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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Shymbayev, Magzhan Alimzhanov, Yermek Astana It University Department of Computational and Data Science Astana7211043 Kazakhstan Astana It University Department of Computational and Data Science Astana Kazakhstan

ISBN: (纸本)9798350335040

This article provides research and development of an extractive question answering system based on the BERT-like model for the Kazakh language. Developing an extractive question answering system requires large training datasets - tens of thousands of annotated question-answer pairs. Such datasets are not available in the majority of languages, including Kazakh. To address this issue, the Kazakh Question Answering dataset (KazQA) is introduced, which is based on the Stanford Question Answering dataset (SQuAD) and generated through machine translation using the Google Cloud Translation API. Different large pretrained contextual language models are used as the baseline models - ALBERT and multilingual BERT and are compared with the newly trained monolingual Kazakh model KazBERT. The results demonstrate that the proposed approach can effectively generate question answering systems in low-resourced Kazakh language. © 2023 IEEE.

关键词： Large dataset

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DeepCensored: Deep-Learning Based Probabilistic Forecasting Framework for Censored data 23

DeepCensored: Deep-Learning Based Probabilistic Forecasting ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Tian, Jiahao Porter, Michael D. School of Data Science University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia Dept. of Systems and Information Engineering CharlottesvilleVA United States

ISBN: (纸本)9798350374889

Accurate time series forecasting is critical in various fields, including resource allocation and crime prevention. While traditional approaches often focus on continuous data, count data forecasting, especially with partially observed (censored) data, remains challenging. This paper introduces DeepCensored, a novel deep learning-based framework that combines the Expectation-Maximization (EM) algorithm with deep neural networks to deliver robust probabilistic forecasts. DeepCensored naturally handles interval-censored event data, where exact event times are unknown but fall within a specific interval. Through extensive simulations and real-world crime data analysis, our method significantly outperforms traditional forecasting approaches, reducing the Mean Absolute Error (MAE) by 50% and better detection of emerging crime trends. These results highlight DeepCensored's potential to provide actionable insights for law enforcement and public safety by predicting crime intensity and enabling resource-efficient policing strategies. © 2024 IEEE.

关键词： Expectation maximization algorithm

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Design of Autonomous Weed Elimination using Maching Learning Techniques

Design of Autonomous Weed Elimination using Maching Learning...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Giradkar, Aditya Adpawar, Rahul Agrawal, Rahul Dhule, Chetan Morris, Nekita Chavan G H Raisoni College of Engineering Department of Data Science Nagpur440016 India Department of Data Science Nagpur440016 India

ISBN: (纸本)9798350333602

The abstract presents a system that employs deep learning techniques to automatically detect weeds in agricultural fields. While the system's efficiency in identifying the presence of weeds is highlighted, the challenges faced by such systems are not explicitly mentioned. Recent techniques such as convolutional neural networks (CNN) are used in this system to teach the model using a dataset of images of weeds and crops. However, the challenges faced by such systems, such as the need for large annotated datasets and the potential for misclassifying weeds, are not addressed. The proposed objective of this study is to demonstrate the effectiveness of using deep learning methods to improve weed management techniques in agriculture. By accurately identifying weeds, farmers can use fewer herbicides and reduce the negative environmental impact of weed control. Overall, this study highlights the potential of deep learning methods in enhancing agricultural practices and reducing the negative environmental effects of weed control. © 2023 IEEE.

关键词： Weed control

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Immersive video interaction system: a survey

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Visual Intelligence 2025年第1期3卷 1-14页

作者： Fang, Xiaohan Chen, Peilin Wang, Meng Wang, Shiqi Department of Computer Science City University of Hong Kong Hong Kong School of Data Science Lingnan University Hong Kong

As communication technologies undergo rapid evolution, human interaction technologies have become increasingly efficient and accessible. Videoconferencing, for example, facilitates real-time, face-to-face communication worldwide, providing unprecedented convenience. Nevertheless, traditional two-dimensional (2D) videoconferencing systems frequently fall short of delivering a fully immersive experience, as they lack the ability to convey physical presence, depth perception, and natural interaction—key elements that are of particular significance in replicating the subtleties inherent in in-person communication. This disconnection leaves users yearning for the rich, multi-sensory experiences that real-life interactions can offer. In response, significant research efforts have been dedicated to the development of immersive video interaction systems (IVISs). This paper introduces the concept of IVISs and reviews a spectrum of systems advanced by researchers and industry innovators, each of whom contributes unique insights into the evolution of this field. A comparative analysis of the characteristics of current IVISs is provided, offering insights across multiple dimensions. This survey concludes with a discussion of potential future research directions and the challenges that remain in enhancing the IVIS technology. Through a comprehensive analysis, this work aims to foster further exploration and innovation in immersive communication technologies. © The Author(s) 2025.

