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A biased edge enhancement method for truss-based community search Front. Comput. Sci. (IF 4.2) Pub Date : 2024-03-15 Yuqi Li, Tao Meng, Zhixiong He, Haiyan Liu, Keqin Li
Most truss-based community search methods are usually confronted with the fragmentation issue. We propose a Biased edge Enhancement method for Truss-based Community Search (BETCS) to address the issue. This paper mainly solves the fragmentation problem in truss community query through data enhancement. In future work, we will consider applying the methods in the text to directed graphs or dynamic graphs
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XGCN: a library for large-scale graph neural network recommendations Front. Comput. Sci. (IF 4.2) Pub Date : 2024-03-15 Xiran Song, Hong Huang, Jianxun Lian, Hai Jin
This work introduces a GNN library, XGCN, which is designed to assist users in rapidly developing and running large-scale GNN recommendation models. We offer highly scalable GNN reproductions and include a recently proposed GNN model: xGCN. Experimental evaluations on datasets of varying scales demonstrate the superior scalability of our XGCN library.
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Uncertain knowledge graph embedding: an effective method combining multi-relation and multi-path Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-23 Qi Liu, Qinghua Zhang, Fan Zhao, Guoyin Wang
Uncertain Knowledge Graphs (UKGs) are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs. The research on the embedding of UKG has only recently begun, Uncertain Knowledge Graph Embedding (UKGE) model has a certain effect on solving this problem. However, there are still unresolved issues. On the one hand, when reasoning
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Unsupervised social network embedding via adaptive specific mappings Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Youming Ge, Cong Huang, Yubao Liu, Sen Zhang, Weiyang Kong
In this paper, we address the problem of unsuperised social network embedding, which aims to embed network nodes, including node attributes, into a latent low dimensional space. In recent methods, the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction performance. However, the non-linear property of node attributes and network
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CompactChain: an efficient stateless chain for UTXO-model blockchain Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 B. Swaroopa Reddy, T. Uday Kiran Reddy
In this work, we propose a stateless blockchain called CompactChain, which compacts the entire state of the UTXO (Unspent Transaction Output) based blockchain systems into two RSA accumulators. The first accumulator is called Transaction Output (TXO) commitment which represents the TXO set. The second one is called Spent Transaction Output (STXO) commitment which represents the STXO set. In this work
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Spreadsheet quality assurance: a literature review Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Pak-Lok Poon, Man Fai Lau, Yuen Tak Yu, Sau-Fun Tang
Spreadsheets are very common for information processing to support decision making by both professional developers and non-technical end users. Moreover, business intelligence and artificial intelligence are increasingly popular in the industry nowadays, where spreadsheets have been used as, or integrated into, intelligent or expert systems in various application domains. However, it has been repeatedly
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Decoupled deep hough voting for point cloud registration Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Mingzhi Yuan, Kexue Fu, Zhihao Li, Manning Wang
Estimating rigid transformation using noisy correspondences is critical to feature-based point cloud registration. Recently, a series of studies have attempted to combine traditional robust model fitting with deep learning. Among them, DHVR proposed a hough voting-based method, achieving new state-of-the-art performance. However, we find voting on rotation and translation simultaneously hinders achieving
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Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Yizheng Wang, Xin Zhang, Ying Ju, Qing Liu, Quan Zou, Yazhou Zhang, Yijie Ding, Ying Zhang
Numerous studies have demonstrated that human microRNAs (miRNAs) and diseases are associated and studies on the microRNA-disease association (MDA) have been conducted. We developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning (HSIC-MKL) to solve the problem of the large time commitment and cost of
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EvolveKG: a general framework to learn evolving knowledge graphs Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Jiaqi Liu, Zhiwen Yu, Bin Guo, Cheng Deng, Luoyi Fu, Xinbing Wang, Chenghu Zhou
A great many practical applications have observed knowledge evolution, i.e., continuous born of new knowledge, with its formation influenced by the structure of historical knowledge. This observation gives rise to evolving knowledge graphs whose structure temporally grows over time. However, both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain unexplored
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Hybrid concurrency control protocol for data sharing among heterogeneous blockchains Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Tiezheng Guo, Zhiwei Zhang, Ye Yuan, Xiaochun Yang, Guoren Wang
With the development of information technology and cloud computing, data sharing has become an important part of scientific research. In traditional data sharing, data is stored on a third-party storage platform, which causes the owner to lose control of the data. As a result, there are issues of intentional data leakage and tampering by third parties, and the private information contained in the data
