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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Gaurav, Akshat Gupta, Brij B. Chui, Kwok Tai Ronin Institute MontclairNJ United States Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan University of Economics and Human Science Warsaw Poland Hong Kong

ISBN: (纸本)9798331521165

This work presents a t-SNE-based feature selection and Seagull Optimized Deep Learning Model to improve consumer electronics security in smart homes. Optimized using the Seagull Optimization Algorithm (SOA), the suggested Convolutional Neural Network (CNN) model showed exceptional performance with an accuracy of 92% in identifying cyber risks. The model routinely exceeded in both accuracy and loss measures when tested against conventional machine learning and deep learning models. T-SNE helped to choose important criteria that provide strong identification of harmful activity. Our method presents a major development in smart home security and a dependable instrument for reducing cybersecurity threats in a linked world. © 2025 IEEE.

关键词： Convolutional neural networks

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Localization Based on Floor Line Map Using Monocular Camera for Factory Autonomous Mobile Robot

Localization Based on Floor Line Map Using Monocular Camera ...

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Mechatronics and Robotics Engineering (ICMRE), International Conference on

作者： Daiki Hirayama Shinji Hagio Kimiya Murase Katsuya Sugiura Kazuhiro Kuno Masahiro Noba Akihisa Ohya Takashi Tsubouchi Ayanori Yorozu Intelligent and Mechanical Interaction Systems University of Tsukuba Tsukuba Japan Production Innovation Department AISIN CORPORATION Anjo Japan Department of Computer Science University of Tsukuba Tsukuba Japan Department of Intelligent Interactive Systems University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350394276

ISBN: (纸本)9798350394283

This study presents a localization for factory autonomous mobile robot based on a floor white line map with transforming the line recognition results from a monocular camera mounted on the front of the robot into a bird’s-eye view. The Monte Carlo Localization with particles based on the observation of floor lines is used. However, the projection of white lines onto the floor coordinates through the bird’s-eye view transformation is affected by variations in the robot’s pitch angle and observational noise caused by reflections from the floor lights. Especially when white lines are not sufficiently observed, updating the likelihood can lead particles to converge to incorrect positions, resulting in inaccurate localization. In this study, we propose a method that updates the likelihood only when a sufficient amount of observations is available based on white line recognition results. We conducted real-world experiments in an actual factory environment with white line layouts and compared and discussed the proposed method with a method using a LiDAR.

关键词： Location awareness Monte Carlo methods Laser radar Robot kinematics Robot vision systems Layout Cameras

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Tractable and Expressive Generative Models of Genetic Variation Data 26th

Tractable and Expressive Generative Models of Genetic Vari...

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26th International Conference on Research in Computational Molecular Biology, RECOMB 2022

作者： Dang, Meihua Liu, Anji Wei, Xinzhu Sankararaman, Sriram Van den Broeck, Guy Department of Computer Science UCLA Los Angeles United States Department of Human Genetics School of Medicine UCLA Los Angeles United States Department of Computational Medicine School of Medicine UCLA Los Angeles United States

ISBN: (纸本)9783031047480

关键词： Genes

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Determinants of Cybercrime Victimization: Experiences and Multi-stage Recommendations from a Survey in Cameroon 14th

Determinants of Cybercrime Victimization: Experiences and Mu...

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14th EAI International Conference on Towards new e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2022

作者： Ntsama, Jean Emmanuel Tchakounte, Franklin Tchakounte Tchuimi, Dimitri Faissal, Ahmadou Fotso Kuate, Franck Arnaud Effa, Joseph Yves Udagepola, Kalum Priyanath Atemkeng, Marcellin Department of Mathematics and Computer Science Faculty of Science University of Ngaoundéré Ngaoundéré Cameroon Yaoundé Cameroon Department of Human Resource Economics Faculty of Economics and Management University of Yaoundé II SOA Yaoundé Cameroon Department of Physics Faculty of Science University of Ngaoundéré Ngaoundéré Cameroon Department of Information and Computing Sciences Scientific Research Development Institute of Technology Loganlea Australia Department of Mathematics Rhodes University Makhanda South Africa

