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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Das, Deepraj Mandal, Soumik Golande, Ashutosh Sen, Argha Chakraborty, Sandip Indian Institute of Technology Kharagpur Department of Computer Science and Engineering India

ISBN: (纸本)9798331531195

In this work, we present milliGait, a user identification using gait patterns captured by millimeter wave (mmWave) radar technology. milliGait takes into account the unique movement signatures of individuals to enable privacy secured accurate user identification. Using a Commercial-Off-the-self (COTS) mmWave radar we have collected mmWave features, particularly the range information and range-doppler profile features, extracted from the raw I/Q samples. To categorize people according to their leg movement patterns, we developed a Convolutional Neural Network (CNN) model using the mmWave features. The two features namely the range information and the range-doppler profile is fed into two concurrent CNN models (1D and 2D CNN models respectively). Finally a fully-connecyed classifier is employed to identify the subjects. We have evaluated milliGait with 3 different subjects with an authentication accuracy of over 80%. © 2025 IEEE.

关键词： Convolutional neural networks

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A Multi-path Droplet Routing Protocol for Digital Microfluidic Biochip

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Informatica (Slovenia) 2024年第7期48卷 95-102页

作者： Swain, Jyotiranjan Pyne, Sumanta School of Computing and Information Technology REVA University Bangalore India Department of Computer Science and Engineering National Institute of Technology Rourkela India

Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It involves challenges like moving droplets without violating the fluid constraints. This article proposed a multi-path droplet routing protocol. Multiple routes are explored and validated using special packets. These routes are then classified based on user-defined heuristics. A single route is sequentially selected for each droplet. The routes are then compacted to generate a parallel moving sequence. The simulation result shows 2.087% and 4.952% improvement in the latest arrival time for free and virtual topology, respectively. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Biochips

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OSINT at GenAI Detection Task 1: Multilingual MGT Detection: Leveraging Cross-Lingual Adaptation for Robust LLMs Text Identification 1

OSINT at GenAI Detection Task 1: Multilingual MGT Detection:...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Agrahari, Shifali Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762053

Detecting AI-generated text has become increasingly prominent. This paper presents our solution for the DAIGenC Task 1 Subtask 2, where we address the challenge of distinguishing human-authored text from machine-generated content, especially in multilingual contexts. We introduce Multi-Task Detection (MLDet), a model that leverages Cross-Lingual Adaptation and Model Generalization strategies for Multilingual Machine-Generated Text (MGT) detection. By combining language-specific embeddings with fusion techniques, MLDet creates a unified, language-agnostic feature representation, enhancing its ability to generalize across diverse languages and models. Our approach demonstrates strong performance, achieving macro and micro F1 scores of 0.7067 and 0.7187, respectively, and ranking 15th in the competition1. We also evaluate our model across datasets generated by different distinct models in many languages, showcasing its robustness in multilingual and cross-model scenarios. © 2025 International Conference on Computational Linguistics.

关键词： Computational linguistics

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PierGuard: A Planning Framework for Underwater Robotic Inspection of Coastal Piers

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IEEE Transactions on Automation science and engineering 2025年 22卷 15941-15952页

作者： Wang, Pengyu Lin, Hin Wang Li, Jialu Wang, Jiankun Shi, Ling Meng, Max Q.-H Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Southern University of Science and Technology Jiaxing Research Institute Jiaxing China Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong University of Alberta Canada Department of Electrical and Computer Engineering Canada

Using underwater robots instead of humans for the inspection of coastal piers can enhance efficiency while reducing risks. A key challenge in performing these tasks lies in achieving efficient and rapid path planning within complex environments. Sampling-based path planning methods, such as Rapidly-exploring Random Tree* (RRT*), have demonstrated notable performance in high-dimensional spaces. In recent years, researchers have begun designing various geometry-inspired heuristics and neural network-driven heuristics to further enhance the effectiveness of RRT*. However, the performance of these general path planning methods still requires improvement when applied to highly cluttered underwater environments. In this paper, we propose PierGuard, which combines the strengths of bidirectional search and neural network-driven heuristic regions. We design a specialized neural network to generate high-quality heuristic regions in cluttered maps, thereby improving the performance of the path planning. Through extensive simulation and real-world ocean field experiments, we demonstrate the effectiveness and efficiency of our proposed method compared with previous research. Our method achieves approximately 2.6 times the performance of the state-of-the-art geometric-based sampling method and nearly 4.9 times that of the state-of-the-art learning-based sampling method. Our results provide valuable insights for the automation of pier inspection and the enhancement of maritime safety. (Video1). © 2004-2012 IEEE.

