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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Boby Siswanto Haryono Soeparno Nesti F. Sianipar Widodo Budiharto Computer Science Department BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Food Technology Department Engineering Faculty Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short (<165 cm), and age=middle (45-60 years) are more likely to be affected by cardiovascular disease. The variable gender=woman with height=short indicates a 76.07% probability of developing cardiovascular disease.

关键词： computer science Correlation Itemsets Cardiovascular diseases data mining Information technology

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Unsupervised Title Generation from Unpaired data with N-Gram Discriminators 7

Unsupervised Title Generation from Unpaired Data with N-Gram...

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7th International Conference on Signal and Image Processing, ICSIP 2022

作者： Hsieh, Tsai-Feng Jwo, Jung-Sing Lee, Cheng-Hsiung Lin, Ching-Sheng Tunghai University Master Program of Digital Innovation Taichung40704 Taiwan Tunghai University Master Program of Digital Innovation Department of Computer Science Taichung40704 Taiwan

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

A title provides readers a succinct representation of the whole document and consequently helps them get the gist without going through the details. Although the research of title generation has been investigated for decades, it still leaves much space to improve due to its requirements of complex tasks such as language understanding and text synthesis. To perform unsupervised learning of titles, we employ a generative adversarial network (GAN) without paired training data. A sequence-to-sequence model with an auto-encoder architecture is adapted to model the generator. The readability gap between human-written and machine-generated text still needs to reduce. As n-gram model has been successfully used as a language model for solving the language tasks, we present n-gram discriminators in the GAN model to increase the readability of machine-generated titles. The proposed model is tested on the English Gigaword dataset and the experimental results demonstrate that our approach obtains encouraging results. © 2022 IEEE.

关键词： Generative adversarial networks

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A Systematic Review of Chatbot Development for Low-Resource Languages

A Systematic Review of Chatbot Development for Low-Resource ...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Abisola Rukayat Oyewole Chukwuemeka Christian Ugwu Adebayo Olusola Adetunmbi Abimbola Helen Afolayan Department of Computer Science Federal University of Technology Akure Nigeria Department of Data Science Federal University of Technology Akure Nigeria Department of Information Systems Federal University of Technology Akure Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

The development of chatbots for low-resource languages presents unique challenges and opportunities in natural language processing. This systematic review examines the current methodologies, technologies, and frameworks employed in developing chatbots for languages with limited computational resources and linguistic data. By analysing studies published in recent times, the study identified key trends in algorithmic approaches, data augmentation techniques, and the integration of local cultural intricacies. The review also highlighted significant achievements and outlined the substantial gaps and challenges that remain. Recommendations were provided for researchers to enhance the development of linguistically inclusive technologies, aiming to bridge the digital divide and foster equitable access to AI-driven solutions.

关键词： Bridges Training Training data Machine learning Oral communication Chatbots Transformers Digital divide Reliability Systematic literature review

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Charging Scheme for Wireless Sensor Network Based on Area Scanning and Energy Detection

Charging Scheme for Wireless Sensor Network Based on Area Sc...

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作者： Ye Xiaojie Zau Kaming Electronic and Computer Engineering department Hong Kong University of Science and Technology computer engineering program Hong Kong University of Science and Technology

With the development of wireless sensor networks,the demand for wireless sensors increased *** the same time,there is a problem that the storage of energy is relatively low due to the small size of wireless sensors,which means that it needs to replace when sensors are out of *** will cost huge finance and pollute the ***,many scholars propose using wireless sensors that can recharge instead of primary wireless *** this case,choosing a suitable route to charge wireless sensors has become a significantly critical problem as there is a relationship between energy loss and the charging *** it is unstable or unsuitable to use solar energy and wind energy to charge,charging wireless sensors using drones,proposed by Kulaea ***1,Han Xu,and Bang Wang [1],has become a feasible ***,this solution still has some drawbacks as it only uses energy to charge the point with the lowest energy level by drones without considering the priority,the rest of the power,and the efficiency of time and *** report will use the data about the utilization rate of energy and make some comparisons to improve this solution.

关键词： Wireless Communication Networks Drones Priority Based On Energy Optimized Path Energy Transfer

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A Deep Learning model for Question Analysis in Low-resource Languages: A dataset and Case Study for Persian

A Deep Learning model for Question Analysis in Low-resource ...

