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Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

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Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis

arXiv

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arXiv 2021年

作者： Liu, Juhua Zhong, Qihuang Ding, Liang Jin, Hua Du, Bo Tao, Dacheng Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China JD Explore Academy at *** Beijing China Affiliated Hospital of Kunming University of Science and Technology Kunming China School of Computer Science Faculty of Engineering The University of Sydney Sydney Australia

The goal of aspect-based sentiment analysis (ABSA) is to determine the sentiment polarity towards an aspect. Because of the expensive and limited amounts of labelled data, the pretraining strategy has become the de facto standard for ABSA. However, there always exists a severe domain shift between the pretraining and downstream ABSA datasets, which hinders effective knowledge transfer when directly fine-tuning, making the downstream task suboptimal. To mitigate this domain shift, we introduce a unified alignment pretraining framework into the vanilla pretrain-finetune pipeline, that has both instance- and knowledge-level alignments. Specifically, we first devise a novel coarse-to-fine retrieval sampling approach to select target domain-related instances from the large-scale pretraining dataset, thus aligning the instances between pretraining and the target domains (First Stage). Then, we introduce a knowledge guidance-based strategy to further bridge the domain gap at the knowledge level. In practice, we formulate the model pretrained on the sampled instances into a knowledge guidance model and a learner model. On the target dataset, we design an on-the-fly teacher-student joint fine-tuning approach to progressively transfer the knowledge from the knowledge guidance model to the learner model (Second Stage). Therefore, the learner model can maintain more domain-invariant knowledge when learning new knowledge from the target dataset. In the Third Stage, the learner model is finetuned to better adapt its learned knowledge to the target dataset. Extensive experiments and analyses on several ABSA benchmarks demonstrate the effectiveness and universality of our proposed pretraining framework. Our source code and models are publicly available at https://***/WHU-ZQH/UIKA. Copyright © 2021, The Authors. All rights reserved.

关键词： Sentiment analysis

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Transformers in Medical Image Analysis: A Review

arXiv

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arXiv 2022年

作者： He, Kelei Gan, Chen Li, Zhuoyuan Rekik, Islem Yin, Zihao Ji, Wen Gao, Yang Wang, Qian Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis. © 2022, CC BY-SA.

关键词： Diagnosis

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A Novel Three Leg Inverter for High Power Hybrid Inductive and Capacitive Wireless Power Transfer System 45

A Novel Three Leg Inverter for High Power Hybrid Inductive a...

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45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019

作者： Vincent, Deepa Huynh, Phuoc Sang Williamson, Sheldon S. University of Ontario-Institute of Technology Faculty of Engineering and Applied Science Department of Electrical Computer and Software Engineering OshawaONL1G 0C5 Canada

ISBN: (纸本)9781728148786

A novel topology for hybrid wireless power transfer (HPT), which uses both capacitive and inductive action, for electric vehicle charging is proposed in this paper. A three leg inverter topology is used for HPT. Two legs of the inverter, switched at 85 kHz, are used for inductive wireless power transfer (IPT) and the third leg, switched at 1 MHz, is used for capacitive wireless power transfer (CPT). Mixed switching frequency operation of the converter, is the novelty in proposed topology. Series-series compensation is used for IPT and dual LC compensation is used in CPT. The effectiveness of the proposed topology is simulated for a power level of 1 kW. IPT contributes to twice the power transfer than that of CPT. © 2019 IEEE.

关键词： Electromagnetic pulse

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Virtual Cluster Head Based PSO and Fuzzy Logic Approach for WSN to Extend Network Lifetime

Virtual Cluster Head Based PSO and Fuzzy Logic Approach for ...

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2020 International Conference on Computing and Information technology, ICCIT 2020

作者： Manikandan, V. Mavaluru, DInesh Gobinathan, K. Rajanarayanan, S. Jenifer, M. Rohini, R. Lebanese French University Department of Computer Networking Erbil Kurdistan Region Iraq Saudi Electronic University College of Computing and Informatics Department of Information Technology Saudi Arabia Gnanamani Collge of Technology Department of CSE Namakkal India Arba Minch University Dept. of Business Administration and Information System Ethiopia Arba Minch University Arba Minch Institute of Technology Faculty of Computing and Software Engineering Ethiopia Vivekanandha College of Engineering for Women Department of CSE Namakkal India

ISBN: (纸本)9781728126807

In recent times, Wireless Sensor Networks (WSNs) are broadly utilized in targeted monitoring applications. Traditional target coverage techniques usually consider that environment is known completely and every target is covered by single sensor. However those algorithms are inflexible when the sensor is about to die, that is, lack of energy and therefore target has to be covered with more than a sensor which is termed as n coverage crisis. In general, n coverage problem is energy and time consuming process, as well sensor organization is needed all the time. To resolve this crisis, this work anticipates a n coverage model with VCH-PSO (Virtual Cluster Head based Particle Swarm Optimization and EFLM (Energy management based Fuzzy logic Model) to enlarge the lifetime of WSN. During the search of optimal area coverage, diverse factors like node position, energy consumption, and coverage range of sensors are considered significantly. Simulation was carried out in MATLAB environment and compared with prevailing techniques. The proposed model shows better trade off in terms of network lifetime and time needed for switching between various coverage regions. © 2020 IEEE.

