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Querying multimedia information systems using SQL

Querying multimedia information systems using SQL

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international conference on Tools for Artificial Intelligence (ICTAI)

作者： S. Marcus Century Technologies Inc. Falls Church VA USA

Relatively little work has been done on developing the mathematical foundations of multimedia technology. S. Marcus and V.S. Subrahmanian (1995; 1994) have taken some initial steps toward the development of a theoretical basis for multimedia information systems. they define a mathematical model of a media instance. A media instance may be thought of as "glue" residing on top of a specific physical media representation (such as video, audio, documents, etc.) Using this "glue", it is possible to define a general purpose logical query language to query multimedia data. this glue consists of a set of "states" and "features", together with relationships between states and/or features. A structured multimedia database system imposes a certain mathematical structure on the set of features/states. Using this notion of a structure, they are able to define indexing structures for processing queries, methods to relax queries when answers do not exist to those queries, as well as sound, complete and terminating procedures to answer such queries. Using the S. Marcus and V.S. Subrahmanian (1995; 1994) work on multimedia database integration systems, we show how their logical based query language can be redefined as an SQL based query language. As there are numerous commercial SQL database systems, a wide and diverse population of users may access the Marcus and Subrahmanian work using their SQL interface. Also, there has been a great deal of study of query optimization of SQL based languages.

关键词： Multimedia systems information systems Multimedia databases Database languages Query processing Indexing Mathematical model Database systems Data structures Voting

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LiSHT: Non-parametric Linearly Scaled Hyperbolic Tangent Activation Function for Neural Networks 7th

LiSHT: Non-parametric Linearly Scaled Hyperbolic Tangent Act...

引用

7th international conference on Computer Vision and Image processing, CVIP 2022

作者： Roy, Swalpa Kumar Manna, Suvojit Dubey, Shiv Ram Chaudhuri, Bidyut Baran Department of Computer Science and Engineering Jalpaiguri Government of Engineering College West Bengal Jalpaiguri India CureSkin Karnataka Bengaluru India Computer Vision and Biometrics Lab Indian Institute of Information Technology Uttar Pradesh Allahabad India Techno India University Kolkata India Indian Statistical Institute Kolkata India

ISBN: (纸本)9783031314063

the activation function in neural network introduces the non-linearity required to deal with the complex tasks. Several activation/non-linearity functions are developed for deep learning models. However, most of the existing activation functions suffer due to the dying gradient problem and non-utilization of the large negative input values. In this paper, we propose a Linearly Scaled Hyperbolic Tangent (LiSHT) for Neural Networks (NNs) by scaling the Tanh linearly. the proposed LiSHT is non-parametric and tackles the dying gradient problem. We perform the experiments on benchmark datasets of different type, such as vector data, image data and natural language data. We observe the superior performance using Multi-layer Perceptron (MLP), Residual Network (ResNet) and Long-short term memory (LSTM) for data classification, image classification and tweets classification tasks, respectively. the accuracy on CIFAR100 dataset using ResNet model with LiSHT is improved by 9.48, 3.40, 3.16, 4.26, and 1.17% as compared to Tanh, ReLU, PReLU, LReLU, and Swish, respectively. We also show the qualitative results using loss landscape, weight distribution and activations maps in support of the proposed activation function. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image classification

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ML-based failure detection approach for predictive maintenance in an industry 4.0 oriented web manufacturing control application

ML-based failure detection approach for predictive maintenan...

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international conference on advanced Technologies for Signal and Image processing (ATSIP)

作者： Sirine Smaoui Mouna Baklouti CES Laboratory National Engineering School of Sfax University of Sfax Sfax Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

With the increasing reach of Industry 4.0, the Internet of things (IoT) and Artificial Intelligence (AI) and many other technologies, have emerged as pivotal components in the era of information technology. Recent progress in AI, and its subfield machine learning (ML), have influenced numerous research domains inside the industry, achieving improvements that were not possible with conventional optimization techniques, notably illustrated in the case of predictive maintenance. A significant component of predictive maintenance is fault detection. the goal of predictive maintenance is to avoid future machine failure and consequent downtime by anticipating faults in machinery. As a result, the aim of this research is to employ ML models for fault identification process. A concrete dataset was employed with real sensors’ outputs allowing to make analysis of faults in machines mounted with various types of sensors. Four distinct supervised ML algorithms were applied in our work in which their hyper-parameters were tuned with both Grid Search and Random Search methods. A comparative study between the algorithms’ performances was made in order to select the best one with highest metrics percentages. A web-based application was also developed, to monitor data and send alerts when detecting anomalies either via SMS, email or inside the provided web interface.

