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International Conference on Sustainable Technology and advanced computing in Electrical Engineering, ICSTACE 2021

作者： Singh, Shashi Pal Kumar, Ajai Singh, Lenali Angra, Tanya Center for Development of Advanced Computing AAIG Pune India Banasthali Vidyapith Rajasthan Banasthali India

ISBN: (纸本)9789811943638

For developing and using various features provided by the internet by all the sections of the society, it is important to make the technology accessible irrespective of the language. So, the translation between the languages becomes important criterion. Previously, the machine translation has been used to translate between different languages, which eased the communication between people from different linguistic backgrounds. But in the last couple of years, the advancement and enhancement of Deep Neural Network (DNN) allowed us to rewrite our approach for machine translation completely. This paper discusses the process and the methodology to build the Machine translation system using Deep Neural Network (DNN) for translation between different Indian languages, using the combination of two ideas, i.e., Recurrent Neural Networks and Encoding. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Defeasible reasoning with conceptual graphs 91

Defeasible reasoning with conceptual graphs

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1991 ACM Computer Science Conference

作者： Kalathur, S. Center for Development of Advanced Computing 2/1 Brunton Road Bangalore India - 560 025

No abstract available.

ISBN: (纸本)0897913825

No abstract available.

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Data mining: Consumer behavior analysis 3

Data mining: Consumer behavior analysis

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3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018

作者： Singh, Shashi Pal Kumar, Ajai Yadav, Neetu Awasthi, Rachna Aaig Center for Development of Advanced Computing Pune India Banasthali Vidyapith Banasthali Rajasthan India

ISBN: (纸本)9781538624401

The whole world is overloaded via innumerable data like scientific data, financial data, mathematical data, geographic data, employment data, and other various kinds of data. Manually analyzing or summarizing this data is impractical, the reason behind this is incredible increment in data in this generation of information and network distribution. Objective of Data mining is to extract the required information, instruction, and pattern from a huge quantity of information and data by the help of multiple tools and techniques. Therefore, it is very essential to select significant techniques to mine the database, because of the several other varieties of data in data base and every data has their individual mining techniques. In this research paper we are going to analyze the consumer behavior, that can help us not only in the retailer market/industry but we can use it in the medical, online shopping etc. Here we are talking about consumer behavior analysis, so we are taking market industry data. Data mining techniques are most conventional and sufficient technique for behavior analysis. Main target of this paper is to recognize behavior of a consumer, his/her psychological condition, consumer's decision making during purchase and then finding how much suitable methods of data mining are applied to enhance the conventional method. Here association rules are deployed to mine out required rules for customer applied in purchased data in retailers shop or organization. After that the required rule can be used for further required filed line online shopping, medical field etc. © 2018 IEEE.

关键词： Data mining

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Collusion-Proof Result Inference in Crowdsourcing

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Journal of Computer Science & Technology 2018年第2期33卷 351-365页

作者： Peng-Peng Chen Hai-Long Sun Yi-Li Fang Jin-Peng Huai State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing 100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing 100191 China

In traditional crowdsourcing, workers are expected to provide independent answers to tasks so as to ensure the diversity of answers. However, recent studies show that the crowd is not a collection of independent workers, but instead that workers communicate and collaborate with each other. To pursue more rewards with little effort, some workers may collude to provide repeated answers, which will damage the quality of the aggregated results. Nonetheless, there are few efforts considering the negative impact of collusion on result inference in crowdsourcing. In this paper, we are specially concerned with the Collusion-Proof result inference problem for general crowdsourcing tasks in public platforms. To that end, we design a metric, the worker performance change rate, to identify the colluded answers by computing the difference of the mean worker performance before and after removing the repeated answers. Then we incorporate the collusion detection result into existing result inference methods to guarantee the quality of the aggregated results even with the occurrence of collusion behaviors. With real-world and synthetic datasets, we conducted an extensive set of evaluations of our approach. The experimental results demonstrate the superiority of our approach in comparison with the state-of-the-art methods.

关键词： crowdsourcing quality control collusion collaborative crowdsourcing result inference

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Hybrid POS classification using long short term memory 3

Hybrid POS classification using long short term memory

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3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018

作者： Singh, Shashi Pal Kumar, Ajai Lohan, Anu Chaudhary, Jigyasa Daksh, Pragya Aaig Center for Development of Advanced Computing Pune India Banasthali Vidyapith Banasthali Rajasthan India

ISBN: (纸本)9781538624401

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 %. © 2018 IEEE.

关键词： Long short-term memory

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ALDIMS - A language for programming distributed memory multiprocessors 6

ALDIMS - A language for programming distributed memory multi...

引用

6th Distributed Memory computing Conference, DMCC 1991

作者： Kumar, K.G. Kulkarni, D. Basu, A. Paulraj, A. Center for Development of Advanced Computing 2/1 Brunton Road Bangalore560 025 India

ISBN: (纸本)0818622903

In this paper we present ALDIMS, a language that combines the expressibility of general functional (MIMD) parallelism with compact expressibility of data (SPMD) parallelism. It uses distributed data structures for specifying data partitions and single assignment variables as abstract means of inter-process communication. Constructs for unstructured parallelism and process placement specifications make general MIMD parallelism expressible. We describe the issues of implementing process invocation and communication primitive generation. We also discuss source level parallelization and optimization issues and strategies. © 1991 IEEE.

