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Using GPUs to speed-up Levenshtein edit distance computation

Using GPUs to speed-up Levenshtein edit distance computation

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international conference on information and Communication Systems (ICICS)

作者： Khaled Balhaf Mohammed A. Shehab Wala'a T. Al-Sarayrah Mahmoud Al-Ayyoub Mohammed Al-Saleh Yaser Jararweh Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9781467386159

Sequence comparison problems such as sequence alignment and approximate string matching are part of the fundamental problems in many fields such as natural language processing, data mining and bioinformatics. However, the algorithms proposed to address these problems suffer from high computational complexities prohibiting them from being widely used in practical large-scale settings. Many researchers used parallel programming to reduce the execution time of these algorithms. In this paper, we follow this approach and use the parallelism capabilities of the Graphics processing Unit (GPU) to accelerate one of the most common algorithms to compute the edit distance between two strings, which is known as the Levenshtein distance. To take full advantage of the large number of cores in a GPU, we employ a diagonal-based tracing technique which results in even greater improvements in terms of the running time. In fact, our CUDA implementation of the Levenshtein algorithm is about 11X faster than the sequential implementation. this is achieved without affecting the accuracy.

关键词： Graphics processing units Central processing Unit Bioinformatics Acceleration Parallel programming DNA Instruction sets

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Improved dynamic gesture segmentation for thai sign language translation

Improved dynamic gesture segmentation for Thai sign language...

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international conference on Signal processing Proceedings (ICSP)

作者： W. Werapan N. Chotikakamthorn Research Center of Communications and Information Technology Faculty of Information Technology King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand

Sign languages normally make use of both static and dynamic gestures to achieve communication goal. In automated continuous gesture recognition, one of the problems found is how to separate a transitional gesture from a meaningful dynamic gesture. In addition, a variation in speed, absolute position and orientation of a dynamic gesture makes an automatic recognition task nontrivial. this paper proposes a segmentation method that alleviates these problems. the method is based on the fact that many (meaningful) dynamic gestures as appeared in thai and other sign languages, are performed in an approximately (quasi) periodic manner. therefore, such dynamic gestures can be distinguished from a transitional gesture by means of Fourier analysis, performed on samples (discrete signal) of hand shapes, positions and orientation. the analysis, however, requires a good choice of data sample windowing. this paper describes a preprocessing method for estimation of the dynamic gesture data windowing period. the proposed method helps improving the accuracy of segmenting hand shape and location data samples into static, periodic and nonperiodic gestures. In addition, accuracy of the feature extracted from the Fourier analysis of a dynamic gesture signal is improved, thus increasing the recognition rate. Experimental results performed on a real data are included.

关键词： Handicapped aids Hidden Markov models Signal analysis Shape Feature extraction Instruments information technology Communications technology Artificial neural networks Cameras

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Detection of Tunneling in DNS over HTTPS

Detection of Tunneling in DNS over HTTPS

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international conference on Signal processing and Communication (ICSC)

作者： Hrithik Jha Iishi Patel Gang Li Aswani Kumar Cherukuri Sumaiya thaseen Vellore Institute of Technology Vellore India Deakin University Melbourne Australia

ISBN: (纸本)9781665427401

Domain Name Service (DNS) enables users to query domain names which are then converted to IP addresses leading traffic to that specific website on the web. DNS over HTTPS (DoH) is a protocol for performing remote DNS resolution via the HTTP protocol. It enables increased user privacy and security by preventing eavesdropping and manipulation of DNS data by man-in-the-middle attacks. DoH helps keep the requests encrypted and minimizes the information exchanged during queries. Along with the added privacy and security advancements, DoH makes it harder to detect tunneling which can be used by malicious actors to transmit sensitive data out of the victim’s computer. DoH Tunneling enables these actors to insert malware or pass stolen information into DNS queries, creating a covert communication channel that bypasses most firewalls and results in sensitive data leakage. In this paper, we explore methods of data collection and detection of DoH tunneling using machine learning techniques based on the packet sizes and duration of requests.

关键词： Data privacy Protocols Firewalls (computing) Machine learning Tunneling Signal processing Data collection

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Trend Extraction Method Using Co-occurrence Patterns from Tweets

Trend Extraction Method Using Co-occurrence Patterns from Tw...

