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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Hepzibah, Grace Babu, Nirmal Varghese Division of Computer Science Engineering Karunya Institute of Technology and Science Karunya Nagar Tamil Nadu Coimbatore641114 India Division of Data Science and Cyber Security Karunya Institute of Technology and Science Karunya Nagar Tamil Nadu Coimbatore641114 India

ISBN: (纸本)9798331300579

As we know that individuals around the globe work difficult to keep up with this hustling world. In any case, due to this each person is managing with diverse wellbeing issues, one of the foremost known issue is sadness or stretch which may in the long run lead to passing or other brutal exercises. This project focuses on leveraging machine learning algorithms to predict psychological instability based on various attributes related to mental health. Developed as a web application using Django, users can upload datasets containing information such as age, gender, family history, and workplace environment. The data undergoes preprocessing, including handling missing values and label encoding. Subsequently, several classifiers including Decision Tree, Random Forest, SVC, MLP Classifier, KNeighbors Classifier, and Linear Discriminant Analysis are trained on the preprocessed data. Evaluation metrics such as accuracy, precision, recall, and F1-score are computed for each classifier. Users can also input personal data to obtain predictions about their psychological well-being. The system aims to offer insights into mental health conditions, enabling early detection and intervention. This project underscores the potential of machine learning in addressing mental health challenges through data-driven approaches. © Grenze Scientific Society, 2024.

关键词： Decision trees

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The Double Contrast for Unsupervised Person Re-Identification 24

The Double Contrast for Unsupervised Person Re-Identificatio...

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2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Liu, Daocheng Fu, Yanyun Shi, Wenxi Zhu, Zhansheng Wang, Deyong School of Computer Science and Technology Urumqi China Beijing Academy of Science and Technology Beijing China Key Laboratory of Big Data of Xinjiang Social Security Risk Urumqi China

ISBN: (纸本)9798400718199

At present, the most popular unsupervised person re-identification(Re-ID) research mainly uses some clustering methods to gather samples with similar features and generate a pseudo-label for each cluster, and uses the parameterless contrast loss to feedback correlation information between *** previous methods used the memory dictionary to store all instance-dependent features, but This approach does not take into account the different number of positive samples in each cluster, and the progress of each cluster update of the memory dictionary is not the *** order to improve the consistency of the update progress, a method is implemented to store the central feature vector in each cluster, but using a hard-sample feature vector in the cluster to update it, so that a single instance feature cannot represent the central feature well. In this article, we study out a method to combine hard instance contrast and cluster contrast, that is, the central feature vector in each cluster is stored in the memory dictionary, and the hard instance feature vector in each cluster is also stored *** addition, in terms of momentum update, the central feature vector and hard instance feature vector of each cluster in mini-batch are used for *** not only solves the problem of inconsistent update progress, but also better learns the similarity and difference of instances in the same cluster, and better aggregates instances within cluster and instances between clusters. Through three dataset experiments, it is concluded that this method has a good improvement. © 2024 ACM.

关键词： Vectors

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The differential meet-in-the-middle attack on FUTURE and CRAFT 13

The differential meet-in-the-middle attack on FUTURE and CRA...

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13th International Conference on Communication and Network Security, ICCNS 2023

作者： Lin, Han Zou, Jian Li, Jiayin College of Computer and Data Science Fuzhou University China College of Computer and Cyber Security Fujian Normal University China

ISBN: (纸本)9798400707964

The differential meet-in-the-middle (MITM) attack is a new cryptanalysis technique proposed at Crypto 2023. It has achieved good results in attacking some symmetric encryption algorithms. In this paper, we enhance the differential-meet-in-the-middle attack by utilizing MILP to construct the best differential trail for a distinguisher in the offline phase and optimizing the attack process during the online phase. This optimization leads to a reduction in the time complexity of the attack. We apply the differential meet-inthe- middle attack to 8-round FUTURE with 264data, 248 memory, 2124time complexity. Besides, our attack to the on 14-round CRAFT with 264 data, 274 memory, 2104 time complexity. As far as we know, it is the first key recovery attack to FUTURE. © 2023 ACM.