ISBN: (纸本)9783031348952

Cybercrimes are multiplying and spreading at an elusive speed commensurate with the emerging technologies of the fourth revolution. Their sophistication and user’s vulnerability to attacks catalyze their success. Several surveys have been conducted to determine the factors favoring victimization. However, they can only be applied within a contextual framework since each ecosystem has particularities. Attempts in this direction are unavailable in Cameroon where cybercrimes cost 12.2 billion CFA in 2021. This work consists of a semi-direct survey conducted in Cameroon in 2021 to provide the determinants explaining the most frequent cybercriminal techniques, the vulnerabilities left by the users, the most targeted population segments, and the socio-demographic and economic factors justifying this security fragility. The results relate to 370 questionnaires collected throughout the territory. According to descriptive statistics and the chi-square test, the explanatory variables of cybercrime victimization are gender, age, intellectual level, level of digital knowledge, level of Information and Communication Technology (ICT) proficiency, type of equipment used, the mobile and desktop operating system used, the possession of an anti-virus/anti-spam and the marital status. Within this work, we have identified threats and their drivers and a theoretical framework has been provided with several stages that could be followed to contain cybercrime. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Statistical tests

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Design lessons from AI’s two grand goals: human emulation and useful applications

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2020年第2期1卷 73-82页

作者： Shneiderman, Ben Department of Computer Science University of Maryland at College Park College ParkMD20742 United States Human–Computer Interaction Laboratory University of Maryland at College Park College ParkMD20742 United States

Researchers’ goals shape the questions they raise, collaborators they choose, methods they use, and outcomes of their work. This article offers a fresh vision of artificial intelligence (AI) research by suggesting a simplification to two goals: 1) emulation to understand human abilities to build systems that perform tasks as well as or better than humans and 2) application of AI methods to build widely used products and services. Researchers and developers for each goal can fruitfully work along their desired paths, but this article is intended to limit the problems that arise when assumptions from one goal are used to drive work on the other goal. For example, autonomous humanoid robots are prominent with emulation researchers, but application developers avoid them, in favor of tool-like appliances or teleoperated devices for widely used commercial products and services. This article covers four such mismatches in goals that affect AI-guided application development: 1) intelligent agent or powerful tool;2) simulated teammate or teleoperated device;3) autonomous system or supervisory control;and 4) humanoid robot or mechanoid appliance. This article clarifies these mismatches to facilitate the discovery of workable compromise designs that will accelerate human-centered AI applications research. A greater emphasis on human-centered AI could reduce AI’s existential threats and increase benefits for users and society, such as in business, education, healthcare, environmental preservation, and community safety. © 2020 IEEE. All right reserved.

关键词： Anthropomorphic robots

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LEARNING WHAT TO DO BY SIMULATING THE PAST 9

LEARNING WHAT TO DO BY SIMULATING THE PAST

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9th International Conference on Learning Representations, ICLR 2021

作者： Lindner, David Shah, Rohin Abbeel, Pieter Dragan, Anca Department of Computer Science ETH Zurich Switzerland Center for Human-Compatible AI UC Berkeley United States

Since reward functions are hard to specify, recent work has focused on learning policies from human feedback. However, such approaches are impeded by the expense of acquiring such feedback. Recent work proposed that agents have access to a source of information that is effectively free: in any environment that humans have acted in, the state will already be optimized for human preferences, and thus an agent can extract information about what humans want from the state (Shah et al., 2019). Such learning is possible in principle, but requires simulating all possible past trajectories that could have led to the observed state. This is feasible in gridworlds, but how do we scale it to complex tasks? In this work, we show that by combining a learned feature encoder with learned inverse models, we can enable agents to simulate human actions backwards in time to infer what they must have done. The resulting algorithm is able to reproduce a specific skill in MuJoCo environments given a single state sampled from the optimal policy for that skill. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

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Towards a Better Evaluation of Metrics for Machine Translation 5

Towards a Better Evaluation of Metrics for Machine Translati...

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5th Conference on Machine Translation, WMT 2020

作者： Stanchev, Peter Wang, Weiyue Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52056 Germany

ISBN: (纸本)9781948087810

An important aspect of machine translation is its evaluation, which can be achieved through the use of a variety of metrics. To compare these metrics, the workshop on statistical machine translation annually evaluates metrics based on their correlation with human judgement. Over the years, methods for measuring correlation with humans have changed, but little research has been performed on what the optimal methods for acquiring human scores are and how human correlation can be measured. In this work, the methods for evaluating metrics at both system- and segment-level are analyzed in detail and their shortcomings are pointed out. © 2020 Association for Computational Linguistics

关键词： Machine translation

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Obstacle Detection and Avoidance based on 3D Stereoscopic Technique for Autonomous Drone 13

Obstacle Detection and Avoidance based on 3D Stereoscopic Te...

