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Chinese Automation Congress (CAC)

作者： Yangyang Geng Wenwen Liu Yang Liu Qiang Wei Ke Liu Department of Cyberspace Security State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Department of Research and Development Center Beijing sifang automation co.ltd Beijing China

Natural gas is one of the essential energy sources for daily life. The increased reliance on energy makes control systems attractive targets for attackers. This paper explains how the control network works and describes the negative influence of attacking physical process. The paper proposes denial-of-service (DoS) attacks model and integrity attacks for the control system. In addition, our work focus on analyzing triethylene glycol (TEG) dehydration process flow for discovering the influencing factors, operational constraints and safety constraints in the dehydration process. Then, we evaluate physical and economic influences for DoS attacks and integrity attacks on TEG dehydration process in our physical testbed. The consequence shows that these kinds of attacks scenarios will drive the plant into an unsafe state. Finally, we propose an effective mechanism to defense DoS attacks and integrity attacks on TEG dehydration process.

关键词： Process control Sensors Natural gas Integrated circuits Actuators Production

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Concept Analysis for Class Cohesion

Concept Analysis for Class Cohesion

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(CSMR) European Conference on Software Maintenance and Reengineering

作者： Padmaja Joshi Rushikesh K. Joshi Department of Computer Science and Engineering Center of Development of Advanced Computing Indian Institute of Technology Bombay Mumbai India Department of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India

A concept lattice based approach for analysis of class cohesion is presented. The approach facilitates rapid identification of less cohesive classes. It also helps identify less cohesive methods, attributes and classes in one go. Further, the approach guides refactorings such as extract class, move method, localize attributes and remove unused *** effectiveness of the technique is demonstrated through examples.

关键词： Lattices Microscopy Software maintenance Computer science Data mining Information analysis Unified modeling language Object oriented modeling Bridges Feedback

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A Re-configurable Context-Aware Engine for Mobile Devices

A Re-configurable Context-Aware Engine for Mobile Devices

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Sangdo Park Su Myeon Kim Intelligent Computing Laboratory Samsung Advanced Institute of Technology Yongin si South Korea Data Intelligence Laboratory Software Research and Development Center Samsung Electronics Limited Suwon South Korea

Despite the increasing request for intelligent applications with the heavy use of smart phone, little of them are available in daily life. One reason must be the high complexity of understanding user's situation. For application (app) developers, knowing user's context usually takes much more time and efforts than taking some useful actions. To help developing intelligent apps, we have proposed a mobile context-aware engine called Intelligent Mobile Platform (IMP). It provides user's current context to apps as a background service in android mobile phones. Especially, depending on user's current environments such as location, active apps, etc., IMP dynamically extends or shrinks its knowledge base. We believe that this dynamic reconfiguration is essential for mobile environment where user continuously changes his/her location and contexts. In this paper, we describe the reconfiguration part in detail. This feature is tested by three in-house apps with the IMP prototype on SAMSUNG Galaxy S II phone.

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Machine Learning-Driven Prediction of Optical and Physical Properties in Lanthanum and Gold-Doped Zinc Borotellurite Glasses for Optoelectronic Applications

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Ceramics International 2025年

作者： S.N. Nazrin Camellia Doroody Liyana Adilla Burhanuddin Neesha Jothi N. Ibrahim Halimah Badioze Zaman M.H.M. Tahir Manzoore Elahi M. Soudagar S. Ramesh Sagar Shelare Shubham Sharma Institute of Informatics and Computing in Energy (IICE) College of Engineering Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia Institute of Sustainable Energy (ISE) Universiti Tenaga Nasional (UNITEN @ The Energy University) Putrajaya Campus Jalan IKRAM-UNITEN 43000 Kajang Selangor Malaysia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura-140401 Punjab India Zhejiang Xingyu Mechanical and Electrical Technology Company Ltd. Tongqin Wuyi County Jinhua Zhejiang 311400 China Department of Electronic Systems Engineering (ESE) Malaysia–Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Department of Computing College of Computing and Informatics Universiti Tenaga Nasional (UNITEN) Malaysia Dept. of Digital Economy Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman 31900 Kampar Perak Department of English Language and Literature Faculty of Languages and Communication Universiti Pendidikan Sultan Idris 35900 Tanjong Malim Perak Malaysia Center of Advanced Manufacturing and Materials Processing (AMMP) Faculty of Engineering University of Malaya Kuala Lumpur Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India Division of Research and Development Lovely Professional University Phagwara Punjab 144411 India Department of Applied Chemistry Priyadarshini College of Engineering Nagpur Maharashtra 440019 India University Centre for Research and Development Chandigarh University Mohali Punjab 140413 India Department of Technical Sciences Western Caspian University Baku Azerbaijan Jadara University Research Center Jadara University Jordan

