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International Conference on Information Visualisation (IV)

作者： Marian Trutschl Phillip C.S.R. Kilgore Billy A. Tran Hyung W. Nam Eric Clifford Adesewa Akande Urska Cvek Dept. of Computer Science LSU Shreveport Shreveport LA Dept. of Pharmacology Toxicology and Neuroscience LSU Health Shreveport Shreveport LA

ISBN: (纸本)9781665490085

Venn diagrams are a useful method of visualizing Boolean data; however, their data aggregation causes fine detail about the data to be lost. In this paper, we present a method of augmenting Venn diagrams, so that they may depict similarity relationships among individual records in the data using the Self-Organizing Map. We applied this method to a synthetic data set and an empirical proteomics data set. We found that we were able to separate data within each region of the Venn diagram based on dimensional values, and that we can highlight the clustering of $p$ -values in the empirical set.

关键词： Self-organizing feature maps Data visualization Proteomics Data aggregation Synthetic data

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EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving Object

arXiv

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arXiv 2023年

作者： Kim, Hyunseo Yoon, Hye Jung Kim, Minji Han, Dong-Sig Zhang, Byoung-Tak Interdisciplinary Program in Neuroscience Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Dept. of Computer Science and Engineering Seoul National University Korea Republic of AIIS Seoul National University Korea Republic of

Current robotic hand manipulation narrowly operates with objects in predictable positions in limited environments. Thus, when the location of the target object deviates severely from the expected location, a robot sometimes responds in an unexpected way, especially when it operates with a human. For safe robot operation, we propose the EXit-aware Object Tracker (EXOT) on a robot hand camera that recognizes an object's absence during manipulation. The robot decides whether to proceed by examining the tracker's bounding box output containing the target object. We adopt an out-of-distribution classifier for more accurate object recognition since trackers can mistrack a background as a target object. To the best of our knowledge, our method is the first approach of applying an out-of-distribution classification technique to a tracker output. We evaluate our method on the first-person video benchmark dataset, TREK-150, and on the custom dataset, RMOT-223, that we collect from the UR5e robot. Then we test our tracker on the UR5e robot in real-time with a conveyor-belt sushi task, to examine the tracker's ability to track target dishes and to determine the exit status. Our tracker shows 38% higher exit-aware performance than a baseline method. The dataset and the code will be released at https://***/hskAlena/EXOT. Copyright © 2023, The Authors. All rights reserved.

关键词： Belt conveyors

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Dark Web Analytics: A Comparative Study of Feature Selection and Prediction Algorithms 3

Dark Web Analytics: A Comparative Study of Feature Selection...

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3rd International Conference on Intelligent Data science Technologies and Applications, IDSTA 2022

作者： Al-Omari, Ahmad Allhusen, Andrew Wahbeh, Abdullah Al-Ramahi, Mohammad Alsmadi, Izzat Our Lady of the Lake University Cis and Security Program San AntonioTX United States Syracuse University Engineering & Computer Science SyracuseNY United States Slippery Rock University Dept. of Computer Science Slippery RockPA United States Texas A&M San Antonio Dept. of Computing and Cyber Security San AntonioTX United States

ISBN: (数字)9781665499606

ISBN: (纸本)9781665499606

The value and size of information exchanged through dark-web pages are remarkable. Recently Many researches showed values and interests in using machine-learning methods to extract security-related useful knowledge from those dark-web pages. In this scope, our goals in this research focus on evaluating best prediction models while analyzing traffic level data coming from the dark web. Results and analysis showed that feature selection played an important role when trying to identify the best models. Sometimes the right combination of features would increase the model's accuracy. For some feature set and classifier combinations, the Src Port and Dst Port both proved to be important features. When available, they were always selected over most other features. When absent, it resulted in many other features being selected to compensate for the information they provided. The Protocol feature was never selected as a feature, regardless of whether Src Port and Dst Port were available. © 2022 IEEE.

