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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Nadeem, Muhammad Rashid, Junaid Moon, Hyeonjoon Dosset, Arailym Sejong Uviversity Department of Computer Science and Engineering Seoul Korea Republic of Sejong University Department of Data Science Seoul Korea Republic of

ISBN: (纸本)9798350326413

Mental disorders are a prevalent issue among teenagers. The widespread use of smartphones and social media has revolutionized the way individuals communicate and exchange information with millions of people using these technologies every day. As a result, vast amounts of data are generated, which can be harnessed to improve mental health detection. The increasing prevalence of mental health issues and the demand for quality healthcare services have led to research exploring the potential of machine learning (ML) to address these challenges. This paper provides a systematic study of seven ML approaches used in previous studies to detect mental disorders. The study examines the datasets employed, the accuracy achieved, and the limitations of each ML approach. The seven ML approaches studied in this paper are Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO), Long Short-Term Memory (LSTM), Random Forest (RF), Logistic Regression (LR), Artificial Neural Networks (ANN), and eXtreme Gradient Boosting (XGBoost). These approaches have been utilized in various studies to detect mental disorders and this paper aims to provide a comprehensive understanding of their effectiveness. The findings indicate that machine learning approaches have demonstrated significant potential for the detection of mental disorders, with promising implications for enhancing healthcare services. Additionally, the paper discusses the open research challenges and future directions for mental health. © 2023 IEEE.

关键词： Support vector machines

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SAMPLING MULTIMODAL DISTRIBUTIONS WITH THE VANILLA SCORE: BENEFITS OF data-BASED INITIALIZATION 12

SAMPLING MULTIMODAL DISTRIBUTIONS WITH THE VANILLA SCORE: BE...

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12th International Conference on Learning Representations, ICLR 2024

作者： Koehler, Frederic Vuong, Thuy-Duong Department of Statistics Data Science Institute University of Chicago ChicagoIL60637 United States Department of Computer Science Stanford University StanfordCA94305 United States

There is a long history, as well as a recent explosion of interest, in statistical and generative modeling approaches based on score functions - derivatives of the log-likelihood of a distribution. In seminal works, Hyvärinen proposed vanilla score matching as a way to learn distributions from data by computing an estimate of the score function of the underlying ground truth, and established connections between this method and established techniques like Contrastive Divergence and Pseudolikelihood estimation. It is by now well-known that vanilla score matching has significant difficulties learning multimodal distributions. Although there are various ways to overcome this difficulty, the following question has remained unanswered - is there a natural way to sample multimodal distributions using just the vanilla score? Inspired by a long line of related experimental works, we prove that the Langevin diffusion with early stopping, initialized at the empirical distribution, and run on a score function estimated from data successfully generates natural multimodal distributions (mixtures of log-concave distributions). © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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Building a portable parallel asynchronous PDE solver using Kokkos 31

Building a portable parallel asynchronous PDE solver using K...

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31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Bhat, Ranjan Aditya, Konduri Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (纸本)9798331509118

In this study, we outline the design and implementation of a portable massively parallel asynchronous solver for time-dependent partial differential equations (PDEs). The solver is implemented using Kokkos library for portability across different node architectures and is coupled with the communication-avoiding algorithm that relaxes the communication across nodes, thus improving the scalability. Asynchrony-tolerant finite difference schemes for spatial derivatives along with the low storage Runge-Kutta scheme for time integration are used to achieve high-order accuracy. The efficacy of the implementation is demonstrated using a mini-app that solves the three-dimensional diffusion problem. © 2024 IEEE

关键词： Scalability

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A survey on graph neural network-based next POI recommendation for smart cities

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Journal of Reliable Intelligent Environments 2024年第3期10卷 299-318页

作者： Yu, Jian Guo, Lucas Zhang, Jiayu Wang, Guiling Department of Computer Science and Soft Engineering Auckland University of Technology Auckland New Zealand School of Information Science and Technology Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China

Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there’s an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially pivotal in smart cities, these systems aim to enhance user experiences by offering location recommendations tailored to past check-ins and visited POIs. Distinguishing itself from traditional POI recommendations, the next POI approach emphasizes predicting the immediate subsequent location, factoring in both geographical attributes and temporal patterns. This approach, while promising, faces with challenges like capturing evolving user preferences and navigating data biases. The introduction of Graph Neural Networks (GNNs) brings forth a transformative solution, particularly in their ability to capture high-order dependencies between POIs, understanding deeper relationships and patterns beyond immediate connections. This survey presents a comprehensive exploration of GNN-based next POI recommendation approaches, delving into their unique characteristics, inherent challenges, and potential avenues for future research. © The Author(s) 2024.