关键词： Industries Measurement Search methods Prediction algorithms Sensors Internet of things Machinery

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Bi-directional Joint Model for Intent Detection and Slot Filling

Bi-directional Joint Model for Intent Detection and Slot Fil...

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advanced Algorithms and Control Engineering (ICAACE), international conference on

作者： Bin Luo Baiming Feng College of Computer Science and Engineering Northwest Normal University Lanzhou Gansu China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

Natural language understanding plays a very important role in textual information processing systems, and is a necessary module for systems such as recommendation, question and answer, and search. Intent detection and slot filling are two crucial tasks for natural language understanding. Traditionally the two tasks were proceeded independently. Recently some studies have shown that the two tasks are correlative strongly, and some joint models have achieved better performance. However, they still have difficulty in capturing the mutual information between the two tasks adequately. To address the underutilization of mutual information in the above two tasks, this papers proposes a new bi-directional joint model for intent detection and slot filling, which contains an input embedding layer, a slot enhancement layer and an intent enhancement layer, where the input embedding layer uses BERT as the underlying encoder, the slot enhancement layer is to add the intent information of the whole sentence as a guideline to establish a deeper connection between the slot and the intent in the process of slot filling, and the intent enhancement layer is similar. Experimental results on Snips dataset demonstrate that our model achieves state-of-the-art results in slot F1(97.1%) and sentence-level semantic frame accuracy(93.1%), indicating that our model is able to capture the mutual information between the semantic components in the NLU tasks more adequately.

关键词： Semantics Process control Bidirectional control information processing Filling Natural language processing Vectors

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A Chinese Text Classification Method Based on BERT and Convolutional Neural Network

A Chinese Text Classification Method Based on BERT and Convo...

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international conference on Systems and Informatics (ICSAI)

作者： Yiran Cui Chaobing Huang School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9781665426251

Text classification has always been an important task in natural language processing. In recent years, text classification has been widely used in emotion analysis, intention recognition, intelligent question answering and other fields. In this paper, the word vector is generated based on the Bert model, and the text features extracted by Convolutional Neural Network (CNN) are fused to get more effective features, so as to complete the Chinese text classification. Experiments are conducted on the public data set. Compared with the text classification model in recent years, it is proved that the Bert+CNN model can accurately classify Chinese text, effectively prevent over fitting, and has good generalization.

关键词： Training Convolution Text recognition Text categorization Semantics Neural networks Feature extraction

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An Enhanced Decentralized Social Network based on web3 and IPFS using Blockchain

An Enhanced Decentralized Social Network based on Web3 and I...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： D. Palanikkumar G Arun R Arunadevi S R Gayathri P Dharun Computer Science and Engineering Dr. N.G.P Institute of Technology Coimbatore India

Human lives now include a significant amount of social media usage. Social media is used to create communities, strengthen bonds, and stay in touch with distant friends. A single server, which performs all significant processing, serves as the hub of a centralized system. thus, a centralized server becomes an easy target for hackers. Decentralized social networks can provide a more secure environment where users may exercise more privacy control through distribution of information processing across many machines. this study proposes a system where the web3 Library is used to implement the decentralized network. this provides a social graph with all devices in the network. the Ethers library provides functionalities to engage the Ethereum framework. this way an OSN (Online Social Network) service based on blockchain is created and its decentralized operations are performed. Due to a decentralized autonomous organization that is designed for user autonomy, users can democratically self-manage the OSN.

关键词： Performance evaluation Privacy Social networking (online) File systems Decentralized autonomous organization Market research Libraries

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Construction of a ceramic culture knowledge graph dataset

Construction of a ceramic culture knowledge graph dataset

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Computer information Science and Application technology (CISAT), international conference on

作者： Weidong Zhang Qiliang Shu Fubao He Jingdezhen Ceramic University Jingdezhen China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

this paper aims to construct a knowledge graph of ceramics culture. It integrates and displays the history, craftsmanship, and artistic styles of ceramics culture. By analyzing various aspects of ceramics culture, the paper uses knowledge graph technology to systematically classify and associate the collected data. It also explores the application of intelligent technologies (such as 3D printing, digital symbol systems, AI recognition) in the ceramics field. the core process involves data collection, processing, and ontology construction, combined with natural language processing techniques for entity recognition and relationship extraction, and uses the graph database Neo4j for data storage and management. A rich knowledge graph of ceramics culture is successfully constructed, providing a powerful tool for research and dissemination of ceramics culture. through experimental evaluation, the effectiveness and practicality of the graph are verified, and the scale and quality of the dataset are discussed, pointing the way for future research.

