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computing and Intelligent Reality Technologies (ICCIRT), International Conference on

作者： A. Babisha G. Umarani Srikanth D.R. Angel Kiruba R Sundar Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing.

关键词： Sign language Visualization Translation Annotations Generative Pre-trainer transformer Transformers Feature extraction Multilingual Optimization Videos

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Research on key technologies of edge cache in virtual data space across WAN

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Frontiers of computer science 2023年第1期17卷 15-34页

作者： Jiantong HUO Yaowen XU Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China College of Software Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China College of Computer Science and Technology Zhejiang UniversityHangZhou 310013China

The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputing Center in Guangzhou, National Supercomputing Center in Jinan, National Supercomputing Center in Changsha, Shanghai Supercomputing Center, and computer Network Information Center in Chinese Academy of sciences) into a storage system that spans the wide area network (WAN), which realizes the unified management of global storage resources in China. At present, the GVDS has been successfully deployed in the China National Grid environment. However, when accessing and sharing remote data in the WAN, the GVDS will cause redundant transmission of data and waste a lot of network bandwidth resources. In this paper, we propose an edge cache system as a supplementary system of the GVDS to improve the performance of upper-level applications accessing and sharing remote data. Specifically, we first designs the architecture of the edge cache system, and then study the key technologies of this architecture: the edge cache index mechanism based on double-layer hashing, the edge cache replacement strategy based on the GDSF algorithm, the request routing based on consistent hashing method, and the cluster member maintenance method based on the SWIM protocol. The experimental results show that the edge cache system has successfully implemented the relevant operation functions (read, write, deletion, modification, etc.) and is compatible with the POSIX interface in terms of function. Further, it can greatly reduce the amount of data transmission and increase the data access bandwidth when the accessed file is located at the edge cache system in terms of performance, i.e., its performance is close to the performance of the network file system in the local area network (LAN).

关键词： virtual data space system wide area network edge cache redundant data transmission

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Comprehensive Integration of Machine Learning, Deep Learning, and Fine-Tuned Large Language Models for Malayalam News Categorization

Comprehensive Integration of Machine Learning, Deep Learning...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Anushka, C.V. John, Ardra K. Nair, Aarathi Rajagopalan Gupta, Deepa Veena, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

The comprehension of news is substantially improved when it is conveyed in an individual's native language. The increasing volume of news articles, driven by global events, necessitates the effective organization and categorization of vast amounts of information. Dravidian languages, such as Malayalam, characterized by their intricate morphology and diacritical features, pose distinctive challenges in this domain. This research embarks on an innovative initiative to classify Malayalam news articles into four principal categories: business, entertainment, sports, and technology. A synthesis of Deep Learning and Machine Learning methodologies, alongside diverse embedding techniques, is utilized to assess the precision and effectiveness of the categorization endeavor. The ensemble classifiers XGBoost and Stacking, when integrated with the XLM RoBERTa word embedding, attained the highest accuracy rate of 0.91. Similarly, XLM RoBERTa and FastText word embedding techniques, attained a performance with an accuracy rate of 0.93 for almost all the deep learning model. Furthermore, large language models were leveraged, investigating both zero-shot learning and fine-tuning strategies. The fine-tuned BLOOM model reached an accuracy level of 0.89 The research additionally presents a comprehensive examination of the importance of large language models in facilitating the categorization process. Furthermore, the research provides a comprehensive examination of the role of large language models in enhancing the categorization process. By advancing the discipline of Natural Language Processing, this investigation provides significant insights into the utilization of sophisticated technologies for regional news analysis, underscoring their potential to enhance information management and accessibility. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Information management

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Extracting Triplets from Domain-specific Texts using Fine-Tuned LLaMA Models: A Comparative Study

Extracting Triplets from Domain-specific Texts using Fine-Tu...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Veena, G. Rajendran, Aparna Gupta, Deepa Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Dept of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

