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On the simulation of hypervisor instructions for accurate timing simulation of virtualized systems

International Journal of Information technology (Singapore) 2024年 1-10页

作者： Mhatre, Swapneel Chandran, Priya Department of Computer Science and Engineering National Institute of Technology Calicut Kerala India

Architectural simulators help in better understanding the behaviour of existing architectures and the design of new architectures. Virtualization has regained importance and this has put a pressing demand for the simulation of virtualized systems. Existing full-system simulators for virtualized systems simulate the application program instructions and the operating system instructions but abstract the hypercalls or traps to the hypervisor. This leads to inaccuracy in the simulation. This paper proposes an approach to simulate hypervisor instructions in addition to operating system instructions for accurate timing simulation of virtualized systems. The proposed approach is demonstrated by simulating RISC-V binary instructions. The simulator is an execution-driven, functional-first, software-based simulator coded in Python. Three programs—searching (linear search), sorting (bubble sort) and ***_ir workload from SPEC CPU2017 benchmarks are simulated using the developed simulator. It is seen that the number of instructions executed when the simulator is configured for a virtualized system are higher than the number of instructions executed when the simulator is configured for a non-virtualized system. This is due to the hypervisor instructions that get executed in a virtualized system. The paper concludes that the proposed approach leads to accurate timing simulation of virtualized systems. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Architecture Hypercall Hypervisor Operating system Simulation Virtualization

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Deep Learning-Driven Edge-Enabled Serverless Architectures for Animal Emotion Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第7期49卷 33-48页

作者： Benedict, Shajulin Subair, Rubiya Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kerala India Technical University Munich Garching Germany

Animal emotion detection, including elephant emotions, is highly possible, but what the traditional emotion detection approaches highlight is their blatant ignorance of adopting edge-enabled intelligence and serverless-based solutions, both of which are affordable. Treating the emotions of animals increases their productivity, especially among trained elephants when subjected to carrying logs or undertaking gargantuan tasks. However, existing infrastructures are inefficient in handling long-running animal emotion detection-related tasks. This article proposes a deep learning-driven edge-enabled serverless architecture after evaluating several existing animal emotion detection techniques. Additionally, we perform an exploratory study on the cost impact of incorporating serverless-enabled approaches to animal emotion detection architectures. We observed that the proposed edge-enabled serverless architectures saved over 13,000 dollars annually compared to traditional animal emotion detection approaches. In addition, the article provided a few research directions to develop novel edge-enabled serverless architectures that boost socio-economic situations while avoiding human-animal conflicts. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Invertebrates

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Behavior Based Group Recommendation from Social Media Dataset by Using Deep Learning and Topic Modeling

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SN computer science 2024年第6期5卷 1-17页

作者： Mukta, Md. Saddam Hossain Ahmed, Jubaer Raiaan, Mohaimenul Azam Khan Fahad, Nur Mohammad Islam, Muhammad Nazrul Imtiaz, Nafiz Islam, Md. Adnanul Ali, Mohammed Eunus Azam, Sami LUT School of Engineering Sciences Lappeenranta-Lahti University of Technology Department of Computer Science and Engineering United International University Department of Computer Science and Engineering Military Institute of Science and Technology Department of HCC Action Lab Monash University Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Faculty of Science and Technology Charles Darwin University

In this digital era, users frequently share their thoughts, preferences, and ideas through social media, which reflect their Basic Human Values. Basic Human Values (aka values) are the fundamental aspects of human behavior, which define what we consider important, and worth having and pursue them. Existing studies identify the values of individuals from different social network usages such as Facebook and Reddit. However, discovering the similarity (or diversity) of value priorities among the members in a group is important since we can reveal many interesting insights such as finding a set of target customers, identifying the chain of misdeed groups, searching for similar acquaintances in workplaces, etc. In this paper, a graph dataset is compiled using the strongest correlation among the features and then we apply a graph clustering technique to identify a suitable hedonist group (i.e., one dimension of values) for users’ recommendations. Then, we also propose a behavior based (i.e., value) group recommendation technique by analyzing users’ contextual and psychological attributes. Finally, we validate those group members in real life by introducing two hypotheses. In particular, we analyze the tweets of a total of 1140 users collected from Twitter. We obtain a substantial intra-cluster correlation coefficient (ICC) and silhouette clustering coefficient (SCC) scores of 65% and 76%, respectively, among the members in our discovered group. © The Author(s) 2024.

