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Multimedia text classification algorithm using potential Dirichlet distribution in mobile cloud computing environment

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Multimedia Tools and Applications 2019年第13期79卷 9615-9627页

作者： Xiaohong Zhang Yan Gao School of Computer Science Southwest Petroleum University Chengdu China School of Software Engineering Chengdu University of Information Technology Chengdu China

In order to solve the problem of inaccurate description of news content features and user interest features in mobile cloud computing, proposed a multimedia text classification algorithm that utilizes multi-tag potential Dirichlet distribution. The algorithm is based on the traditional latent Dirichlet allocation (LDA) model and assumes a linear relationship between user tags and potential topics. Therefore, a relational matrix is introduced in the LDA model to describe the corresponding relationship between the tag and the topic, so that the probability distribution of the tag on the word can be inferred from the probability distribution of the topic on the word. The algorithm first learns the probability distribution table of label words by Gibbs sampling method, then infers the probability distribution of new documents on labels according to the model parameters, so as to realize the purpose of predicting the corresponding multiple labels of documents. In order to improve the ability of the algorithm to deal with massive data, the parallel algorithm has been improved. Since the bottleneck of the algorithm lies mainly in the serial nature of global variable updating and communication, the core idea of our parallelization is that in massive text training, global delay updating and asynchronous communication will not affect the final training results. Experiments show that the proposed algorithm has greatly improved the training efficiency. The classification accuracy is higher than that of Naive Bayesian algorithm and Support Vector Machine (SVM) algorithm proposed in other literatures. The average classification accuracy can achieve at about 95%, and it can be used as a general parallel framework of supervised LDA algorithm.

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Fine-grained iterative attention network for temporal language localization in videos

arXiv

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arXiv 2020年

作者： Qu, Xiaoye Tang, Pengwei Zou, Zhikang Cheng, Yu Dong, Jianfeng Zhou, Pan Xu, Zichuan School of Electronic Information and Communication Huazhong University of Science and Technology Huawei Tech. Hangzhou China School of Electronic Information and Communication Huazhong University of Science and Technology China Baidu Inc. China Microsoft Dynamics 365 AI Research RedmondWA United States School of Computer and Information Engineering Zhejiang Gongshang University China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Dalian University of Technology China

Temporal language localization in videos aims to ground one video segment in an untrimmed video based on a given sentence query. To tackle this task, designing an effective model to extract grounding information from both visual and textual modalities is crucial. However, most previous attempts in this field only focus on unidirectional interactions from video to query, which emphasizes which words to listen and attends to sentence information via vanilla soft attention, but clues from query-by-video interactions implying where to look are not taken into consideration. In this paper, we propose a Fine-grained Iterative Attention Network (FIAN) that consists of an iterative attention module for bilateral query-video in-formation extraction. Specifically, in the iterative attention module, each word in the query is first enhanced by attending to each frame in the video through fine-grained attention, then video iteratively attends to the integrated query. Finally, both video and query information is utilized to provide robust cross-modal representation for further moment localization. In addition, to better predict the target segment, we propose a content-oriented localization strategy instead of applying recent anchor-based localization. We evaluate the proposed method on three challenging public benchmarks: ActivityNet Captions, TACoS, and Charades-STA. FIAN significantly outperforms the state-of-the-art approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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ReGO: Reference-Guided Outpainting for Scenery Image

arXiv

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arXiv 2021年

作者： Wang, Yaxiong Wei, Yunchao Qian, Xueming Zhu, Li Yang, Yi The School of Software Engineering Xi’an Jiaotong University Xi'An710049 China Institute of Information Science Beijing Jiaotong University Beijing100000 China The Key Laboratory for Intelligent Networks and Network Security Ministry of Education Xi’an Jiaotong University Xi'An710049 China The SMILES Laboratory Xi’an Jiaotong University Xi'An710049 China Zhibian Technology Co. Ltd. Taizhou317000 China The School of Software Xi’an Jiaotong University Xi'An710049 China School of Computer Science and Technology Zhejiang University Hangzhou310000 China

We aim to tackle the challenging yet practical scenery image outpainting task in this work. Recently, generative adversarial learning has significantly advanced the image outpainting by producing semantic consistent content for the given image. However, the existing methods always suffer from the blurry texture and the artifacts of the generative part, making the overall outpainting results lack authenticity. To overcome the weakness, this work investigates a principle way to synthesize texture-rich results by borrowing pixels from its neighbors (i.e. , reference images), named Reference-Guided Outpainting (ReGO). Particularly, the ReGO designs an Adaptive Content Selection (ACS) module to transfer the pixel of reference images for texture compensating of the target one. To prevent the style of the generated part from being affected by the reference images, a style ranking loss is further proposed to augment the ReGO to synthesize style-consistent results. Extensive experiments on two sceneary benchmarks, NS6K [3], NS8K [9] and SUN Attribute [25], well demonstrate the effectiveness of our ReGO. Our code is available at https://***/wangyxxjtu/ReGOPytorch. © 2021, CC BY.

