integration, namely backbone source-side fusion, target-side fusion, and both-side fusion, to integrate the backbone information into Transformer-based models for various downstream tasks. Our evaluation on benchmark datasets shows that the proposed explicit and implicit text compression approaches improve results in comparison to strong baselines. We therefore conclude, when comparing the encodings to the baseline models, text compression helps the encoders to learn better language representations. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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A smart filtering-based adaptive optimized link state routing protocol in flying ad hoc networks for traffic monitoring

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Journal of King Saud University - computer and Information sciences 2024年第4期36卷

作者： Hosseinzadeh, Mehdi Ali, Saqib Rahmani, Amir Masoud Lansky, Jan Nulicek, Vladimir Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Sulaymaniyah Kurdistan region Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Nowadays, the use of drones as a fundamental element of smart cities has attracted the attention of many researchers to monitor and control the traffic of vehicles. Because of the high flexibility of multi-drone systems, like flying ad hoc networks (FANETs), they provide various services and improve modern life in smart cities. However, due to the unique features of FANET, especially the high speed of drones and rapid changes in network topology, communication reliability is a serious challenge in this network. Hence, traditional routing protocols, such as optimized link state routing (OLSR) scheme, cannot work well in these networks. In this paper, a smart filtering-based adaptive optimized link state routing (SFA-OLSR) scheme is proposed in FANETs. To increase adaptability to the FANET environment, SFA-OLSR provides a new solution to adjust the hello broadcast period so that each flying node specifies its broadcast period based on a new scale called cosine similarity between real and predicted positions. Furthermore, in SFA-OLSR, each flying node develops a filtering algorithm based on two parameters, namely link lifetime and remaining energy. The purpose of this algorithm is to reduce the size of the single-hop neighboring set of each flying node and minimize the search space when finding multi-point relays (MPRs). This increases the convergence speed of the algorithm. Then, SFA-OLSR exploits the sparrow search algorithm (SSA) to single out the best MPRs. This algorithm introduces a multi-objective function by focusing on three components, including energy, link lifespan, and neighbor degree. Lastly, the simulation process of SFA-OLSR is performed by the NS3 simulator. This process evaluates the performance of the proposed method and three schemes, namely Gangopadhyay et al., P-OLSR, and OLSR-ETX. These evaluations show that SFA-OLSR has a good performance in terms of three scales, namely packet delivery ratio, delay, and throughput, but its overhead is more than

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Meta-heuristic algorithms Reliability Routing

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Can i trust you? A user study of robot mediation of a support group

arXiv

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arXiv 2020年

作者： Birmingham, Chris Hu, Zijian Mahajan, Kartik Reber, Eli Matarić, Maja J. Interaction Lab CS Department University of Southern California Los AngelesCA United States Interaction Lab CS Department University of Southern California Los AngelesCA United States Interaction Lab CS Department University of Southern California Los AngelesCA United States Pennsylvania State University State CollegePA United States PI of the Interaction Lab Professor of Computer Science Neuroscience and Pediatrics University of Southern Cali-fornia

Socially assistive robots have the potential to improve group dynamics when interacting with groups of people in social settings. This work contributes to the understanding of those dynamics through a user study of trust dynamics in the novel context of a robot mediated support group. For this study, a novel framework for robot mediation of a support group was developed and validated. To evaluate interpersonal trust in the multi-party setting, a dyadic trust scale was implemented and found to be uni-factorial, validating it as an appropriate measure of general trust. The results of this study demonstrate a significant increase in average interpersonal trust after the group interaction session, and qualitative post-session interview data report that participants found the interaction helpful and successfully supported and learned from one other. The results of the study validate that a robot-mediated support group can improve trust among strangers and allow them to share and receive support for their academic stress. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamics

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Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds (a short report)

arXiv

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arXiv 2021年

作者： Nguyen, Long H. Pham, Nhat Truong Do, Van Huong Nguyen, Liu Tai Nguyen, Thanh Tin Do, Van Dung Nguyen, Hai Nguyen, Ngoc Duy Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam ASICLAND Suwon Korea Republic of Human Computer Interaction Lab Sejong University Seoul Korea Republic of Hanoi University of Industry Hanoi Viet Nam Khoury College of Computer Science Northeastern University Boston United States Institute for Intelligent Systems Research and Innovation Deakin University VIC Australia

