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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lv, Weipeng Jiang, Changkun Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9781665468190

The discovery of protein-protein interaction sites (PPIs) is vital for exploring the principle of PPI and understanding the nature of life activities. Developing computational approaches to predict PPIs can effectively compensate for the shortcomings of biological experiments, which are mostly time-consuming and vulnerable to noise. In recent years, deep learning has been used to develop PPIs prediction models. Most of them consider the contextual information of the target amino acid residues and use a local protein sequence to represent the targets. However, the traditional deep-learning techniques, e.g., deep neural networks (DNNs) and convolutional neural networks (CNNs), disregard the important spatial hierarchies contained in the features of protein sequences, leading to their failure to effectively distinguish the interaction sites from different residue regions. In this work, we design MSE-CapsPPISP, a new deep-learning model to address the PPIs prediction with spatial hierarchies. The key idea of MSE-CapsPPISP is to take into account the hierarchical relationships between the features of protein sequences. We characterize the hierarchical relationships by designing a tailored Capsule Network (CapsNet) model, which is a novel type of neural network with vector neurons. Moreover, to make the network representation more robust, MSE-CapsPPISP uses multi-scale CNNs to extract multi-scale features of protein sequences and Squeeze-and-Excitation blocks to recalibrate the features. The validation results show that our MSE-CapsPPISP outperforms the baseline CNNs-based architecture DeepPPISP and other competing schemes in the PPIs prediction task. © 2022 IEEE.

关键词： Forecasting

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A Gait Recognition Method Based on the Combination of Human Body Posture and Human Body Contour 2

A Gait Recognition Method Based on the Combination of Human ...

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2nd International Conference on Artificial Intelligence and Computer Science, AICS 2020

作者： Ma, Yue Wei, Chenghao Long, Hao Ministry of Public Security China Big Data Institute College of Computer Science and Software Engineering Shenzhen University China

Aiming at the issues that affect the gait recognition, such as outfit changes and carry-on-objects during gait recognition, this paper proposes a gait recognition method based on the improved GaitSet network, which relies on the fusion of human posture and human contour. This method introduces key points of human posture, and improves the precision of human contour extraction by introducing the key points of human posture. At the same time, the human posture map composed of the key points of human posture is used as the synchronous attribute of the contour map to extract gait features. Because key points focus on the inherent walking characteristics of human body, they are not affected by the external information such as clothing and carrying objects. Combined with the rich gait change attributes of human contour, it can effectively improve the accuracy and robustness of the gait recognition model. In scenes of human body wearing coats, our experimental results show that the accuracy of the proposed method in CASIA-B gait data set and self-built database can reach over 73.5% accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Gait analysis

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Evaluation and enhancement of smart community system based on big data 3

Evaluation and enhancement of smart community system based o...

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3rd International Conference on Electronic Information engineering, big data, and Computer Technology, EIBDCT 2024

作者： Wu, Huiwen Huang, Xiaoping Natural Language Processing Software Engineering Institute of Guangzhou Guangzhou China Big Data Nanfang College Guangzhou China

ISBN: (纸本)9781510680449

With the rapid development of big data technology, the application of smart community system is more and more extensive, but it also faces the problem of evaluation and enhancement. This paper aims to explore the evaluation and enhancement of smart community system based on big data. First, it introduces the basic concepts and characteristics of smart community system, then expounds the index system and methods of smart community system evaluation based on big data, then analyzes the key technologies and strategies of smart community system enhancement, and finally puts forward the future research direction and prospect. Improve the efficiency of community management and service. Through big data analysis, the services and management of the community can be intelligently optimized, so as to improve the efficiency of community management and service. Improve the quality of life of community residents: The smart community system can provide a more convenient and comfortable living environment for community residents through various intelligent means. For example, through the smart home system, the remote control of home equipment can be achieved to improve the convenience of life. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： big data

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DAAL: Domain Adversarial Active Learning Based on Dual Features for Rumor Detection 12th

DAAL: Domain Adversarial Active Learning Based on Dual Feat...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Zhang, Chaoran Gao, Min Huang, Yinqiu Jiang, Feng Wang, Jia Wen, Junhao School of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Business Vocational College Chongqing China

