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IEEE World Forum on Internet of Things (WF-IoT)

作者： Melchizedek Alipio Gianne Marcus Obusan Julianne Abbe Ong Samuel Perez Anton Luis Garces Miroslav Bures Department of Computer Science System Testing Intelligent Laboratory FEE Czech Technical University in Prague Prague 2 Czech Republic Department of Electronics and Computer Engineering De La Salle University Manila Philippines

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Goat farming shows potential as one of the so-lutions for providing quality livestock products in developing countries. Goat health is essential in determining the quality of goods obtained from the goat. Smart livestock farming has been a reliable solution to achieve high-yield and high-quality goat products. However, current smart livestock monitoring systems do not consider the feeding pattern and behavior of the animal as a factor in its overall health. Hence, this study aims to develop a system that effectively monitors the feeding behavior of the goat using wearable devices, machine learning, and a visualization interface. The wearable device consists of an accelerometer, gyrometer, and temperature sensors to monitor the goat's feeding behavior and movement. The data collected by the wearable device are wirelessly transmitted to a base station to build a sensor-based dataset. This dataset is used to generate a k-means clustering model to classify the feeding behavior of the goats. Results show that the system was able to classify the feeding behavior of the goat with an accuracy of 98.02 % and a reliability of 100% in comparison with the actual goat behavior on the farm. Lastly, a graphical user interface is developed to provide monitoring and other relevant information to help the farmers in grazing the goats.

关键词： Temperature sensors Temperature measurement Temperature distribution Animals Agriculture Reliability Wearable devices

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

arXiv

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arXiv 2024年

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China JinYinHu Laboratory Wuhan China

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy

arXiv

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arXiv 2024年

作者： Huo, Wei Liu, Changxin Ding, Kemi Johansson, Karl Henrik Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

This paper investigates the use of the cubic-regularized Newton method within a federated learning framework while addressing two major concerns that commonly arise in federated learning: privacy leakage and communication bottleneck. We introduce a federated learning algorithm called Differentially Private Federated Cubic Regularized Newton (DP-FCRN). By leveraging second-order techniques, our algorithm achieves lower iteration complexity compared to first-order methods. We also incorporate noise perturbation during local computations to ensure privacy. Furthermore, we employ sparsification in uplink transmission, which not only reduces the communication costs but also amplifies the privacy guarantee. Specifically, this approach reduces the necessary noise intensity without compromising privacy protection. We analyze the convergence properties of our algorithm and establish the privacy guarantee. Finally, we validate the effectiveness of the proposed algorithm through experiments on a benchmark dataset. © 2024, CC BY.

关键词： Federated learning

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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International Conference on computers, Communications, and systems (ICCCS)

作者： Hong Qian Wei Wu Qi Zhu Fei Wang Jinwei Zhao Yan Zheng School of Computer Science and Technology University of Science and Technology of China Hefei China System Software Research Department Jiangnan Institute of Computing Technology Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs.

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Enhancing EEG Artifact Removal Efficiency by Introducing Dense Skip Connections to IC-U-Net

Enhancing EEG Artifact Removal Efficiency by Introducing Den...

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

作者： Kong-Yi Chang Yung-Chia Huang Chun-Hsiang Chuang Institute of Information Systems and Applications College of Electrical Engineering and Computer Science NTHU Taiwan Research Center for Education and Mind Sciences College of Education NTHU Taiwan Department of Engineering and System Science NTHU Taiwan

Electroencephalographic (EEG) data is considered contaminated with various types of artifacts. Deep learning has been successfully applied to developing EEG artifact removal techniques to increase the signal-to-noise ratio (SNR) and enhance brain-computer interface performance. Recently, our research team has proposed an end-to-end UNet-based EEG artifact removal technique, IC-U-Net, which can reconstruct signals against various artifacts. However, this model suffers from being prone to overfitting with a limited training dataset size and demanding a high computational cost. To address these issues, this study attempted to leverage the architecture of UNet++ to improve the practicability of IC-U-Net by introducing dense skip connections in the encoder-decoder architecture. Results showed that this proposed model obtained superior SNR to the original model with half the number of parameters. Also, this proposed model achieved comparable convergency using a quarter of the training data size.

