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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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Keeping Pace with Ever-Increasing data: Towards Continual Learning of Code Intelligence Models

arXiv

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arXiv 2023年

作者： Gao, Shuzheng Zhang, Hongyu Gao, Cuiyun Wang, Chaozheng School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Big Data and Software Engineering Chongqing University China

—Previous research on code intelligence usually trains a deep learning model on a fixed dataset in an offline manner. However, in real-world scenarios, new code repositories emerge incessantly, and the carried new knowledge is beneficial for providing up-to-date code intelligence services to developers. In this paper, we aim at the following problem: How to enable code intelligence models to continually learn from ever-increasing data? One major challenge here is catastrophic forgetting, meaning that the model can easily forget knowledge learned from previous datasets when learning from the new dataset. To tackle this challenge, we propose REPEAT, a novel method for continual learning of code intelligence models. Specifically, REPEAT addresses the catastrophic forgetting problem with representative exemplars replay and adaptive parameter regularization. The representative exemplars replay component selects informative and diverse exemplars in each dataset and uses them to retrain model periodically. The adaptive parameter regularization component recognizes important parameters in the model and adaptively penalizes their changes to preserve the knowledge learned before. We evaluate the proposed approach on three code intelligence tasks including code summarization, software vulnerability detection, and code clone detection. Extensive experiments demonstrate that REPEAT consistently outperforms baseline methods on all tasks. For example, REPEAT improves the conventional fine-tuning method by 1.22, 5.61, and 1.72 on code summarization, vulnerability detection and clone detection, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Cloning

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Dual Interaction and Kernel-Diverse Network for Accurate Drug-Target Binding Affinity Prediction

Dual Interaction and Kernel-Diverse Network for Accurate Dru...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yulong Wu Jin Xie Jing Nie Jian Hu Yuansong Zeng School of Big Data and Software Engineering Chongqing University Chongqing China School of School of Microelectronics and Communication Engineering Chongqing University Chongqing China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-target binding affinities measure the binding strength between drugs and targets, serving as the foundation for computer-aided drug design. The existing methods based on neural network rarely incorporate knowledge from biochemistry, which limits their performance. In this paper, we propose a dual interaction and kernel-diverse network for drug-target binding affinity prediction, termed DTANet, drawing inspiration from the biochemical properties of drugs and targets. Specifically, since functional groups and peptide chains are pivotal in determining the properties of drugs and their targets, and their lengths vary significantly. Drawing inspiration from this, we propose a Kernel-diverse Two-stream Network (KTN) and a Cross-Scale Interaction Module (CSIM) to extract the features of drugs and targets. The binding site of drug and target is crucial for precise affinity prediction. Inspired by this, we introduce the Drug-Target Interaction Module (DTIM) to explore the relationships between drugs and targets to focus on binding sites. Experiments have been conducted on two popular benchmarks: KIBA and Davis. Our DTANet obtains superior performance compared to existing methods on both datasets. Source codes are available at https://***/202324131016T/DTANet.

关键词： Drugs Knowledge engineering Convolutional codes Peptides Source coding Neural networks Deep architecture Feature extraction Physiology Kernel

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Unlearnable 3D point clouds: class-wise transformation is all you need 24

Unlearnable 3D point clouds: class-wise transformation is al...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xianlong Wang Minghui Li Wei Liu Hangtao Zhang Shengshan Hu Yechao Zhang Ziqi Zhou Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (纸本)9798331314385

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples; (ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC.

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Vanadium dioxide-based phase gradient-switchable coding metasurface

Vanadium dioxide-based phase gradient-switchable coding meta...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yujie Zhang Tao Wei Zhenyu Sun Hongyi Ge Xiaodi Ji Yuying Jiang Yuwei Bu Xuyang Wu Keke Jia College of Information Science and Engineering HAUT Zhengzhou China College of Information Science and Engineering School of Software HAUT HAUE Zhengzhou China College of Intelligence and Big Data HAUT Zhengzhou China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

In this paper, we design a phase gradient switchable coding metasurface based on vanadium dioxide (VO2). Each unit structure of the metasurface consists of three layers: a metal-VO2 composite structural layer, a polytetrafluoroethylene dielectric layer and a metal reflective layer. The metasurface utilizes the phase transition property of VO2 to dynamically reconstruct the phase gradient of the metasurface and realize the switching of two far-field beam functions. Simulation results show that when VO2 is in the insulating state, the phase difference between the units of the metasurface at 1.5 THz frequency is 90°, which can realize the anomalous deflection function of the 2-bit coding metasurface. When VO2 is transformed into a metallic state, the phase difference between the metasurfaces at the 2.1 THz frequency is 180°, which reconfigures the metasurface into a 1-bit phase-gradient coding metasurface and realizes the dual-beam beam splitting function. The phase gradient switchable coding metasurface designed in this paper enriches the flexibility of beam modulation and provides a new efficient and flexible beam modulation scheme for terahertz communication with potential applications.

关键词： Wireless communication Phase modulation Simulation Metals Switches Vanadium Transforms Metasurfaces Encoding Terahertz communications

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Housing Profile and Recommendation for Housing Provident Fund Using Two-Tower Neural Collaborative Filtering

Housing Profile and Recommendation for Housing Provident Fun...

