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

作者： Zhang, Panyu Jung, Gyuwon Ahmed, Uzair Lee, Uichin Graduate School of Data Science KAIST Daejeon South Korea School of Computing KAIST Daejeon South Korea

ISBN: (纸本)9798400713958

Nowadays, it’s possible to deliver interventions through mobile technologies to improve users’ mental and physical health. Causal analysis may help researchers identify the potential causes of the health issues and design proper interventions. However, in previous studies, causal analysis is mainly conducted between single sensor data features such as walking activity duration and perceived stress. There is a lack of research into causal analysis between more complicated behavior features which could be derived from multiple sensor features and target well-being labels. To address this gap, we propose CausalCFF, a framework that investigates causal relationships between contextually filtered behavioral features (e.g., walking duration at workplace locations) and well-being outcomes (e.g., stress). Our analysis identifies frequent workplace visits during periods of reduced home time as the most salient cause for elevated stress levels, highlighting the framework’s ability to target context-specific behavioral biomarkers for human well-being. The code is also made available¹ © 2025 Copyright held by the owner/author(s).

关键词： Causal Analysis Contextually Filtered Features Human Behavior Mobile Sensor data Stress

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Short Time Series Forecasting Method Based on Genetic Programming and Kalman Filter 1st

Short Time Series Forecasting Method Based on Genetic Progr...

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1st International Conference on Complex Computational Ecosystems, CCE 2023

作者： Kone, Lalla Aicha Leonteva, Anna Ouskova Diallo, Mamadou Tourad Haouba, Ahmedou Collet, Pierre ICUBE Laboratory Strasbourg University Strasbourg France Reasearch Unit of Scientific Computing Computer Science and Data Science University of Nouakchott Nouakchott Mauritania

ISBN: (纸本)9783031443541

Accurate forecasting of the baccalaureate admission statistics is a crucial step towards an improvement of the educational system in Mauritania and its responsiveness to the economical needs. Since an available historical information is collected only over last ten years, an accurate forecasting technique for short time series is required. Addressing this issue, the presented paper proposes a tool based on the genetic programming and Kalman filter. This tool allows to make accurate short term prediction for short time series and easily set up experiments. A tool validation on different data sets is presented, where the provided tool provides more robust forecasting results comparatively with the state-of-the-art techniques. It is expected that this tool can help to make a qualitative jump in the improvement of Mauritanian education system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Kalman filters

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Harnessing Deep Neural Networks for Micro-Blog Sentiment Analysis: A Novel Approach

Harnessing Deep Neural Networks for Micro-Blog Sentiment Ana...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Syed Ismail, A. Nikkath Bushra, S. Department of Data Science and Business System India Srm Institute of Science and Technology Department of Computing Technologies SRM Nagar Kattankulathur603203 India

ISBN: (纸本)9798350313987

Analyzing sentiment in micro-blogs, such as tweets or short social media posts, is a challenging yet important task in natural language processing (NLP). Deep learning techniques have shown great promise in this domain. Here's a novel approach for sentimental analysis using a deep neural network for micro-blogs. With the exponential growth of user-generated content on online platforms, YouTube has emerged as a dominant space for both creators and viewers to interact. One crucial aspect of this interaction is the comment section, where viewers express their thoughts and sentiments. This research presents a robust sentiment analysis model tailored specifically for YouTube video comments, aiming to extract valuable insights from this rich source of user-generated content. Ultimately, our novel deep neural network approach for micro-blog sentiment analysis demonstrates promising results, offering valuable insights into public sentiment across various social media platforms Using a deep neural network, this study presents a sentiment analysis model for YouTube video comments. This research contributes to the advancement of sentiment analysis methodologies, addressing the unique challenges posed by the concise and informal nature of micro-blogs. Our method, a mathematical model, and individuals were utilized to categorize the comments on various blogs. It was assumed that the classification of humans was entirely accurate. The results showed that, with classification accuracy ranging from 90% to 97%, our model is more precise than the numerical model. © 2023 IEEE.

