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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Trung T. Nguyen Amartya Hatua Andrew H. Sung Department of Computer Science Winona State University Winona MN USA AI Center of Excellence Fidelity Investments Boston MA USA School of Computing Sciences and Computer Engineering The University of Southern Mississippi Hattiesburg MS USA

Recent advances in large language models (LLMs) have significantly improved the quality of synthetic text data. LLMs imitate human writing patterns to produce highly natural text, raising serious ethical, moral, legal, social, and economic concerns in various industries. To address these issues, we present two methods to distinguish Synthetically Generated Text (SGT) from Human-Written Text (HWT): machine learning and text similarity. Our methods include procedures for dataset creation, feature engineering, model training, and result analysis with feature importance analysis and explanation models. As part of our research, we created two datasets - a Wikipedia-based and a US Election 2024 news article-based dataset using ChatGPT, from which we obtained promising results of up to ***% accuracy. These datasets can be used as open-source datasets in future studies. We also identified a set of handcrafted features that can serve as the baseline feature set for future research.

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Machine Learning in E-health: A Comprehensive Survey of Anxiety

Machine Learning in E-health: A Comprehensive Survey of Anxi...

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2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

作者： Teelhawod, Bibi Nushrina Akhtar, Faijan Heyat, Md Belal Bin Tripathi, Pragati Mehrotra, Rajat Asfaw, Ashamo Betelihem Shorman, Omar Al Masadeh, Mahmoud University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan China Shenzhen University IoT Research Center College of Computer Science and Software Engineering Guangdong Shenzhen China Gautam Buddha University Department of Electrical Engineering Greater Noida India Gl Bajaj Institute of Technology and Management Aktu UP India Northwestern Polytechnical University School of Software Shaanxi Xian China Najran University College of Engineering and A1 Shrouk Trading Company Najran Saudi Arabia Yarmouk University Computer Engineering Department Irbid Jordan

ISBN: (纸本)9781665416566

Anxiety is the sixth major disorder that arises due to sleeplessness, inspiration, energy, hunger loss and desperate ideas. This study aims to define the extent of the research carried on anxiety with machine learning (ML) and to generate a database. We highlight serious problems for additional research obligations and communications. This paper designs a perception-based image of ML We have investigated deep insights of material to re-claim the current understanding of anxiety with ML from engineering detection to clinical treatment. We found 18 research articles and 28 closest keywords in the Web of Science (WoS) database. In addition, we extracted the information including objective, subject, major, scoring technique, classifier, and performance measure from previously published 18 articles. This study has discovered the research gap and future direction, so far focused on anxiety with ML. In addition, it also delivers objectives for the upcoming work to be competent in a significant and scientific manner. © 2021 IEEE.

关键词： Machine learning

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Research on Key Word Information Retrieval Based on Inverted Index 8th

Research on Key Word Information Retrieval Based on Inverted...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Qi, Meihan Fang, Wei Zhao, Yongming Sha, Yu Sheng, Victor S. School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing100081 China China Meteorological Administration Training Center Beijing China Department of Computer Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9783031067600

With the advent of the era of big data, data has penetrated into every aspect of social life and become an important production factor in various industries. However, while providing convenience to our life, massive information also brings difficulties to information retrieval, which has problems of low retrieval efficiency and poor retrieval accuracy. Therefore, how to improve the efficiency and accuracy of information retrieval has become a key technical problem to be solved. In this paper, we propose a key word information retrieval scheme based on inverted index, which uses the inverted index segmentation algorithm based on keywords to realize the block retrieval of information, improves the efficiency of information retrieval, and improves the accuracy of retrieval content through reasonable error correction of keywords. The results of evaluation indicate that compared with the mainstream information retrieval methods, the keyword information retrieval scheme based on inverted index can carry out faster and more accurate information retrieval. Experimental results show that the keyword information retrieval scheme with inverted index can further improve the efficiency and accuracy of information retrieval. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information retrieval

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Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 638-655页

作者： Panayiotis M.Papadopoulos Vasso Reppa Marios M.Polycarpou Christos G.Panayiotou IEEE KIOS Research and Innovation Center of Excellence Department of Electrical and Computer EngineeringUniversity of CyprusNicosia 1678Cyprus Department of Maritime and Transport Technology Delft University of TechnologyDelft2628 CDThe Netherlands

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants' productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning(HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building's energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC *** the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.