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IEEE International Conference on Cloud Computing and big data Analysis (ICCCBDA)

作者： Zhijun Xie Zhenting Guo Xiaoxia Hong Zuoming Zhao Juan Liu Xiaoling Li Chongqing Housing Provident Fund Management Center Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China

The housing provident fund system has provided tremendous assistance in helping provident fund users take out loans for home purchase services since its inception. Currently, as digitalization and information technology continue to strengthen, the country emphasizes the need to use digital technology (e.g., big data, artificial intelligence, etc.) extensively in government management services and to drive innovation in housing fund management, service, and supervision. Profile and recommendation technologies were mostly used in the field of financial technology, such as e-commerce platforms and commercial banks, but we innovatively apply the big data profile and artificial intelligence recommendation methods in the provident fund. We propose a multi-dimensional profile model based on the big data from the Housing Provident Fund Center and establish a housing profile labeling system to help the center in digital management better. At the same time, based on the two-tower neural network collaborative filtering algorithm, we built a housing recommendation model based on the past purchase transaction history, which can be used to improve the digital service level of the center. After experiments, our model is much more accurate than the compared traditional collaborative filtering algorithms. Through our study, the Housing Provident Center can enhance digital management techniques, improve service level, and promote intelligent government construction.

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A Novel Multi-layered Minutiae Extractor Based on OCT Fingerprints 16th

A Novel Multi-layered Minutiae Extractor Based on OCT Finge...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Zeng, Wenfeng Zhang, Wentian Liu, Feng Tan, Xu Li, Qin College of Computer Science and Software Engineering Shenzhen University Shenzhen China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Senzhen China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9783031202322

Fingerprints of Optical Coherence Tomography (OCT) imaging provide 3D volume data which have the nature property of multi-layered tissue structure. This paper, for the first time, attempts to extract minutiae for OCT-based fingerprint by making full use of the merits multi-layered structure of OCT imaging and powerful convolution al neural network (CNN). In particular, a novel multi-layered feature fusion minutiae extraction network is proposed, involving a multi-layered feature extractor to integrate the rich information of multiple subsurface fingerprints, and a two-stage object detection framework using reweighted concatenation feature to detect minutiae points. Compared with the results achieved by typical traditional and learning-based methods, our method outperforms the best one by about 2% of F1-score in minutiae extraction, and achieves the improvement of 0.09% than the best one in matching performance, which further demonstrate the effectiveness and robustness of our proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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Analysis of converting -circuit into -circuit

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EPJ Quantum Technology 2025年第1期12卷 1-14页

作者： Luo, Qing-bin Ding, Lang Yang, Guo-wu Li, Xiao-yu College of Intelligent Systems Science and Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

A $\mathfrak{C^{*}}$ -circuit, which was proposed in Asiacypt 2022 by Huang and Sun (Advances in cryptology – ASIACRYPT 2022, pp. 614–644, 2022), can directly perform calculations with the existing quantum states, thereby reducing the use of quantum resources in quantum logic synthesis. We theoretically prove how to convert a $\mathfrak{C^{0}}$ -circuit into the corresponding $\mathfrak{C^{*}}$ -circuit through two lemmas and one theorem. The first lemma proves the interchangeability of CNOT gates and NOT gates by using the equivalence of quantum circuits. The second lemma proves that adding CNOT gates to the front of a quantum circuit whose initial states are all $|0\rangle $ s will not change the output states of the circuit. The theorem is used to describe what kind of $\mathfrak{C^{0}}$ -circuit can be transformed into $\mathfrak{C^{*}}$ -circuit, and the correctness of this transformation is proved. Our work will provide a theoretical basis for converting $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit. Then applying the theoretical analysis results to the multiplication over $\text{GF}(2^{8})$ , the constructed quantum circuit needs 27 Toffoli gates and 118 CNOT gates, which is 15 fewer Toffoli gates and 43 CNOT gates than the current best result. This shows that the method of constructing quantum circuits by using the conversion of $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit is very efficient.

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy data preprocessing software algorithms Distributed databases Parallel processing software Partitioning algorithms

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Deep Reinforcement Learning for Dynamic Demand in Shared Bicycle Scheduling Problem 9

Deep Reinforcement Learning for Dynamic Demand in Shared Bic...

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Jiao, Zixiao Xiao, Wenhua Xin, Hailu Liu, Daqian Yan, Hui Chen, Chao National University of Defense Technology Laboratory for Big Data and Decision Changsha China Academy of Military Sciences Beijing China National University of Defense Technology College of Systems Engineering Changsha China Liaoning Technical University Software College Liaoning China Central South University School of Traffic and Transportation Engineering Changsha China

ISBN: (纸本)9798350330076

In contemporary shared bicycle scheduling systems, a common occurrence is a severe imbalance between regions, with one area suffering from a dearth of bicycles while another area is burdened with an excess. The task of balancing the distribution of vehicles can be abstracted as The Vehicle Routing Problem with Pickup and Delivery and Time Windows (VRPSPDTW). The primary objective is to efficiently schedule vehicles between multiple sites to minimize the overall transportation cost. However, in practical scheduling scenarios, the demand at each site undergoes dynamic fluctuations over time, thereby presenting a formidable challenge for problem-solving. Hence, this study employs a deep reinforcement learning algorithm equipped with a pointer network, constructing a computational environment capable of accommodating the dynamic nature of demand and lifting the constraints on the number of scheduled vehicles. Furthermore, the integration of a node masking mechanism enables the acquisition of high-quality scheduling strategies for addressing the shared bicycle scheduling predicament. Two distinct scheduling strategies are proposed, one prioritizing cost optimization and the other emphasizing user demand fulfillment. Empirical findings substantiate that the proposed model surpasses other conventional algorithms in terms of performance efficacy and computational efficiency. © 2023 IEEE.

关键词： Reinforcement learning

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