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1st International Recent Trends in Technology, Engineering and Computing Conference, IRTTEC 2020

作者： Ghazi, Alaan Aljunid, S.A. Idrus, Syed Zulkarnain Syed Fareed, Alaa Al-Dawoodi, Aras Mohsin, Aram Hewa School of Human Development and Techno Communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia School of Computing University Utara Malaysia Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Computer Technical Engineering Alkitab University Iraq Criminal Evidence Department Al-Qalam University College Iraq

Various optical communication technologies use Optical-Code Division Multiple Access (Optical-CDMA) as a channel access technique, which allows transmission of different users simultaneously over the optical communication channel. Therefore, Optical-CDMA permits free undue interference transmission of several users. Whereas Wavelength-division multiple access (WDMA) is a technique that can be implemented on how the optical channel can be divided into subchannels. This paper proposes a Hybrid WDMA-Optical-CDMA over multi-mode fiber (MMF) transmission system for short haul-local area network. The WDMA system is carried out by using four Laguerre-Gaussian (LG) modes that operate over four main wavelengths (1551 nm, 1550 nm, 1549 nm and 1548 nm). Additionally, the Optical-CDMA system is carried out by using one dimension Zero Cross-Correlation (ZCC) code to overcome multiple access interference (MAI) between the four ZCC codes. The performance of our system is evaluated based on eye diagrams, Q-Factor and bit-error rates (BER) measurements. © Published under licence by IOP Publishing Ltd.

关键词： Multiple access interference

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Rehabilitation Assistant - a Toolkit for Maintaining Health and Improving the Reliability of Personnel at Critical Infrastructure Facilities 13

Rehabilitation Assistant - a Toolkit for Maintaining Health ...

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Zaslavskyi, Volodymyr Horbunov, Oleh Makarchuk, Mykola Osadchyy, Yevgen Tereshchenko, Vasyl Holub, Valentin Taras Shevchenko National University of Kyiv Department of Mathematical Informatics Faculty of Computer Science and Cybernetics Kyiv Ukraine Taras Shevchenko National University of Kyiv Educational and Scientific Centre Institute of Biology and Medicine Department of Human and Animal Physiology Kyiv Ukraine V. I. Vernadsky Taurida National University Department of Physical Education Sports and Health of the People Kyiv Ukraine

ISBN: (纸本)9798350396119

The article is devoted to the development of medical transformative technology and the study of the possibility of applying the type-variety principle, a systematic analysis of the influence of the human factor and personnel errors in ensuring the highly reliable functioning and safety of critical systems. Minimizing staff errors and monitoring the health of employees at critical infrastructure facilities in the face of new challenges associated with the COVID-19 pandemic and barbaric military aggression can be achieved through the joint use of a variety of physical practices and exercises that affect all components of human health. Based on expert analysis of video images of a person and his movements, which are recorded periodically, strategies are formed to influence both his physical and psychological state, as well as his social and communicative activity, which thereby ensures the reliability, safety and sustainability of the functioning of critical systems. © 2023 IEEE.

关键词： Critical infrastructures

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Proposing an Interactive Audit Pipeline for Visual Privacy Research

Proposing an Interactive Audit Pipeline for Visual Privacy R...

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IEEE International Conference on Big Data

作者： Jasmine DeHart Chenguang Xu Christan Grant Lisa Egede School of Computer Science University of Oklahoma Norman Oklahoma USA Human Computer Interaction Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9781665445993

In an ideal world, deployed machine learning models will enhance our society. We hope that those models will provide unbiased and ethical decisions that will benefit everyone. However, this is not always the case; issues arise during the data preparation process throughout the steps leading to the models’ deployment. The continued use of biased datasets and biased processes will adversely damage communities and increase the cost to fix the problem later. In this work, we walk through the decision making process that a researcher should consider before, during, and after a system deployment to understand the broader impacts of their research in the community. Throughout this paper, we discuss fairness, privacy, and ownership issues in the machine learning pipeline, assert the need for a responsible human-over-the-loop methodology to bring accountability into machine learning pipeline, and finally, reflect on the need to explore research agendas that have harmful societal impacts. We examine visual privacy research and draw lessons that can apply broadly to artificial intelligence. Our goal is to provide a systematic analysis of the machine learning pipeline for visual privacy and bias issues. With this pipeline, we hope to raise stakeholder (e.g., researchers, modelers, corporations) awareness as these issues propagate in the various machine learning phases.

关键词： Training Visualization Privacy Data privacy Pipelines Decision making Machine learning

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Could human Gaze Augment Detectors of Synthetic Images?

Could Human Gaze Augment Detectors of Synthetic Images?

