检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Zhang, Chuan Zhu, Liehuang Xu, Chang Sharif, Kashif Liu, Ximeng Du, Xiaojiang Guizani, Mohsen Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Application School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Information Systems Singapore Management University College of Mathematics and Computer Science Fuzhou University Department of Computer and Information Sciences Temple University Philadelphia United States Department of Electrical and Computer Engineering University of Idaho MoscowID United States

The recent proliferation of smart devices has given rise to ubiquitous computing, an emerging computing paradigm which allows anytime & anywhere computing possible. In such a ubiquitous computing environment, customers release different computing or sensing tasks, and people, also known as data processors, participate in these tasks and get paid for providing their idle computing and communication resources. Thus, how to select an appropriate and reliable customer while not disclosing processors' privacy has become an interesting problem. In this article, we present a trust-based and privacy-preserving customer selection scheme in ubiquitous computing, called TPCS, to enable potential processors select the customers with good reputation. The basic concept of TPCS is that each data processor holds a trust value, and the reputation score of the customer is calculated based on processors' trust values and feedbacks via a truth discovery process. To preserve processors' privacy, pseudonyms and Paillier cryptosystem are applied to conceal each processor's real identity. In addition, three authentication protocols are designed to ensure that only the valid data processors (i.e., the processors registering in the system, holding the truthful trust values, and joining the computing tasks) can pass the authentication. A comprehensive security analysis is conducted to prove that our proposed TPCS scheme is secure and can defend against several sophisticated attacks. Moreover, extensive simulations are conducted to demonstrate the correctness and effectiveness of the proposed scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Ubiquitous computing

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Building Knowledge-Grounded Dialogue Systems with Graph-Based Semantic Modeling

arXiv

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arXiv 2022年

作者： Yang, Yizhe Huang, Heyan Gao, Yang Li, Jiawei School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Southeast Academy of Information Technology Beijing Institute of Technology Fujian Putian351100 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

The knowledge-grounded dialogue task aims to generate responses that convey information from given knowledge documents. However, it is a challenge for the current sequence-based model to acquire knowledge from complex documents and integrate it to perform correct responses without the aid of an explicit semantic structure. To address these issues, we propose a novel graph structure, Grounded Graph (G2), that models the semantic structure of both dialogue and knowledge to facilitate knowledge selection and integration for knowledge-grounded dialogue generation. We also propose a Grounded Graph Aware Transformer (G2AT) model that fuses multi-forms knowledge (both sequential and graphic) to enhance knowledge-grounded response generation. Our experiments results show that our proposed model outperforms the previous state-of-the-art methods with more than 10% gains in response generation and nearly 20% improvement in factual consistency. Further, our model reveals good generalization ability and robustness. By incorporating semantic structures as prior knowledge in deep neural networks, our model provides an effective way to aid language generation. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Unsupervised Hashing with Semantic Concept Mining

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Proceedings of the ACM on Management of Data 2023年第1期1卷 1-19页

作者： Rong-Cheng Tu Xian-Ling Mao Kevin Qinghong Lin Chengfei Cai Weize Qin Wei Wei Hongfa Wang Heyan Huang Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications & Beijing Institute of Technology Southeast Academy of Information Technology Beijing China Beijing Institute of Technology Beijing China National University of Singapore Singapore Singapore Zhejiang University Zhejiang China Institute of Computing Technology Chinese Academy of Sciences Beijing China Huazhong University of Science and Technology Wuhan China

Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised hashing methods have been proposed by designing a semantic similarity matrix, which is based on the similarities between image features extracted by a pre-trained CNN model. However, most of these methods tend to ignore high-level abstract semantic concepts contained in images. Intuitively, concepts play an important role in calculating the similarity among images. In real-world scenarios, each image is associated with some concepts, and the similarity between two images will be larger if they share more identical concepts. Inspired by the above intuition, in this work, we propose a novel Unsupervised Hashing with Semantic Concept Mining, called UHSCM, which leverages a VLP model to construct a high-quality similarity matrix. Specifically, a set of randomly chosen concepts is first collected. Then, by employing a vision-language pretraining (VLP) model with the prompt engineering which has shown strong power in visual representation learning, the set of concepts is denoised according to the training images. Next, the proposed method UHSCM applies the VLP model with prompting again to mine the concept distribution of each image and construct a high-quality semantic similarity matrix based on the mined concept distributions. Finally, with the semantic similarity matrix as guiding information, a novel hashing loss with a modified contrastive loss based regularization item is proposed to optimize the hashing network. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task.