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IIAI international conference on advanced Applied Informatics (IIAIAAI)

作者： Shotaro Noda Katsuhide Fujita Department of Computer and Information Sciences Tokyo University of Agriculture and Technology Koganei Tokyo Japan

ISBN: (纸本)9781479999590

We can feel free to post the information such as personal events using Twitter one of the popular micro-blogging service. However, the collection of information is limited by the human power only, therefore, the method of collecting trends automatically is important. Existing web services focus on the number of tweets for getting trends. However, a time lag was occurred for extracting the trends. In this paper, we propose the trend extraction method for twitter in real time by paying attention to the co-occurrence patterns. Our system can learn the new key patterns at the same time not only using the picked up trend biterms, previously. Furthermore, we evaluate the efficiency of the proposed method of extracting the trends from twitter by the comparative experiments. We demonstrate that our proposed method can extract accurately and widely without time-lags compared with the existing service (Real time Yahoo Search).

关键词： Market research Twitter Real-time systems HTML Computers web services Natural language processing

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Predicting the Stock Market Prices Using Ensemble and Fbprophet Model 7th

Predicting the Stock Market Prices Using Ensemble and Fbprop...

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7th international conference on information and Communication technology for Competitive Strategies, ICTCS 2022

作者： Shareef, Shaik Ahmad Kumar, Chinchili tharun Harika, Kotha Yellisetti, Sandeep Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Kanuru India

ISBN: (纸本)9789811993039

Stock price prediction through machine learning is an emerging area of research. In the emerging economy, stock investment is useful for individuals and institutions for stabilising their income on investments. Many fund houses hire professionals to analyse the stocks. the machine learning models are more useful and effective than any other traditional techniques. However, many researchers have done price prediction based on various ML algorithms and techniques such as classification and regression. In this proposed work, we were comparing two main types of stock market analysis methods, technical analysis and fundamental analysis. For technical analysis, ensemble classification was used. More care has been taken on choosing the classifier to get more accuracy of prediction. ML algorithm decision tree, KNN and random forest have been used for creating ensemble models. three ensemble classifiers are chosen such as AdaBoost, bagging and voting classifiers. For the fundamental analysis, we have chosen the Fbprophet price forecast with sentiment on stock from New York Times news for stock ‘Apple’. the proposed ensemble models and Fbprophet model are compared to find the best on accurate predictions. Natural language processing (NLP), sentiment analysis technique is used for finding the stock sentiment as positive, negative or neutral. this sentiment value is compounded for each date and added as the additional feature for stock historical prices downloaded from Yahoo. From the experimental results, it is observed that voting-based ensemble classifiers are more efficient and have achieved the highest accuracy of 79.98%. this tells us for short-term market prediction technical analysis gives us better results than fundamental analysis. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Natural language processing: Concepts and Applications using Chatbot

Natural Language Processing: Concepts and Applications using...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： R. Srinivasan M. Kavitha Uma S Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai

A chatbot is a paradigm of human-computer interaction (HCI) and artificial intelligence software. A chatbot is described as "computer software designed to simulate human-to-human interaction, particularly over the Internet." Chatbot is a popular computer programme that is used to promote human user interaction and communication, particularly via the internet. As a computer programme application, it is becoming more popular. Previously, a user would ask a query, and a computer programme would respond. By entering in the question, the user can answer it. If a user asks a question that isn't answered in the database, the chat bot conducts a Google search and responds with pertinent information. When a user enters a query, the bot compares and correlates the query terms with words in the database using pattern matching. User's input will be used to analyses the terms in the phrase for similarity. As a result, the highest score is printed. NLP (Natural language processing) is a technique for helping computers understand human language. this document also offers a summary of the metrics used to evaluate bot performance. this contributes in the development of more effective bots.

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i-AEGIS-128: An Improved Authenticated Encryption Based on AEGIS-128

i-AEGIS-128: An Improved Authenticated Encryption Based on A...

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international conference on Optimization and Applications (ICOA)

作者： Muhammad Ghulam Ghouse Sapiee Jamel Muhammad Aamir Muhammad Zulqarnain Mustafa Mat Deris Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia College of Science and Engineering University of Derby United Kingdom Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia

ISBN: (纸本)9781665431156

Authenticated Encryption (AE) is the latest cryptographic algorithm standard introduced through CAESAR (Com-petition for Authenticated Encryption: Security, Applicability, and Robustness). AE schemes provide a process for simultaneously achieving the security goals of confidentiality and integrity assurance. the constructions of authentication encryption is secure and efficient to protect the modern electronic information systems. In this research paper, we proposed an Improved AEGIS-128 Algorithm (i-AEGIS-128) for improving the standard AEGIS-128 algorithm to enhance the performance in which quarter round-based AES (advanced Encryption Standard) is used for the encryption and decryption process. the proposed i-AEGIS-128 is based on an AEGIS-128 AES-round permutation modification for hardware implementation. the Results of i-AEGIS-128 on Intel(R)Core(TM) i7-3700 Processor running at 3.40 GHz speed with 16GB of RAM using C/C++ and HDL (Hardware Description language) languages and platforms to determine the encryption and decryption time, throughput processing unit memory during encryption and decryption. Finally we compared the efficiency and power analysis of the hardware. the i-AEGIS-128 algorithm relies on the use of ¼ AES-round in authentication encryption to reduce the area consumed during processing, perform fast encryption and decryption, use less power and provide the best efficiency.