This research centers on extracting triplets (Subject, Verb, Object) from domain-specific texts, particularly focusing on the film industry. We compiled a corpus of 4,300 sentences from 500 well-known movie articles and Wikipedia entries. The study targeted five key relations: Composed by, Written by, Released on, Made f rom, and Released in, along with their respective entities. Dependency parsing was employed to extract triplets from this dataset. These extracted triplets were then used for supervised fine- tuning of the LLaMa model. We assessed the evaluation loss across different versions of the LLaMA models and found that LLaMA 3 outperformed the others, showing the best performance for this task. For testing, the fine-tuned LLaMA model is used to extract triplets from unseen sentences. The extracted triplets can be used to create knowledge graphs, offering structured representations of information by mapping relationships between entities. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Finetuning LLaMA LLM NLP Zero-shot

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How Do HCI Researchers Study Cognitive Biases? A Scoping Review 25

How Do HCI Researchers Study Cognitive Biases? A Scoping Rev...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Boonprakong, Nattapat Tag, Benjamin Goncalves, Jorge Dingler, Tilman School of Computing and Information Systems University of Melbourne ParkvilleVIC Australia School of Computer Science and Engineering University of New South Wales SydneyNSW Australia School of Computing and Information Systems University of Melbourne Melbourne Australia Industrial Design Engineering Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400713941

computing systems are increasingly designed to adapt to users' cognitive states and mental models. Yet, cognitive biases affect how humans form such models and, therefore, they can impact their interactions with computers. To better understand this interplay, we conducted a scoping review to chart how Human-computer Interaction (HCI) researchers study cognitive biases. Our findings show that computing systems not only have the potential to induce and amplify cognitive biases but also can be designed to steer users' behaviour and decision-making by capitalising on biases. We describe how HCI researchers develop algorithms and sensing methods to detect and quantify the effects of cognitive biases and discuss how we can use their understanding to inform system design. In this paper, we outline a research agenda for more theory-grounded research and highlight ethical issues when researching and designing computing systems with cognitive biases in mind as they affect real-world behaviour. © 2025 Copyright held by the owner/author(s).

关键词： Real time systems

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Enabling an On-demand Access to Community Sentiments using LSTM RNNs Web Service Architecture

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Procedia computer science 2023年 230卷 584-597页

作者： N Prabakaran A. Anbarasi N. Deepa P Pandiaraja School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India 632014 Department of computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai 603203 Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai 603203 Department of Computer Science and Engineering M. Kumarasamy college of Engineering Karur India 639113

Analyzing community response has always played an important role in marketing and development of various products ranging from services to manufacturing. Analyzing the responses and interpreting them helps improve the quality of the product. This task is traditionally done by community managers using surveys and focus groups. One other hands-off solution would be using the technology to analyze sentiments from users’ digital interactions in various online communities. Sentiment Analysis is the use of Natural Language Processing to process text to identify and retrieve sentiments behind the textual data. Sentiment Analysis can be approached either by supervised classification where a machine learning model is trained using labeled data or lexicon-based unsupervised analysis where a sentiment dictionary is used to find the overall sentiment. The idea is to build a system which presents community sentiment results on discussion threads from reddit on demand with the end user not to concern about data processing. We propose a streamlined pipeline architecture for a web application with several closely connected components in the backend that allows users to simply send the reddit discussion thread's URL, which the backend receives and scrapes the comments using the Reddit API, creates a dataset, cleans it and performs sentiment analysis using a RNNs. We used a Long Short-Term Memory (LSTM) a variant of RNN like Bidirectional RNNs and Multi-layered RNNs are also used to get better results.

关键词： Sentiment Analysis Community Response Analysis Social Media Mining Monitor sentiments Sentiment analysis architecture LSTM RNN On demand architecture

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FARMER: Forecasting Agricultural Results with Machine Learning Evaluation and Regression

FARMER: Forecasting Agricultural Results with Machine Learni...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Naman Chauhan Bharathi Mohan G V. Sulochana R. Prasanna Kumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Chennai India Anna Administrative Staff College Dehradun India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

Agriculture plays an important role, in Indias economy providing support to a portion of the population and contributing significantly to the GDP. It is crucial to predict crop yields in order to make decisions and promote sustainable agricultural practices. To achieve this a research study proposes an approach that utilizes machine learning techniques such as Decision Tree, Support Vector Machine (SVM) Random Forest, Multiple Linear Regression and Partial Least Squares Regression. The study carefully evaluates the performance of these models. Reveals some findings while comparing them to determine the best model. Multiple Linear Regression achieves the accuracy at 81.2% followed by Decision Tree at 83.5% SVM at 92.1% Random Forest at 91.5%. Partial Least Squares Regression at 85.3%. Notably SVM emerges as the performing model demonstrating its potential in improving precision, for predicting agricultural yields. Ultimately an Android application is developed using Kotlin and Android Studio for an user experience.