关键词： information science Databases Symbols Memory Knowledge graphs Ontologies three-dimensional printing Natural language processing Ceramics History

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Design of a Tourism Area Dining Recommendation System Based on Autoabstract technology

Design of a Tourism Area Dining Recommendation System Based ...

引用

Computer information Science and Application technology (CISAT), international conference on

作者： Jilin Qin Joey S. Aviles Graduate School Angeles University Foundation Angeles City Philippines

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

this study aims to address the issue of tourists choosing suitable restaurants and food in tourist areas, and proposes a tourist area dining recommendation system based on automatic summarization technology. the system utilizes web content scraping robots, a topic-aware Transformer model, and automatic summarization technology to automatically obtain introductions and summary comments for various restaurants in tourist areas, and to provide automatic dining recommendations. the research also combines Hadoop platform technology to establish parallel processing for real-time provision of food reviews and recommendation services. the innovation of this study lies in the use of automatic summarization technology and parallel processing to achieve rapid acquisition and recommendation of food information in tourist areas, providing convenient and practical services for tourists. this system has certain application prospects and promotional value in the field of tourist area dining recommendations.

关键词： Technological innovation information science Reviews Computational modeling Text summarization Parallel processing Transformers Real-time systems Recommender systems Robots

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Human-Machine Collaboration in Healthcare Investment Integrating Large language Models

Human-Machine Collaboration in Healthcare Investment Integra...

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Machine Learning and Natural language processing (MLNLP), international conference on

作者： Hua Wang International Business school Zhejiang University Haining China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

Large language models (LLMs) have rapidly advanced and are increasingly utilized in Finance. these technologies offer innovative decision-making tools for financial investors, particularly as traditional methods often prove insufficient. this study proposes an investment analysis model for the pharmaceutical and biotechnology industry that combines LLMs with human analysts' expertise. By comprehensively analyzing research reports, technical indicators, and corporate governance data, our approach effectively extracts key information such as market sentiment, corporate governance status, and technological innovation, generating investment recommendations. An empirical test with real-world data on 493 listed pharmaceutical companies shows that the GPT-based investment strategy performs excellently in terms of return rates, Sharpe ratios, and maximum drawdowns. Specifically, the cautious investment strategy resulted in a reduction of maximum drawdowns by 9% to 30% and volatility by 5% to 10%. the results indicate that the GPT model significantly improves investment analysis efficiency, accurately identifies high-potential companies, and effectively manages risks. Overall, this study demonstrates the strong potential of human-machine collaboration in addressing the complexity and information density of the pharmaceutical industry, paving the way for intelligent investment analysis.

关键词： Analytical models Technological innovation Large language models Human-machine systems Collaboration Companies Medical services Natural language processing Investment Pharmaceuticals

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Classification of Unsafe Children’s Toys Based on NLP-Generated Corpus

Classification of Unsafe Children’s Toys Based on NLP-Gener...

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international conference on information technology (INCIT)

作者： Htoo thiri Wai Surapong Uttama School of Information Technology Mae Fah luang University Chiang Rai Thailand Center of Excellence in AI and Emerging Technologies School of Information Technology Mae Fah luang University Chiang Rai Thailand

Playing with toys is essential for children as it is an integral part of the learning process and development. However, young children especially toddlers and babies are susceptible to dangers due to unsafe toys. this research aims to classify unsafe children’s toys based on a corpus derived by Natural language processing (NLP) techniques using different classification algorithms. the study collected 19087 unsafe and safe children’s toys data from the Europe Commission Rapid Alert System (Safety Gates) and Amazon online retail store. three different scenarios in feature selection: ‘Brands’, ‘Unsafe Terms,’ and a combination of ‘Brands’ and ‘Unsafe Terms’ were used for classification. ‘Brands’ did not serve a vital role in the classification of safe and unsafe children’s toys compared to the classification with ‘Unsafe Terms’. Although combining ‘Brands’ and ‘Unsafe Terms’ or only ‘Unsafe Terms’ provided nearly similar accuracy, classification with only ‘Brands’ did not provide good accuracy. the classification accuracy of k-Nearest Neighbor is 78%, Logistic Regression is 83%, Support Vector Machine is 82%, Decision Tree is 82%, Naïve Bayes is 50%, and Random Forest is 81% respectively. the results showed that Logistic Regression revealed the best performance as it outperformed other algorithms.

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