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International Conference on Digital Signal Processing (DSP)

作者： Nikolaos Fotopoulos Clara Riedmiller Efe Bozkir Panagiotis Tsinganos Dimitris Ampeliotis Gjergji Kasneci Enkelejda Kasneci Athanassios Skodras Department of Electrical and Computer Engineering University of Patras Greece Department of Computer Science Eberhard Karls University of Tübingen Germany Human-Centered Technologies for Learning Technical University of Munich Germany Department of Digital Media and Communication Ionian University Greece Responsible Data Science Technical University of Munich Germany

Recent advances in generative adversarial networks (GANs) allow for the synthesis of extremely photo-realistic face images, deceiving even the most experienced observers, let alone the unsuspecting internet user. Due to this, there has been a considerable effort by the image forensics community to design appropriate tools for the detection of these images. This paper first implements one such detection technique based on spatial and cross-band co-occurrence matrices and convolutional neural networks (CNNs), and then attempts to improve it by introducing additional information obtained from the human gaze. We show that in cases where human observers correctly decide whether an image is real or fake, eye movement information in combination with spatial and cross-band co-occurrence matrices derived from observation regions can be informative towards the task of detecting fake images. However, only a limited increase in the detection accuracy is achieved.

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A Value-of-Information-based management framework for fog services

A Value-of-Information-based management framework for fog se...

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作者： Poltronieri, Filippo Tortonesi, Mauro Morelli, Alessandro Stefanelli, Cesare Suri, Niranjan Engineering Department University of Ferrara Ferrara Italy Department of Mathematics and Computer Science University of Ferrara Ferrara Italy Florida Institute for Human and Machine Cognition PensacolaFL United States United States Army Research Laboratory AdelphiMD United States

The management of fog computing applications is a challenging task that requires dealing with a dynamic and resource-scarce environment. We argue that approaches leveraging Value-of-Information (VoI) concepts and tools are particularly interesting to support the realization of that objective. This paper describes innovative methodologies and reference models for the service fabric management of fog computing services. First, we formalize the VoI concept and discuss its adoption in fog computing environments. Then, we propose a model that aims at maximizing the allocation of fog services from a value-based perspective. To overcome the complexity of this model, we present two possible solutions (a simplified user-specific VoI model and a distance-based heuristic), and we compare them by adopting meta-heuristics as optimization techniques. Then, we investigate the adoption of a hybrid model, which combines the distance-based heuristic with the simplified user-specific VoI model. Experimental results prove the validity of all presented approaches and highlight the soundness of the distance-based heuristic, which is capable of reaching the 91% performance of the simplified user-specific VoI model in a very short amount of time. This makes it a suitable approach for online optimization of resources in fog computing. © 2021 John Wiley & Sons, Ltd.

关键词： Fog computing

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Incorporating simulated spatial context information improves the effectiveness of contrastive learning models

arXiv

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arXiv 2024年

作者： Zhu, Lizhen Wang, James Z. Lee, Wonseuk Wyble, Brad Data Science and Artificial Intelligence Area College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Human-Computer Interaction Area College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Department of Communication and Media School of Social Sciences and Humanities Loughborough University Leicestershire Loughborough United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States Department of Psychology The Pennsylvania State University University ParkPA United States

Visual learning often occurs in a specific context, where an agent acquires skills through exploration and tracking of its location in a consistent environment. The historical spatial context of the agent provides a similarity signal for self-supervised contrastive learning. We present a unique approach, termed Environmental Spatial Similarity (ESS), that complements existing contrastive learning methods. Using images from simulated, photorealistic environments as an experimental setting, we demonstrate that ESS outperforms traditional instance discrimination approaches. Moreover, sampling additional data from the same environment substantially improves accuracy and provides new augmentations. ESS allows remarkable proficiency in room classification and spatial prediction tasks, especially in unfamiliar environments. This learning paradigm has the potential to enable rapid visual learning in agents operating in new environments with unique visual characteristics. Potentially transformative applications span from robotics to space exploration. Our proof of concept demonstrates improved efficiency over methods that rely on extensive, disconnected datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： computer vision

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Eye Drop Instillation Success and Hand Function in Adults with Glaucoma: A Pilot Study

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Ophthalmology Glaucoma 2025年第3期8卷 275-284页

作者： Weber, Madeline K. Kabil, Gül G. Niziol, Leslie M. Urrego, Daniel Duque Haire, Cameron Cain, Stephen M. Sample, Alanson P. Burke, David T. Brown, Susan H. Newman-Casey, Paula Anne University of Michigan Medical School Ann Arbor MI United States University of Michigan School of Kinesiology Ann Arbor MI United States Department of Ophthalmology and Visual Sciences University of Michigan W.K. Kellogg Eye Center Ann Arbor MI United States Department of Chemical and Biomedical Engineering West Virginia University Morgantown WV United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Department of Human Genetics University of Michigan Ann Arbor MI United States

