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2024 International Conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Deepu, S.S. Chithra, P. Panicker, Praveen Suboodh, P.S. Binu, P.J. Dhanya, M.R. Anusree, V. Archana, M.S. Athira, B.K. Health and Technology Group Center for Development of Advanced Computing Kerala Thiruvanathapuram India Government Medical College Department of Neurology Kerala State Thiruvananthapuram India

ISBN: (纸本)9798331540067

This research develops a real-time Brain-Computer Interface (BCI) for interpreting eye blinks as a means of hands-free communication, particularly useful for individuals with limited mobility or in high-focus tasks. Combining machine learning (ML) and signal processing, the study detects voluntary eye blinks with high accuracy. Data from 20 participants were analyzed using a random forest classifier, achieving accuracy between 85.29% and 92.15%. A peak detection method further improved blink detection accuracy to over 98%. This system enhances accessibility in various interactive environments, improving life quality for those with disabilities. © 2024 IEEE.

关键词： Adversarial machine learning

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Dynamic network embedding via incremental skip-gram with negative sampling

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Science China(Information Sciences) 2020年第10期63卷 89-107页

作者： Hao PENG Jianxin LI Hao YAN Qiran GONG Senzhang WANG Lin LIU Lihong WANG Xiang REN Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Computer Science University of Southern California

Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. The fundamental problem of continuously capturing the dynamic properties in an efficient way for a dynamic network remains unsolved. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. The first order moment of the objective difference converges in order of O（1/n;）, and the second order moment of the objective difference can be stabilized in order of O（1）. Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up to

关键词： dynamic network embedding bound and convergence analysis multi-label classification link prediction

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Identifying topologies and system parameters of uncertaintime-varying delayed complex networks

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Science China(Technological Sciences) 2019年第1期62卷 94-105页

作者： WANG Xiong GU HaiBo WANG QianYao Lü JinHu LSC Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Node dynamics and network topologies play vital roles in determining the network features and network dynamical *** it is of great theoretical significance and practical value to recover the topology structures and system parameters of uncertain complex networks with available information. This paper presents an adaptive anticipatory synchronization-based approach to identify the unknown system parameters and network topological structures of uncertain time-varying delayed complex networks in the presence of noise. Moreover, during the identification process, our proposed scheme guarantees anticipatory synchronization between the uncertain drive and constructed auxiliary response network simultaneously. Particularly, our method can be extended to several special cases. Furthermore, numerical simulations are provided to verify the effectiveness and applicability of our method for reconstructing network topologies and node parameters. We hope our method can provide basic insight into future research on addressing reconstruction issues of uncertain realistic and large-scale complex networks.

关键词： system parameters and network topologies identification anticipatory synchronization uncertain time-varying delayed complex networks noise-perturbed complex networks

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System architecture for high-performance permissioned blockchains

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Frontiers of Computer Science 2019年第6期13卷 1151-1165页

作者： Libo FENG Hui ZHANG Wei-Tek TSAI Simeng SUN State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China Digital Society&Blockchain Laboratory School of Computer ScienceBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China School of Computing Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA

Blockchain(BC),as an emerging distributed database technology with advanced security and reliability,has attracted much attention from experts who devoted to efinance,intellectual property protection,the internet of things(IoT)and so ***,the inefficient transaction processing speed,which hinders the BC’s widespread,has not been well tackled *** this paper,we propose a novel architecture,called Dual-Channel Parallel Broadcast model(DCPB),which could address such a problem to a greater extent by using three methods which are dual communication channels,parallel pipeline processing and block broadcast *** the dual-channel model,one channel processes transactions,and the other engages in the execution of *** parallel pipeline processing allows the system to operate *** block generation strategy improves the efficiency and speed of *** experiments have been applied to BeihangChain,a simplified prototype for BC system,illustrates that its transaction processing speed could be improved to 16K transaction per second which could well support many real-world scenarios such as BC-based energy trading system and Micro-film copyright trading system in CCTV.