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A probabilistic generative model for tracking multi-knowledge concept mastery probability Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Hengyu Liu, Tiancheng Zhang, Fan Li, Minghe Yu, Ge Yu
Knowledge tracing aims to track students’ knowledge status over time to predict students’ future performance accurately. In a real environment, teachers expect knowledge tracing models to provide the interpretable result of knowledge status. Markov chain-based knowledge tracing (MCKT) models, such as Bayesian Knowledge Tracing, can track knowledge concept mastery probability over time. However, as
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A hybrid memory architecture supporting fine-grained data migration Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Ye Chi, Jianhui Yue, Xiaofei Liao, Haikun Liu, Hai Jin
Hybrid memory systems composed of dynamic random access memory (DRAM) and Non-volatile memory (NVM) often exploit page migration technologies to fully take the advantages of different memory media. Most previous proposals usually migrate data at a granularity of 4 KB pages, and thus waste memory bandwidth and DRAM resource. In this paper, we propose Mocha, a non-hierarchical architecture that organizes
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An anonymous authentication and secure data transmission scheme for the Internet of Things based on blockchain Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Xingxing Chen, Qingfeng Cheng, Weidong Yang, Xiangyang Luo
With the widespread use of network infrastructures such as 5G and low-power wide-area networks, a large number of the Internet of Things (IoT) device nodes are connected to the network, generating massive amounts of data. Therefore, it is a great challenge to achieve anonymous authentication of IoT nodes and secure data transmission. At present, blockchain technology is widely used in authentication
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Accelerating BERT inference with GPU-efficient exit prediction Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22
Abstract BERT is a representative pre-trained language model that has drawn extensive attention for significant improvements in downstream Natural Language Processing (NLP) tasks. The complex architecture and massive parameters bring BERT competitive performance but also result in slow speed at model inference time. To speed up BERT inference, FastBERT realizes adaptive inference with an acceptable
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FedDAA: a robust federated learning framework to protect privacy and defend against adversarial attack Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Shiwei Lu, Ruihu Li, Wenbin Liu
Federated learning (FL) has emerged to break data-silo and protect clients’ privacy in the field of artificial intelligence. However, deep leakage from gradient (DLG) attack can fully reconstruct clients’ data from the submitted gradient, which threatens the fundamental privacy of FL. Although cryptology and differential privacy prevent privacy leakage from gradient, they bring negative effect on communication
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Group control for procedural rules: parameterized complexity and consecutive domains Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Yongjie Yang, Dinko Dimitrov
We consider GROUP CONTROL BY ADDING INDIVIDUALS (GCAI) in the setting of group identification for two procedural rules—the consensus-start-respecting rule and the liberal-start-respecting rule. It is known that GCAI for both rules are NP-hard, but whether they are fixed-parameter tractable with respect to the number of distinguished individuals remained open. We resolve both open problems in the affirmative
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A disk I/O optimized system for concurrent graph processing jobs Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Xianghao Xu, Fang Wang, Hong Jiang, Yongli Cheng, Dan Feng, Peng Fang
In order to analyze and process the large graphs with high cost efficiency, researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity computer. On the other hand, with the rapidly growing need of analyzing graphs in the real-world, graph processing systems have to efficiently handle massive concurrent graph processing (CGP) jobs. Unfortunately
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Protein acetylation sites with complex-valued polynomial model Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-22 Wenzheng Bao, Bin Yang
Protein acetylation refers to a process of adding acetyl groups (CH3CO-) to lysine residues on protein chains. As one of the most commonly used protein post-translational modifications, lysine acetylation plays an important role in different organisms. In our study, we developed a human-specific method which uses a cascade classifier of complex-valued polynomial model (CVPM), combined with sequence
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GRAMO: geometric resampling augmentation for monocular 3D object detection Front. Comput. Sci. (IF 4.2) Pub Date : 2024-01-15 He Guan, Chunfeng Song, Zhaoxiang Zhang
Data augmentation is widely recognized as an effective means of bolstering model robustness. However, when applied to monocular 3D object detection, non-geometric image augmentation neglects the critical link between the image and physical space, resulting in the semantic collapse of the extended scene. To address this issue, we propose two geometric-level data augmentation operators named Geometric-Copy-Paste
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Research on performance optimization of virtual data space across WAN Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-28 Jiantong Huo, Zhisheng Huo, Limin Xiao, Zhenxue He