SARS-CoV-2 is colloquially known as COVID-19 that had an initial outbreak in December 2019. The deadly virus has spread across the world, taking part in the global pandemic disease since March 2020. In addition, a recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths over the world. Therefore, it is vital to possess a selftesting service of SARS-CoV-2 at home. In this study, we introduce Fruit-CoV, a two-stage vision framework, which is capable of detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, we convert sounds into Log-Mel Spectrograms and use the EfficientNet-V2 network to extract its visual features in the first stage. In the second stage, we use 14 convolutional layers extracted from the large-scale Pretrained Audio Neural Networks for audio pattern recognition (PANNs) and theWavegram-Log-Mel-CNN to aggregate feature representations of the Log-Mel Spectrograms. Finally, we use the combined features to train a binary classifier. In this study, we use a dataset provided by the AICovidVN 115M Challenge, which includes a total of 7371 recorded cough sounds collected throughout Vietnam, India, and Switzerland. Experimental results show that our proposed model achieves an AUC score of 92.8% and ranks the 1st place on the leaderboard of the AICovidVN Challenge. More importantly, our proposed framework can be integrated into a call center or a VoIP system to speed up detecting SARS-CoV-2 infections through online/recorded cough sounds. © 2021, CC BY.

关键词： SARS

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Creating expert knowledge by relying on language learners: A generic approach for mass-producing language resources by combining implicit crowdsourcing and language learning 12

Creating expert knowledge by relying on language learners: A...

引用

12th International Conference on Language Resources and Evaluation, LREC 2020

作者： Nicolas, Lionel Lyding, Verena Borg, Claudia Forascu, Corina Fort, Karën Zdravkova, Katerina Kosem, Iztok Cibej, Jaka Holdt, Špela Arhar Millour, Alice König, Alexander Rodosthenous, Christos Sangati, Federico Ul Hassan, Umair Katinskaia, Anisia Barreiro, Anabela Aparaschivei, Lavinia HaCohen-Kerner, Yaakov Institute for Applied Linguistics Eurac Research Bolzano Italy Faculty of Information and Communication Technology University of Malta Msida Malta Faculty of Computer Science Alexandru Ioan Cuza University of Iasi Romania Sorbonne Université STIH - EA 4509 France Ss. Cyril and Methodius University Faculty of Computer Science and Engineering Macedonia Faculty of Arts University of Ljubljana Slovenia CLARIN ERIC Netherlands Computational Cognition Lab Open University of Cyprus Cyprus Orientale University of Naples Italy Insight Centre for Data Analytics National University of Ireland Galway Ireland Department of Computer Science University of Helsinki Finland Human Languages Technologies Lab INESC-ID Lisbon Portugal Israel

ISBN: (纸本)9791095546344

We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach by explaining its core paradigm that consists in pairing specific types of LRs with specific exercises, by detailing both its strengths and challenges, and by discussing how much these challenges have been addressed at present. Accordingly, we also report on on-going proof-of-concept efforts aiming at developing the first prototypical implementation of the approach in order to correct and extend an LR called ConceptNet based on the input crowdsourced from language learners. We then present an international network called the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect) that provides the context to accelerate the implementation of the generic approach. Finally, we exemplify how it can be used in several language learning scenarios to produce a multitude of NLP resources and how it can therefore alleviate the long-standing NLP issue of the lack of LRs. © European Language Resources Association (ELRA), licensed under CC-BY-NC

关键词： Crowdsourcing

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Future Vector Enhanced LSTM Language Model for LVCSR

arXiv

引用

arXiv 2020年

作者： Liu, Qi Qian, Yanmin Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

Language models (LM) play an important role in large vocabulary continuous speech recognition (LVCSR). However, traditional language models only predict next single word with given history, while the consecutive predictions on a sequence of words are usually demanded and useful in LVCSR. The mismatch between the single word prediction modeling in trained and the long term sequence prediction in read demands may lead to the performance degradation. In this paper, a novel enhanced long short-term memory (LSTM) LM using the future vector is proposed. In addition to the given history, the rest of the sequence will be also embedded by future vectors. This future vector can be incorporated with the LSTM LM, so it has the ability to model much longer term sequence level information. Experiments show that, the proposed new LSTM LM gets a better result on BLEU scores for long term sequence prediction. For the speech recognition rescoring, although the proposed LSTM LM obtains very slight gains, the new model seems obtain the great complementary with the conventional LSTM LM. Rescoring using both the new and conventional LSTM LMs can achieve a very large improvement on the word error rate. Copyright © 2020, The Authors. All rights reserved.

关键词： Long short-term memory

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