关键词： Convolution neural network Deep learning model Micro-blogs NLP Sentimental analysis

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Colour Correction and Detail Enhancement in Underwater Images using Hybrid Real-ESRGAN 3

Colour Correction and Detail Enhancement in Underwater Image...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Chandar, Vijay Nair, Vishnu G Rajeswari, D. SRM Institute of Science and Technology Kattankulathur School of Computing College of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350343632

The motive of underwater image enhancement is to improve and enhance the quality of photographs taken underwater. Various traditonal image processing techniques and deep learning models have been developed to work on this task. But most often the deep learning models and conventional methods of image processing work in silos, leading to subpar results. This research proposes a strategy that utilizes a combination of both approaches to solve this issue. Initially, traditional image processing techniques like colour correction and image stretching are used to enhance the colour of the images. The enhanced images are then fed into a custom Real-ESRGAN model that has been fine-tuned on an underwater image dataset to optimize performance in this context. This hybrid approach has the potential to outperform the models utilizing either traditional or deep learning methods in isolation by leveraging the strengths of both. In addition, batch processing is used in the pipeline to efficiently handle multiple images at the same time. This effectively addresses the scalability problem that arises frequently in image enhancement tasks. With eye-catching visuals, the proposed methodology achieves a Peak Signal-to-Noise Ratio (PSNR) of 23.66 and a Mean Squared Error (MSE) of 0.28, suggesting excellent image restoration and noise reduction. The qualitative assessments show that the improved image graphs have better colour, contrast, and clarity than the originals. © 2023 IEEE.

关键词： Batch Processing Image Enhancement Image Stretching MSI PSNR Real-ESRGAN

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A Modified EACOP Implementation for Real-Parameter Single Objective Optimization Problems 13

A Modified EACOP Implementation for Real-Parameter Single Ob...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Tangherloni, Andrea Coelho, Vasco Buffa, Francesca M. Cazzaniga, Paolo Bocconi Institute for Data Science and Analytics Bocconi University Department of Computing Sciences Milan Italy University of Milano-Bicocca Department of Informatics Systems and Communication Milan Italy Bocconi University Department of Computing Sciences Milan Italy Bocconi Institute for Data Science and Analytics Italy University of Bergamo Department of Human and Social Sciences Bergamo Italy

ISBN: (纸本)9798350308365

Evolutionary algorithms are effective techniques for optimizing non-linear and complex high-dimensional problems. However, most of them require a precise fine-tuning of their functioning settings to achieve satisfactory results. In this work, we propose a modified version of an evolutionary approach called the Evolutionary Algorithm for COmplex-process oPtimization (EACOP), designed to have a limited number of hyper-parameters. The base version of EACOP (bEACOP) combines different strategies, including the scatter search methodology, local searches, and a novel combination method based on path relinking to balance the exploration and exploitation phases. Our improved version (iEACOP) intensifies the exploration phase to escape from suboptimal search space areas where, on the contrary, bEACOP gets stuck. Our results show that iEACOP outperforms bEACOP on 27 out of 29 CEC 2017 test suite benchmark functions, exhibiting comparable performance against the three best algorithms of the CEC 2017 competition on single-objective bound-constrained real-parameter numerical optimization. The source code of bEACOP and iEACOP will be made publicly available on GitHub upon acceptance. © 2024 IEEE.

关键词： Optimization algorithms

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Music Recommendation based on Facial Expressions using data Augmentation 3

Music Recommendation based on Facial Expressions using Data ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kapoor, Tanisha Ganguly, Arnaja Rajeswari, D. SRM Institute of Science and Technology Kattankulathur School of Computing College of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350343632

Emotion recognition from facial expressions plays a pivotal role in human-computer interaction, psychology, marketing, and various other domains. This research paper presents a comprehensive study and implementation of an automated emotion recognition system based on deep learning techniques. The project addresses the challenge of accurately classifying a wide range of emotions from facial images in real-time. Leveraging Convolutional Neural Networks (CNNs) and modern deep learning approaches, the system demonstrates high accuracy in identifying emotions, including happiness, sadness, anger, and more. This project used the image dataset FER-2013 for the mood prediction portion of the project, and Spotify music data to predict emotions for music recommendation. Both the datasets utilized are freely available on Kaggle. The proposed system can achieve an accuracy of 91.23% in predicting the users' emotion based on their facial expressions. © 2023 IEEE.