关键词： Location based services

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An Empirical Investigation of Docker Sockets for Privilege Escalation and Defensive Strategies 5

An Empirical Investigation of Docker Sockets for Privilege E...

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5th International Conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Rajyashree, R. Mathi, Senthilkumar Saravanan, G. Sakthivel, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India

Cloud-based infrastructures often leverage virtualization, but its implementation can be expensive. Traditional coding methods can lead to issues when transitioning code from one computing environment to another. In response, the container paradigm emerged to offer cost-effective and agile delivery. Containers differ from full machine virtualization by compactly encapsulating the entire software and its dependencies. Leveraging containers, developers can create more secure and efficient applications. Docker, a prominent containerization platform, facilitates the execution of docker images. The Docker Hub serves as a popular repository for various images. Given the importance of application security, especially in the face of threats like malware, ransomware, and data breaches, ensuring robust security is imperative. The paper investigates vulnerabilities within Docker containers and proposes defensive strategies to mitigate potential breaches. In addition, it investigates attacks involving Docker sockets and suggests preventive measures for non-root users. © 2024 The Authors. Published by Elsevier B.V.

关键词： Containers

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FESR - Framework to evaluate Software Reliability based on software metrics 24

FESR - Framework to evaluate Software Reliability based on s...

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8th International Conference on High Performance Compilation, Computing and Communications, HP3C 2024

作者： Fahad, Hurriya Ahmed, Irfan Mahmood, Jabar Saman Batool, Khadija Ali, Qasim Department of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310007 China

ISBN: (纸本)9798400716904

Evaluating reliability of software is hot concern for decision makers and software engineers seeing as if we assess, it cannot be mastered. It is common that reliability of system stratum could be employed for evaluating growth of testing of system with help of comparison of failure of currently implemented software with required system. It is also generic that reliability of system echelon assures improved utilization by the user consequently enhanced contentment of customers. In this paper, our goal is to put forward a meticulously analysis of reliability measurement process. As the existing approaches still lack software reliability, quality and its growth, successful generation of test cases, scalability with large software systems, cost and customer satisfaction. we have projected a new framework of software reliability measurement process based on software metrics. Our model is evaluated by comparing it with an existing model and a published case study. The control experiment is used to evaluate FESR Framework. © 2024 ACM.

关键词： Software quality

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Deformable Dual Graph Aggregation Transformer Convolutional Networks with Spider Wasp Optimizer for Ovarian Tumor Classification Using Magnetic Resonance Imaging

Biomedical Materials and Devices

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Biomedical Materials and Devices 2025年 1-19页

作者： Shanmugaveni, V. Jotheeswari, M. Abarnaswara, R. VijayaKumar, M. Manojanani, A. Department of Computer Science and Engineering (Cyber Security) Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Artificial Intelligence & Data Science Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering RVS Technical Campus Tamil Nadu Coimbatore India

The diagnosis of Ovarian Tumor (OT) remains a significant challenge as there is presently no practical non-invasive technique to determine true benign or malignant lesions before treatment. This study proposes a unique Deformable Dual Graph Aggregation Transformer Convolutional Network (DeDGATCNet) with Spider Wasp Optimizer (SWO) for the classification of ovarian tumors acquired from magnetic resonance imaging (MRI). In contrast to traditional deep learning models, DeDGATCNet + SWO directly integrates bending graph-based forming transformer convolutional learning and improves representation for better classification. The SWO is combined to optimize the parameters of the network, thereby improving its performance. The procedures include pre-processing the MRI data from the study of ovarian tumor TCGA-OV datasets with an Adaptive Bilateral Texture Filter (ABTF) for removing artifacts and normalization of intensity, accompanied by a segment using a Masked-Attention Mask Transformer (MAMT). Feature extraction is achieved through a One-Dimensional Quaternion Discrete Fourier Transform (1D-QDFT), which captures features from both the spatial and frequency domains. DeDGATCNet + SWO performance is way ahead and sets a new accuracy record of 99.9% for state-of-the-art models. This research introduces a new type of hybridization between deformable graph transformers, quaternion-based spectral analysis, and nature-inspired optimization techniques, setting non-invasive OT at a new height for classification and improving early diagnosis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive bilateral texture filter Deformable dual graph aggregation transformer convolutional networks Masked-attention mask transformer One-dimensional quaternion discrete fourier transform

