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A Non-normal Warning System for Dam Operation Using Machine Learning 24

A Non-normal Warning System for Dam Operation Using Machine ...

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24th IEEE/ACIS International Winter Conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2022

作者： Chang, Meng-Wei Liu, I-Hsien Liu, Chuan-Kang Lin, Wei-Min Su, Zhi-Yuan Li, Jung-Shian Inst. Computer and Communication Engineering National Cheng Kung University Dept. of Electrical Engineering Tainan Taiwan National Chin-Yi University of Technology Dept. of Artificial Intelligence and Computer Engineering Taichung Taiwan Chia Nan University of Pharmacy and Science Dept. of Multimedia and Game Development Tainan71710 Taiwan

ISBN: (纸本)9798350310412

A country's critical infrastructures are heavily related to the quality of life and safety of the people. As a result, the security protection aspect of critical infrastructure has gained more and more attention nowadays, especially the security of its industrial control system (ICS). To avoid the abnormal condition happening in the critical infrastructure which could put people in great danger, a system that is capable of detecting any abnormal state of the ICS promptly is needed. Fortunately, due to the dramatic growth of the applications of machine learning in recent years, some researchers have already proposed anomaly detection methods with machine learning to provide inst.nt warning and protection for ICS. However, most of the existing anomaly detection research tends to only target one cause that harms the system, such as attacks on the network or physical equipment failures. The ICS will be more comprehensively secured if the anomaly detection system can cover multiple aspects of the ICS. Therefore, we have established a non-normal warning system with the Generative Adversarial Network (GAN) for dam operations in this study, which can detect various types of non-normal operations and notify relevant personnel right away. Note that we use real historical data to make predictions and verify our warning system, and we improve it even more by implementing the visual analysis method, which makes up the indecipherable results often found in unsupervised learning. © 2022 IEEE.

关键词： Critical infrastructures

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Toward EEG-Based Brain State Recognition for Personalized Neuromodulation

Toward EEG-Based Brain State Recognition for Personalized Ne...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Yu-Cheng Chang Pin-Hsuan Chao Sin-Horng Chen Chun-Shu Wei Dept. of Computer Science Inst. of Biomedical Engineering (BME) NYCU Inst. of BME NYCU Dept. of Electronics and Electrical Engineering

ISBN: (纸本)9781665465007

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive antidepressant neuromodulation therapy for treatment-resistant depression (TRD). However, the remission rate of patients remains unsatisfactory possibly due to the suboptimal configuration of conventional rTMS protocol. This work aims to design a close-loop TMS system and validate the practicability of brain-state-dependent stimulation based on real-time monitoring of electroencephalogram (EEG). We propose a novel method of phase estimation to enhance the precision of EEG phase-triggered firing of TMS. Our implementation supports subsequent studies on personalized brain-state-dependent neuromodulation for clinical applications.

关键词： Protocols Transcranial magnetic stimulation Multicore processing Firing Phase estimation Medical treatment Depression

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Public-data assisted private stochastic optimization: power and limitations 24

Public-data assisted private stochastic optimization: power ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Enayat Ullah Michael Menart Raef Bassily Cristóbal Guzmán Raman Arora Meta Department of Computer Science & Engineering The Ohio State University and Department of Computer Science University of Toronto Vector Institute Department of Computer Science & Engineering Translational Data Analytics Institute (TDAI) The Ohio State University Inst. for Mathematical and Comput. Eng. Fac. de Matemáticas and Esc. de Ingeniería Pontificia Universidad Católica de Chile Department of Computer Science Johns Hopkins University