关键词： Websites

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MERNet: Multimodal Emotion Recognition in Conversation Network via Cross-Modal Knowledge Distillation and Contrastive Learning

MERNet: Multimodal Emotion Recognition in Conversation Netwo...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Sooyeon Hwang Gahgene Gweon Eunseong Kwon Interdisciplinary Program in Artificial Intelligence Seoul National University South Korea Graduate School of Convergence Science and Technology Seoul National University South Korea Dept. of Computer Science and Engineering POSTECH South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Multimodal Emotion Recognition in Conversation (ERC) is a task of predicting the emotion of each utterance in a conversation by utilizing both verbal and non-verbal modalities. However, existing approaches often struggle to bridge cross-modal gaps, resulting in misaligned features and frequent misclassification of minority emotions into semantically similar majority emotions. To address these challenges, we propose MERNet, a framework that employs cross-modal knowledge distillation and contrastive learning to align multimodal features and effectively distinguish subtle emotions in conversations. Our framework consists of two stages: 1) guiding non-verbal modalities with the text modality to transfer knowledge and align their features, and 2) applying contrastive learning with emotion labels as anchors to distinguish subtle differences between similar emotions and address the class imbalance problem. Experiments conducted on two benchmark datasets, IEMOCAP and MELD, demonstrate that our MERNet outperforms existing state-of-the-art models.

关键词： Knowledge engineering Bridges Emotion recognition Visualization Computational modeling Contrastive learning Benchmark testing Linguistics Big Data

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Disclosure Privacy Information Social Media on TikTok 9

Disclosure Privacy Information Social Media on TikTok

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Syamsuar, Dedy Fahrezi, Fazri Favian, Ahnaf Irwansyah, Edy Irawan, Achmad Fakhri School of Information System Bina Nusantara University Faculty of Engineering Professional Engineer Program Dept. Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information;nevertheless, perceived risks can greatly discourage users. © 2024 IEEE.

关键词： Differential privacy

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DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance 11

DCGAN-based Medical Image Augmentation to Improve ELM Classi...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Rando Setiawan, Noor Akhmad Permanasari, Adhistya Erna Rulaningtyas, Riries Suksmono, Andriyan Bayu Sitanggang, Imas Sukaesih Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Biomedical Engineering Program Dept. of Physics Universitas Airlangga Surabaya Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Ipb University Department of Computer Science Bogor Indonesia

ISBN: (纸本)9781665460309

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%. © 2022 IEEE.

关键词： Generative adversarial networks

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Reliable Data Transmission through Private CBRS Networks

arXiv

引用

arXiv 2023年

作者： Kuo, Hsun-Yu Liu, Szu-Yu Huang, Chin-Ya Chen, Yu-Chi Xie, Meng-Hua The Bachelor Degree Program of Applied Science and Technology National Taiwan University of Science and Technology Taiwan The Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan

We consider the use of a domain proxy assisted private citizen broadband radio service (CBRS) network and propose a Maximum Transmission Continuity (MTC) scheme to transmit Internet of Things (IoT) data reliably. MTC dynamically allocates available CBRS channels to sustain the continuity of data transmission without violating the channel access requirements. MTC allocates the granted CBRS channels according to the priority of each user, the instant channel access status, interference among users, and the fairness. The simulation results demonstrate the improvement in managing reliable IoT data transmission in the private CBRS network. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Explainable AI model reveals disease-related mechanisms in single-cell RNA-seq data

arXiv

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arXiv 2025年

作者： Usman, Mohammad Varea, Olga Radeva, Petia I. Canals, Josep M. Abante, Jordi Ortiz, Daniel Dept. of Mathematics & Computer Science Universitat de Barcelona Barcelona Spain Dept. of Biomedical Sciences Faculty of Medicine and Health Sciences Institute of Neuroscience University of Barcelona Barcelona Spain Creatio Production and Validation Center of advanced therapies Universitat de Barcelona Barcelona Spain Barcelona Spain