关键词： Support vector machines Biological system modeling Linear regression Sociology Vectors User experience Reliability

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Prophet: Conflict-Free Sharding Blockchain via Byzantine-Tolerant Deterministic Ordering 42

Prophet: Conflict-Free Sharding Blockchain via Byzantine-Tol...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Hong, Zicong Guo, Song Zhou, Enyuan Zhang, Jianting Chen, Wuhui Liang, Jinwen Zhang, Jie Zomaya, Albert The Hong Kong Polytechnic University Department of Computing Hong Kong The Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong Purdue University Computer Science Department United States Sun Yat-sen University Pengcheng Laboratory School of Computer Science and Engineering Shenzhen China The University of Sydney School of Computer Science Australia

ISBN: (纸本)9798350334142

Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards' independent and random scheduling for cross-shard transactions results in numerous conflicts and aborts, since cross-shard transactions from different shards may access the same account. A deterministic ordering can eliminate conflicts by determining a global order for transactions before processing, as proved in the database field. Unfortunately, due to the intertwining of the Byzantine environment and information isolation among shards, there is no trusted party able to predetermine such an order for cross-shard transactions. To tackle this challenge, this paper proposes Prophet, a conflict-free sharding blockchain based on Byzantine-tolerant deterministic ordering. It first depends on untrusted self-organizing coalitions of nodes from different shards to pre-execute cross-shard transactions for prerequisite information about ordering. It then determines a trusted global order based on stateless ordering and post-verification for pre-executed results, through shard cooperation. Following the order, the shards thus orderly execute and commit transactions without conflicts. Prophet orchestrates the pre-execution, ordering, and execution processes in the sharding consensus for minimal overhead. We rigorously prove the determinism and serializability of transactions under the Byzantine and sharded environment. An evaluation of our prototype shows that Prophet improves the throughput by 3.11× and achieves nearly no aborts on 1 million Ethereum transactions compared with state-of-the-art sharding. © 2023 IEEE.

关键词： Blockchain

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Distributed Denial of Service Attack Detection by Machine Learning Techniques 22

Distributed Denial of Service Attack Detection by Machine Le...

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22nd International Learning and Technology Conference, L and T 2025

作者： Jewani, Varkha K. Ajmire, Prafulla E. Atique, Mohd. Sharma, Bhisham Dhaou, Imed Ben Awadhesh Chaurasia, Suhashini Sant Gadge Baba Amravati University Amravati India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia RTM Nagpur University Department of Computer Science Nagpur India

ISBN: (纸本)9798331532215

The exponential expansion of computer networks and the internet makes it clear that there is a chance of being attacked and harmed. In the meantime, one of the most crucial defensive measures against the more complex and frequent network attacks is the intrusion detection and prevention systems (IDSs and IPSs). Due to insufficient datasets, anomaly-based research in intrusion detection systems is plagued by imprecise deployment, analysis, and evaluation. The researchers analyzed a variety of datasets, including DARPA98, KDD99, ISC2012, and ADFA13, to assess how well their suggested intrusion detection and intrusion prevention techniques performed. There are several issues: lack of adequate attack coverage, unrepresentative payloads, and anonymised packet information, insufficient traffic volume and diversity, or a deficiency in feature set and metadata. This study is focused on CICIDS-2017 which is the recently updated IDS dataset that meets real-world requirements and is accessible publicly. In addition to benign network flows and distributed denial of service (DDoS) attacks, it contains seven common attack network flows as well. To provide the optimal combination of features for the identification of the attack category, it also assesses the efficacy of several machine learning (ML) algorithms and network traffic feature sets. Even though various ML techniques like Linear Regression, Random Forests, and Decision Tree can be used, and performed very well, Hybrid Algorithm is designed to get better analysis on dataset. © 2025 IEEE.

关键词： Network intrusion

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