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Combined compression and encryption of linear wireless sensor network data using autoencoders

Scientific Reports 2025年第1期15卷 1-16页

作者： Shylashree, N. Kumar, Sachin Min, Hong Department of Electronics and Communication RV College of Engineering Affiliated to VTU Belagavi Bengaluru 560059 India Big Data and Machine Learning Lab South Ural State University Chelyabinsk Russian Federation Akian College of Science and Engineering American University of Armenia Yerevan 0019 Armenia School of Computing Gachon University Seongnam South Korea

In a linear wireless sensor network (LWSN), sensor nodes are deployed in a linear fashion to monitor and gather data along a linear path or route. Generally, the base station collects the data from the contiguously placed nodes in the same order. When the sensors are deployed closely and with a gradual variation of sensor data along the route, a high degree of correlation exists among the sensed data. The sensed data sequence can be compressed with very low loss in such a situation. In this paper, a joint compression and encryption method for LWSN data is presented. The method is based on the dimension reduction property of an autoencoder at the bottleneck section. The Encoder part of the trained Autoencoder, housed at the Base Station (BS), reduces the number of data samples at the encoded output. Hence, the data gets compressed at the output of the Encoder. The output of the Encoder is encrypted using an asymmetric encryption that provides immunity to the Chosen Plaintext Attack. Thus, both data compression and encryption are achieved together at the BS. Therefore, the procedure at the BS is denoted as joint compression and encryption. The encrypted data is sent to the Cloud Server for secured storage and further distribution to the End User, where it is decrypted and subsequently decompressed by the Decoder part of the trained Autoencoder. The decompressed data sequence is very nearly equal to the original data sequence. The proposed lossy compression has a mean square reconstruction error of less than 0.5 for compression ratios in the range of 5 to 10. The compression time taken is short even though the Autoencoder training process, which occurs once in a while, takes a relatively long time. © The Author(s) 2025.

关键词： Autoencoder with supervised learning Chosen plaintext attack Integer matrix keys Joint compression and encryption Linear wireless sensor network Secured cloud storage

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ChatGPT-guided Semantics for Zero-shot learning

ChatGPT-guided Semantics for Zero-shot Learning

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Fahimul Hoque Shubho Townim Faisal Chowdhury Ali Cheraghian Morteza Saberi Nabeel Mohammed Shafin Rahman Dept. of Electrical and Computer Engineering North South University Bangladesh Australian Institute for Machine Learning University of Adelaide Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia School of Computer Science and DSI University of Technology Sydney Australia

Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes to the unseen classes. Existing methods of obtaining class semantics include manual attributes or automatic word vectors from language models (like word2vec). We know attribute annotation is costly, whereas automatic word vectors are relatively noisy. To address this problem, we explore how ChatGPT, a large language model, can enhance class semantics for ZSL tasks. ChatGPT can be a helpful source to obtain text descriptions for each class containing related attributes and semantics. We use the word2vec model to get a word vector using the texts from ChatGPT. Then, we enrich word vectors by combining the word embeddings from class names and descriptions generated by ChatGPT. More specifically, we leverage ChatGPT to provide extra supervision for the class description, eventually benefiting ZSL models. We evaluate our approach on various 2D image (CUB and AwA) and 3D point cloud (ModelNet10, ModelNet40, and ScanObjectNN) datasets and show that it improves ZSL performance. Our work contributes to the ZSL literature by applying ChatGPT for class semantics enhancement and proposing a novel word vector fusion method.

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ChatGPT-guided Semantics for Zero-shot learning

arXiv

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arXiv 2023年

作者： Shubho, Fahimul Hoque Chowdhury, Townim Faisal Cheraghian, Ali Saberi, Morteza Mohammed, Nabeel Rahman, Shafin Dept. of Electrical and Computer Engineering North South University Bangladesh Australian Institute for Machine Learning University of Adelaide Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia School of Computer Science and DSI University of Technology Sydney Australia

关键词： Zero-shot learning

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Analysis of the Research Status of Information Technology Education Literature in China and Abroad 24

Analysis of the Research Status of Information Technology Ed...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Yulin Liu, Kai Li, Junke School of Information Engineering Suqian University Jiangsu Suqian223800 China School of Computer and Information Qiannan Normal University for Nationalities Duyun558000 China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu 223800 China Key Laboratory of Machine Learning and Unstructured Data Processing of Qiannan Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000 China

ISBN: (纸本)9798350319934

Information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad. © 2022 IEEE.

关键词： Informatization

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BIM: Improving Graph Neural Networks with Balanced Influence Maximization

BIM: Improving Graph Neural Networks with Balanced Influence...

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International Conference on data engineering

作者： Wentao Zhang Xinyi Gao Ling Yang Meng Cao Ping Huang Jiulong Shan Hongzhi Yin Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Key Lab of High Confidence Software Technologies Peking University Apple Inc. Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score.