关键词： Graph neural network (GNN) Group recommendation Psychological attributes Social media Topic modeling Twitter Values

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Extensive examination of hyper-parameters setting using neural-based methods for limited resources language: Nyishi-English

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International Journal of Information technology (Singapore) 2024年第6期16卷 3901-3911页

作者： Kakum, Nabam Sambyo, Koj Department of Computer science and Engineering National Institute of Technology Arunachal Pradesh India

Taking advantage of the enormous advancement of the transformer model using a neural-based approach for text translation, we extent another research project into the extremely limited resources of the Indian language, Nyishi-English. In this work, we compare the quality of translation between the baseline RNN and Transformer model by employing the fine-tune hyperparameter optimization technique. Further, the quality of system prediction has been judged using both automatic metrics evaluation (BLEU) and human evaluation methods based on sentence length. Each parameter combination has been scrutinized, and the translation quality has been identified using BLEU. The human evaluation method has been used to verify the system prediction based on simple, complex and long sentences. The study concluded with the identification of the optimal hyperparameter optimization combination utilizing the limited resource language Nyishi. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： BLEU Hyperparameters NMT Nyishi Transformer model

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Space robotics hybrid conceptual model for tracking and estimation using IoRT and AI-based control system

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Materials Today: Proceedings 2023年 72卷 958-965页

作者： Sinha, Anurag Garg, Namit Singh, N.K. Kumar, Biresh Banerjee, Pallab Verma, Achyut Kumar Department of Computer Science IGNOU Delhi India Department of Mathematics and Computing Indian Institute of Technology Kanpur India Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India Department of Computer Science and Engineering Amity University Jharkhand Ranchi Jharkhand India Department of Chemical Engineering Indian Institute of Technology Kanpur India

Robotics is an amalgamation of mechanical engineering and computer science. Mechanical engineering helps to design and develop mechanical parts and devices for control systems in robots. Space robots and robotics are known as devices that can enhance the manipulation, functionalities, and control exercised by astronauts, and thus can be called their artificial assistants for real-time exploration of space conditions. Control systems in robots like gestures and action become predominant for control, thus human-robot interaction comes into existence. AI and reinforcement learning have been used to control functionality in robots in various fields, but IoRT (Internet of robotic Things) is an emerging area of IoT that has the capabilities to monitor a variety of courses of action in robots. In this paper, we are proposing a conceptual framework based on an IoRT control system integrated with AI and reinforcement learning algorithms, which has been accessed based on multiple papers in the same domain, and it will help future researchers develop and simulate such a prototype. We are also using AKF (Adaptive Kalman Filtering) for robot tracking and noise deduction in sensors integrated with the A* algorithm. Overall, this is a concept-based framework to be designed and simulated. © 2023

关键词： Reinforcement learning

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Optimizing crowd evacuation: evaluation of strategies for safety and efficiency

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Journal of Reliable Intelligent Environments 2025年第1期11卷 1-19页

作者： Oliveira, Hugo S. Faculty of Sciences University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science INESC TEC Porto Portugal

Predicting and controlling crowd dynamics in emergencies is one of the main objectives of simulated emergency exercises. However, during emergency exercises, there is often a lack of sense of danger by the actors involved and concerns about exposing real people to potentially dangerous environments. These problems impose limitations in running an emergency drill, harming the collection of valuable information for posterior analysis and decision-making. This work aims to mitigate these problems by using Agent Based Modelling (ABM) simulator to deepen the comprehension of human actions when exposed to a sudden variation in extensive crowded environmental conditions and how evacuation strategies affect evacuation performance. To assess the impact of the evacuation strategy employed, we propose a modified informed leader-flowing approach and compare it with common evacuation strategies in a simulated environment, replicating stadium benches with narrow corridors leading to different exit points. The objective is to determine the impact of each set of configurations and evacuation strategies and compare them against other established ones. Our experiments determined that agents following the crowd generally lead to a higher number of victims due to the rise of herding phenomena near the exits, which was significantly reduced when agents were guided towards the exit via knowing the exit beforehand or following leader agent with real-time information regarding exit location and exit current state, proving that relevant and controlled information in combination with Follow Leader strategies can be crucial in an emergency evacuation scenario with limited evacuation exit capabi and distribution. © The Author(s) 2024.

关键词： Decision making

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A hybrid convolutional neural network for sarcasm detection from multilingual social media posts

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Multimedia Tools and Applications 2025年第16期84卷 15867-15895页

作者： Pandey, Rajnish Kumar, Abhinav Singh, Jyoti Prakash Tripathi, Sudhakar Department of Information Technology IET Dr Rammanohar Lohia Awadh University Ayodhya Uttar Pradesh Faizabad India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj211004 India Department of Computer Science and Engineering National Institute of Technology Patna Patna India Department of Information Technology Rajkiya Engineering College Uttar Pradesh Ambedkarnagar India

Sarcasm in social media postings significantly impacts automated sentiment extraction due to its potential to invert the overall polarity of phrases. It poses a formidable challenge in extracting genuine sentiments from informal multilingual comments about events or individuals, especially when the message is in a multilingual form. The prevalence of noisy datasets further complicates this task. This work proposes a hybrid convolutional neural network (CNN-H) model to classify sarcastic and non-sarcastic statements. Unlike traditional CNN models, CNN-H integrates character and word embeddings, enhancing its capacity to handle the complexities of multilingual content. Evaluation on five benchmark datasets, including English (news headlines, Ghosh Tweet, Riloff Tweet), Hindi, and multilingual Hindi-English (Hinglish), demonstrates the superior performance of CNN-H. The model surpasses various state-of-the-art conventional machine-learning and deep-learning models, including CNN, achieving an impressive F1-score of 93.0% on the multilingual dataset. The incorporation of character and word embeddings proves pivotal in mitigating the impact of noisy datasets, prevalent in social media posts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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Severity of natural calamities and crop yield prediction using hybrid deep learning model in Uttar Pradesh