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Performance Improvements of Machine Learning-Based Crime Prediction, A Case Study in Bangladesh

Performance Improvements of Machine Learning-Based Crime Pre...

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Computing and Machine Intelligence (ICMI), International Conference on

作者： SM Nuruzzaman Nobel S M Masfequier Rahman Swapno Md Babul Islam V. P. Meena Francesco Benedetto Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of EEE Amrita School of Engineering Bengaluru India Economics Dept SP4TE Laboratory University of ROMA TRE Via Silvio Italy

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Crime observation examines information on criminal episodes to identify trends in their types, locations, and timing. Forecasting probable criminal activity in some places exploits previous data and data analysis to predict future criminal activity. Crime prediction faces several challenges, and accuracy can be elusive, mainly when dealing with diverse locations. Here, we propose a machine learning-based method, namely the extra tree regressor model, for crime prediction, with specific emphasis on different crime categories like dacoity, robbery, and murder, as well as geographical areas, including metropolitan regions and divisions. We decided to focus our analysis on a particular case study, i.e., crime data about Bangladesh, thus exploiting the crime data taken from the Bangladesh police department website. Our results and comparisons with state-of-the-art methods, such as linear regression, randomforestregressor, XGBregressor, and gradientboosting regressor, demonstrate the superiority of the proposed approach. In particular, our method can reach an accuracy rate of 99.95 %, thus proving to be very effective in predicting and analyzing Bangladesh's criminal activity and understanding crime patterns and trends.

关键词： Learning systems Accuracy Law enforcement Linear regression Predictive models Market research Timing

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Internet of Things for Digital Forensics Application in Saudi Arabia

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Advances in Internet of Things 2023年第1期13卷 1-11页

作者： Faihan B. Bindrwish Amer Nizar Abu Ali Wed H. Ghabban Alaaldin Alrowwad Najmah Adel Fallatah Omair Ameerbakhsh Ibrahim M. Alfadli Information System Department College of Computer Science and Engineering Taibah University Madina Saudi Arabia Applied College University of Tabuk Tabuk Saudi Arabia Department of Business School The University of Jordan Aqaba Branch Aqaba Jordan

Despite the extensive empirical literature relating to the Internet of Things (IoT), surprisingly few attempts have sought to establish the ways in which digital forensics can be applied to undertake detailed examinations regarding IoT frameworks. The existing digital forensic applications have effectively held back efforts to align the IoT with digital forensic strategies. This is because the forensic applications are ill-suited to the highly complex IoT frameworks and would, therefore, struggle to amass, analyze and test the necessary evidence that would be required by a court. As such, there is a need to develop a suitable forensic framework to facilitate forensic investigations in IoT settings. Nor has considerable progress been made in terms of collecting and saving network and server logs from IoT settings to enable examinations. Consequently, this study sets out to develop and test the FB system which is a lightweight forensic framework capable of improving the scope of investigations in IoT environments. The FB system can organize the management of various IoT devices found in a smart apartment, all of which is controlled by the owner’s smart watch. This will help to perform useful functions, automate the decision-making process, and ensure that the system remains secure. A Java app is utilized to simulate the FB system, learning the user’s requirements and security expectations when installed and employing the MySQL server as a means of logging the communications of the various IoT devices.

关键词： Smart Home Internet of Things Digital Forensic FB Framework

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Empirical Analysis for the Selection of Baseline Performances for Short Text Classification

Empirical Analysis for the Selection of Baseline Performance...

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International Conference on Industrial and Information systems, ICIIS

作者： JRKC Jayakody VGTN Vidanagama Indika Perera HMLK Herath Department of Computing and Information System University of Wayamba Sri Lanka Department of Computer Science and Engineering University of Moratuwa Sri Lanka Department of Agribusiness Management University of Wayamba Sri Lanka