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Pattern Recognition Systems (ICPRS), International Conference on

作者： Fatemeh Ebrahimi Khaksefidi Afsaneh Fatemi Mohammad Ali Nematbakhsh Mahsa Abazari Kia Faculty of Computer Engineering Department University of Isfahan Isfahan Iran Faculty of Computer and Data Science Northeastern University London London United Kingdom

ISBN: (数字)9798350375657

ISBN: (纸本)9798350375664

Question-answering systems, characterized by their fundamental functions of question classification, information retrieval, and answer selection, demand refinement to enhance precision in retrieving exact answers. Question classification, a cornerstone task, anticipates the probable answer to a posed query. However, the performance of question classification algorithms is hampered, particularly in agglutinative languages with complex morphology like Persian, where linguistic resources are limited. In this study, we propose a novel multi-layer Long-short-term memory (LSTM) Attention Convolutional Neural Network (CNN) (LACNN) classifier, tailored to extract pertinent information from Persian language contexts. Notably, this model operates autonomously, obviating the need for prior knowledge or external features. Moreover, we introduce UIMQC, the first medical question dataset in Persian, derived from the English GARD dataset. The inquiries within UIMQC are inherently intricate, often pertaining to rare diseases necessitating specialized diagnosis. Our experimental findings demonstrate a notable enhancement over baseline methods, with a 9% performance increase on the UTQC dataset, and achieving 67.08% accuracy on the UIMQC dataset. Consequently, we advocate for the adoption of the LACNN model in various morphological analysis tasks across low-resource languages, as in Question Answering systems it improves the performance for retrieving accurate answers to the users’ queries.

关键词： Analytical models Accuracy Refining Morphology Manuals data augmentation Question answering (information retrieval)

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MPEG-DASH Multi-View Video and Audio Transmission QoE Over HTTP/2 Stream

MPEG-DASH Multi-View Video and Audio Transmission QoE Over H...

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International Conference on Advanced Technologies for Communications, ATC

作者： Yamato Wakayama Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

In this paper, we consider multi-view video and audio streaming using MPEG-DASH, which enables to transmit video tailored to the network conditions over HTTP communication. This paper uses HTTP/2 instead of HTTP/1.1, which the authors previously used. HTTP/2 manages a series of request-response exchanges called a stream, which is assigned a unique stream ID. We perform a subjective experiment under various network conditions to evaluate application-level QoS and QoE. From the evaluation results, we investigate the effect of the HTTP/2 stream on the video and audio quality of the users in the multi-view video transmission scenario.

关键词： Transport protocols Bit rate Transform coding Quality of service Streaming media HTTP Quality assessment Delays Quality of experience Video recording

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Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models

arXiv

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arXiv 2024年

作者： Wu, Dennis Hu, Jerry Yao-Chieh Hsiao, Teng-Yun Liu, Han Department of Computer Science University of Northwestern Evanston United States Department of Physics National Taiwan University Taipei Taiwan Department of Statistics and Data Science University of Northwestern Evanston United States

We propose a two-stage memory retrieval dynamics for modern Hopfield models, termed U-Hop, with enhanced memory capacity. Our key contribution is a learnable feature map Φ which transforms the Hopfield energy function into kernel space. This transformation ensures convergence between the local minima of energy and the fixed points of retrieval dynamics within the kernel space. Consequently, the kernel norm induced by Φ serves as a novel similarity measure. It utilizes the stored memory patterns as learning data to enhance memory capacity across all modern Hopfield models. Specifically, we accomplish this by constructing a separation loss LΦ that separates the local minima of kernelized energy by separating stored memory patterns in kernel space. Methodologically, U-Hop memory retrieval process consists of: (Stage I) minimizing separation loss for a more uniformed memory (local minimum) distribution, followed by (Stage II) standard Hopfield energy minimization for memory retrieval. This results in a significant reduction of possible metastable states in the Hopfield energy function, thus enhancing memory capacity by preventing memory confusion. Empirically, with real-world datasets, we demonstrate that U-Hop outperforms all existing modern Hopfield models and SOTA similarity measures, achieving substantial improvements in both associative memory retrieval and deep learning tasks. Code is available at GitHub;future updates are on arXiv.1 © 2024, CC BY-NC-SA.