关键词： Integer programming

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Multimodal Machine Learning for Early Detection of Autism in Children: Integrating Gestures and Mood Analysis 2

Multimodal Machine Learning for Early Detection of Autism in...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Puri, Chetan Ghodeswar, Ujwala Reddy, K.T.V. Sharma, Mansi Warudkar, Sahil Department of Computer Science and Design Wardha India Yeshwantrao Chavan College of Engineering Department of Electronics Engineering Nagpur India Department of Artificial Intelligence and Data Science Wardha India

ISBN: (纸本)9798331528713

Autism spectrum disorder (ASD) is characterized by neurological disorders and challenges with interpersonal communication, communication, and schedule behaviour. Early distinguishing proof of ASD is vital to optimize the intervention and progress long-term results. This paper analyses the potential of numerous machine learning procedures for early location of ASD in children. We propose a novel approach that combines gesture assessment with cognitive evaluation to capture a comprehensive picture of a child's behaviour. Side effect analysis will focus on highlights such as body language, hand movements, and eye contact, which can give profitable bits of knowledge into the social and communication designs of children with ASD. Therapy will utilize facial expressions, tone of voice, and body language to survey a child's emotional state. The combination of these strategies empowers the machine learning show to recognize complex structures that will be characteristic of ASD. This consider has the potential to move forward the exactness and productivity of ASD diagnosis in children. The proposed strategy can be connected in different situations, such as during routine testing or through a portable application. Early and precise conclusion can clear the way for convenient intervention and back for children with ASD, eventually driving to positive long-term results. © 2024 IEEE.

关键词： Diseases

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Secure and Lightweight Signcryption Scheme With Group Equality Test for Heterogeneous WBAN

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IEEE Internet of Things Journal 2025年第13期12卷 25314-25332页

作者： Zhang, Qijia Zhang, Jianhong Yang, Nan Tian, Youliang Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang050025 China North China University of Technology College of Information Sciences and Technology Beiing100144 China

Wireless body area network (WBAN) integrates body sensors to collect and upload various health indicators, which facilitates timely remote healthcare. To realize the real-time monitoring of the patient’s health status while ensuring the security of sensitive data, signcryption schemes with equality test provide a feasible solution. Nevertheless, most of the existing schemes suffer from heavy computational burdens and are vulnerable to some inherent threats, such as offline message recovery attack and known session temporary key attack. Additionally, some of them cannot provide the essential security properties. To cope with these problems, this paper constructs a secure and lightweight signcryption scheme with group equality test, which is suitable for the heterogeneous environment. To formally define the resistance to offline message recovery attack and known session temporary key attack, we first propose two corresponding security models. Then, we introduce a group mechanism to the equality test to support the aforementioned security properties. Besides, we adopt the online/offline construction to avert the time-consuming operations in the real-time data processing phase. Furthermore, our scheme supports message aggregation to reduce the computational overhead of handling multiple messages. Formal security proof shows that our scheme satisfies confidentiality and unforgeability. Compared to several recent schemes, the experimental results demonstrate that our scheme enjoys the best performance in terms of both computational overhead and communication cost. With implementation, our scheme is proved to be feasible for lightweight devices in real-world applications. © 2014 IEEE.

关键词： Electronic health record

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Few-shot Link Prediction on Hyper-relational Facts 30

Few-shot Link Prediction on Hyper-relational Facts

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wei, Jiyao Guan, Saiping Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Hyper-relational facts, which consist of a primary triple (head entity, relation, tail entity) and auxiliary attribute-value pairs, are widely present in real-world Knowledge Graphs (KGs). Link Prediction on Hyper-relational Facts (LPHFs) is to predict a missing element in a hyper-relational fact, which helps populate and enrich KGs. However, existing LPHFs studies usually require an amount of high-quality data. They overlook few-shot relations, which have limited instances, yet are common in real-world scenarios. Thus, we introduce a new task, Few-Shot Link Prediction on Hyper-relational Facts (FSLPHFs). It aims to predict a missing entity in a hyper-relational fact with limited support instances. To tackle FSLPHFs, we propose MetaRH, a model that learns Meta Relational information in Hyper-relational facts. MetaRH comprises three modules: relation learning, support-specific adjustment, and query inference. By capturing meta relational information from limited support instances, MetaRH can accurately predict the missing entity in a query. As there is no existing dataset available for this new task, we construct three datasets to validate the effectiveness of MetaRH. Experimental results on these datasets demonstrate that MetaRH significantly outperforms existing representative models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge graph

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JUMBF Type Checker: A Versatile Tool for Identifying JPEG Systems Parts in JPEG Images

JUMBF Type Checker: A Versatile Tool for Identifying JPEG Sy...

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2024 International Conference on Engineering and Computing, ICECT 2024

作者： Jamil, Sonain Ullah, Faiz Kwon, Oh-Jin Yaseen Lee, Jinhee Afnan Abbas, Muhammad Sohail Department of Computer Science Gjovik2815 Norway Sejong University Department of Electronics Engineering Seoul05006 Korea Republic of Department of Data Science and Artificial Engineering Islamabad Pakistan

ISBN: (纸本)9798350349719

JPEG pictures may now include complicated meta-data using the JPEG Universal Metadata Box Format (JUMBF), which requires tools to recognize different JUMBF kinds inside the image. We offer the JUMBF Type Checker, a software tool designed to meet this demand. The system distinguishes between JPEG pictures with various JUMBF information types, including JPEG Privacy and Security, JPEG 360, and JPEG Snack. The program works with MATLAB R2023a and later, provides an effective way for users to test and evaluate the type of JUMBF included in their JPEG files. Future improvements will go beyond MATLAB to include web browsers and improve dataset validation capabilities. This application is essential for researchers and practitioners in digital image processing because it provides a simple way to organize and analyze JPEG pictures with integrated JUMBF. (https://***/sonainjameel/JUMBF-Type-Checker) © 2024 IEEE.