关键词： Textures

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Model-based Automated Testing of JavaScript Web Applications via Longer Test Sequences

arXiv

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arXiv 2019年

作者： Gao, Pengfei Song, Fu Chen, Taolue Zeng, Yao Su, Ting School of Information Science and Technology ShanghaiTech University School of computer Science and Engineering Nanyang Technological University School of Computer Science and Software Engineering East China Normal University

—JavaScript has become one of the most widely used languages for Web development. However, it is challenging to ensure the correctness and reliability of Web applications written in JavaScript, due to their dynamic and event-driven features. A variety of dynamic analysis techniques for JavaScript Web applications have been proposed, but they are limited in either coverage or scalability. In this paper, we propose a model-based automated approach to achieve high code coverage in a reasonable amount of time via testing with longer event sequences. We implement our approach as the tool LJS, and perform extensive experiments on 21 publicly available benchmarks (18,559 lines of code in total). On average, LJS achieves 86.4% line coverage in 10 minutes, which is 5.4% higher than that of JSDEP, a breadth-first search based automated testing tool enriched with partial order reduction. In particular, on large applications, the coverage of LJS is 11-18% higher than that of JSDEP. Our empirical finding supports that longer test sequences can achieve higher code coverage in JavsScript testing. Copyright © 2019, The Authors. All rights reserved.

关键词： Automation

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Matrix Classifier On Dynamic Functional Connectivity For Mci Identification

Matrix Classifier On Dynamic Functional Connectivity For Mci...

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IEEE International Conference on Image Processing

作者： Lei Zhou Liang Zhang Xiao Bai Jun Zhou School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Software Development Environment Beihang University Beijing School of Information and Communication Technology Griffith University Nathan

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

One of the most popular method for Alzheimer's disease (AD) diagnosis is exploring the Brain functional connectivity (FC) from resting-state functional magnetic resonance imaging (RS-fMRI). To early prevent AD, it is crucial to distinguish AD and and its preclinical stage, mild cognitive impairment (MCI) and early MCI (eMCI). In many existing works, dynamic functional connectivity (dFC) which contains rich spatiotemporal information has been exploited for the MCI and eMCI identification. However, most of these dFC based methods only consider the correlation between discrete brain status while ignore the valuable spatiotemporal information contained in dFC. To overcome this limitation, we propose a matrix classifier based method on the dFC signal for MCI and eMCI identification. Specifically, we first represent the dFC correlations by matrix features which contain rich spatiotemporal information and then learn the support matrix machines (SMM) to classify AD and its preclinical stage. Experiments on 600 real people data provide by the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed matrix classifier based method outperforms other FC and dFC based methods for both normal controls (NC)/MCI identification and NC/eMCI identification.

关键词： Feature extraction Correlation Dementia Microsoft Windows Spatiotemporal phenomena Training

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Joint image synthesis and fusion with converted features for Alzheimer's disease diagnosis

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Chen, Zhaodong Wang, Mingxia Nan, Fengtao Yang, Yun Li, Shunbao Zhou, Menghui Qi, Jun Wang, Hanwen Yang, Po National Pilot School of Software Yunnan University Kunming650500 China School of Computer Science University of Sheffield SheffieldS1 4DP United Kingdom Northwest A&F University College of information engineering Northwest A&F University Xianyang712199 China Department of Computing School of Advanced Technology Xi'an JiaoTong-Liverpool University Suzhou215028 China

The effectiveness of complete multi-modal neuroimaging data in the diagnosis of Alzheimer's disease has been extensively demonstrated and applied. Dealing with incomplete modalities poses a common challenge in multi-modal neuroimaging diagnosis. The mainstream approaches aim to synthesize missing neuroimaging data in order to make full use of all available samples. However, these methods treat image synthesis and disease diagnosis as two independent tasks, overlooking the potential feature of cross-modality image synthesis for downstream tasks. To this end, we propose the Joint Image Synthesis and Classification Learning method to jointly optimize image synthesis and disease diagnosis using incomplete neuroimaging modalities. Our approach comprises a submodule for synthesizing missing neuroimaging data and a decision fusion submodule that integrates features from different modalities and the high-level/converted features generated during synthesis. Experimental results demonstrate that our joint optimization approach outperforms conventional two-stage methods. Our method is capable of handling arbitrary neuroimaging modality missing scenarios and achieves state-of-the-art performance in both Alzheimer's Disease identification and mild cognitive impairment conversion classification tasks. Finally, we further explored the importance of different converted features. This highlights the effectiveness of our approach in addressing the challenges of Alzheimer's Disease diagnosis and provides insights for future research in multi-modal medical image analysis. © 2025

