2022 15th International Conference on Human System Interaction (HSI)最新文献_第7页

Preferred Benchmarking Criteria for Systematic Taxonomy of Embedded Platforms (STEP) in Human System Interaction Systems 人机交互系统中嵌入式平台系统分类(STEP)的首选基准标准

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-07-28 DOI: 10.1109/HSI55341.2022.9869470

A. Kwaśniewska, Sharath Raghava, Carlos Davila, Mikael Sevenier, D. Gamba, J. Rumiński

{"title":"Preferred Benchmarking Criteria for Systematic Taxonomy of Embedded Platforms (STEP) in Human System Interaction Systems","authors":"A. Kwaśniewska, Sharath Raghava, Carlos Davila, Mikael Sevenier, D. Gamba, J. Rumiński","doi":"10.1109/HSI55341.2022.9869470","DOIUrl":"https://doi.org/10.1109/HSI55341.2022.9869470","url":null,"abstract":"The rate of progress in the field of Artificial Intelligence (AI) and Machine Learning (ML) has significantly increased over the past ten years and continues to accelerate. Since then, AI has made the leap from research case studies to real production ready applications. The significance of this growth cannot be undermined as it catalyzed the very nature of computing. Conventional platforms struggle to achieve greater performance and efficiency, what causes a surging demand for innovative AI accelerators, specialized platforms and purpose-built computes. At the same time, it is required to provide solutions for assessment of ML platform performance in a reproducible and unbiased manner to be able to provide a fair comparison of different products. This is especially valid for Human System Interaction (HSI) systems that require specific data handling for low latency responses in emergency situations or to improve user experience, as well as for preserving data privacy and security by processing it locally. Taking it into account, this work presents a comprehensive guideline on preferred benchmarking criteria for evaluation of ML platforms that include both lower level analysis of ML models and system-level evaluation of the entire pipeline. In addition, we propose a Systematic Taxonomy of Embedded Platforms (STEP) that can be used by the community and customers for better selection of specific ML hardware consistent with their needs for better design of ML-based HSI solutions.","PeriodicalId":282607,"journal":{"name":"2022 15th International Conference on Human System Interaction (HSI)","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132743587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Personalised Physiotherapy Rehabilitation using Artificial Intelligence and Virtual Reality Gaming 使用人工智能和虚拟现实游戏的个性化物理治疗康复

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-07-28 DOI: 10.1109/HSI55341.2022.9869454

Thimal Kempitiya, Daswin De Silva, E. Rio, Richard Skarbez, D. Alahakoon

{"title":"Personalised Physiotherapy Rehabilitation using Artificial Intelligence and Virtual Reality Gaming","authors":"Thimal Kempitiya, Daswin De Silva, E. Rio, Richard Skarbez, D. Alahakoon","doi":"10.1109/HSI55341.2022.9869454","DOIUrl":"https://doi.org/10.1109/HSI55341.2022.9869454","url":null,"abstract":"Healthcare systems and services are rapidly transitioning towards a patient-centred care approach. As an allied health profession that is highly individualised, physiotherapy, requires technological innovations to advance into this space. Virtual Reality (VR) technologies have been explored and applied successfully towards achieving this transition, specifically in single-player and multi-player gaming configurations. However, most VR games are not customised to the individuals and not appropriate for the rehabilitation of patients with unique needs. Given the large volumes of data generated by the patient during an engagement with a VR game, Artificial Intelligence (AI) can be leveraged to deliver personalised physiotherapy rehabilitation. This is a work-in-progress paper that presents the design and development of a framework for personalised physiotherapy rehabilitation using AI and VR, in the setting of a single-player VR game. The completed framework will be trialled in a public hospital setting to evaluate its effectiveness in providing patient-centred physiotherapy rehabilitation for diverse patient cohorts.","PeriodicalId":282607,"journal":{"name":"2022 15th International Conference on Human System Interaction (HSI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132834581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

HSI 2022 Cover Page 恒指2022年封面

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-07-28 DOI: 10.1109/hsi55341.2022.9869494