Purpose: To investigate hand function and eye drop instillation success in adults with and without glaucoma. Design: Cross-sectional pilot study. Subjects: Adults aged ≥ 65 years with glaucoma who use eye drops daily and adults aged 65+ without glaucoma who do not regularly use eye drops. Methods: Hand function was evaluated using the Saehan Hydraulic Pinch Gauge, Jamar Hand Dynamometer, Grooved Pegboard Test (GPT), Arthritis Hand Function Tests (AHFT), Semmes-Weinstein Monofilament Test, and a tactile pattern recognition device. Eye drop instillation success was analyzed through videography. Main Outcome Measures: Proportion of successful eye drop instillation trials assessed by (1) overall success in getting at least one drop in the eye;(2) average number of drops dispensed;and (3) bottle tip contact with the eye, eyelashes, or skin. Hand function measures: mean pinch and grip strength, GPT and AHFT completion times, smallest detected monofilament, tactile pattern identification time, and accuracy. Results: A total of 25 participants with glaucoma and 79 participants without glaucoma were included. There was no difference in the proportion of trials where at least one drop was successfully instilled, disregarding bottle tip contact (glaucoma: 0.95, nonglaucoma: 0.91;P = 0.88). Participants with glaucoma were more likely to make contact between the bottle tip and the eye, eyelashes, or skin compared with those without (glaucoma: 0.49, nonglaucoma: 0.28;P = 0.01). Participants with glaucoma dispensed a similar number of drops as those without (glaucoma: 1.37, nonglaucoma: 1.46;P = 0.47). Participants with glaucoma had significantly reduced pinch strength (glaucoma: 4.8 kg, nonglaucoma 6.1 kg, P = 0.01), grip strength (glaucoma: 23.4 kg, nonglaucoma: 27.7 kg, P = 0.02), longer completion times for the GPT (glaucoma: 113.5 seconds, nonglaucoma: 85.5 seconds, P = 0.02), and specific AHFT tasks of fastening/unfastening buttons (glaucoma: 36.6 seconds, nonglaucoma: 27.7

关键词： Eye drop instillation success Glaucoma Hand function Sensorimotor deficits

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SCANS: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering

arXiv

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arXiv 2024年

作者： Cao, Zouying Yang, Yifei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

Safety alignment is indispensable for Large Language Models (LLMs) to defend threats from malicious instructions. However, recent researches reveal safety-aligned LLMs prone to reject benign queries due to the exaggerated safety issue, limiting their helpfulness. In this paper, we propose a Safety-Conscious Activation Steering (SCANS) method to mitigate the exaggerated safety concerns in aligned LLMs. First, SCANS extracts the refusal steering vectors within the activation space and utilizes vocabulary projection to anchor some specific safety-critical layers which influence model refusal behavior. Second, by tracking the hidden state transition, SCANS identifies the steering direction and steers the model behavior accordingly, achieving a balance between exaggerated safety and adequate safety. Experiments show that SCANS achieves new state-of-the-art performance on XSTest and OKTest benchmarks, without impairing their defense capability against harmful queries and maintaining almost unchanged model capability. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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EmotionMap:Visual Analysis of Video Emotional Content on a Map

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Journal of computer science & Technology 2020年第3期35卷 576-591页

作者： Cui-Xia Ma Jian-Cheng Song Qian Zhu Kevin Maher Ze-Yuan Huang Hong-An Wang State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China Academy of Arts and Design Tsinghua UniversityBeijing 100084China

Emotion plays a crucial role in gratifying users’needs during their experience of movies and TV series,and may be underutilized as a framework for exploring video content and *** this paper,we present EmotionMap,a novel way of presenting emotion for daily users in 2D geography,fusing spatio-temporal information with emotional *** interface is composed of novel visualization elements interconnected to facilitate video content exploration,understanding,and *** allows understanding of the overall emotion at a glance while also giving a rapid understanding of the ***,we develop EmotionDisc which is an effective tool for collecting audiences’emotion based on emotion representation *** collect audience and character emotional data,and then integrate the metaphor of a map to visualize video content and emotion in a hierarchical *** combines sketch interaction,providing a natural approach for users’active *** novelty and the effectiveness of EmotionMap have been demonstrated by the user study and experts’feedback.

关键词： video visualization emotion analysis visual analysis sketch interaction

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A Machine Learning Approaches for Natural Language Processing and Sentiment Analysis in Social Media

A Machine Learning Approaches for Natural Language Processin...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Geetha Manoharan Prithu Sarkar Punit Pathak Khadri Syed Faizz Ahmad Arun Singh Vaishnavi M School of Business SR University Warangal Telangana India Amity School of Communication Amity University Kolkata School of Liberal Arts and Human Sciences Auro University Surat department Computer Science and Engineering SRM University Andhra Pradesh Vikrant University Gwalior M.P Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

In the recent decade, there has been a rise in social media and various companies are using it as a primary tool for marketing. In order to better understand the emotion and views of the customers, social media sentiment analysis has been used. Machine learning is a rising technology which helps in sentiment analysis in the social media which helps to easily categorize the views and opinions of the customers in certain categories. The major objectives of the study include exploring Machine Learning Approaches for Natural Language Processing and Sentiment Analysis in social media. In this research, the methods of sentiment analysis in online social networks using ML and techniques for grouping have been studied in detail. A secondary research approach has been followed for the research. The major benefits of sentiment analysis on social media have also been presented as a part of the report. In the research it has been found that there are two major types of clarifying sentiments which are supervised and unsupervised learning. The research also reveals that the best learning model for evaluating the Twitter dataset is determined to be CNN using Word to Vector and under instruction.

关键词： Sentiment analysis Analytical models Social networking (online) Computational modeling Blogs Machine learning Companies Vectors Informatics Unsupervised learning

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