关键词： Neurodegenerative diseases

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An Efficient Access Privacy Protocol for Healthcare Patient information System

An Efficient Access Privacy Protocol for Healthcare Patient ...

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International Conference on Wavelet Active Media Technology and information Processing (ICWAMTIP)

作者： Muhammad Irfan Sharif Jian Ping Li Sana Ullah Amin Ul Haq Gulzar Alam School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Department of Information and Computer Science KFUPM Saudi Arabia

ISBN: (数字)9781728142425

ISBN: (纸本)9781728142432

This paper discusses different healthcare information access record systems development techniques and sharing techniques with patient and physician. It provides a theoretical study to find out the latest techniques used by healthcare information sharing organizations which have a legal access and keep the patient data secured. We have proposed a system, which give access to the electronic record of a patient by using two-factor authentication protocol which is an efficient and more secure access method. This system is for both literate and illiterate people, which requires the National Identity Card Number (CNIC) and mobile device. The physician needs computer or mobile phone to access the model services. The electronic medical records are stored in a database which can be accessed by the authorized person physician or patient through two-factor authentication protocol. Each individual accessed through the two-factor authentication process, which makes this system secure and non-vulnerable. It provides a onetime password, which is only valid for only one login session.

关键词： Authentication Electronic healthcare Privacy Electronic medical records Bibliographies

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Binary neural networks: A survey

arXiv

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arXiv 2020年

作者： Qin, Haotong Gong, Ruihao Liu, Xianglong Bai, Xiao Song, Jingkuan Sebe, Nicu State Key Lab of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China

The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety of algorithms have been proposed, and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error. We also investigate other practical aspects of binary neural networks such as the hardware-friendly design and the training tricks. Then, we give the evaluation and discussions on different tasks, including image classification, object detection and semantic segmentation. Finally, the challenges that may be faced in future research are prospected. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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ADAT: Time-Series-Aware Adaptive Transformer Architecture for Sign Language Translation

arXiv

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arXiv 2025年

作者： Shahin, Nada Ismail, Leila Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates National Water and Energy United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates

Current sign language machine translation systems rely on recognizing hand movements, facial expressions and body postures, and natural language processing, to convert signs into text. Recent approaches use Transformer architectures to model long-range dependencies via positional encoding. However, they lack accuracy in recognizing fine-grained, short-range temporal dependencies between gestures captured at high framerates. Moreover, their high computational complexity leads to inefficient training. To mitigate these issues, we propose an Adaptive Transformer (ADAT), which incorporates components for enhanced feature extraction and adaptive feature weighting through a gating mechanism to emphasize contextually relevant features while reducing training overhead and maintaining translation accuracy. To evaluate ADAT, we introduce MedASL, the first public medical American Sign Language dataset. In sign-to-gloss-to-text experiments, ADAT outperforms the encoder-decoder transformer, improving BLEU-4 accuracy by 0.1% while reducing training time by 14.33% on PHOENIX14T and 3.24% on MedASL. In sign-to-text experiments, it improves accuracy by 8.7% and reduces training time by 2.8% on PHOENIX14T and achieves 4.7% higher accuracy and 7.17% faster training on MedASL. Compared to encoder-only and decoder-only baselines in sign-to-text, ADAT is at least 6.8% more accurate despite being up to 12.1% slower due to its dual-stream structure. © 2025, CC BY.

关键词： Neural machine translation

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Forward to the special issue of the 9th International Symposium on Cyberspace Safety and Security (CSS 2017)

Forward to the special issue of the 9th International Sympos...

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作者： Wen, Sheng Castiglione, Aniello Wang, Tian Bhuiyan, Md Zakirul Alam School of Software and Electrical Engineering Swinburne University of Technology HawthornVIC Australia Department of Science and Technology University of Naples Parthenope Italy National Huaqiao University College of Computer Science and Technology Xiamen China Department of Computer and Information Sciences Fordham University New YorkNY United States

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