关键词： Training Industries data engineering Graph neural networks

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Condition Monitoring and Fault Diagnosis of BLDC Motor in Electric Vehicles Using Artificial Intelligence

Condition Monitoring and Fault Diagnosis of BLDC Motor in El...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Swapnil Gundewar Meher Langote Swapna Kamble Prashant Kamble Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Maharashtra India Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Maharashtra India Department of Information Technology Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India Department of Mechanical Technology Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Due to their high benefits in terms of environment and standards of battery advancement, Electric Vehicles (EVs) are extremely important in the shift towards transportation. Here the Brushless DC (BLDC) motor is an integrated part of the EVs, and it has become the most popular choice due to better efficiency, reliability, and reduced maintenance requirements than the others. The major difference from traditional motors is that BLDC motors make use of electronic commutation, so brushes are not employed; this results in lower amounts of wear. This not only helps the motor to have a longer working life but also to work more efficiently, or to maintain their efficiency. However, one crucial drawback to using BLDC motors is the fact that they tend to perform and have great durability if the condition monitoring and fault diagnosis systems when utilized top-notch. These systems are important for the identification of abnormalities and possible defects that if not attended to can lead to major failures and long periods of non-productive time. The ability to track the state of BLDC motors in real time enables one to have preventive measures that cut the chances of the motor breaking down and consequently the costs attached to this eventuality. Condition monitoring involves continuous assessment of motor attributes such as; vibration, temperature, and electrical patterns in a bid to detect changes in healthy motor status. Whereas the existing literature on the level of faults goes beyond it by identifying the specific nature and location of the faults. Altogether, the procedures composed play the basic role of a predictive maintenance program and check BLDC motors’ performance and reliability throughout their usage. Given the importance of BLDC motors in EVs, improvements in the reliability of condition monitoring and fault diagnosis are unpreventable for the enhancement and sustainability of electric mobility.

关键词： Fault diagnosis Temperature measurement Condition monitoring Temperature sensors Vibrations machine learning Motors Electric vehicles Maintenance Monitoring

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RVPD: An Automated System for Calculating the Tortuosity and Bifurcation Angles of Retinal Vessels to Predict Diseases

RVPD: An Automated System for Calculating the Tortuosity and...

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IEEE International Symposium on Biomedical Imaging

作者： Guangzhengao Yang Xingyu Luo Jie Zhao Fangfang Fan Haoshen Li Bin Dong Li Zhang Yan Zhang Center for Data Science Peking University China Department of Cardiology Peking University First Hospital China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Hypertension and diabetes are known to potentially cause morphological changes in the retinal capillary system, yet quantifying these changes presents significant challenges. This research addresses this issue by designing and developing an automated system capable of analyzing retinal images to detect and measure these changes. Our system automatically segments vessels and the optic disc, calculates tortuosity and bifurcation angles, and performs disease prediction based on these measurements along with clinical features. For tortuosity calculation, we adopted a novel curve fitting method, which provides more stable convergence compared to traditional discrete numerical methods. The system integrates clinical data and conducts prediction experiments on hypertensive and diabetic patients with varying sample sizes. Our results demonstrate the system's strong performance and scalability: in the hypertension group, the AUC reached a maximum of 0.814 ± 0.023, with an ACC of 0.735 ± 0.033, while in the diabetes group, the AUC reached a maximum of 0.710 ± 0.043, with an ACC of 0.641 ± 0.052. These findings suggest that our automated system could serve as a valuable tool for early detection and risk assessment of hypertension and diabetes, potentially improving patient outcomes through timely intervention.

关键词： Hypertension Image segmentation Scalability Bifurcation Optical variables measurement Optical imaging Retinal vessels Diabetes Risk management Diseases

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Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping

arXiv

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arXiv 2023年

作者： Butke, Joshua Hashimoto, Noriaki Takeuchi, Ichiro Miyoshi, Hiroaki Ohshima, Koichi Sakuma, Jun Machine Learning and Data Mining Lab University of Tsukuba Japan RIKEN Center for Advanced Intelligence Project Japan Department of Mechanical Systems Engineering Nagoya University Japan Department of Pathology Kurume University Japan Department of Computer Science Tokyo Institute of Technology Japan

Whole-slide image analysis via the means of computational pathology often relies on processing tessellated gigapixel images with only slide-level labels available. Applying multiple instance learning-based methods or transformer models is computationally expensive as, for each image, all instances have to be processed simultaneously. The MLP-Mixer is an under-explored alternative model to common vision transformers, especially for large-scale datasets. Due to the lack of a self-attention mechanism, they have linear computational complexity to the number of input patches but achieve comparable performance on natural image datasets. We propose a combination of feature embedding and clustering to preprocess the full whole-slide image into a reduced prototype representation which can then serve as input to a suitable MLP-Mixer architecture. Our experiments on two public benchmarks and one inhouse malignant lymphoma dataset show comparable performance to current state-of-the-art methods, while achieving lower training costs in terms of computational time and memory load. Code is publicly available at https://***/butkej/ProtoMixer. Copyright © 2023, The Authors. All rights reserved.

关键词： Mixers (machinery)

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Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

arXiv

引用

arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

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Quantization of Bandlimited Graph Signals

Quantization of Bandlimited Graph Signals

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International Conference on Sampling Theory and Applications (SampTA)

作者： Felix Krahmer He Lyu Rayan Saab Anna Veselovska Rongrong Wang Department of Mathematics & Munich Data Science Institute Technical University of Munich and Munich Center for Machine Learning Garching/Munich Germany Department of Mathematics & Halicioglu Data Science Institute University of California San Diego San Diego USA Department of Computational Mathematics Science and Engineering & Department of Mathematics Michigan State University East Lansing USA

Graph models and graph-based signals are becoming increasingly important in machine learning, natural sciences, and modern signal processing. In this paper, we address the problem of quantizing bandlimited graph signals. We introduce two classes of noise-shaping algorithms for graph signals that differ in their sampling methodologies. We demonstrate that these algorithms can be efficiently used to construct quantized representatives of bandlimited graph-based signals with bounded amplitude. Moreover, for one of the algorithms, we provide theoretical guarantees on the relative error between the quantized representative and the true signal.

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