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Automatic Evaluation for Machine Translation 1

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7th International Conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Singh, Shashi Pal Kumar, Ajai Saxena, Aarti Verma, Richa AAIG Center for Development of Advanced Computing Pune India Banasthali Vidyapith Rajasthan Banasthali India

ISBN: (数字)9789819907694

ISBN: (纸本)9789819907687

Language is the primary mode of communication. Communication is the only way to convey our thoughts and emotions to others. But there are many languages that we don't know how to speak and we can't learn all languages so quickly which is why Machine Translation systems were invented to help us communicate with anyone from anywhere. Researchers started working on Machine Translation Systems in the 1950s and have developed various amazing techniques to make our communication easy. Machine Translation Evaluation (MTE) Methodology checks the accuracy of the translations done by Machine Translation Tools. It is extremely important because while developing the translation systems, it constantly checks the performance and helps us to make appropriate changes in the system for bringing accuracy. Mostly it is done by comparing the output of machine translation systems with the translation(s) done by Human beings, there are also some techniques that do not require any reference sentences. In our project, we are using four Automated Machine Translation Evaluation metrics which are TER (Translation Error Rate), METEOR (Metric for Evaluation of Translation with Explicit Ordering), BLEU (Bilingual Evaluation Understudy), and NIST (National Institution of Standards and Technology) for English-Hindi Translation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Technology transfer

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Parallelization of low-level computer vision algorithms on clusters

Parallelization of low-level computer vision algorithms on c...

引用

2nd Asia International Conference on Modelling and Simulation, AMS 2008

作者： Kadam, Sanjay Scientific and Engineering Computing Group Center for Development Advanced Computing Pune University Campus Ganeshkhind Pune 411 007 Maharashtra India

ISBN: (纸本)9780769531366

In this paper we present parallel implementations of some representative low level vision algorithms on a cluster of workstations. These include convolution operation and the image restoration algorithm using Markov random field models. The convolution operation has been parallelized using the Farmer-Worker paradigm, while the image restoration algorithm has been parallelized through the Master-Worker pattern. Parallel implementations of both these algorithms have shown promising results, where the observed speedups are reasonably close to the ideal speedups. The parallel convolution operation has shown good scalability with respect to the problem size and number of processors used in parallelization. The paper elaborates on different parallelization results of the convolution operation observed after varying the image size, mask size and processor load on individual workstations. The image restoration algorithm is communication intensive. However, as the computing time between successive communications at each worker process is relatively high, the actual speedups observed are very close to the ideal speedups in this algorithm. © 2008 IEEE.

关键词： Convolution

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Secured Automatic Meter Reading for Implementation of SAMAST Framework in India 1

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6th International Conference and Exhibition on Smart Grids and Smart Cities, ISUW 2020

作者： Sidhartha, Dodla Mahendra, Lagineni Mohan, Katta Jagan Kumar, R. K. Senthil Bindhumadhava, B.S. Center for Development of Advanced Computing Knowledge Park No 1 Old Madras Road Byappanahalli Bangalore India

ISBN: (数字)9789811690082

ISBN: (纸本)9789811690075

The next level of the smart grid contains essential components like advanced Interface Energy Meters (IEMs), which play a major role in SAMAST (Scheduling, Accounting, Metering and Settlement of Transactions in Electricity) implementation in India. Automatic meter reading (AMR) is one of the very important modules considered in the implementation of the SAMAST Framework in India. As per SAMAST guidelines, all IEMs data should be available at corresponding State Load Dispatch center (SLDC) in defined periodic intervals. Various communication mediums like PLCC, Fiber Optics, VSAT, and GSM/GPRS/3G/4G are used to transfer data from substation IEMs to the control center. Communication between the IEMs and Control center is vulnerable for various attacks like Meter spoofing attack, Authentication attacks, Man-In-The-Middle (MITM) attacks, Reply attacks, De-pseudonymization attacks, etc. These attacks on communication have a cascading effect on other modules of SAMAST such as scheduling, forecasting, accounting, and deviation settlement of the transactions. The proposed solution helps to achieve high-secure data communication between IEMs and control center by retaining interoperability and also addresses various attacks by using various security suites of DLMS/COSEM High-Security mechanism without compromising the availability, integrity, and confidentiality of the data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electric load dispatching

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Forensic data carving

Forensic data carving

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2nd International Conference on Digital Forensics and Cyber Crime, ICDF2C 2010

作者： Povar, Digambar Bhadran, V.K. Center for Development of Advanced Computing Trivandrum Ministry of Communications and Information Technology Govt. of India India

ISBN: (纸本)9783642195129

File or data carving is a term used in the field of Cyber forensics. Cyber forensics is the process of acquisition, authentication, analysis and documentation of evidence extracted from and/or contained in a computer system, computer network and digital media. Extracting data (file) out of undifferentiated blocks (raw data) is called as carving. Identifying and recovering files based on analysis of file formats is known as file carving. In Cyber Forensics, carving is a helpful technique in finding hidden or deleted files from digital media. A file can be hidden in areas like lost clusters, unallocated clusters and slack space of the disk or digital media. To use this method of extraction, a file should have a standard file signature called a file header (start of the file). A search is performed to locate the file header and continued until the file footer (end of the file) is reached. The data between these two points will be extracted and analyzed to validate the file. The extraction algorithm uses different methods of carving depending on the file formats. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2011.

关键词： Digital storage

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