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3rd IEEE International Conference on human-Machine Systems, ICHMS 2022

作者： Pham, Daniel Menon, Vineetha Tenhundfeld, Nathan Weger, Kristin Mesmer, Bryan Gholston, Sampson Davis, Thomas The University of Alabama in Huntsville Department of Computer Science HuntsvilleAL United States The University of Alabama in Huntsville Department of Psychology HuntsvilleAL United States The University of Alabama in Huntsville Department of Iseem HuntsvilleAL United States Human Systems Integration Division Devcom Data and Analysis Center HuntsvilleAL United States

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

human interactions with Artificial Intelligence (AI) have become a ubiquitous phenomenon in our society. It plays a major role in defense and espionage in the context of AI-based autonomous systems such as drones and ground robots that are sent to the front lines to navigate terrains, map the surroundings, and identify adversaries in the environment. In these types of scenarios, hostages may be spread out over a larger geographical area. Reliance on ground vision alone may not only be obscured and insufficient but also can expose soldiers or human rescuers to danger. Since time is of great essence, such an intense situation calls for decisions to be made swiftly and efficiently as the lives of hostages are at stake. This makes such a scenario ideal to evaluate the effectiveness of an AI-driven drone autonomous system for combat search and rescue (CSAR) missions. Here the focus is to gather information about the operational environment (detect hostages and other targets-of-interest) in a short amount of time that spans a varying geographic terrain. This allows for targeted rescue operations with AI-informed decision-making outcomes to ensure that the rescuers at ground level can be directed along the most optimal path, and casualties will be either minimized or entirely averted. In this work, we study the feasibility of our drone-based AI target detection autonomous system to evaluate the classification accuracy of multi-view targets-of-interest in the CSAR environment. The overarching goal of this study is to understand the role of AI-driven autonomous systems as an assistive support tool for efficient, swift and accurate decision making, navigating a complex and ever-changing environment, and identification of targets. © 2022 IEEE.

关键词： Object detection

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Critically Analyzing the Nature of Deep Learning, The Way, It is Adopted in Organizational Developments 3

Critically Analyzing the Nature of Deep Learning, The Way, I...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Lourens, Melanie Krishna, Somanchi Hari Dhumale, R.B. Bhatia, Sonakshi Joshi, Kapil Tripathi, Vikas Durban University of Technology Department of Human Resources Management Durban South Africa Vignana Bharathi Institute of Technology Department of Business Management Aushapur Village Ghatkesar Mandal Malkangiri Medchal Dist. Telangana Hyderabad India AISSMS Institute of Information Technology Maharashtra Pune India Uttaranchal University Department of Management Uttarakhand Dehradun India Uttaranchal University Uttaranchal Institute of Technology India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The ultimate focus of this study is to assess deep teaching approach and how its benefits business outcomes with a focus on the projects' economic value as deep learning-based transformations. The actions required to finish this research were as follows: Following are the aspects of reinforcement learning: Learning based: What exactly is it? What happens when it works properly? How methods have been employed? What are the drawbacks and issues? What impact may machine learning has upon your organization's performance? vii) Deep learning instances;and (vi) paying close attention neural network. In order to understand how DL business value affects organizational performance, our study has made use of the concept of information technology capabilities. It encompasses a broad spectrum of technological advancements, including algorithms that evolve on their own and chat bots and language processing. The idea of the operation of replying to or agitating their world while contemporaneously increasing and enhancing their strategic and commercial advantages has been the driving force behind the deployment of ai and machine learning technological breakthroughs by enterprises. Due to its ability to detect, anticipate, and interact with people, DL surpasses the adversary when it comes towards improving the effectiveness of current processes and boosting the impact of automation, data, and revolutionary advances. © 2023 IEEE.

关键词： Neural networks

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Predicting behavioral competencies automatically from facial expressions in real-time video-recorded interviews

Predicting behavioral competencies automatically from facial...

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作者： Su, Yu-Sheng Suen, Hung-Yue Hung, Kuo-En Department of Computer Science and Engineering National Taiwan Ocean University Keelung City Taiwan Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei City Taiwan

This work aims to develop a real-time image and video processor enabled with an artificial intelligence (AI) agent that can predict a job candidate’s behavioral competencies according to his or her facial expressions. This is accomplished using a real-time video-recorded interview with a histogram of oriented gradients and support vector machine (HOG-SVM) plus convolutional neural network (CNN) recognition. Different from the classical view of recognizing emotional states, this prototype system was developed to automatically decode a job candidate’s behaviors by their microexpressions based on the behavioral ecology view of facial displays (BECV) in the context of employment interviews using a real-time video-recorded interview. An experiment was conducted at a Fortune 500 company, and the video records and competency scores were collected from the company’s employees and hiring managers. The results indicated that our proposed system can provide better predictive power than can human-structured interviews, personality inventories, occupation interest testing, and assessment centers. As such, our proposed approach can be utilized as an effective screening method using a personal-value-based competency model. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Support vector machines