ISBN: (纸本)9798331314385

We study the limits and capability of public-data assisted differentially private (PA-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO) with either labeled or unlabeled public data. For complete/labeled public data, we show that any (ε, δ)-PA-DP has excess risk $\tilde{\Omega}\big(\min(\frac{1}{\sqrt{n_{\text{pub}}}},\frac{1}{\sqrt{n}}+\frac{\sqrt{d}}{n\epsilon} ) \big)$, where d is the dimension, npub is the number of public samples, npriv is the number of private samples, and n = npub + npriv. These lower bounds are established via our new lower bounds for PA-DP mean estimation, which are of a similar form. Up to constant factors, these lower bounds show that the simple strategy of either treating all data as private or discarding the private data, is optimal. We also study PA-DP supervised learning with unlabeled public samples. In contrast to our previous result, we here show novel methods for leveraging public data in private supervised learning. For generalized linear models (GLM) with unlabeled public data, we show an efficient algorithm which, given Õ(nprivε) unlabeled public samples, achieves the dimension independent rate $\tilde{O}\big(\frac{1}{\sqrt{{n_{\text{priv}}}}} + \frac{1}{\sqrt{{n_{\text{priv}}}\epsilon}}\big)$. We develop new lower bounds for this setting which shows that this rate cannot be improved with more public samples, and any fewer public samples leads to a worse rate. Finally, we provide extensions of this result to general hypothesis classes with finite fat-shattering dimension with applications to neural networks and non-Euclidean geometries.

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Chinese Emergency Event Extraction Based on Contrastive Learning with Event Semantic Features 19

Chinese Emergency Event Extraction Based on Contrastive Lear...

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19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Sheng, Xinyi Gu, Jinguang Yan, Youcheng Xu, Fangfang Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan430065 China The Key Lab. of Rich-Media Knowledge Org. and Serv. of Digit. Publ. Content Inst. of Sci. and Tech. Info. of China Beijing100038 China

ISBN: (纸本)9798350304398

Extracting emergency events from a large amount of unstructured information is essential for improving early warning and emergency response. Existing event extraction methods for specialist fields often rely on well-designed syntactic dependencies or external knowledge graphs, which can provide high accuracy in their specialist fields. However, these approaches may have poor generalization capabilities and are difficult to apply directly to the emergency field. In addition, most existing methods ignore the interdependencies between event triggers and event arguments, which may be crucial semantic features for event extraction. To solve these problems, we propose a Chinese emergent event extraction method based on contrastive learning with event semantic features(CNEmergEE). Firstly, the proposed approach automatically expands the pre-trained model's vocabulary on a domain corpus. Then, continuing learning on the same corpus allows the model to complete domain adaptation. Finally, we use contrast learning to enable models to learn semantic representations of events. Experimental results on the CEC (Chinese Emergency Corpus) dataset show that our approach achieves better improvement. © 2023 IEEE.

关键词： Semantics

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A Robust Cold-Start Approach Using Phylogram

A Robust Cold-Start Approach Using Phylogram

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International Conference on Optimization and Applications (ICOA)

作者： Breno Caetano da Silva Jorge Luiz Franco Hermes Manoel Galvão Castelo Branco Eric Tokuda Alexandre Cláudio Botazzo Delbem Computer Science Department Federal Institute of Maranhão Teresina Brazil Inst. of Math. and Computer Science University of São Paulo São Carlos Brazil Electrical Engineering Department Federal University of Piaui Teresina Brazil

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

In Active Learning (AL) applications, it is crucial to develop a model that efficiently represents data using a limited number of labeled samples. In this context, an oracle is often employed to label samples that are deemed representative and informative. However, one of the primary limitations of AL lies in the need for an initial set of labeled samples, which may not be representative of the entire data space. To address this issue, cold-start strategies are implemented with the goal of selecting an appropriate initial set of samples for labeling, ensuring that the initial model is robust and effective. We propose a novel approach – PhyIL – that harness the advantages of phylogram analysis and community detection techniques to smartly perform the cold-start selection in AL. The method facilitates the choice of samples that best represent the diversity and complexity of the data, which is fundamental for the subsequent success of the active learning process. We evaluated the proposed approach using nine real-world databases and nine traditional ML models. The findings indicate that PhyIL offers an effective and viable solution for the initial sample selection, when compared to the random selection.