关键词： Associative storage

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HAR-CTGAN: A Mobile Sensor data Generation Tool for Human Activity Recognition

HAR-CTGAN: A Mobile Sensor Data Generation Tool for Human Ac...

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IEEE International Conference on Big data

作者： Joshua DeOliveira Walter Gerych Aruzhan Koshkarova Elke Rundensteiner Emmanuel Agu Data Science Program Worcester Polytechnic Institute Worcester MA Department of Computer Science Worcester Polytechnic Institute Worcester MA

ISBN: (纸本)9781665480468

Human activity recognition (HAR) is the process of using mobile sensor data to determine the physical activities performed by individuals. HAR is the backbone of many mobile healthcare applications, such as passive health monitoring systems, early diagnosing systems, and fall detection systems. Effective HAR models rely on deep learning architectures and big data in order to accurately classify activities. Unfortunately, HAR datasets are expensive to collect, are often mislabeled, and have large class imbalances. State-of-the-art approaches to address these challenges utilize Generative Adversarial Networks (GANs) for generating additional synthetic data along with their labels. Problematically, these HAR GANs only synthesize continuous features — features that are represented by real numbers — recorded from gyroscopes, accelerometers, and other sensors that produce continuous data. This is limiting since mobile sensor data commonly has discrete features that provide additional context such as device location and the time-of-day, which have been shown to substantially improve HAR classification. Hence, we studied Conditional Tabular Generative Adversarial Networks (CTGANs) for data generation to synthesize mobile sensor data containing both continuous and discrete features, a task never been done by state-of-the-art approaches. We show HAR-CTGANs generate data with greater realism resulting in allowing better downstream performance in HAR models, and when state-of-the-art models were modified with HAR-CTGAN characteristics, downstream performance also improves.

关键词： Training Medical services Big data Generative adversarial networks data models Stability analysis Human activity recognition

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Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees

arXiv

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arXiv 2024年

作者： Smith, Chandler Cai, HanQin Tasissa, Abiy Department of Mathematics Tufts University MedfordMA02155 United States Department of Statistics and Data Science Department of Computer Science University of Central Florida OrlandoFL32816 United States

The problem of determining the configuration of points from partial distance information, known as the Euclidean Distance Geometry (EDG) problem, is fundamental to many tasks in the applied sciences. In this paper, we propose two algorithms grounded in the Riemannian optimization framework to address the EDG problem. Our approach formulates the problem as a low-rank matrix completion task over the Gram matrix, using partial measurements represented as expansion coefficients of the Gram matrix in a non-orthogonal basis. For the first algorithm, under a uniform sampling with replacement model for the observed distance entries, we demonstrate that, with high probability, a Riemannian gradient-like algorithm on the manifold of rank-r matrices converges linearly to the true solution, given initialization via a one-step hard thresholding. This holds provided the number of samples, m, satisfies m ≥ O(n7/4r2 log(n)). With a more refined initialization, achieved through resampled Riemannian gradient-like descent, we further improve this bound to m ≥ O(nr2 log(n)). Our analysis for the first algorithm leverages a non-self-adjoint operator and depends on deriving eigenvalue bounds for an inner product matrix of restricted basis matrices, leveraging sparsity properties for tighter guarantees than previously established. The second algorithm introduces a self-adjoint surrogate for the sampling operator. This algorithm demonstrates strong numerical performance on both synthetic and real data. Furthermore, we show that optimizing over manifolds of higher-than-rank-r matrices yields superior numerical results, consistent with recent literature on overparameterization in the EDG problem. © 2024, CC BY.

关键词： Eigenvalues and eigenfunctions

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Domain Adaptation in Color-Shifted datasets Through Adversarial Learning Balancing

Domain Adaptation in Color-Shifted Datasets Through Adversar...

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International Conference on Artificial Intelligence and data sciences (AiDAS)

作者： Hidayaturrahman Agung Trisetyarso Iman Herwidiana Kartowisastro Widodo Budiharto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusatara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusatara University Jakarta Indonesia Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation.

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