关键词： CNN Emotion recognition Facial expression analysis FER-2013 Music Recommendation

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Predicting the Impact of Road Conditions on Battery Health Via Transfer Learning 3

Predicting the Impact of Road Conditions on Battery Health V...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Nair, Rahul Anil Suresh, Dev Karan Rajeswari, D. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9781665462167

Battery life of EV (electric vehicles) depends on various factors. These factors include temperature of the battery, duration of use or the type and condition of the road traversed by the vehicle. This may cause the battery to discharge faster than it used to. Understanding what may cause the battery life to shorten can help us alleviate the given problem and can preserve the battery for longer time. This research focuses on the effects road conditions have on battery life to be able to recommend the favourable conditions to extend it. It proposes to find the routes taken by the vehicles and classifying them based on their battery health upon reaching the destination. In this paper, an attempt has been made to predict the category of the routes by employing Transfer learning via a pre-trained model VGG-16. Observations indicate that paths which have more turns and curves are generally worse for battery life as compared to straight and narrow paths. Another model which is an artificial neural network has been developed to predict the based on the average vehicle speed and the distance covered by the vehicle. The VGG-16 model predicts the data with which it is validated with an accuracy of 84.29% and the testing data with an accuracy of 83.56%. The artificial neural network model predicts the category of the data with an accuracy of 88.02%. © 2023 IEEE.

关键词： Deep learning

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HeterMM: applying in-DRAM index to heterogeneous memory-based key-value stores

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Frontiers of Computer science 2024年第4期18卷 215-217页

作者： Yunhong JI Wentao HUANG Xuan ZHOU School of Information Renmin University of ChinaBeijing 100872China School of Computing National University of SingaporeSingapore 117417Singapore School of Data Science and Engineering East China Normal UniversityShanghai 200062China

1 Introduction Emerging byte-addressable storage technologies,such as NVM(Non-Volatile Memory),provide a more cost-effective and larger-capacity alternative to DRAM(Dynamic Random Access Memory)[1],presenting new opportunities to address the high cost,limited capacity,and volatility of in-memory key-value(KV)*** efforts have been dedicated to redesigning conventional structures on ***,they were challenged by the substantial engineering cost and increased complexity to be integrated into existing ***,a general framework to apply existing indexes to KV stores on NVM becomes more attractive[2].

关键词： stores applying value

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Battery Health Prediction Using Deep Hybrid Learning 3

Battery Health Prediction Using Deep Hybrid Learning

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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Nair, Rahul Anil Suresh, Dev Karan Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350322842

The type and condition of the road the vehicle travels;how long the battery is used for are just a few of the variables that affect the battery life of electric cars (EVs). These elements might affect the battery's lifespan by causing it to drain more quickly than usual. By understanding the various factors that impact battery life, work can be done to mitigate these issues and extend the useful life of the battery. To find the best circumstances for increasing the battery's longevity, this research paper is concentrated on examining the impacts of road conditions on battery life. The previous article classified routes with an accuracy of 83.56% based on battery health while arriving at the destination [1]. This article examines EV routed and categorises them on the basis of the number of brakes applied upon arrival at the destination. By doing so, it aims to develop recommendations for drivers on how to optimize their driving habits to extend the battery's lifespan. Observations indicate that bad routes generally involve a lot more brakes as compared to good routes. Vehicle speed has a negative correlation with heater power in a bad route and a positive correlation in a good route. Battery voltage and Battery state- of-charge have opposing correlations in both categories. In this research, a prediction of the route category was attempted by using Deep Hybrid Learning to integrate three pre-trained models., VGGI6, Resnet50 and VGG19 with two boosting algorithms:- XGBoost and AdaBoost. The accuracy of the six deep hybrid learning models in predicting the category of these routes are as follows:-0.87625, 0.84875, 0.80125, 0.7925, 0.8425 and 0.855. © 2023 IEEE.

关键词： Deep learning

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VIVA: A Benchmark for Vision-Grounded Decision-Making with Human Values

VIVA: A Benchmark for Vision-Grounded Decision-Making with H...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Hu, Zhe Ren, Yixiao Li, Jing Yin, Yu Department of Computing The Hong Kong Polytechnic University Hong Kong Research Centre for Data Science & Artificial Intelligence Hong Kong Department of Computer and Data Sciences Case Western Reserve University United States

ISBN: (纸本)9798891761643

Large vision language models (VLMs) have demonstrated significant potential for integration into daily life, making it crucial for them to incorporate human values when making decisions in real-world situations. This paper introduces VIVA, a benchmark for VIsion-grounded decision-making driven by human VAlues. While most large VLMs focus on physical-level skills, our work is the first to examine their multimodal capabilities in leveraging human values to make decisions under a vision-depicted situation. VIVA contains 1,240 images depicting diverse real-world situations and the manually annotated decisions grounded in them. Given an image there, the model should select the most appropriate action to address the situation and provide the relevant human values and reason underlying the decision. Extensive experiments based on VIVA show the limitation of VLMs in using human values to make multimodal decisions. Further analyses indicate the potential benefits of exploiting action consequences and predicted human values. Our code and dataset are available at https://***/Derekkk/VIVA_EMNLP24. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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