关键词： Motion planning

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SlidEar: Exploring eSense in-Ear Wearable for Voice-Assisted Smart Slide Supervision 17

SlidEar: Exploring eSense in-Ear Wearable for Voice-Assisted...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Mondal, Susmita Zingre, Sameeran Ravishankar Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

In corporate settings, conferences, or classrooms, an orator relies on manual slide transitions, which can disrupt their presentation flow. Presentation devices can be inaccessible due to the physical limitations of individuals, reducing engagement with the audience. Traditionally used remote-controlled wireless presenters are hand-held devices that usually require a compatible connector slot for the presentation device and partially engage one hand of the orator. To ease the hand-free presentation of slides, we introduce SlidEar, a voice-assisted smart slide supervision successfully tested on eSense in-ear wearables. SlidEar leverages voice recognition fusion with wearable sensors to facilitate seamless slide operations. It allows real-time feedback on natural language commands during the presentation of slides. Compared with conventional methods, SlidEar enhances engagement and accessibility through hand-free in-ear placement and helps the orator focus on delivering their content without any disruption or distraction. The evaluation shows promising results that improve the presentation experience profusion. SlidEar significantly reduces errors and can be widely adopted in diverse presentation scenarios. © 2025 IEEE.

关键词： Speech recognition

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Leveraging the Potential of Large Language Models

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Informatica (Slovenia) 2024年第8期48卷 1-16页

作者： Prasad, Shreya Gupta, Himank Ghosh, Arup Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Information Technology Graceland University LamoniIA United States

This study focuses on enhancing Natural Language Processing (NLP) in generative AI chatbots through the utilization of advanced pre-trained models. We assessed five distinct Large Language Models (LLMs): TRANSFORMER MODEL, FALCON 7B, LAMINI-FLAN-T5-783M, LLAMA-2-7B, and LLAMA-2-13B to identify the most effective one. Our findings revealed that the LLAMA Model excels in comprehending user queries and delivering precise responses during conversations. The article elucidates the methodology employed to evaluate and select various models for our chatbot. Through rigorous testing, we determined that the LLAMA-2-13B model exhibits enhanced response time and accuracy. Additionally, we employed tools such as Facebook Artificial Intelligence Similarity Search (FAISS) and experimented with user interfaces like Streamlit and Chainlit to enhance the chatbot's user-friendliness. The research underscores the significance of selecting the appropriate model for crafting efficient chatbots. Ultimately, the LLAMA-13B model emerged as the standout performer, showcasing superior performance. Benchmark assessments, including HellaSwag and WinoGrande, which gauge common sense reasoning, were employed to evaluate our chatbot's capabilities. The study concludes that LLAMA-based models hold significant promise for the development of innovative and user-friendly chatbots in the future. Povzetek: Študija se osredotoča na izboljšanje obdelave naravnega jezika (NLP) v generativnih AI klepetalnicah z uporabo naprednih vnaprej usposobljenih modelov. Analizirali so pet modelov: TRANSFORMER MODEL, FALCON 7B, LAMINI-FLAN-T5-783M, LLAMA-2-7B in LLAMA-2-13B. Model LLAMA-13B se je izkazal kot najboljši z vrhunskimi rezultati na testih HellaSwag in WinoGrande. Raziskava uporablja orodja, kot je FAISS, in uporabniške vmesnike, kot sta Streamlit in Chainlit za izboljšanje uporabniške izkušnje. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Machine learning

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Indian sign language recognition using TensorFlow and MediaPipe 1st

Indian sign language recognition using TensorFlow and MediaP...

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Kanyal, Hoshiyar Singh Gupta, Shubham Singh, Smriti Kumari, Sarita Department of Computer Science and Engineering ABES Institute of Technology Ghaziabad India

ISBN: (纸本)9781032874036

With this work, a technical method for identifying Indian Sign Language is attempted to be developed. This work attempted to create a system for recognizing the hand movements from Indian Sign Language using machine learning techniques. An amalgamation of two- and three-dimensional illustrations of Indian Sign Language motions has been employed. The dataset was manually assembled. To identify landmarks in the pictures, the Media Pipe framework has been used. The findings show that the approach used in this study is successful in identifying the other Sign Language alphabets and gestures. The signs and gestures recognition for several more Indian regional languages could also be investigated and explored using this method. © 2025 the Author(s).