关键词： Fuzzy logic

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

引用

arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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Monocular Vision Based UAV Target Detection and Ranging System Implemented on OpenCV and Tensor Flow

Monocular Vision Based UAV Target Detection and Ranging Syst...

引用

第十八届分布式计算及其应用国际学术研讨会

作者： Zhirui Jiang Yuanbo Liu Bin Wu Quanyin Zhu School of Intelligent Engineering Zhengzhou University of Aeronautics Deep Blue Honors School Jiangsu University of Science and Technology Faculty of Computer and Software Engineering Huaiyin Institute of Technology

In recent years,UAV(Unmanned Aerial Vehicle)becomes more and more popular and it is put into operation in various fields,such as agricultural irrigation,geological exploration and so *** this paper,an unmanned aerial vehicle target detection and ranging system based on OpenCV and Tensor Flow is discussed,which can detect and avoid obstacles in time,improving the service life and intelligence level of the unmanned aerial ***,gray-scale need to be put on the image captured by the *** Gaussian denoising,Sobel operator is applied for edge ***,target recognition is carried out by using OpenCV and Tensor Flow,following by which is distance measurement,on the basis of OpenCV and video ***,the measurement results can be obtained.

关键词： Unmanned Aerial Vehicle OpenCV Tensor Flow Edge Detection Algorithms

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Improving recommender systems accuracy in social networks using popularity 20

Improving recommender systems accuracy in social networks us...

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20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019

作者： Yazdi, Kasra Majbouri Yazdi, Adel Majbouri Khodayi, Saeed Hou, Jingyu Zhou, Wanlei Saedy, Saeid Rostami, Mehrdad School of Information Technology Deakin University Melbourne Australia Department of Computing Kharazmi University Tehran Iran Faculty of Computer and Electrical Engineering Azad University Qazvin Iran School of Software University of Technology Sydney Sydney Australia Faculty of Engineering Khavaran Higher Education Institute Mashad Iran Faculty of Computer Engineering University of Kurdistan Sanandaj Iran

ISBN: (纸本)9781728126166

With the rapid advancement of World Wide Web, people can share their knowledge and information via online tools such as sharing systems and ecommerce applications. Many approaches have been proposed to process and organize information. Recommender systems are good successful examples of such tools in providing personalized suggestions. The main purpose of a recommender system is to identify and introduce desired items of a user among many other options (e.g. music, movies, books, news and etc). The goal of our proposed method is to provide a recommender system based on information diffusion and popularity in social networks. By adding popularity, similarity and users' trusts a more efficient system is proposed. This approach makes an improvement in tackling the issues and defects of the previous methods such as prediction accuracy and coverage. The evaluation of the simulated proposed method on MovieLens and Epinions datasets shows that it provides more accurate recommendations in comparison to other approaches. © 2019 IEEE.

关键词： Recommender systems

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Preface

Smart Innovation, Systems and Technologies

引用

Smart Innovation, Systems and Technologies 2023年 313卷 ix-x页

作者： Sivakumar Reddy, V. Kamakshi Prasad, V. Wang, Jiacun Reddy, K.T.V. Malla Reddy College of Engineering and Technology Telangana Hyderabad India Department of CSE Jawaharlal Nehru Tec hnological University Hyderabad Telangana Hyderabad India Department of Computer Science and Software Engineering Monmouth University West Long BranchNJ United States Faculty of Engineering and Technology Datta Meghe Institute of Medical Sciences Maharashtra Sawangi India

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Review of Machine Learning Applications for Predicting the Quality Biomass Briquettes for Sustainable and Low-Carbon Energy Solutions

Green Energy and Resources

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Green Energy and Resources 2025年

作者： Constance Nakato Nakimuli Fred Kaggwa Johan De Greef David Kilama Okot Julien Blondeau Simon Kawuma Department of Information Technology Mbarara University of Science and Technology Uganda Brussels Institute for Thermal-Fluid Systems and Clean Energy(BRITE) Vrije Universiteit Brussel Belgium Thermodynamics and Fluid Mechanics Group Faculty of Engineering Vrije Universiteit Brussel (VUB) Belgium Department of Computer Science Mbarara University of Science and Technology Uganda Department of Materials Engineering – Leuven Group T Campus KU Leuven Leuven Belgium Department of Physics Mbarara University of Science and Technology Uganda Department of Software Engineering Mbarara University of Science and Technology Uganda

This review discusses how Machine Learning is applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon solutions. Traditional methods of assessment of briquette quality involve destructive laboratory experiments, do not favor sample reuse, are time-consuming, and labor-intensive, posing barriers to efficient production. This paper has reviewed literature covering various Machine Learning models applied for predicting and optimizing briquette quality parameters including combustion, physical, and emission properties. Several Machine Learning models have shown promising results in predicting and optimizing these key parameters for example Random Forest with R2 of 0.9936 in deformation energy prediction and Artificial Neural Networks with R2 of 0.8936 in the prediction of impact resistance. By enhancing the accuracy and efficiency of briquette quality predictions, Machine Learning algorithms contribute to the development of high-quality biomass briquettes thereby creating sustainable and low-carbon energy systems. This review points to critical literature gaps regarding model generalizability across diverse biomass feedstock and integration of broader quality parameters. Addressing these gaps will advance AI-based solutions, promote greener energy practices, and support sustainable development. The findings are intended to aid researchers, industry professionals, and policy makers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.

关键词： Machine learning briquettes sustainable energy low-carbon solutions agricultural waste municipal waste

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