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HYBRID SPIKING NEURAL NETWORK FINE-TUNING FOR HIPPOCAMPUS SEGMENTATION

arXiv

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arXiv 2023年

作者： Yue, Ye Baltes, Marc Abujahar, Nidal Sun, Tao Smith, Charles D. Bihl, Trevor Liu, Jundong School of Electrical Engineering and Computer Science Ohio University United States Department of Neurology University of Kentucky United States Department of Biomedical Industrial & Human Factors Engineering Wright State University United States

Over the past decade, artificial neural networks (ANNs) have made tremendous advances, in part due to the increased availability of annotated data. However, ANNs typically require significant power and memory consumptions to reach their full potential. Spiking neural networks (SNNs) have recently emerged as a low-power alternative to ANNs due to their sparsity nature. SNN, however, are not as easy to train as ANNs. In this work, we propose a hybrid SNN training scheme and apply it to segment human hippocampi from magnetic resonance images. Our approach takes ANN-SNN conversion as an initialization step and relies on spike-based backpropagation to fine-tune the network. Compared with the conversion and direct training solutions, our method has advantages in both segmentation accuracy and training efficiency. Experiments demonstrate the effectiveness of our model in achieving the design goals. © 2023, CC BY-NC-SA.

关键词： Image segmentation

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Performance measurement of processes and threads controlling, tracking and monitoring based on shared-memory parallel processing approach 3

Performance measurement of processes and threads controlling...

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3rd International Conference on Engineering Technology and its Applications, IICETA 2020

作者： Sharif, Karzan H. Zeebaree, Subhi R. M. Haji, Lailan M. Zebari, Rizgar R. University of Human Development Computer Science Department Sulaymaniyah Iraq Duhok Polytechnic University Duhok Iraq University of Zakho Computer Science Department KRG Iraq

ISBN: (纸本)9781728182315

In this paper, a professional integrated operating system performance measurement system is proposed, designed, and implemented in order to monitor processes and threads. Because monitoring itself is not enough, so this system provides full control of the CPU forcing, Priority-changing, and pausing-resuming-killing of these processes/threads. During these complex operations, it provides the ability to measure/compute the details of Total-execution-time, CPU-time, User-time, Kernel-time, context switching, CPU -usage, and the priority of processes/threads. The proposed system can be used with any architecture of multicore processors. Despite that measuring all these parameters in one system is very difficult and complex especially the Kernel-time and Context-switching, but all of them have been applied and determined successfully. Adding to the above features that never been integrated (by any previous work) in one system, and in order to illustrate the real abilities of this system, a shared-memory parallel processing approach has been proposed and applied. The proposed system prepared to run all possible cases of processes and threads that construct under-tested-program which may be one of the: Single-Process-Single-Thread, Single-Process-Multi- Thread, Multi-Process-Single-Thread, Multi-Process-Multi- Thread, and Multi- Process-Single-Multi-Thread. The algorithms of this system are designed and implemented via C++ programming language(using C++ Standard Library Thread) which is the nearest programming to the OSs provides a more increasing processing speed. © 2020 IEEE.

关键词： Software testing

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Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning

arXiv

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arXiv 2022年

作者： Wu, Hongqiu Ding, Ruixue Zhao, Hai Chen, Boli Xie, Pengjun Huang, Fei Zhang, Min 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 School of Computer Science and Technology Soochow University China Damo Academy Alibaba Group China

Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling, which serves the ultimate purpose of pre-trained language models (PrLMs), generalizing well on a mass of scenarios. However, learning multiple training objectives in a single model is challenging due to the unknown relative significance as well as the potential contrariety between them. Empirical studies have shown that the current objective sampling in an ad-hoc manual setting makes the learned language representation barely converge to the desired optimum. Thus, we propose MOMETAS, a novel adaptive sampler based on meta-learning, which learns the latent sampling pattern on arbitrary pre-training objectives. Such a design is lightweight with negligible additional training overhead. To validate our approach, we adopt five objectives and conduct continual pre-training with BERT-base and BERT-large models, where MOMETAS demonstrates universal performance gain over other rule-based sampling strategies on 14 natural language processing tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Modeling languages

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Exploring the "Freedom to be Me" through Design Sprints with Neurodivergent Scholars 24

Exploring the "Freedom to be Me" through Design Sprints with...