Improvements of classification performance for short text have become increasingly popular due to explosive growth of social media and other online communication platforms. As a result, recent research works attempt to improve the classification performances over a selected baseline. Unfortunately, latest research works related to short text classification performance improvements with Neural networks [CNN, LSTM,RNN] and, Embeddings as feature representation haven't use common baseline to compare the performance improvements with their experiments. Therefore, it's hard to compare one work with the other. Hence this research work was carried out to identify the appropriate n-grams, feature representation, preprocessing technique and the machine learning algorithm which suits for the given short text type dataset which can be used as a baseline to compare latest experimented results. We used seven short text types datasets which are publicly available. We compared different n-grams [1-gram,2-gram,3-gram etc], feature representation techniques such as [TF] term frequency and [TF-IDF]term frequency with inverse document frequency techniques as well as the impact of using stop words. Moreover, we compared the classification performances among different traditional machine learning based algorithms. Our conclusions are, combining 1-gram with 2-gram word features gave high performances compared to other n-grams. [2] out of two feature representations techniques TF perform well compared to TF IDF. [3] further, removal of stop word support to improve the classification performances when feature representation type is TF.[4] Moreover, out of traditional machine learning algorithms, SVM gave good results. Finding of our research work can be used to compute baseline classification performances with future research work.

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Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography

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光：科学与应用（英文版） 2025年第2期14卷 490-508页

作者： Shiyi Cheng Shuaibin Chang Yunzhe Li Anna Novoseltseva Sunni Lin Yicun Wu Jiahui Zhu Ann C.McKee Douglas L.Rosene Hui Wang Irving J.Bigio David A.Boas Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Department of Computer Science Boston UniversityBostonMA 02215USA Boston University Alzheimer's Disease Research Center and CTE Center Boston University School of MedicineBostonMA 02118USA Department of Neurology Boston University School of MedicineBostonMA 02118USA VA Boston Healthcare System U.S.Department of Veteran AffairsBostonMA 02130USA Department of Pathology and Laboratory Medicine Boston University School of MedicineBostonMA 02118USA Department of Anatomy & Neurobiology Boston University School of MedicineBostonMAUSA Department of Radiology Athinoula A.Martinos Center for Biomedical ImagingMassachusetts General HospitalBostonMA 02129USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Neurophotonics Center Boston UniversityBostonMA 02215USA

A major challenge in neuroscience is visualizing the structure of the human brain at different *** histology reveals micro-and meso-scale brain features but suffers from staining variability,tissue damage,and distortion,which impedes accurate 3D *** emerging label-free serial sectioning optical coherence tomography(S-OCT)technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical ***,we present a novel 3D imaging framework that combines S-OCT with a deep-learning digital staining(DS)*** enhanced imaging modality integrates high-throughput 3D imaging,low sample variability and high interpretability,making it suitable for 3D histology *** develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images for translating S-OCT to Gallyas silver *** demonstrate DS on various human cerebral cortex samples,achieving consistent staining quality and enhancing contrast across cortical layer ***,we show that DS preserves geometry in 3D on cubic-centimeter tissue blocks,allowing for visualization of meso-scale vessel networks in the white *** believe that our technique has the potential for high-throughput,multiscale imaging of brain tissues and may facilitate studies of brain structures.

关键词： tomography serial imaging digital human brain coherence enhanced multiscale optical

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Radar Based Fall Detection with Imbalance Data Handling and Data Augmentation

Radar Based Fall Detection with Imbalance Data Handling and ...

引用

IEEE Sensors Applications Symposium, SAS

作者： Hamidreza Sadreazami Abhishek Khoyani Marzieh Amini Sreeraman Rajan Miodrag Bolic Bioengineering Department McGill University Montreal Canada System and Computer Engineering Department Carleton University Ottawa Canada School of Information Technology Carleton University Ottawa Canada School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

Radar-based fall detection is helping seniors to live independently. In this paper, a new method for fall detection from human activities is proposed using data augmentation and class imbalance handling. Data collected from a radar signal is processed and a time series is obtained by aggregating the individual time series in the fast-time of the radar returns. This time series is used as input to several classifiers to distinguish fall from non-fall activities. To this end, we augment the radar time series data using a Mu-Sigma method. To handle the imbalanced data, we apply the synthetic minority over-sampling technique and class weighting strategy. A comprehensive study is performed to build a supervised learning method with or without data augmentation and imbalanced data handling. The performance of the proposed method is compared with some of the other existing methods using different classifiers including k-nearest neighbors, decision trees, naive Bayes, support vector machine and multi-layer perceptron. The results demonstrate that the proposed fall detection method outperforms the other methods in terms of providing higher accuracy, precision, sensitivity and specificity values.

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