关键词： 3D vision Immersive video interaction systems Survey Video communication

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Hierarchical Structure of SiO2 by In Situ Modification Combining Quartz Sand and Methyltrimethoxysilane

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Defect and Diffusion Forum 2023年 428卷 69-74页

作者： Silvia, Linda Awathifi, Lana Mughayyirah, Yuniar Zainuri, Mochamad Darminto Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

SiO2 were successfully synthesized by sol–gel process via in-situ modification. The SiO2 consists of two kinds of co-precursor namely raw material from quartz sand and methyltrimethoxysilane (MTMS) as main SiO2 source. By introducing SiO2 with different sizes of particle micro-submicron scale, it was suggested to construct hierarchical structure. The sample was characterized by X-Ray Diffraction (XRD), Scanning Electron Microscope (SEM), and Atomic Force Microscope (AFM) to analyze crystal structure, morphology of particles, and surface roughness of the samples. In this research, SiO2 co-precursor from quartz sand were extraction and synthesis to be SiO2 quartz (SQ) phase and SiO2 amorphous (SA) phase respectively which is combine with SiO2 based MTMS by sol–gel process via in-situ modification. Based on the results, it can be concluded that SiO2 with high purity levels maintains its crystal structure even after in-situ modification, resulting in an increase in particle size to approximately 12-20 μm. SEM images showed that particle was irregular shape due to agglomeration which is SiO2 based quartz sand stick by SiO2 based MTMS. AFM showed the surface roughness of MTMS/SQ have a larger roughness than MTMS/SA with Sa 3.3 ± 0.8 nm and Sq 5.0 ± 1.0 nm. The formation of hierarchical structure layers based on SiO2 has a promising potential for wide applications in various fields such as hydrophobic surface. © 2023 Trans Tech Publications Ltd, Switzerland.

关键词： Scanning electron microscopy

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Bootstrapping Language-Audio Pre-training for Music Captioning

Bootstrapping Language-Audio Pre-training for Music Captioni...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lanzendörfer, Luca A. Pinkl, Constantin Perraudin, Nathanaël Wattenhofer, Roger ETH Zurich Switzerland Swiss Data Science Center Switzerland

ISBN: (纸本)9798350368741

We introduce BLAP, a model capable of generating high-quality captions for music. BLAP leverages a fine-tuned CLAP audio encoder and a pre-trained Flan-T5 large language model. To achieve effective cross-modal alignment between music and language, BLAP utilizes a Querying Transformer, allowing us to obtain state-of-the-art performance using 6x less data compared to previous models. This is a critical consideration given the scarcity of descriptive music data and the subjective nature of music interpretation. We provide qualitative examples demonstrating BLAP's ability to produce realistic captions for music, and perform a quantitative evaluation on three datasets. BLAP achieves a relative improvement on FENSE compared to previous models of 3.5%, 6.5%, and 7.5% on the MusicCaps, Song Describer, and YouTube8m-MTC datasets, respectively. The codebase is available at https://***/ETH-DISCO/blap. © 2025 IEEE.

关键词： Contrastive Language-Audio Pre-training Language Models Music Captioning

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IQABC-Based Hybrid Deployment Algorithm for Mobile Robotic Agents Providing Network Coverage

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Tsinghua science and Technology 2024年第2期29卷 589-604页

作者： Shuang Xu Xiaojie Liu Dengao Li Jumin Zhao College of Computer Science and Technology(College of Data Science) Taiyuan University of TechnologyTaiyuan 030024China Pengcheng Laboratory Shenzhen 518055China College of Information and Computer Taiyuan University of TechnologyTaiyuan 030024China

Working as aerial base stations,mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target ***,a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users,while considering the mobility of on-ground *** this paper,to solve this issue,we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage ***,we propose a hybrid deployment algorithm based on the improved quick artificial bee *** algorithm is composed of a centralized deployment algorithm and a distributed *** proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically *** results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.