Recent advancements in glass science have focused on optimizing optical and structural properties through the incorporation of rare oxide nanoparticles, such as lanthanum and gold oxide, to enhance transparency, refractive index, and band gap energy for photonic applications. Additionally, the integration of machine learning techniques in material design accelerates the prediction and optimization of these properties, enabling more efficient discovery and development of advanced glass systems for optoelectronic and photonic devices. The melt-quenching technique was employed to fabricate zinc borotellurite glasses doped with lanthanum oxide and gold oxide. The glass's amorphous nature was confirmed through structural analysis, which detected absorption bands corresponding to TeO 3 , BO 4 , and BO 3 units. A drop in molar volume with an increase in gold oxide quantity prompted a suggestion for structural reorganisation. Optical investigations indicated an increase in the optical band gap energy and a blue shift in the absorption edge. Four machine learning models—Random Forest, Gradient Boosting, XGBoost, and Artificial Neural Networks—were employed to forecast critical optical and physical parameters, such as refractive index, density, molar volume, optical band gap, and Urbach energy, to expedite the discovery process. This data-driven approach illustrates the transformative capabilities of machine learning in materials science, facilitating the swift modification of glass compositions to enhance optical performance for photonic and optoelectronic applications. The enhanced optical properties of the synthesized glass system make it a promising candidate for photonic and optoelectronic applications, including optical fibers, solid-state lasers, and planar waveguides , where its tunable refractive index and improved transparency can optimize signal transmission and device efficiency. Additionally, the presence of gold oxide nanoparticles suggests potential use in nonl

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Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection

arXiv

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arXiv 2022年

作者： Vivek, Yelleti Ravi, Vadlamani Mane, Abhay Anand Naidu, Laveti Ramesh Centre for Artificial Intelligence Machine Learning Institute for Development and Research in Banking Technology Castle Hills Road #1 Masab Tank Hyderabad500076 India Center for Development of Advanced Computing #1 Old Madras Road Karnataka Bangalore560038 India

Customers’ empathy and trust are very critical in the financial domain. Frequent occurrence of fraudulent activities affects these two factors. Hence, financial organizations and banks must take utmost care to mitigate them. Among them, ATM fraudulent transaction is a common problem faced by today’s banks. There following are the critical challenges involved in fraud datasets: the dataset is highly imbalanced, the fraud pattern is changing, etc. Owing to the rarity of fraudulent activities, Fraud detection can be formulated as either a binary classification problem or One class classification (OCC). In this study, we handled these techniques on an ATM transactions dataset collected from India. In binary classification, we investigated the effectiveness of various over-sampling techniques, such as the Synthetic Minority Oversampling Technique (SMOTE) and its variants, Generative Adversarial Networks (GAN), to achieve oversampling. Further, we employed various machine learning techniques viz., Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting Tree (GBT), Multi-layer perceptron (MLP). GBT outperformed the rest of the models by achieving 0.963 AUC, and DT stands second with 0.958 AUC. DT is the winner if the complexity and interpretability aspects are considered. Among all the oversampling approaches, SMOTE and its variants were observed to perform better. In OCC, IForest attained 0.959 CR, and OCSVM secured second place with 0.947 CR. Further, we incorporated explainable artificial intelligence (XAI) and causal inference (CI) in the fraud detection framework and studied it through various *** Codes 68T99, 62D20, 91-08 © 2022, CC BY-NC-ND.

关键词： Decision trees

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ATM Fraud Detection using Streaming Data Analytics

arXiv

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arXiv 2023年

Gaining the trust and confidence of customers is the essence of the growth and success of financial institutions and organizations. Of late, the financial industry is significantly impacted by numerous instances of fraudulent activities. Further, owing to the generation of large voluminous datasets, it is highly essential that underlying framework is scalable and meet real time needs. To address this issue, in the study, we proposed ATM fraud detection in static and streaming contexts respectively. In the static context, we investigated a parallel and scalable machine learning algorithms for ATM fraud detection that is built on Spark and trained with a variety of machine learning (ML) models including Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting Tree (GBT), and Multi-layer perceptron (MLP). We also employed several balancing techniques like Synthetic Minority Oversampling Technique (SMOTE) and its variants, Generative Adversarial Networks (GAN), to address the rarity in the dataset. In addition, we proposed a streaming based ATM fraud detection in the streaming context. Our sliding window based method collects ATM transactions that are performed within a specified time interval and then utilizes to train several ML models, including NB, RF, DT, and K-Nearest Neighbour (KNN). We selected these models based on their less model complexity and quicker response time. In both contexts, RF turned out to be the best model. RF obtained the best mean AUC of 0.975 in the static context and mean AUC of 0.910 in the streaming context. RF is also empirically proven to be statistically significant than the next-best performing *** Codes 37M10, 62M10, 91B84 © 2023, CC BY-NC-ND.

关键词： Decision trees

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Efficient Block Pruning Based on Kernel and Feature Stablization

Efficient Block Pruning Based on Kernel and Feature Stabliza...