关键词： blockchain concurrency performance dualchannel model parallel pipeline consensus algorithm

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Design of an environmental information system for monitoring water and air quality in urban areas

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Disaster Prevention and Management: An International Journal 2005年第3期14卷 326-342页

作者： Patra, B. Pradhan, B. Center for Development of Advanced Computing (CDAC) Pune University Pune India Faculty of Engineering and Computer Technology Asian Institute of Medicine Science and Technology (AIMST) Kedah Darul Aman Malaysia

Purpose – Over the past 50 years India has been experiencing rapid population growth, causing the migration of a large part of the population to the cities looking for livelihood. This resulted in massive increments of population in the cities that has led to the increase of pollution. Gujarat, being a highly industrialized state, is a case in point. The systems for treatment and water disposal of this state are highly challenged. The northwestern state of Gujarat has no effective systems for treatment or disposal of waste water. The purpose of this article is to address this problem, introducing a geographic information system (GIS) approach to record the characterization, analyze the needs and generate a conceptual GIS database in the state. Design/methodology/approach – This paper outlines the background, suggested methodology for the development of a GIS database pollution dependent control of water pollution in the state of Gujarat in India. The present research is to install a document management system that has been developed in providing organizing chart, sorting, querying and retrieving of key data. A computerized laboratory information system on monitoring of quality of ambient air has been developed. Findings – An integrated GIS database has been generated involving creation of pollutant contours, querying and visualizing the query output in spatial and nonspatial form. Originality/value – The authors have created a complete geospatial database for the environmental monitoring for the whole state of Gujarat. They have dealt with nearly 36,000 different files from different sources and put them together to create the database. A computerized laboratory information system on monitoring of quality of ambient air has been developed. Frontend application programs have been developed in Visual Basic and the backend database to integrate the laboratory data and the existing data in oracle database. © 2005, Emerald Group Publishing Limited

关键词： Geographic information systems India Pollution control

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Dynaldroid: A system for automated dynamic analysis of Android applications

Dynaldroid: A system for automated dynamic analysis of Andro...

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National Conference on Recent Advances in Electronics & Computer Engineering (RAECE)

作者： K.P Reddy Babu Rajesh V Himanshu Pareek Mahesh U Patil Center for Development of Advanced Computing (CDAC) Hyderabad India

ISBN: (纸本)9781509021772

Android is an extensively used mobile platform and with evolution it has also witnessed an increased influx of malicious applications in its market place. Availability of multiple sources for downloading applications has aided the malware developers by increasing the consumer base for their applications. This has caused users falling victim to malicious applications. A major hindrance in blocking the entry of malicious applications into the Android market place is scarcity of effective mechanisms to detect malicious applications. Static analysis of an Android application gives a good idea as to what the application is capable of, but it's effectiveness decreases if the application is obfuscated. Dynamic analysis of an Android application shows the true behavior of the application during execution. Thus dynamic analysis is more useful in uncovering the threats that the user has from an application. The contribution of this work includes automation of Android application execution on an emulator to facilitate dynamic analysis of the application. Based on information collected during dynamic analysis of malware samples, we derived a set of patterns which indicate the occurrence of malicious activity. These patterns allowed us to develop `Dynanlroid', a dynamic analysis tool for detecting a set of malicious actions performed by Android applications. The network activity carried out by the application is also analyzed by the Dynaldroid.

关键词： Cryptography Androids Humanoid robots Layout Semantics Performance evaluation

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Vision Based Detection and Classification of Disease on Rice Crops Using Convolutional Neural Network

Vision Based Detection and Classification of Disease on Rice...