关键词： Performance evaluation Power demand Authentication Random access memory throughput Hardware Robustness

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Youtube Comment Summarizer and Post Content Analysis

Youtube Comment Summarizer and Post Content Analysis

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international conference on Computation System and information technology for Sustainable Solutions (CSITSS)

作者： Aneesh Adiga S Anish S Chaithanya Ganesh Abhin Divakar RV College of Engineering

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

this paper presents a unified approach to analyzing online discourse by leveraging advanced natural language processing techniques, focusing on both YouTube comments and Amazon reviews. We introduce a YouTube Comment Analyzer and an Amazon Review Summarizer, both employing bag-of-words analysis to unveil sentiment trends, topic dynamics, and actionable insights within user-generated content. through the utilization of state-of-the-art machine learning algorithms, our tools categorize comments and reviews, extract key sentiments, and identify prevalent topics. By synthesizing salient features from both platforms, we offer a comprehensive understanding of user engagement, content reception, and product performance. Our findings contribute to a deeper comprehension of online discourse dynamics and provide valuable insights for content creators, marketers, businesses, and platform administrators in navigating the digital landscape effectively.

关键词： Video on demand Machine learning algorithms Reviews Navigation User-generated content Focusing Market research Natural language processing web sites information technology

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Extensions to the Pig data processing platform for scalable RDF data processing using Hadoop

Extensions to the Pig data processing platform for scalable ...

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international conference on Data Engineering Workshops

作者： Yusuke Tanimura Akiyoshi Matono Steven Lynden Isao Kojima Information Technology Research Institute National Institute for Advanced Industrial Science and Technology Tsukuba Ibaraki Japan

In order to effectively handle the growing amount of available RDF data, a scalable and flexible RDF data processing framework is needed. We previously proposed a Hadoop-based framework, which takes advantages of scalable and fault-tolerant distributed processing technologies, originally proposed as Google's distributed file system and MapReduce parallel model. In this paper, we present a method extending the Pig data processing platform on top of the Hadoop infrastructure. Pig compiles programs written in a high level language, called Pig Latin, into MapReduce programs that can be executed by Hadoop. In order to support RDF, Pig was extended with the ability to load and store RDF data efficiently. Furthermore, as reasoning is an important requirement for most systems storing RDF data, support for inferring new triples using entailment rules was also added. In this paper, we describe these extensions and present an evaluation of their performance.

关键词： Data processing Resource description framework Relational databases File systems Scalability Semantic web Open source software OWL information technology Distributed processing

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Can information Retrieval techniques automatic assessment challenges?

Can Information Retrieval techniques automatic assessment ch...

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international conference on Computer and information technology (ICCIT)

作者： Md Maruf Hasan School of Technology Shinawatra University Thailand

In information Retrieval (IR), the similarity scores between a query and a set of documents are calculated, and the relevant documents are ranked based on their similarity scores. IR systems often consider queries as short documents containing only a few words in calculating document similarity score. In Computer Aided Assessment (CAA) of narrative answers, when model answers are available, the similarity score between Students' Answers and the respective Model Answer may be a good quality-indicator. With such an analogy in mind, we applied basic IR techniques in the context of automatic assessment and discussed our findings. In this paper, we explain the development of a web-based automatic assessment system that incorporates 5 different text analysis techniques for automatic assessment of narrative answers using vector space framework. We apply Uni-gram, Bi-gram, ***, Keyphrase Extraction, and Keyphrase with Synonym Resolution before representing model answers and students' answers as document vectors; and then we compute document similarity scores. the experimental results based on 30 narrative questions with 30 model answers, and 300 student's answers (from 10 students) show that the correlation of automatic assessment with human assessment is higher when advanced text processing techniques such as Keyphrase Extraction and Synonym Resolution are applied.

关键词： information retrieval Text analysis Data mining Computer aided analysis Natural language processing Automatic testing System testing information technology Humans Text processing

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