引用次数: 0

Face De-identification Scheme Using Landmark-Based Inpainting 基于地标的人脸去识别方法

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-07-28 DOI: 10.1109/HSI55341.2022.9869487

Hyeonwoo Kim, Junsuk Lee, Eenjun Hwang

引用次数: 0

A Comparison of Time-Frequency Distributions for Deep Learning-Based Speech Assessment of Aphasic Patients 基于深度学习的失语症患者语音评估时频分布比较

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-07-28 DOI: 10.1109/HSI55341.2022.9869452

Akshay Kumar, S. Mahmoud, Yin Wang, S. Faisal, Qiang Fang

{"title":"A Comparison of Time-Frequency Distributions for Deep Learning-Based Speech Assessment of Aphasic Patients","authors":"Akshay Kumar, S. Mahmoud, Yin Wang, S. Faisal, Qiang Fang","doi":"10.1109/HSI55341.2022.9869452","DOIUrl":"https://doi.org/10.1109/HSI55341.2022.9869452","url":null,"abstract":"Speech impairment assessment is an essential part of the rehabilitation of aphasic patients. As the number of stroke incidents is increasing year after year, it is essential to develop automatic speech impairment assessment (ASIA) methods. Deep learning, together with time-frequency distribution (TFD) representation of speech data, can be a promising solution for developing ASIA methods. However, before making further progress, it is essential to assess various TFDs in terms of their effectiveness for ASIA. Therefore, this paper assessed and compared various TFD methods for ASIA of Mandarin speech. Various state-of-the-art computer vision convolutional neural network models were trained, using TFDs of speech data of thirty-four healthy participants and twelve aphasic patients, to assess the effectiveness of TFDs. The automatic speech recognition rate was used as a measure for evaluating the performance of TFDs. Results showed that Mel spectrogram-based TFDs perform significantly better than the previously used Hyperbolic-T distribution TFDs, for automatic speech recognition. The results indicate that Mel spectrogram TFDs, instead of Hyperbolic-T distribution TFDs, can improve the ASIA performance. The findings presented will help improve the performance of deep learning- and TFD-based ASIA methods.","PeriodicalId":282607,"journal":{"name":"2022 15th International Conference on Human System Interaction (HSI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133704807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

How Virtual Body Continuity with Different Hand Representations Influence on User Perceptions and Task Performance 不同手表征的虚拟身体连续性如何影响用户感知和任务绩效

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-07-28 DOI: 10.1109/HSI55341.2022.9869486

Jingjing Zhang, Mengjie Huang, Xiaohang Tang, Yiqi Wang, Rui Yang

{"title":"How Virtual Body Continuity with Different Hand Representations Influence on User Perceptions and Task Performance","authors":"Jingjing Zhang, Mengjie Huang, Xiaohang Tang, Yiqi Wang, Rui Yang","doi":"10.1109/HSI55341.2022.9869486","DOIUrl":"https://doi.org/10.1109/HSI55341.2022.9869486","url":null,"abstract":"Virtual avatars or hands in virtual reality connect users’ physical bodies and virtual worlds. Changes in the virtual hand representations (body continuity and hand realism) may affect user perceptions and task performance. However, there is no agreed conclusion on how they influence user perceptions (the sense of embodiment and presence) and limited evidence of task performance. Therefore, this paper investigates the impact of body continuity (connected and disconnected virtual hand) with three hand realism levels on user perceptions and task performance by self-report and objective performance data in virtual reality. The results revealed no significant results about body continuity on user perceptions, while a significant effect of hand realism levels on sense of embodiment and presence was found. Moreover, the abstract disconnected and connected hands reported lower task scores than those realistic hands from task performance data. Overall, this study provides new insights into further understanding user perceptions and task performance under the connected and disconnected hands, and it has practical reference value for exploring the later research on virtual hand representations.","PeriodicalId":282607,"journal":{"name":"2022 15th International Conference on Human System Interaction (HSI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123871649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Specialist vs Generalist: A Transformer Architecture for Global Forecasting Energy Time Series 专家vs通才:用于全球预测能源时间序列的变压器架构