关键词： image retrieval semantic concept mining unsupervised hashing

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Using multiple reference audios and style embedding constraints for speech synthesis

arXiv

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arXiv 2021年

作者： Gong, Cheng Wang, Longbiao Ling, Zhenhua Zhang, Ju Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei China Co. Ltd Japan Advanced Institute of Science and Technology Ishikawa Japan

The end-to-end speech synthesis model can directly take an utterance as reference audio, and generate speech from the text with prosody and speaker characteristics similar to the reference audio. However, an appropriate acoustic embedding must be manually selected during inference. Due to the fact that only the matched text and speech are used in the training process, using unmatched text and speech for inference would cause the model to synthesize speech with low content quality. In this study, we propose to mitigate these two problems by using multiple reference audios and style embedding constraints rather than using only the target audio. Multiple reference audios are automatically selected using the sentence similarity determined by Bidirectional Encoder Representations from Transformers (BERT). In addition, we use "target" style embedding from a pre-trained encoder as a constraint by considering the mutual information between the predicted and "target" style embedding. The experimental results show that the proposed model can improve the speech naturalness and content quality with multiple reference audios and can also outperform the baseline model in ABX preference tests of style similarity. © 2021, CC BY.

关键词： Embeddings

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Enhancing participant selection through caching in mobile crowd sensing

Enhancing participant selection through caching in mobile cr...

引用

International Workshop on Quality of Service

作者： Hanshang Li Ting Li Fan Li Weichao Wang Yu Wang Department of Computer Science University of North Carolina at Charlotte Charlotte NC USA Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China School of Computer Science Beijing Institute of Technology Beijing China

With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of participants from the huge user pool to perform the tasks and achieve certain level of coverage. In this paper, we introduce a new MCS architecture which leverages the cached sensing data to fulfill partial sensing tasks in order to reduce the size of selected participant set. We present a newly designed participant selection algorithm with caching and evaluate it via extensive simulations with a real-world mobile dataset.

关键词： Sensors Mobile communication Memory Algorithm design and analysis Smart phones Probabilistic logic Data models

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cloud-based approximate constrained shortest distance queries over encrypted graphs with privacy protection

arXiv

引用

arXiv 2018年

作者： Shen, Meng Ma, Baoli Zhu, Liehuang Mijumbi, Rashid Du, Xiaojiang Hu, Jiankun Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science Beijing Institute of Technology Beijing China Bell Labs CTO Nokia Dublin Ireland Department of Computer and Information Sciences Temple University Philadelphia United States Canberra Australia

Constrained shortest distance (CSD) querying is one of the fundamental graph query primitives, which finds the shortest distance from an origin to a destination in a graph with a constraint that the total cost does not exceed a given threshold. CSD querying has a wide range of applications, such as routing in telecommunications and transportation. With an increasing prevalence of cloud computing paradigm, graph owners desire to outsource their graphs to cloud servers. In order to protect sensitive information, these graphs are usually encrypted before being outsourced to the cloud. This, however, imposes a great challenge to CSD querying over encrypted graphs. Since performing constraint filtering is an intractable task, existing work mainly focuses on unconstrained shortest distance queries. CSD querying over encrypted graphs remains an open research problem. In this paper, we propose Connor, a novel graph encryption scheme that enables approximate CSD querying. Connor is built based on an efficient, tree-based ciphertext comparison protocol, and makes use of symmetric-key primitives and the somewhat homomorphic encryption, making it computationally efficient. Using Connor, a graph owner can first encrypt privacy-sensitive graphs and then outsource them to the cloud server, achieving the necessary privacy without losing the ability of querying. Extensive experiments with real-world datasets demonstrate the effectiveness and efficiency of the proposed graph encryption scheme. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphic methods

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Rapid diagnosis of osteoarthritis using serum Raman spectroscopy based on MFCC transformation combined with MCNN 4

Rapid diagnosis of osteoarthritis using serum Raman spectros...

引用

4th International Conference on Optics and Image processing, ICOIP 2024

作者： Xu, Hongze Chen, Chen Tian, Xuecong He, Liang Zuo, Enguang Chen, Xiaomei Wu, Lijun Chen, Cheng Lv, Xiaoyi College of Software Xinjiang University Urumqi830046 China College of Information Science and Engineering Xinjiang University Urumqi830046 China Key Laboratory of signal detection and processing Xinjiang University Urumqi830046 China Xinjiang Cloud Computing Application Laboratory Karamay834099 China Department of Rheumatology and Immunology People's Hospital of Xinjiang Uygur Autonomous Region Urumqi830001 China Xinjiang Clinical Research Center for Rheumatoid Arthritis Urumqi830001 China Department of Electronic Engineering Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China School of Information Science and Engineering Xinjiang University Ürümqi China

ISBN: (纸本)9781510682481

Osteoarthritis (OA) is a common chronic degenerative disease worldwide. Currently, traditional diagnostic methods for OA have disadvantages such as high cost and long time consumption. For this reason, it is urgent to have a method that can diagnose osteoarthritis quickly and accurately. During this study, we suggest a multi-column convolutional neural networks (MCNN) model based on Mel-frequency cepstral coefficient (MFCC). We first compare the effectiveness of Partial Least Squares (PLS) and MFCC in preprocessing the data and comprehensively evaluate the performance of the model by five-fold cross-validation. To further improve the ability of recognizing spectral features, We utilize the Spectral Segmentation Method (SSM) to construct the MFCC feature sequences into 2D spectral feature maps and input them into the MCNN for classification. The final experimental results show that our proposed mfc - ssm - mcnn model has a high classification efficiency in diagnosing osteoarthritis with an accuracy of 95.0%. © 2024 SPIE.