Neurodegenerative diseases (NDDs) are complex and lack effective treatment due to their poorly understood mechanism. The increasingly used data analysis from Single nucleus RNA Sequencing (snRNA-seq) allows to explore transcriptomic events at a single cell level, yet face challenges in interpreting the mechanisms underlying a disease. On the other hand, Neural Network (NN) models can handle complex data to offer insights but can be seen as black boxes with poor interpretability. In this context, explainable AI (XAI) emerges as a solution that could help to understand disease-associated mechanisms when combined with efficient N models. However, limited research explores XAI in single-cell data. In this work, we implement a method for identifying disease-related genes and the mechanistic explanation of disease progression based on model combined with SHAP. We analyze available Huntington's disease (HD) data to identify both HD-altered genes and mechanisms by adding Gene Set Enrichment Analysis (GSEA) comparing two methods, differential gene expression analysis (DGE) and NN combined with SHAP approach. Our results show that DGE and SHAP approaches offer both common and differential sets of altered genes and pathways, reinforcing the usefulness of XAI methods for a broader perspective of disease. © 2025, CC BY-NC-ND.

关键词： Gene expression

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On the Stimulation Artifact Reduction during Electrophysiological Recording of Compound Nerve Action Potentials *

On the Stimulation Artifact Reduction during Electrophysiolo...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Raphael Panskus Lukas Holzapfel Wouter A. Serdijn Vasiliki Giagka Dept. of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology The Netherlands Dept. of System Integration and Interconnection Technologies Fraunhofer Institute for Reliability and Microintegration IZM Berlin Germany Neuroscience Dept. Erasmus Medical Center Rotterdam the Netherlands

Recording neuronal activity triggered by electrical impulses is a powerful tool in neuroscience research and neural engineering. It is often applied in acute electrophysiological experimental settings to record compound nerve action potentials. However, the elicited neural response is often distorted by electrical stimulus artifacts, complicating subsequent analysis. In this work, we present a model to better understand the effect of the selected amplifier configuration and the location of the ground electrode in a practical electrophysiological nerve setup. Simulation results show that the stimulus artifact can be reduced by more than an order of magnitude if the placement of the ground electrode, its impedance, and the amplifier configuration are optimized. We experimentally demonstrate the effects in three different settings, in-vivo and in-vitro.

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An IoT-based Parameter Extraction Platform for Powder Metallurgy Sintering Furnace

An IoT-based Parameter Extraction Platform for Powder Metall...

引用

International Conference on Internet of Things: Systems, Management and Security

作者： Shih-Man Chang Hao Pu Lin Chin-Chuan Han Yu-Chi Wu Dept. of Computer Science and Information Engineering National United University Miao-Li Taiwan Program in Material and Chemical Engineering National United University MiaoLi Taiwan Dept. of Electrical Engineering National United University Miao-Li Taiwan

ISBN: (数字)9798350366501

ISBN: (纸本)9798350366518

Internet of Things (IoT) technology has widely used in industrial production. Most cases focused on designing new equipment or processes. However, small and medium enterprises especially the powder metallurgy manufacturers in Taiwan would not replace existing production equipment due to the finical problems. In this study, an IoT-based platform is conducted to extract the sintering parameters for technology to augment sintering furnaces by installing sensors and data transmission modules, setting a sintering parameter extraction platform for powder metallurgy sintering furnace. Long-term monitoring and remote control are the main technologies for solving three problems during the sintering process, i.e., temperature, atmosphere, and time. Monitoring targets include checking whether the heaters are damaged, ensuring stable air flow meter readings, and verifying the normal speed of the conveyor belt. Additionally, remote control technology has been developed to execute cooling and reheating processes on the sintering furnace. The staying time on-site for personnel is reduced and the sintering time is increased for production increase during working hours.

关键词： Temperature sensors Powders Furnaces Sintering Process control Metallurgy Internet of Things Remote control Monitoring Parameter extraction

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