World Water Policy

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World Water Policy 2024年第1期10卷 244-279页

作者： Kumar, Rajneesh Mahapatra, Rajendra Prasad Department of Computer Science and Engineering SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

Crop yield prediction has gained major potential for global food production. Predicting crop yields based on specific parameters like soil, environment, crop, and water has been an interesting research topic in recent decades. To accurately predict crop yields, measuring the severities of natural calamities including water level is mainly required. However, the existing studies failed to predict crop yields accurately because of various issues like overfitting problems, difficulty in training, inability to handle large data, and reduced learning capability. Thus, the proposed study develops an efficient mechanism for accurately predicting crop yields by analyzing several natural calamities. Here, the input samples are initially pre-processed to remove unwanted noises using data normalization and standardization. To enhance the performance of crop yield prediction, natural calamities are computed by using an Extreme Gradient Boosting (XGBoost) model based on parameters like the Palmer Drought Severity Index (PDSI), Severe Hail Index (SHI), and Storm Severity Index (SSI). Also, the hyperparameters of XGBoost model are tuned by utilizing Sheep Flock Optimization Algorithm (SFOA). Finally, the crop yield is predicted by proposing a new one-dimensional convolutional gated recurrent unit neural network (1D-CGRU). The proposed classifier predicts the crop yields with reduced error rates like mean square error (MSE) of 0.4363, root mean square error (RMSE) of 0.1904, normalized root mean squared error (NRMSE) of 0.00101, mean absolute error (MAE) of 0.2437, and R-squared (R²) of.2756. Also, the significant findings of the proposed study positively indicate that this study can be applicable to real-time agricultural practices and is highly suitable for water quality predictions. Also, it can assist farmers and farming businesses in predicting the yield of crops in a specific season when to harvest and crop a plant for attaining improved crop yields. © 2024 Policy

关键词： crop yield prediction deep learning network extreme gradient boosting Palmer Drought Severity Index Severe Hail Index Storm Severity Index

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Parkinson’s disease diagnosis using deep learning model by analyzing the channels of electroencephalography signals from substansia niagra and ventral tegmental area regions of human brain

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International Journal of Information technology (Singapore) 2025年第4期17卷 2129-2152页

作者： Kumar, Kaushal Ghosh, Rajib Department of Computer Science and Engineering National Institute of Technology Patna Patna 800005 India

Parkinson’s disease (PD) is a progressive neurological disorder primarily affecting motor functions and mobility. Early diagnosis is crucial for reducing disease progression and treatment costs. This paper proposes a bi-directional long short-term memory (BLSTM)-based system for PD diagnosis using electroencephalography (EEG) signals collected exclusively from the substantia nigra (SN) and ventral tegmental area (VTA), key brain regions associated with dopamine production. Mel-frequency cepstral coefficients (MFCC) features are extracted from the EEG data, and the BLSTM classifier is employed to differentiate PD subjects from healthy individuals. The performance of the proposed system is evaluated on three public datasets, namely, the University of Iowa (UI) dataset, Parkinson’s Disease Gait (PDG) dataset, and the UC San Diego Resting State (USDRS) dataset, demonstrating superior classification accuracy compared to existing methods. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Electroencephalography signal Mel-frequency cepstral coefficients features Parkinson’s disease Recurrent neural network Substansia niagra and ventral tegmental area

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Causal Inference for Social and engineering Systems

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Performance Evaluation Review 2023年第3期50卷 7-11页

作者： Agarwal, Anish Massachusetts Institute of Technology Electrical Engineering and Computer Science MA United States

What will happen to Y if we do A? A variety of meaningful social and engineering questions can be formulated this way: What will happen to a patient's health if they are given a new therapy? What will happen to a country's economy if policy-makers legislate a new tax? What will happen to a data center's latency if a new congestion control protocol is used? We explore how to answer such counterfactual questions using observational data-which is increasingly available due to digitization and pervasive sensors-and/or very limited experimental data. The two key challenges are: (i) counterfactual prediction in the presence of latent confounders;(ii) estimation with modern datasets which are high-dimensional, noisy, and sparse. The key framework we introduce is connecting causal inference with tensor completion. In particular, we represent the various potential outcomes (i.e., counterfactuals) of interest through an order-3 tensor. The key theoretical results presented are: (i) Formal identification results establishing under what missingness patterns, latent confounding, and structure on the tensor is recovery of unobserved potential outcomes possible. (ii) Introducing novel estimators to recover these unobserved potential outcomes and proving they are finite-sample consistent and asymptotically normal. Finally, we discuss connections between matrix/tensor completion and time series analysis and reinforcement learning;we believe this could serve as a basis to do counterfactual forecasting, and building data-driven simulators for reinforcement learning. © 2023 Copyright is held by the owner/author(s).

关键词： Reinforcement learning

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