关键词： Metadata

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News-Driven Price Movement Forecasting with Label-Prior Graph Attention 24

News-Driven Price Movement Forecasting with Label-Prior Grap...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Liou, Yi-Ting Chen, Chung-Chi Huang, Hen-Hsen Chen, Hsin-Hsi Graduate Program of Data Science National Taiwan University Academia Sinica Taiwan AIST Japan Institute of Information Science Academia Sinica Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

ISBN: (纸本)9798400701726

This paper introduces a novel approach to stock movement prediction using multi-label classification, leveraging the interconnections between news articles and related company stocks. We present the Label-Prior Graph Attention (LPGA) model, which significantly enhances the performance of news-driven stock price movement forecasting. This model is comprised of a unique graph attention architecture, incorporating a label encoder and a text encoder, designed to effectively capture and utilize the relationships between labels in a graph-based context. Our model demonstrates superior performance over several benchmark models. The LPGA model’s efficacy is further validated through experiments on two multilabel datasets. The model outperforms established baseline models across various evaluation metrics. The success of the LPGA model in both stock movement prediction and general multi-label classification tasks indicates its potential as a versatile tool in the realm of machine learning and financial analysis. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graphic methods

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S5ELBP: Supervised SAR Image Classification using SVM from SSELBP Texture Features

S5ELBP: Supervised SAR Image Classification using SVM from S...

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Bandi, Sudheer Reddy Anbarasan, M. Merlin Linda, G. Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Swarna Bharathi Institute of Science and Technology Department of Computer Science and Engineering Khammam India

ISBN: (纸本)9798350348910

Synthetic Aperture Radar (SAR) and Optical sensors are very popular depending on their strengths and application needs. The ocean surface will be covered with clouds most of the time and optical sensors cannot capture the information as they cannot operate in dust and cloudy conditions. So, SAR sensors come into existence to capture the atmospheric phenomena in those regions. There is a wide range of texture extraction techniques such as traditional-based wavelet analysis, statistical analysis, local pixel-based approaches, and dimensional reduction approaches. However, these approaches have limitations in boundary extraction, high-dimensional feature extraction, and uniformity representation. In recent times, deep learning (DL) approaches to extract features which varies based on the applications, image interpretation, and so on. At the same time, DL approaches are limited in transparency, computation requirements, and hyperparameter tuning. In contrast, the proposed S5ELBP i.e., Supervised SAR image classification using the integration of Support Vector Machine (SVM) and Scale Selective Extended Local Binary Pattern (SSELBP) extraction techniques shall overcome various challenges of existing traditional and DL approaches such as adaptive thresholding, less computational resources, and noise robustness. Hence, the objective of the proposed methodology is to extract the image features from the Scale Selective Extended Local Binary Pattern (SSELBP). Also, the SSELBP features are integrated with SVM to show the strength of the proposed approach in SAR image classification. Additionally, the performance of the present work is enhanced by comparing the extracted results with various classifiers. Finally, the work is concluded by recommending the scope of future work. © 2023 IEEE.

关键词： Support vector machines

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A Hybrid Model Using Coordinate Attention Mechanisms for Stock Price Prediction 24

A Hybrid Model Using Coordinate Attention Mechanisms for Sto...

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2024 International Conference on Digital Economy, Blockchain and Artificial Intelligence, DEBAI 2024

作者： Sun, Ying Zhang, Bowen Peng, Cheng The Institute for Sustainable Development Macau University of Science and Technology China College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Computer Science University of Electronic Science and Technology of China Zhongshan China

ISBN: (纸本)9798400710261

Predicting stock prices is a complex and challenging task in financial markets due to the intricate and non-linear nature of stock price movements. This study explores the application of Coordinate Attention mechanisms within advanced Temporal Convolutional Network (TCN) frameworks for stock price prediction based solely on historical stock price data. We present a hybrid model that combines the advantages of modern TCNs with Coordinate Attention mechanisms to improve the precision and robustness of stock prediction models. Extensive experiments are conducted on the well-known TSLA stock dataset. The results show varying levels of alignment with actual stock prices, highlighting the strengths and weaknesses of each model. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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