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Proceedings of the 26th International ACM SIGACCESS Conference on computers and Accessibility

作者： LouAnne Boyd Annuska Zolyomi Saad Hassan Seray B Ibrahim Guande Wu Kay Kender Learn.Adapt.Build Chapman University United States Information School University of Washington United States Tulane University United States Department of Informatics King's College London United Kingdom Tandon School of Engineering New York University United States Human Computer Interaction Group TU Wien Austria

ISBN: (纸本)9798400706776

Social stigma negatively impacts the well-being of neurodivergent individuals. Specifically for autistic people, the social isolation and pressure to conform to normative ways of being can take a tremendous toll; such as a thwarted sense of belonging to the point of higher rates of suicide. Yet, few technologies are directly targeting the problem of stigma. Socio-technical systems have tremendous potential to shift public perception of traditionally marginalized populations. However, these systems are not consistently designed to reflect the values and needs of neurodivergent individuals. This work explores the use of design sprints to envision CT– a spectrum of technology that could reduce social stigma by increasing the public’s awareness, accommodations, acceptance, advocacy, and appreciation. This work reports on two design sprints across 25 HCI community members with varying lived experiences with neurodiversity and knowledge of design practice. The resulting design concepts were discussed in the groups and then analyzed to reflect on how they might combat stigma. Results reveal designs that support the freedom to be oneself via (1) safe spaces (2) public understanding, and (3) authentic expression of strengths, challenges, and needs.

关键词： Celebratory Technology

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Towards Robust Family-Infant Audio Analysis Based on Unsupervised Pretraining of Wav2vec 2.0 on Large-Scale Unlabeled Family Audio

arXiv

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arXiv 2023年

作者： Li, Jialu Hasegawa-Johnson, Mark McElwain, Nancy L. Department of Electrical and Computer Engineering University of Illinois United States Beckman Institute for Advanced Science and Technology University of Illinois United States Department of Human Development and Family Studies University of Illinois United States

To perform automatic family audio analysis, past studies have collected recordings using phone, video, or audio-only recording devices like LENA, investigated supervised learning methods, and used or fine-tuned general-purpose embeddings learned from large pretrained models. In this study, we advance the audio component of a new infant wearable multi-modal device called LittleBeats (LB) by learning family audio representation via wav2vec 2.0 (W2V2) pretraining. We show given a limited number of labeled LB home recordings, W2V2 pretrained using 1k-hour of unlabeled home recordings outperforms oracle W2V2 pretrained on 960-hour unlabeled LibriSpeech in terms of parent/infant speaker diarization (SD) and vocalization classifications (VC) at home. Extra relevant external unlabeled and labeled data further benefit W2V2 pretraining and fine-tuning. With SpecAug and environmental speech corruptions, we obtain 12% relative gain on SD and moderate boost on VC. Code and model weights are available. © 2023, CC BY.

关键词： Learning systems

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UMAP and Tree Physiology Optimization for Effective Water Quality Management in Consumer Electronics

UMAP and Tree Physiology Optimization for Effective Water Qu...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Brij B. Gupta Akshat Gaurav Shavi Bansal Jinsong Wu Varsha Arya Kwok Tai Chui Department of Computer Science and Information Engineering Asia University Taichung Taiwan University of Economics and Human Science Warsaw Poland Ronin Institute Montclair NJ USA Insights2Techinfo India Department of Electrical Engineering Universidad de Chile Chile Hong Kong Metropolitan University (HKMU) Hong Kong

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Leveraging UMAP for feature selection and Tree Physiology Optimization (TPO) for hyperparameter tuning, we proposed model for efficient water quality control in consumer electronics. We balanced the data with SMOTE from a Kaggle water quality dataset and using UMAP to find important characteristics. The TPO method then was used to maximize the parameters of the deep learning model. With an eye on few false positives, our algorithm was very accurate in spotting clean water. Still, the confusion matrix shows that there are difficulties precisely identifying dangerous water.

关键词： Deep learning Electric potential Accuracy Water quality Feature extraction Physiology Safety Consumer electronics Optimization Tuning

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Classification of Infant Sleep/Wake States: Cross-Attention among Large Scale Pretrained Transformer Networks using Audio, ECG, and IMU Data

arXiv

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arXiv 2023年

作者： Chang, Kai Chieh Hasegawa-Johnson, Mark McElwain, Nancy L. Islam, Bashima Department of Electrical and Computer Engineering University of Illinois Urbana Champaign United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States Department of Human Development and Family Studies University of Illinois Urbana-Champaign United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute United States

Infant sleep is critical to brain and behavioral development. Prior studies on infant sleep/wake classification have been largely limited to reliance on expensive and burdensome polysomnography (PSG) tests in the laboratory or wearable devices that collect single-modality data. To facilitate data collection and accuracy of detection, we aimed to advance this field of study by using a multi-modal wearable device, LittleBeats (LB), to collect audio, electrocardiogram (ECG), and inertial measurement unit (IMU) data among a cohort of 28 infants. We employed a 3-branch (audio/ECG/IMU) large scale transformer-based neural network (NN) to demonstrate the potential of such multi-modal data. We pretrained each branch independently with its respective modality, then finetuned the model by fusing the pretrained transformer layers with cross-attention. We show that multi-modal data significantly improves sleep/wake classification (accuracy = 0.880), compared with use of a single modality (accuracy = 0.732). Our approach to multi-modal mid-level fusion may be adaptable to a diverse range of architectures and tasks, expanding future directions of infant behavioral research. © 2023, CC BY.

关键词： Electrocardiograms

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