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2019 International Conference on Cutting-Edge Technologies in Engineering, ICon-CuTE 2019

作者： Verma, Gaurav Taluja, Charu Saxena, Abhishek Kumar Center for development of Advanced Computing Academic Consultancy Services Division Mohali India Shri Ramswaroop Memorial University Department of Electronics and Communication Engineering Barabanki India

ISBN: (纸本)9781728100005

This paper presents a combination of K-mean s clustering, SVM and CNN based classification techniques for the crop disease / infection with target as Rice crop. Agricultural crops show variation in features of healthy crops in color, texture and growth. Manual inspection and monitoring of crops is incompatible for large farm sizes so aerial monitoring or camera imaging can prove to be helpful for agriculturists in pre-determination of crop diseases. The use of image processing algorithms involving K-means clustering, Support vector machine and Convolutional Neural Networks enables classification process. The supervised classification techniques involve prior training process which determines the accuracy. The training dataset involved initially with 10 image set which was increased to over 50 to obtain accuracy over 95 percent. © 2019 IEEE.

关键词： K-means clustering

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Loop partitioning unimodular transformations for distributed memory multiprocessors

Loop partitioning unimodular transformations for distributed...

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International Symposium on Parallel Processing

作者： D. Kulkarni K.G. Kumar A. Basu A. Paulraj Center for Development of Advanced Computing Bangalore Karnataka India

Presents a unimodular transformation called rotation to partition the iteration space of a perfectly nested loop. The transformation captures the individual transformations like loop interchange, reversal, and skewing in a uniform framework. Though the transformation is for any architecture, the authors have specifically addressed its application to distributed memory machines in this paper. The authors prove that the transformation is free from deadlocks. They finally describe the choice of an optimal rotation based on the parallelism factor, load imbalance and the volume of communication in the transformed space.< >

关键词： Parallel processing Distributed computing System recovery Shape

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Simultaneous gene selection and cancer classification using a hybrid intelligent water drop approach

Simultaneous gene selection and cancer classification using ...

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5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013

作者： Kumar, Manish Ghosh, Shameek Valadi, Jayaraman Siarry, Patrick Bioinformatics Center University of Pune Pune India Centre for Development of Advanced Computing Pune India Shiv Nadar University Uttar Pradesh - 203207 India France

ISBN: (纸本)9783642450617

Computational Analysis of gene expression data is extremely difficult, due to the existence of a huge number of genes and less number of samples (limited number of patients). Thus,it is of significant importance to provide a subset of the most informative genesto a learning algorithm, for constructing robust prediction models. In this study, we propose a hybrid Intelligent Water Drop (IWD) - Support Vector Machines (SVM) algorithm, with weighted gene ranking as a heuristic, for simultaneous gene subset selection and cancer prediction. Our results, evaluated on three cancer datasets, demonstrate that the genes selected by the IWD technique yield classification accuracies comparable to previously reported algorithms. © Springer-Verlag 2013.

关键词： Gene expression

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Hybrid POS Classification Using Long Short Term Memory

Hybrid POS Classification Using Long Short Term Memory

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IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology

作者： Shashi Pal Singh Ajai Kumar Anu Lohan Jigyasa Chaudhary Pragya Daksh AAIG Center for development of Advanced Computing Banasthali Vidyapith

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

A Part of Speech classifier is an important tool that is used to develop many NLP tasks. In this paper we described Deep Neural Network based architecture, that given a sentence, outputs a part-of-speech tag sequence. For Part of Speech classification, we trained a LSTM network by semi-supervised learning. We further applied a probabilistic approach to improve Part of Speech prediction of the LSTM network. Word2vec deep learning tool is used for learning high-quality distributed vector representations of words that capture a large number of precise word relationships. By using a small amount of labeled dataset of words and applying cosine similarity we assign Part of Speech tags to words. If some words don't show nearness to any word in labeled dataset, then we apply probabilistic approach for classification of unclassified words of input sentence. This hybrid neural Part of Speech classifier is evaluated over a corpus of 1000 sentences having a total of 7,400 words;part of speech classification system achieved the accuracy of 94.71%.

关键词： Natural language processing (NLP) Machine learning Long short-Term memory (LSTM) Cosine similarity Stemming Part of speech (POS) Stemming Word Speech Taxonomy Short-Term Memory vector representation Machine Learning Artificial Intelligence Semi-supervised Learning

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