关键词： wireless robotic networks network coverage deployment algorithm improved quick artificial bee colony

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Enhancing Plant Health through Deep Neural Network based Leaf Disease Detection 2

Enhancing Plant Health through Deep Neural Network based Lea...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Ingole, Rohit V. Nikhate, Samidha S. Agrawal, Rahul Dhule, Chetan Morris, Nekita Chavan G H Raisoni College of Engineering Department of Data Science Nagpur440016 India Department of Data Science Nagpur440016 India

ISBN: (纸本)9781665456302

Farming is a vital industry that not only provides food but also raw materials for numerous industries while contributing significantly to the economy. Plant diseases and infestations can severely impact crop production, leading to economic losses for farmers and food shortages for consumers. Early detection of these diseases and infestations is crucial to preventing their spread and protecting the quality of the produce. In this context, machine learning and deep learning approaches have shown promising results in detecting plant diseases. This comprehensive study provides a detailed analysis of prior research. It compares the state-of-the-art deep learning models' performance on the plant leaf image dataset. This study also explores various data preprocessing and augmentation techniques with the goal of improving data quality and enabling optimal training of the models. © 2023 IEEE.

关键词： Losses

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Key-Value Store Coupled with an Operating System for Storing Large-Scale Values

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Computers, Materials & Continua 2022年第11期73卷 3333-3350页

作者： Jeonghwan Im Hyuk-Yoon Kwon Graduate School of Data Science Seoul National University of Science and TechnologySeoulKorea Department of Industrial Engineering Graduate School of Data ScienceResearch Center for Electrical and Information ScienceSeoul National University of Science and TechnologySeoulKorea

The key-value store can provide flexibility of data types because it does not need to specify the data types to be stored in advance and can store any types of data as the value of the key-value *** types of studies have been conducted to improve the performance of the key-value store while maintaining its ***,the research efforts storing the large-scale values such as multimedia data files(e.g.,images or videos)in the key-value store were *** this study,we propose a new key-value store,WR-Store++aiming to store the large-scale values ***,it provides a new design of separating data and index by working with the built-in data structure of the Windows operating system and the file *** utilization of the built-in data structure of the Windows operating system achieves the efficiency of the key-value store and that of the file system extends the limited space of the storage *** also present chunk-based memory management and parallel processing of WR-Store++to further improve its performance in the GET *** the experiments,we show that WR-Store++can store at least 32.74 times larger datasets than the existing baseline key-value store,WR-Store,which has the limitation in storing large-scale data ***,in terms of processing efficiency,we show that WR-Store++outperforms not only WR-Store but also the other state-ofthe-art key-value stores,LevelDB,RocksDB,and BerkeleyDB,for individual key-value operations and mixed workloads.

关键词： Key-value stores large-scale values chunk-based memory management parallel processing

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Credit card fraud detection: an evaluation of SMOTE resampling and machine learning model performance

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International Journal of Business Intelligence and data Mining 2023年第1期23卷 1-13页

作者： Alshameri, Faleh Xia, Ran Department of Information Technology Data Science and Cybersecurity School of Business and Technology Marymount University United States

Credit card fraud has been a noted security issue that requires financial organisations to continuously improve their fraud detection system. In most cases, a credit transaction dataset is expected to have a significantly larger number of normal transactions than fraud transactions. Therefore, the accuracy of a fraud detection system depends on building a model that can adequately handle such an imbalanced dataset. The purpose of this paper is to explore one of the techniques of dataset rebalancing, the synthetic minority oversampling technique (SMOTE). To evaluate the effects of this technique on model training, we selected four basic classification algorithms, complement naïve Bayes (CNB), K-nearest neighbour (KNN), random forest and support vector machine (SVM). We then compared the performances of the four models trained on the rebalanced and original dataset using the area under precision-recall curve (AUPRC) plots. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Support vector machines

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