关键词： Training Databases Data models Complexity theory Labeling Standards Optimization Context modeling

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Prediction of Parkinson's Disease using Machine Learning

Prediction of Parkinson's Disease using Machine Learning

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Naresh Alapati N. Anusha P Joharika N. Jenny Jerusha P Tanuja Department of Computer Sc. and Engineering Vignan’s Nirula Institute of Technology and Science For Women Guntur India Department of Computer Sc. and Engineering Vignan’s Nirula Inst. of Technology and Science For Women Guntur India

The disruption of the brain cells that create the chemical dopamine, which enables brain cells to communicate with one another, causes Parkinson's illness. Dopamine-producing cells in the brain are what give movements control, adaptability, and fluidity. One of the most deadly and progressive nervous system illnesses that impact movement is Parkinson's. It is the second most prevalent neurological illness that impairs function, shortens lifespan, and currently lacks a treatment. Speech problems, tremor, rthymic shaking, and delayed movement impact over 90% of those who have this disease (bradykinesia). When considering the word "Parkinson's," speech traits are the major idea. Parkinson's disease can be predicted using a variety of machine learning approaches, including SVM, KNN, RF, and LR Models. The dataset is constructed using user input as well as algorithmic input. They employ the KNN Model in the current system to predict PD. The Proposed System makes use of the RF Model. Prediction is crucial, and the proposed system has a higher accuracy rate for recovering patients in the early phases. Machine learning can be used to complete this procedure.

关键词： Radio frequency Training Support vector machines Renewable energy sources Machine learning algorithms Parkinson's disease Predictive models

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Prediction of Brain Stroke in Human Beings using Machine Learning

Prediction of Brain Stroke in Human Beings using Machine Lea...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： T. Navya Deepthi Sk. Sharmila M. Swarna M. Gouthami Ch. Lavanya Akshaya Department of Computer Sc. and Engineering Vignan’s Nirula Institute of Technology and Science for Women Guntur India Department of Computer Sc. and Engineering Vignan’s Nirula Inst. of Technology and Science For Women Guntur India

Blood vessels in brain serve a major function in supplying the brain with nutrients and oxygen. All body parts are meant to be worked out actively. One of the deadliest diseases in the world is a brain stroke. Most strokes fall within the ischemic embolic and haemorrhagic categories. A blood clot that originates away from the patient's brain, typically in the heart, travels through the patient's bloodstream to lodge in the brain's smaller arteries to cause an ischemic stroke. The second is haemorrhagic stroke occurs when a brain artery bursts or releases blood. When a blood vessel either bursts or becomes blocked by a clot, a stroke develops. This study has collected a variety of patients' datasets. It includes a number of medical factors. There are a variety of machine learning algorithms available for making predictions, here the K-Nearest Neighbour with Random Forest algorithms are considered.

关键词： Heart Radio frequency Renewable energy sources Machine learning algorithms Stroke (medical condition) Prediction algorithms Glucose

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Chinese Medical Record Entity Recognition Based on Lexicon and Self-attention

Chinese Medical Record Entity Recognition Based on Lexicon a...

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Sensors, Electronics and computer engineering (ICSECE), IEEE International Conference on

作者： Le Wang Yahui Zhao Rongyi Cui Guozhe Jin Jinxiang Wang Inst. of Intelligent Information Processing Yanbian University Yanji China Computer Science and Technology College of Engineering Yanbian University Yanji China