ISBN: (纸本)9783031446955

The widespread of rumors has a negative impact on society, and rumor detection has attracted significant attention. When a new event appears, the scarcity of corresponding labeled data causes a severe challenge to rumor detection. It is necessary to query high-quality unlabeled data and annotate it for detection. Previous studies on active learning for rumor detection can query the unlabeled samples on the decision boundary based on the textual features of posts. These studies, when selecting the optimal samples, have not sufficiently considered that new event posts are usually far from old events and differ in terms of sentiment, which often plays a key role in rumor detection. Therefore, overlooking these characteristics could potentially lead to sub-optimal performance in rumor detection based on these active learning methods. To this end, we propose domain adversarial active learning based on dual features (DAAL) for rumor detection, considering these characteristics. Specifically, we first extract dual features, including affective and textual features, to obtain representations of the posts. We then propose a new active learning method that selects the samples furthest from all labeled samples based on their dual features. This method helps our rumor detection model gain more labels from new, distant event posts for training. Finally, we introduce adversarial domain training, a method designed to extract transferable features across different events (or domains), to enhance the adaptability of our rumor detection model to new events. Experimental results demonstrate DAAL can select high-quality candidates and achieve superior performance compared to existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature extraction

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A framework for accurate and efficient classification of manufacturing resources in the industrial internet platform

A framework for accurate and efficient classification of man...

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2022 International Symposium on Intelligent Robotics and Systems, ISoIRS 2022

作者： Zhang, Zhe Wang, Xu Wu, Yingbo Chongqing University College of Mechanical and Vehicle Engineering Chongqing China Chongqing University School of Big Data & Software Engineering Chongqing China

ISBN: (纸本)9781665455213

Manufacturing Resource Classification (MRC) is a prerequisite for optimal manufacturing resource allocation in the Industrial Internet Platform (IIP). Some key challenges are the inaccurate description of digital manufacturing resources (MRs) and capabilities, the large scope of resource search, and the slow response time of the cloud platform. To address these issues, this paper proposes a framework for classifying resources based on Video Semantic Description (VSD) and Event Description (ED) and related implementations to support the optimal allocation of MRs in the IIP. Firstly, we propose a framework for accurately and efficiently classifying MRs, the structure of which mainly consists of real-time digital descriptions of MRs, construction of manufacturing event clouds, and similar classification of manufacturing events. In addition, we propose a real-time numerical description method for MRs to address the real-time transmission and accurate description of IIP platforms, an event cloud construction method to shorten the search scope of resource allocation, and an event cloud similarity classification method to reduce the associated computational redundancy, which can support the accurate and efficient classification of MRs. © 2022 IEEE.

关键词： Semantics

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of Computer Science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Context-Aware Collaborative Ranking 15th

Context-Aware Collaborative Ranking

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15th Asia Information Retrieval Societies Conference, AIRS 2019

作者： Dai, Wei Pan, Weike Ming, Zhong National Engineering Laboratory for Big Data System Computing Technology College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783030428341

Recommender systems (RS) are being used in a broad range of applications, from online shopping websites to music streaming platforms, which aim to provide users high-quality personalized services. Collaborative filtering (CF) is a promising technique to ensure the accuracy of a recommender system, which can be divided into specific tasks such as rating prediction and item ranking. However, there is a larger volume of published works studying the problem of rating prediction, rather than item ranking though it is recognized to be more appropriate for the final recommendation in a real application. On the other hand, many studies on item ranking devoted to leveraging implicit feedback are limited in performance improvements due to the uniformity of implicit feedback. Hence, in this paper, we focus on item ranking with informative explicit feedback, which is also called collaborative ranking. In particular, we propose a novel recommendation model termed context-aware collaborative ranking (CCR), which adopts a logistic loss function to measure the predicted error of ranking and exploits the inherent preference context derived from the explicit feedback. Moreover, we design an elegant strategy to distinguish between positive and negative samples used in the process of model training. Empirical studies on four real-world datasets clearly demonstrate that our CCR outperforms the state-of-the-art methods in terms of various ranking-oriented evaluation metrics. © Springer Nature Switzerland AG 2020.

关键词： Collaborative filtering

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Gesture Recognition Based on Accelerometer and Gyroscope and Its Application in Medical and Smart Homes

Gesture Recognition Based on Accelerometer and Gyroscope and...