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Evaluating Explanation Methods for Vision-and-Language Navigation 26

Evaluating Explanation Methods for Vision-and-Language Navig...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Chen, Guanqi Yang, Lei Chen, Guanhua Pan, Jia Department of Computer Science The University of Hong Kong Hong Kong Hong Kong Department of Statistics and Data Science Southern University of Science and Technology China

ISBN: (纸本)9781643684369

The ability to navigate robots with natural language instructions in an unknown environment is a crucial step for achieving embodied artificial intelligence (AI). With the improving performance of deep neural models proposed in the field of vision-and-language navigation (VLN), it is equally interesting to know what information the models utilize for their decision-making in the navigation tasks. To understand the inner workings of deep neural models, various explanation methods have been developed for promoting explainable AI (XAI). But they are mostly applied to deep neural models for image or text classification tasks and little work has been done in explaining deep neural models for VLN tasks. In this paper, we address these problems by building quantitative benchmarks to evaluate explanation methods for VLN models in terms of faithfulness. We propose a new erasure-based evaluation pipeline to measure the step-wise textual explanation in the sequential decision-making setting. We evaluate several explanation methods for two representative VLN models on two popular VLN datasets and reveal valuable findings through our experiments. © 2023 The Authors.

关键词： Navigation

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Probabilistic time series forecasting with deep non-linear state space models

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 3-13页

作者： Heming Du Shouguo Du Wen Li School of Computer Science College of Engineering and Computer ScienceAustralian National UniversityCanberraAustralian Capital TerritoryAustralia Shanghai Municipal Big Data Center ShanghaiChina Department of Economic Statistics School of Statistics and InformationShanghai University of International Business and EconomicsShanghaiChina

Probabilistic time series forecasting aims at estimating future probabilistic distributions based on given time series *** is a widespread challenge in various tasks,such as risk management and decision *** investigate temporal patterns in time series data and predict subsequent probabilities,the state space model(SSM)provides a general *** of SSM achieve considerable success in many fields,such as engineering and ***,since underlying processes in real-world scenarios are usually unknown and complicated,actual time series observations are always irregular and ***,it is very difficult to determinate an SSM for classical statistical *** this paper,a general time series forecasting framework,called Deep Nonlinear State Space Model(DNLSSM),is proposed to predict the probabilistic distribution based on estimated underlying unknown processes from historical time series *** fuse deep neural networks and statistical methods to iteratively estimate states and network parameters and thus exploit intricate temporal patterns of time series *** particular,the unscented Kalman filter(UKF)is adopted to calculate marginal likelihoods and update distributions recursively for non-linear *** that,a non-linear Joseph form covariance update is developed to ensure that calculated covariance matrices in UKF updates are symmetric and positive ***,the authors enhance the tolerance of UKF to round-off errors and manage to combine UKF and deep neural *** this manner,the DNLSSM effectively models non-linear correlations between observed time series data and underlying dynamic *** in both synthetic and real-world datasets demonstrate that the DNLSSM consistently improves the accuracy of probability forecasts compared to the baseline methods.

关键词： Artificial Intelligence machine learning time series

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Enhance Efficient of Support Vector Machine with CNN and PSO for solving Handwritten Digits Recognition 28

Enhance Efficient of Support Vector Machine with CNN and PSO...

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28th International computer science and Engineering Conference, ICSEC 2024

作者： Janprasit, Siwach Punkong, Narong Ratanavilisagul, Chiabwoot Kosolsombat, Somkiat Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand Digital Technology for Business Faculty of Management Science Kanchanaburi Thailand Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand Thammasat University Data Science and Innovation College of Interdisciplinary Studies Thailand

ISBN: (纸本)9798350366860

handwritten digit recognition is a crucial task in various fields such as postal mail sorting, bank check processing, and digitizing handwritten documents. This research aims to compare the effectiveness of using Convolutional Neural Network (CNN) for feature extraction combined with Support Vector Machine (SVM) for classification, against the traditional method of using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for feature extraction. Additionally, Particle Swarm Optimization (PSO) is applied to optimize SVM parameters to further enhance model performance. The experiments are conducted on two datasets: The MNIST dataset and an individual handwriting dataset from 50 participants. The results indicate that the proposed method of using CNN for feature extraction and PSO-optimized SVM achieves superior accuracy compared to traditional DWT and DCT methods. Specifically, the proposed method achieved an accuracy of 99.05% on the MNIST dataset and 67.79% on the individual handwriting dataset, demonstrating its robustness and effectiveness in handwritten digit recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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