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-07-28 DOI: 10.1109/HSI55341.2022.9869463

Prabod Rathnayaka, Harsha Moraliyage, Nishan Mills, Daswin De Silva, Andrew Jennings

{"title":"Specialist vs Generalist: A Transformer Architecture for Global Forecasting Energy Time Series","authors":"Prabod Rathnayaka, Harsha Moraliyage, Nishan Mills, Daswin De Silva, Andrew Jennings","doi":"10.1109/HSI55341.2022.9869463","DOIUrl":"https://doi.org/10.1109/HSI55341.2022.9869463","url":null,"abstract":"Time series forecasting is a critical requirement for the optimal operation of energy grids, systems, and platforms, where the forecasting challenge itself can span across energy consumption, renewables generation, and energy utilisation. Artificial Intelligence (AI) algorithms and models have been leveraged to predict these time series forecasts with increasing levels of accuracy. In contrast to local models that are developed separately for each time series, Global Models, which are trained across many sets of time series drawing on characteristics of ’relatedness’, have produced more accurate forecasts. In this paper, we propose a transformer architecture based global model as a generalist forecaster of energy time series data, where we frame a sequence forecasting model and represent numerical values of the corresponding time series as vector embeddings in this model. We evaluate this transformer architecture based global model on real-world time-series energy data generated by the La Trobe Energy AI platform (LEAP), a functional and operational microgrid deployed in the multicampus tertiary education setting of La Trobe University, Australia. The results of these experiments confirm that the proposed generalist forecasting approach outperforms specialist local models trained on individual time series. We also demonstrate the ability of this approach to forecast dissimilar time series from the same model.","PeriodicalId":282607,"journal":{"name":"2022 15th International Conference on Human System Interaction (HSI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124508770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Data-Correlation Energy Management of Smart Homes with Rooftop Photovoltaic and Plug-in Electric Vehicles 基于屋顶光伏和插电式电动车的智能家居数据关联能源管理

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-07-28 DOI: 10.1109/hsi55341.2022.9869443

Zipeng Liang, Haoyong Chen

引用次数: 0

Vehicle Identification and Classification for Smart Transportation using Artificial Intelligence- A Review 基于人工智能的智能交通车辆识别与分类研究综述

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-07-28 DOI: 10.1109/HSI55341.2022.9869476

S. Rajput, J. Patni

{"title":"Vehicle Identification and Classification for Smart Transportation using Artificial Intelligence- A Review","authors":"S. Rajput, J. Patni","doi":"10.1109/HSI55341.2022.9869476","DOIUrl":"https://doi.org/10.1109/HSI55341.2022.9869476","url":null,"abstract":"The vehicles are the core elements of smart transportation, and vehicle identification & classification is an essential task in many sub-areas of smart transportation. With the change in technologies, it is time to shift in the implementing methods for smart transportation systems, such as toll management systems and advanced traffic management systems, from traditional methods to artificial intelligence (AI) based methods. This paper presents various AI-based object detectors that detect the objects using recorded or real-time images and are suitable for vehicle identification and classification. This paper suggests use of single-stage object detectors as the preferred choice over two-stage object detectors. For processing images and videos, we have also presented a comparative study between single stage-object detectors You Only Look Once (YOLO) and Single Shot Multi-Box Detector (SSD), including their incremental versions and differences between them. Furthermore, YOLOv3, from the family of single-stage object detectors, has been suggested for the task of identifying & classifying of vehicles for toll management systems and advanced traffic management systems. This paper compares single-stage object detector algorithms YOLO & SSD and suggests that YOLO being faster than SSD and comparable mAP makes YOLO a suitable algorithm for use in the toll management system.","PeriodicalId":282607,"journal":{"name":"2022 15th International Conference on Human System Interaction (HSI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125895115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Communication Efficient Federated Learning Framework with Local Momentum 具有局部动量的沟通高效联邦学习框架

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-07-28 DOI: 10.1109/HSI55341.2022.9869493

Renyou Xie, Xiaojun Zhou

引用次数: 0