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

作者： Sheng Xu Hanlin Chen Kexin Liu Jinhu Lii Baochang Zhang School of Automatic Science and Electrical Engineering Beihang University China State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

With the development of computer vision research, the architecture of convolutional neural network becomes more and more complex to reach the state-of-the-art performance. Is the complexity of the model necessarily proportional to its accuracy? To answer this, the compression of the network has attracted much attention in the academy and industry. Existing network pruning methods mostly rely on the scoring mechanism of complexity or diversity of kernels to compress the network, and then build the network model after removing the kernels by tuning or training on the input data. These methods are cumbersome and depend on a well-trained pre-trained model. In this paper, we propose an end-to-end block pruning method based on kernel and feature stability by pruning blocks efficiently. To accomplish this, we firstly introduce a mask to scale the output of the blocks, and the L1 regularization term to monitor the mask update. Second, we introduce the center Loss to guarantee that the feature does not deviate greatly during learning. To converge fast, we introduce fast iterative shrinkage-thresholding algorithm (FISTA) to optimize the mask, by which a more fast and reliable pruning process is achieved. We implement experiments on different datasets, including CIFAR-10 and ImageNet ILSVRC2012. All the experiments have achieved the state-of-the-art accuracy.

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Anomaly Prediction in Passenger Flow with Knowledge Transfer Method

Anomaly Prediction in Passenger Flow with Knowledge Transfer...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Zhipu Xie Weifeng Lv Syed Muhammad Asim Ali Bowen Du Runhe Huang Key State Laboratory of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing Hosei University Tokyo Japan

ISBN: (纸本)9781538675199

Predicting anomaly in travel demand is a crucial finding from smart card data analytics. The output of these predictions is a significant contribution to planning sustainable public transport system and generating possible knowledge for transportation learning models. This paper investigates the anomaly effects of the surge in bus passengers demand and compare it with an increase in taxi demand. Indeed, both short-term and long-term demands reveal different patterns of passengers in uncertain situations. In pursuit of our goal, we estimated the similarity in stations by both selected and latent features where pre-trained knowledge are combined as an ensemble with different weights. We present Surge Prediction and Knowledge Transfer (SPKT) model that uses Seq2Seq method combined with Multi-source Transfer Learning method on travel patterns extracted from smart card data to classify source stations and target station. To illustrate the demands blueprint, we considered multiple source stations as input to the predictor, to develop a mechanism that bridges the knowledge transfer learning with the targeted stations. To exemplify our method, we use a case study of an event with passenger surge. From experiments, we found that transferring knowledge can make the surge prediction better compared to only limited training data for the target stations. The results have proved the effectiveness of surge predictions and knowledge transfer for learning models.

关键词： Surges Knowledge transfer Predictive models Public transportation Feature extraction Real-time systems

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Handwritten Assamese numeral recognizer using HMM & SVM classifiers

Handwritten Assamese numeral recognizer using HMM & SVM clas...

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National Conference on Communications (NCC)

作者： Bandita Sarma Kapil Mehrotra R. Krishna Naik S. R. M. Prasanna Swapnil Belhe Chitralekha Mahanta Department of Electronics & Electrical Engineering Indian Institute of Technology Guwahati Guwahati GIST Center for Development of Advanced Computing Pune Department of Electronics & Electrical Engineering Indian Institute of Technology Guwahati 781039 India

This work describes the development of Assamese online numeral recognition system using Hidden Markov Models (HMM) and Support Vector Machines (SVM). Preprocessed (x, y) coordinates and their first and second derivatives at each point are used as features for both the modeling techniques. The two systems are developed individually using HMM and SVM. The results from both the systems are then combined using two different approaches. In the first approach, the scores from both the classifiers are directly merged and an improvement in performance is observed in the combined system (Comb - 1). In the second approach, the confusion patterns from HMM and SVM classifiers are also analyzed. Based on this, the results are further combined to obtain a final hybrid numeral recognizer with an enhanced performance (Comb - 2). The HMM, SVM, Comb-1 and Comb-2 systems provide average recognition performance of 96.5, 96.8, 98 and 98.3, respectively.

关键词： Hidden Markov models Support vector machines Handwriting recognition Character recognition Accuracy Testing Feature extraction

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Signal approximation with Pascal’s triangle and sampling

Signal approximation with Pascal’s triangle and sampling

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第32届中国控制与决策会议

作者： Lei Chen Xinghuo Yu Jinhu Lü School of Automation Science and Electrical Engineering State Key Laboratory of Software Development EnvironmentBeijing Advanced Innovation Center for Big Data and Brain ComputingBeihang University School of Engineering RMIT University

This brief explores the approximation properties of a unique basis expansion based on Pascal’s triangle,which realizes a sampled-data driven approach between a continuous-time signal and its discrete-time *** roles of certain parameters,such as sampling time interval or model order,and signal characteristics,i.e.,its curvature,on the approximation are *** errors in one and multiple-step predictions are ***,time-variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.

关键词： Signal approximation Sampling Discretization

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