Named Entity Recognition (NER) is a pivotal challenge in the field of natural language processing. In the medical field, NER plays a crucial role in identifying medical entities, such as diagnoses and drugs, from electronic medical records. To address the issue of incomplete and inaccurate word recognition in Chinese electronic medical records, we propose a BERT-BiLSTM-CRF model that incorporates both a lexicon and self-attention *** our approach, we first utilize the BERT model for preprocessing to generate word vectors. These vectors capture important semantic information about the words. Next, we employ a BiLSTM network to further capture contextual information from the input sequence. This helps to enhance the understanding of the text's meaning. To reinforce the significance of the sequence information, we introduce a self-attention layer that learns to prioritize relevant contextual details. Subsequently,the extracted features are fed into a CRF model for final training. The CRF layer takes into account the dependencies among the labels and improves the overall sequence labeling accuracy. Given the specialized nature of the medical domain, we integrate a medical lexicon, incorporating external knowledge features into the model. This inclusion enables better recognition of medical terms and enhances performance on medical-related *** extensive experiments on the CCKS2019 dataset, our proposed model demonstrates favorable performance. It get relatively excellent results in identifying medical entities accurately and effectively from Chinese electronic medical records.

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How to Construct Pseudorandom Bit Sequences from Mazes by a Method Digging Out Walls

How to Construct Pseudorandom Bit Sequences from Mazes by a ...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Takeru Miyazaki Shunsuke Araki Satoshi Uehara Faculity of Management and Information Sciences Kyushu Inst. of Information Sciences Fukuoka Japan Faculty of Computer Science and Systems Engineering Kyushu Inst. of Technology Fukuoka Japan Faculty of Environmental Engineering The Univ. of Kitakyushu Fukuoka Japan

In this paper, we propose a new pseudorandom bit sequence generator, which is based on a method digging out walls. Although each of these sequences lacks enough randomness, synthesized sequences of them can pass all of the NIST statistical test suite. We also describe a conjectural relation between the number of types on the square mazes and their sizes.

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Private algorithms for stochastic saddle points and variational inequalities: beyond euclidean geometry 24

Private algorithms for stochastic saddle points and variatio...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Raef Bassily Cristóbal Guzmán Michael Menart Department of Computer Science & Engineering Translational Data Analytics Institute (TDAI) The Ohio State University Inst. for Mathematical and Comput. Eng. Fac. de Matemáticas and Esc. de Ingeniería Pontificia Universidad Católica de Chile Department of Computer Science & Engineering The Ohio State University and Department of Computer Science University of Toronto Vector Institute

ISBN: (纸本)9798331314385

In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of (ε, δ)-differential privacy (DP) in both Euclidean and non-Euclidean setups. We first consider Lipschitz convex-concave SSPs in the ℓp/ℓq setup, p, q ∈ [1,2]. That is, we consider the case where the primal problem has an ℓp-setup (i.e., the primal parameter is constrained to an ℓp bounded domain and the loss is ℓp-Lipschitz with respect to the primal parameter) and the dual problem has an ℓq setup. Here, we obtain a bound of $\tilde{O}\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$ on the strong SP-gap, where n is the number of samples and d is the dimension. This rate is nearly optimal for any p, q ∈ [1,2]. Without additional assumptions, such as smoothness or linearity requirements, prior work under DP has only obtained this rate when p = q = 2 (i.e., only in the Euclidean setup). Further, existing algorithms have each only been shown to work for specific settings of p and q and under certain assumptions on the loss and the feasible set, whereas we provide a general algorithm for DP SSPs whenever p, q ∈ [1, 2]. Our result is obtained via a novel analysis of the recursive regularization algorithm. In particular, we develop new tools for analyzing generalization, which may be of independent interest. Next, we turn our attention towards SVIs with a monotone, bounded and Lipschitz operator and consider ℓp-setups, p ∈ [1, 2]. Here, we provide the first analysis which obtains a bound on the strong VI-gap of $\tilde{O}\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$. For p - 1 = Ω(1), this rate is near optimal due to existing lower bounds. To obtain this result, we develop a modified version of recursive regularization. Our analysis builds on the techniques we develop for SSPs as well as employing additional novel components which handle difficulties arising from adapting the recursive regularizat

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