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International Conference on computing: Theory and Applications, ICCTA

作者： Yumnam Kirani Singh Center for Development of Advanced Computing Kolkata India

Proposed here is a new subband coding scheme entitled ISPIHT (Improved SPIHT). It is simpler in its coding approach yet it is more efficient in time and memory keeping the performance of SPIHT preserved. It requires less no of compression operations during the coding. The memory requirement for ISPIHT is about two times less than SPIHT

关键词： Image coding Transform coding Testing Entropy Bit rate Distortion Wavelet transforms Redundancy Process control Decoding

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Modeling Web Attachment Storage for Web Applications

Modeling Web Attachment Storage for Web Applications

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Asia-Pacific Conference on Software Engineering

作者： Vijay Jain Amol Kolambkar Center for Development of Advanced Computing Mumbai India

Web application performance plays vital role in application usability and adaptability to its intended users. There are many factors, which attributes towards overall performance enhancements. One of them is that how efficiently a web application under peak load stores or retrieves the web-attached documents. In this paper, we have described a document storage and retrieval technique for improving web application performance. A technique suggested for the document storage purpose is based on the verification of results performed with various types and sizes of document attachments. Applying the suggested technique has shown an overall performance improvement for applications with web attachments.

关键词： Conferences Web servers Performance evaluation Testing Computational modeling

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Automatic 3D modeling and exploration of indoor structures from panoramic imagery 24

Automatic 3D modeling and exploration of indoor structures f...

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2024 SIGGRAPH Asia 2024 Courses, SA Courses 2024

作者： Gobbetti, Enrico Pintore, Giovanni Agus, Marco National Research Center in High-Performance Computing Big Data and Quantum Computing Cagliari Italy Visual and Data-intensive Computing Center for Advanced Studies Research and Development in Sardinia Cagliari Italy College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9798400711350

Surround-view panoramic imaging delivers extensive spatial coverage and is widely supported by professional and commodity capture devices. Research on inferring and exploring 3D indoor models from 360° images has recently flourished, resulting in highly effective solutions. Nevertheless, challenges persist due to the complexity and variability of indoor environments and issues with noisy and incomplete data. This course provides an up-to-date integrative view of the field. After introducing a characterization of input sources, we define the structure of output models, the priors exploited to bridge the gap between imperfect input and desired output, and the main characteristics of geometry reasoning and data-driven approaches. We then identify and discuss the main sub-problems in indoor reconstruction from panoramas and review and analyze state-of-the-art solutions for indoor capture, room modeling, integrated model computation, visual representation generation, and immersive exploration. Relevant examples of implemented pipelines are described, focusing on deep-learning solutions. We finally point out relevant research issues and analyze research trends. © 2024 Copyright held by the owner/author(s).

关键词： 3D reconstruction

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Applications of Support Vector Machines In Chemo And Bioinformatics

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AIP Conference Proceedings 2010年第1期1298卷 18-23页

作者： V. K. Jayaraman V. Sundararajan aCSIR Emeritus Scientist Center For Development of Advanced Computing Pune University Campus Pune‐411007 India bGroup Leader Center For Development of Advanced Computing Pune University Campus Pune‐411007 India

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed‐forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples.

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PosMed: A biomedical entity prioritisation tool based on statistical inference over literature and the Semantic Web 6

PosMed: A biomedical entity prioritisation tool based on sta...

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6th International Workshop on Semantic Web Applications and Tools for Life Sciences, SWAT4LS 2013

作者： Kobayashi, Norio Makita, Yuko Ishii, Manabu Matsushima, Akihiro Mochizuki, Yoshiki Doi, Koji Nishikata, Koro Gifford, David Takatsuki, Terue Masuya, Hiroshi Toyoda, Tetsuro RIKEN Advanced Center for Computing and Communication Integrated Database Unit Wako Japan RIKEN BioResource Center Technology and Development Unit for Knowledge Base of Mouse Phenotype Tsukuba Japan

Positional MEDLINE (PosMed) is a web application that quickly prioritises biomedical entities such as genes and diseases based on statistical signicance of associations between these and a user-specied keyword by employing our original search engine named General and Rapid Association Study Engine (GRASE). GRASE search is modelled as an extension of SPARQL search with statistical analysis, which enables searching over semantic data including not only linked datasets but also signicant extracted semantic links over multiple biomedical documents. PosMed was originally implemented for in silico positional cloning stud- ies by prioritizing genes. Further applications include bioresource search with associated genetic functions or ontologies, and functional interpreta- Tion of gene variants found from exome sequencing of personal genomes. PosMed is available at http://***/PosMed/.

关键词： Genes

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Text analysis for honeypot misuse inference 5

Text analysis for honeypot misuse inference

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5th IEEE International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2020

作者： Kamati, Toivo Herman Jat, Dharm Singh Chamotra, Saurabh Namibia University of Science and Technology Department of Computer Science Windhoek Namibia Center for Development of Advanced Computing Department of Cyber Security Technology Pune India

ISBN: (纸本)9781728194370

Transformation of raw text is required for computational text analysis using Natural Language Processing methods. Computational text analysis leverage on human brain limitations to automatically index documents for retrieval and topic generation for topic distribution correlations in corpus of voluminous documents. Natural language non-parametric and parametric Topic modeling with Expectancy Maximization and Gibbs sampling render technique to build Machine Learning models for evaluation with log-likelihood, topic coherence and coefficient of determination of held-out document. This research extends the concept of Natural Language Processing to automate analysis of High interaction honeypot system call documents to deduce system resources misuse by malcode during real-time engagement with the user-space applications of the deployed honeypot. © 2020 IEEE.

关键词： Learning algorithms

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EEG-Based Eye Blink Detection and Interpretation for Human-Computer Interaction and Communication

EEG-Based Eye Blink Detection and Interpretation for Human-C...

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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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