关键词： Raman spectroscopy

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Certificate-aware encrypted traffic classification using Second-Order Markov Chain

Certificate-aware encrypted traffic classification using Sec...

引用

International Workshop on Quality of Service

作者： Meng Shen Mingwei Wei Liehuang Zhu Mingzhong Wang Fuliang Li Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Beijing Institute of Technology Beijing P. R. China Faculty of Arts and Business University of the Sunshine Coast Queensland Australia College of Information Science and Engineering Northeastern University P. R. China

With the prosperity of network applications, traffic classification serves as a crucial role in network management and malicious attack detection. The widely used encryption transmission protocols, such as the Secure Socket Layer/Transport Layer Security (SSL/TLS) protocols, leads to the failure of traditional payload-based classification methods. Existing methods for encrypted traffic classification suffer from low accuracy. In this paper, we propose a certificate-aware encrypted traffic classification method based on the Second-Order Markov Chain. We start by exploring reasons why existing methods not perform well, and make a novel observation that certificate packet length in SSL/TLS sessions contributes to application discrimination. To increase the diversity of application fingerprints, we develop a new model by incorporating the certificate packet length clustering into the Second-Order homogeneous Markov chains. Extensive evaluation results show that the proposed method lead to a 30% improvement on average compared with the state-of-the-art method, in terms of classification accuracy.

关键词： Markov processes Protocols Servers Ciphers Ports (Computers) Encryption

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LPTD: Achieving lightweight and privacy-preserving truth discovery in CIoT

arXiv

引用

arXiv 2018年

作者： Zhang, Chuan Zhu, Liehuang Xu, Chang Sharif, Kashif Du, Xiaojiang Guizani, Mohsen Beijing Eng. Research Center of Massive Language Information Processing and Cloud Computing App. School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States Department of Electrical and Computer Engineering University of Idaho MoscowID United States

In recent years, cognitive Internet of Things (CIoT) has received considerable attention because it can extract valuable information from various Internet of Things (IoT) devices. In CIoT, truth discovery plays an important role in identifying truthful values from large scale data to help CIoT provide deeper insights and value from collected information. However, the privacy concerns of IoT devices pose a major challenge in designing truth discovery approaches. Although existing schemes of truth discovery can be executed with strong privacy guarantees, they are not efficient or cannot be applied in real-life CIoT applications. This article proposes a novel framework for lightweight and privacy-preserving truth discovery called LPTD-I, which is implemented by incorporating fog and cloud platforms, and adopting the homomorphic Paillier encryption and oneway hash chain techniques. This scheme not only protects devices' privacy, but also achieves high efficiency. Moreover, we introduce a fault tolerant (LPTD-II) framework which can effectively overcome malfunctioning CIoT devices. Detailed security analysis indicates the proposed schemes are secure under a comprehensively designed threat model. Experimental simulations are also carried out to demonstrate the efficiency of the proposed schemes. Copyright © 2018, The Authors. All rights reserved.

关键词： Internet of things

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Exploring explainable selection to control abstract generation

arXiv

引用

arXiv 2020年

作者： Haonan, Wang Yang, Gao Yu, Bai Lapata, Mirella Heyan, Huang School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Institute for Language Cognition and Computation School of Informatics University of Edinburgh 10 Crichton Street EdinburghEH8 9AB United Kingdom Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications

It is a big challenge to model long-range input for document summarization. In this paper, we target using a select and generate paradigm to enhance the capability of selecting explainable contents (i.e., interpret the selection given its semantics, novelty, relevance) and then guiding to control the abstract generation. Specifically, a new designed pair-wise extractor is proposed to capture the sentence pair interactions and its centrality. Furthermore, the generator is hybrid with the selected content and is jointly integrated with a pointer distribution that is derived from a sentence deployment's attention. The abstract generation can be controlled by an explainable mask matrix that determines to what extent the content can be included in the summary. Encoders are adaptable with both Transformerbased and BERT-based configurations. Overall, both results based on ROUGE metrics and human evaluation gain outperformance over several state-of-The-Art models on two benchmark CNN/DailyMail and NYT datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Abstracting

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