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Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and big data, APWeb-WAIM 2018

作者： Su, Huaizhou Li, Yande Liu, Li School of Big Data and Software Engineering Chongqing University Chongqing China College of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (纸本)9783030012977

In recent years, with the rapid development of science and technology, artificial intelligence has gradually entered various fields, and medicine is no exception. For the patients with hand or leg disability after hand injury surgery, the rapid development of artificial intelligence undoubtedly also contributes to improving their life a lot. Lately, gesture-based human-computer interaction has further accelerated its research due to its natural and intuitive interaction, but building a powerful gesture recognition system is still based on traditional visual methods such as the one proposed in [1] based on multiple cameras which uses color matching for motion detection and tracking and applies it to vehicle control;and another gesture recognition algorithm using stereo imaging and color vision proposed in [2]. The popularity of accelerometers and gyroscopes opens up a new path for gesture recognition. This paper presents a gesture recognition algorithm based on neural network using accelerometer and gyroscope, and applies it to a simple mobile game and smart socket. The mobile game will be used in the hand rehabilitation training of patients after hand injury surgery, leg disability patients inconvenience, smart socket will help them more easily and quickly manage household electricity. © 2018, Springer Nature Switzerland AG.

关键词： Human computer interaction

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Rethinking the Sample Relations for Few-Shot Classification

arXiv

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arXiv 2025年

作者： Yin, Guowei Huang, Sheng Huangfu, Luwen Zhang, Yi Zhang, Xiaohong School of Big Data and Software Engineering Chongqing University No.55 Daxuecheng South Rd. Shapingba Chongqing401331 China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University No.174 Shazhengjie Shapingba Chongqing400044 China Fowler College of Business San Diego State University San DiegoCA92182 United States San Diego State University San DiegoCA92182 United States AI4Business Lab San Diego State University San DiegoCA92182 United States

Feature quality is paramount for classification performance, particularly in few-shot scenarios. Contrastive learning, a widely adopted technique for enhancing feature quality, leverages sample relations to extract intrinsic features that capture semantic information and has achieved remarkable success in Few-Shot Learning (FSL). Nevertheless, current few-shot contrastive learning approaches often overlook the semantic similarity discrepancies at different granularities when employing the same modeling approach for different sample relations, which limits the potential of few-shot contrastive learning. In this paper, we introduce a straightforward yet effective contrastive learning approach, Multi-Grained Relation Contrastive Learning (MGRCL), as a pretraining feature learning model to boost few-shot learning by meticulously modeling sample relations at different granularities. MGRCL categorizes sample relations into three types: intra-sample relation of the same sample under different transformations, intra-class relation of homogenous samples, and inter-class relation of inhomogeneous samples. In MGRCL, we design Transformation Consistency Learning (TCL) to ensure the rigorous semantic consistency of a sample under different transformations by aligning predictions of input pairs. Furthermore, to preserve discriminative information, we employ Class Contrastive Learning (CCL) to ensure that a sample is always closer to its homogenous samples than its inhomogeneous ones, as homogenous samples share similar semantic content while inhomogeneous samples have different semantic content. Our method is assessed across four popular FSL benchmarks, showing that such a simple pre-training feature learning method surpasses a majority of leading FSL methods. Moreover, our method can be incorporated into other FSL methods as the pre-trained model and help them obtain significant performance gains. © 2025, CC BY-NC-SA.

关键词： Contrastive Learning

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FedRec++: Lossless Federated Recommendation with Explicit Feedback 35

FedRec++: Lossless Federated Recommendation with Explicit Fe...

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35th AAAI Conference on Artificial Intelligence, AAAI 2021

作者： Liang, Feng Pan, Weike Ming, Zhong National Engineering Laboratory for Big Data System Computing Technology College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781713835974

With the marriage of federated machine learning and recommender systems for privacy-aware preference modeling and personalization, there comes a new research branch called federated recommender systems aiming to build a recommendation model in a distributed way, i.e., each user is represented as a distributed client where his/her original rating data are not shared with the server or the other clients. Notice that, besides the sensitive information of a specific rating score assigned to a certain item by a user, the information of a user’s rated set of items shall also be well protected. Some very recent works propose to randomly sample some unrated items for each user and then assign some virtual ratings, so that the server can not identify the scores and the set of rated items easily during the server-client interactions. However, the virtual ratings assigned to the randomly sampled items will inevitably introduce some noise to the model training process, which will then cause loss in recommendation performance. In this paper, we propose a novel lossless federated recommendation method (FedRec++) by allocating some denoising clients (i.e., users) to eliminate the noise in a privacy-aware manner. We further analyse our FedRec++ in terms of security and losslessness, and discuss its generality in the context of existing works. Extensive empirical studies clearly show the effectiveness of our FedRec++ in providing accurate and privacy-aware recommendation without much additional communication cost. Copyright © 2021, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Recommender systems

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