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13th International Conference on Information Technology and Electrical Engineering, ICITEE 2021

作者： Nabandit, Thanawut Saiyod, Saiyan Nonsakhoo, Woottichai Sirisawat, Piyanat Khon Kaen University Hardware-Human Interface and communication laboratory Department of Computer Science Khon Kaen Thailand

ISBN: (纸本)9781665443067

Autonomous drones are drones that can fly on their own without the need for human supervision. Once the drone is assigned a flight mission, it will automatically fly to its destination. The ability to quickly maneuver obstacles is essential to complete the task assigned to it. Obstacle analysis processes need to be efficient and require fewer computing resources due to the hardware and power constraints of drones. This work presents a simple but effective way to detect obstacles using a 3D Stereoscopic camera and a less complicated image processing method, which can run on a small single-board computer. On-site flight tests show that the proposed method can be used efficiently. It can detect obstacles at a distance of 2 meters, 5 frames per second, 79.55% of detection accuracy, and maneuver effectively. © 2021 IEEE.

关键词： Drones

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Data Collection and Real-Time Facial Emotion Recognition in iOS Apps with CNN-Based Models

Data Collection and Real-Time Facial Emotion Recognition in ...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Valles, Damian Umali, Lois Adrianne R. Paveglio, Thomas Brinson, Josiah N. Hyder, Mohammed Jackson, Gavin E. Hall, Dylan Farrell, John W. Li, Yumeng Aslan, Semih Resendiz, Maria D. Liu, Ting Texas State University Ingram School of Engineering San MarcosTX78666 United States Texas State University Department of Communication Disorders Round RockTX78665 United States Wake Forest University Department of Physics Winston-SalemNC27109 United States Rhodes College Department of Mathematics and Computer Science MemphisTN38112 United States Texas State University Department of Computer Science San MarcosTX78666 United States Texas State University Department of Health and Human Performance San MarcosTX78666 United States

ISBN: (纸本)9798350337617

This study analyzes and compares deep learning models, including Naïve-CNN, VGG16, EfficientNetV2, and MobileNetV2, for facial emotion recognition using the FER2013, and collected Zoom datasets are presented. The paper discusses the data collection of human subjects over Zoom to gather quality samples for the seven emotions targeted with the classifiers. Preprocessing steps are considered to enhance histogram information, brightness contrast, and augmentation of both datasets. The performance of these models was evaluated based on their accuracy, precision, recall, and F1-score. An iOS app was developed to test the trained models in real-time with YouTube videos. The Naïve-CNN and VGG16 models demonstrated the highest performance, while the EfficientNetV2 and MobileNetV2 models showed potential for further improvement during training and testing. The iOS app implementation showed the expected weak results of the trained models but observed capacities that the model provided for actual utilization on mobile devices. The work provides valuable insights into the challenges faced by deep learning CNN-based models for facial emotion recognition and suggests directions for future research. © 2023 IEEE.

关键词： Emotion Recognition

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Designing Online Multiplayer Games with Haptically and Virtually Linked Tangible Robots to Enhance Social interaction in Therapy

Designing Online Multiplayer Games with Haptically and Virtu...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Arzu Guneysu Ozgur Hala Khodr Mehdi Akeddar Michael Roust Pierre Dillenbourg Computer Human Interaction in Learning and Instruction Lab (CHILI) EPFL Lausanne Switzerland Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

The social aspects of therapy and training are important for patients to avoid social isolation and must be considered when designing a platform, especially for home-based rehabilitation. We proposed an online version of the previously proposed tangible Pacman game for upper limb training with haptic-enabled tangible Cellulo robots. Our main objective is to enhance motivation and engagement through social integration and also to form a gamified multiplayer rehabilitation at a distance. Thus, allowing relatives, children, and friends to connect and play with their loved ones while also helping them with their training from anywhere in the world. As well as connecting therapists to their patients through haptically linking capabilities. This is especially relevant when there are social distancing measures which might isolate the elderly population, a majority of all rehabilitation patients.

关键词： Training Pediatrics Sociology Medical treatment Games human factors Social factors

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