关键词： Gesture recognition

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Illicit Events Evaluation on the Dark Web Using NSGA-2 Algorithms Based on Energy Consumption

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Informatica (Slovenia) 2024年第18期48卷 77-96页

作者： Rawat, Romil Rajavat, Anand Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India

The proposed work objective is to adopt the non-dominated sorting genetic algorithm II (NSGA-II), a type of MOEA (multi-objective evolutionary algorithms), to reduce the dimensionality and identify the most relevant features. The objectives and hypotheses tested using NSGA-II and MOEA are to develop predictive models that best fit the problem of traffic analysis, analyse the set of features associated with the dataset, and apply NSGA techniques to reduce the data dimensionality. The work used the datasets and considered the Transnational Terrorist Hostage Event (TTHE) and Counter Trafficking Data Collaborative (CTDC) datasets, which relate to global human trafficking (HTr). The method used is focused on analysing the energy consumption (Econ) with the carbon footprint (CFP) of the proposed algorithm for different values of the main parameters on different hardware platforms, where the quality of each individual is computed based on ML algorithms. The result represents the "F-measure" metric obtained from support vector machines (SVM) and the number of features in order to determine the dimensionality, the execution time, the Econ, and the CFP during the feature engineering process. To estimate performance, the parameter values are computed using *** proposed work concludes that cybercrime employs and represents susceptible behaviour on the OSN Channels network. We identified the threat patterns by using NSGA-II and Econ analysis to establish connections between the variables in the dataset. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Support vector machines

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Protecting Recommendations: Combating BIAS and Manipulation in Recommendation 8

Protecting Recommendations: Combating BIAS and Manipulation ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Infant, P.Alwin Kumar, P.Mohan Jainish, G.R. Hindustan Institute of Technology & Sciencce Department of Computer Science and Engineering Chennai India Loyola Icam College of Engineering & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Recommender systems assist consumers in navigating the deluge of information by helping them find services and goods. The effectiveness of recommender systems has been thoroughly examined in research currently available from a number of angles. In the research community, robustness against malicious attack is becoming a more important concern. Collaborative Filtering (CF) recommender systems have been established to accomplish this objective. However, due to the fact that they are collaborative recommender systems are open to attack. To weaken the system, attackers can introduce fictitious and biased profiles. An examination of the random attack, Bandwagon attack, Mixed Attack and Noise Injection on recommender systems is presented in this work. Numerous algorithms are susceptible to profile injection attacks. Some of the earlier approaches suffer from only looking at one aspect of user rating values while assessing the rating matrix, ignoring alternative viewpoints. First, a rating matrix in this research that takes into account both genuine and fictitious user ratings is created. Next, using user rating time as a basis, the rating matrix is examined. Next, build a model to distinguish between real and fraudulent users using a variety of classifiers, including Principal Component Analysis (PCA), K-Nearest Neighbour (KNN), and Bayesian Networks (BN). Ultimately, a number of tests are conducted, and a summary of the findings comparing different approaches' abilities to detect small-scale attacks and detect overall attacks is presented. © 2024 IEEE.

关键词： Collaborative filtering

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Phish Check: Content-based Phishing Website Detection Web App 6

Phish Check: Content-based Phishing Website Detection Web Ap...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Ghadiyaram, Anil Kumar Reddy, G. Srikanth Basith, Mohammed Abdul Reddy, Naini Rithvik Department of Computer Science and Engineering Vignana Bharathi Institute of Technology India

ISBN: (纸本)9798331522667

This research proposes an innovative method for categorizing phishing and legitimate websites based on content features extracted from their HTML structure. Leveraging data collection with the requests library and HTML parsing using the Beautiful Soup module, our study scrutinizes 26,584 websites sourced from "***" and "***." Employing seven diverse machine learning classifiers from the sci-kit-learn library and implementing k-fold cross-validation, this research study assesses the efficacy of different models in accurately distinguishing phishing websites. Evaluation metrics include confusion matrices, accuracy, precision, and recall scores. The proposed approach refrains from using URL-based attributes, focusing solely on the characteristics of HTML content. This study presents the results and comparisons of various classifiers, highlighting the effectiveness of our methodology. © 2025 IEEE.

关键词： Phishing

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