关键词： Building automation fault diagnosis fault location smart homes

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Data Hiding Scheme using Difference Expansion and Modulus Function

Data Hiding Scheme using Difference Expansion and Modulus Fu...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Md. Sagar Hossen Tohari Ahmad Ntivuguruzwa Jean De La Croix Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh African Center of Excellence in Internet of Things University of Rwanda Kigali Rwanda

Our information is constantly under threat when transmitted through public networks. So, research to keep information secret has been carried out. Mainly, steganography, which consists of hiding data in digital media, receives much attention. Existing steganographic systems identified the need to improve performance by reducing a tradeoff between the peak signal-to-noise ratio (PSNR) and bits per pixel (BPP). In this paper, we propose a new steganographic scheme to embed the bits of secret messages in a digital image’s pixels. Our method expands the differences between the neighbouring pixels for secret data concealment. We group the pixels in blocks of size $1\times 3$, and two of the three pixels of the block are candidates to hold the secret bit. We also propose extracting the hidden data to validate our data concealment scheme. To extract the secret data, we also arrange the neighbouring pixels into blocks of three and use their differences, and a modulus function, based on pixels identified carrying the secret data based on the key generated during data concealment. To evaluate the performance of our scheme, we consider the PSNR and the BPP as metrics. The experimental results showed better performance over the existing methods with 68.7790 dB for the PSNR and 0.1562 BPP.

关键词： Measurement Image quality Steganography Technological innovation PSNR Digital images Media

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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Predicting Parkinson’s disease trajectory using clinical and functional MRI features: a reproduction and replication study

arXiv

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arXiv 2024年

作者： Germani, Elodie Bhagwat, Nikhil Dugré, Mathieu Gau, Rémi Montillo, Albert A. Nguyen, Kevin P. Sokolowski, Andrzej Sharp, Madeleine Poline, Jean-Baptiste Glatard, Tristan Univ Rennes Inria CNRS Inserm France Department of Neurology and Neurosurgery McGill University Montreal Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada Lyda Hill Department of Bioinformatics University of Texas Southwestern Medical Center Dallas United States

Parkinson’s disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability related for instance to cohort selection or image analysis. In this context, an evaluation of the robustness of such biomarkers to variations in the data processing workflow is essential. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD. Here, we attempt to fully reproduce (reimplementing the experiments with the same methods, including data collection from the same database) and replicate (different data and/or method) the models described in [1] to predict individual’s PD current state and progression using demographic, clinical and neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We use the Parkinson’s Progression Markers Initiative dataset (PPMI, ***), as in [1] and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. We also investigated methodological variations in cohort selection, feature extraction pipelines and sets of input features. Different criteria were used to evaluate the reproduction attempt and compare the results with the original ones. Notably, we obtained significantly better than chance performance using the analysis pipeline closest to that in the original study (R2 > 0), which is consistent with its findings. In addition, we performed a partial reproduction using derived data provided by the authors of the original study, and we obtained results that were close to the original ones. The challenges encountered while attempting to reproduce (fully and partially) and replicating the original work are likely exp

关键词： Neurodegenerative diseases

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DAM-Net: Global Flood Detection from SAR Imagery Using Differential Attention Metric-Based Vision Transformers

arXiv

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arXiv 2023年

作者： Saleh, Tamer Weng, Xingxing Holail, Shimaa Hao, Chen Xia, Gui-Song Wuhan University Wuhan430079 China National Engineering Research Center for Multi-media Software School of Computer Science Institute of Artificial Intelligence Wuhan University Wuhan430072 China Geomatics Engineering Department Faculty of Engineering at Shoubra Benha University Cairo1196 Egypt School of Computer Science Wuhan University Wuhan430072 China

The detection of flooded areas using high-resolution synthetic aperture radar (SAR) imagery is a critical task with applications in crisis and disaster management, as well as environmental resource planning. However, the complex nature of SAR images presents a challenge that often leads to an overestimation of the flood extent. To address this issue, we propose a novel differential attention metric-based network (DAM-Net) in this study. The DAM-Net comprises two key components: a weight-sharing Siamese backbone to obtain multi-scale change features of multi-temporal images and tokens containing high-level semantic information of water-body changes, and a temporal differential fusion (TDF) module that integrates semantic tokens and change features to generate flood maps with reduced speckle noise. Specifically, the backbone is split into multiple stages. In each stage, we design three modules, namely, temporal-wise feature extraction (TWFE), cross-temporal change attention (CTCA), and temporal-aware change enhancement (TACE), to effectively extract the change features. In TACE of the last stage, we introduce a class token to record high-level semantic information of water-body changes via the attention mechanism. Another challenge faced by data-driven deep learning algorithms is the limited availability of flood detection datasets. To overcome this, we have created the S1GFloods open-source dataset, a global-scale high-resolution Sentinel-1 SAR image pairs dataset covering 46 global flood events between 2015 and 2022. The experiments on the S1GFloods dataset using the proposed DAM-Net showed top results compared to state-of-the-art methods in terms of overall accuracy, F1-score, and IoU, which reached 97.8%, 96.5%, and 93.2%, respectively. Our dataset and code will be available online at https://***/Tamer-Saleh/S1GFlood-Detection. © 2023, CC BY.

关键词： Floods

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The Applications of Federated Learning Algorithm in the Federated Cloud Environment: A Systematic Review

The Applications of Federated Learning Algorithm in the Fede...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Ademolu Ajao Oluranti Jonathan Emmanuel Adetiba Department of Computer and Information Sciences Covenant Applied and Informatics Africa Center of Excellence (CApIC-ACE) Covenant University Ota Nigeria Department of Computer and Information Sciences Covenant University Covenant University Ota Nigeria Department of Electrical & Information Engineering Covenant Applied Informatics and Communications (CApIC-ACE) Covenant University Ota Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed to protect the privacy of each device. This paper provides an overview of federated learning systems and how it can be applied in the federated cloud environment its frameworks, architectures, and applications, we were able to identify from various literature the Federated Learning technology was most used in the Federated Cloud/Multi-Cloud Environment, we also identified the use cases, additionally, we were able to identify the future directions Federated Learning could lean towards.

关键词： Performance evaluation Cloud computing Federated learning Smart contracts Training data Blockchains Internet of Things

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CTSSeg: Consistent Teacher-Student model for magnetic resonance image Segmentation

CTSSeg: Consistent Teacher-Student model for magnetic resona...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chenbin Zhang Qingyuan He Kun Yan Meng Ma Defeng Liu Ping Wang School of Computer Science Peking University Beijing China Department of Radiology Peking University Third Hospital Beijing China School of Software and Microelectronics Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Center for Reproductive Medicine Peking University Third Hospital Beijing China School of Software and Microelectronics Peking University and the KeyLaboratory of High Confidence Software Technologies (PKU) Ministry of Education

Segmentation of magnetic resonance images is an essential way of measuring the volume of tissues and lesions, which can improve the efficiency of diagnosis. The mainstream image segmentation methods are based on deep learning, which requires a large amount of labeled data. However, labeling magnetic resonance images is expensive and time-consuming. Therefore, we propose a consistent teacher-student model for magnetic resonance image segmentation, which is abbreviated as CTSSeg. Specifically, the CTSSeg includes a student network and a teacher network, where the student network learns supervised from labeled data, while the teacher network utilizes unlabeled data to improve the student network via contrastive learning and pseudo-label learning. We evaluate the proposed CTSSeg on the Atrial Segmentation Challenge dataset and a local clinical dataset. The experimental results show that our method can make full use of both labeled and unlabeled data and yield state-of-the-art performance.

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