For the high-performance computing in a WAN environment, the geographical locations of national supercomputing centers are scattered and the network topology is complex, so it is difficult to form a unified view of resources. To aggregate the widely dispersed storage resources of national supercomputing centers in China, we have previously proposed a global virtual data space named GVDS in the project
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Neural partially linear additive model Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-28 Liangxuan Zhu, Han Li, Xuelin Zhang, Lingjuan Wu, Hong Chen
Interpretability has drawn increasing attention in machine learning. Most works focus on post-hoc explanations rather than building a self-explaining model. So, we propose a Neural Partially Linear Additive Model (NPLAM), which automatically distinguishes insignificant, linear, and nonlinear features in neural networks. On the one hand, neural network construction fits data better than spline function
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A general tail item representation enhancement framework for sequential recommendation Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-28 Mingyue Cheng, Qi Liu, Wenyu Zhang, Zhiding Liu, Hongke Zhao, Enhong Chen
Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems (SRS). However, the current deep model structures are limited in their ability to learn high-quality embeddings with insufficient data. Meanwhile, highly skewed long-tail distribution is very common in recommender systems. Therefore, in this paper, we focus on enhancing the
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Rts: learning robustly from time series data with noisy label Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-28 Zhi Zhou, Yi-Xuan Jin, Yu-Feng Li
Significant progress has been made in machine learning with large amounts of clean labels and static data. However, in many real-world applications, the data often changes with time and it is difficult to obtain massive clean annotations, that is, noisy labels and time series are faced simultaneously. For example, in product-buyer evaluation, each sample records the daily time behavior of users, but
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A program logic for obstruction-freedom Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-28 Zhao-Hui Li, Xin-Yu Feng
Though obstruction-free progress property is weaker than other non-blocking properties including lock-freedom and wait-freedom, it has advantages that have led to the use of obstruction-free implementations for software transactional memory (STM) and in anonymous and fault-tolerant distributed computing. However, existing work can only verify obstruction-freedom of specific data structures (e.g., STM
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Aggregation-based dual heterogeneous task allocation in spatial crowdsourcing Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-28 Xiaochuan Lin, Kaimin Wei, Zhetao Li, Jinpeng Chen, Tingrui Pei
Spatial crowdsourcing (SC) is a popular data collection paradigm for numerous applications. With the increment of tasks and workers in SC, heterogeneity becomes an unavoidable difficulty in task allocation. Existing researches only focus on the single-heterogeneous task allocation. However, a variety of heterogeneous objects coexist in real-world SC systems. This dramatically expands the space for
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Federated learning-outcome prediction with multi-layer privacy protection Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-28
Abstract Learning-outcome prediction (LOP) is a longstanding and critical problem in educational routes. Many studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection issue. To this end, this study proposes a distributed grade prediction model, dubbed FecMap, by exploiting the federated
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IP2vec: an IP node representation model for IP geolocation Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-28 Fan Zhang, Meijuan Yin, Fenlin Liu, Xiangyang Luo, Shuodi Zu
IP geolocation is essential for the territorial analysis of sensitive network entities, location-based services (LBS) and network fraud detection. It has important theoretical significance and application value. Measurement-based IP geolocation is a hot research topic. However, the existing IP geolocation algorithms cannot effectively utilize the distance characteristics of the delay, and the nodes’
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A new design of parity-preserving reversible multipliers based on multiple-control toffoli synthesis targeting emerging quantum circuits Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-28 Mojtaba Noorallahzadeh, Mohammad Mosleh, Kamalika Datta
With the recent demonstration of quantum computers, interests in the field of reversible logic synthesis and optimization have taken a different turn. As every quantum operation is inherently reversible, there is an immense motivation for exploring reversible circuit design and optimization. When it comes to faults in circuits, the parity-preserving feature donates to the detection of permanent and
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Model gradient: unified model and policy learning in model-based reinforcement learning Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-27
Abstract Model-based reinforcement learning is a promising direction to improve the sample efficiency of reinforcement learning with learning a model of the environment. Previous model learning methods aim at fitting the transition data, and commonly employ a supervised learning approach to minimize the distance between the predicted state and the real state. The supervised model learning methods,
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Graph-Segmenter: graph transformer with boundary-aware attention for semantic segmentation Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-23 Zizhang Wu, Yuanzhu Gan, Tianhao Xu, Fan Wang
The transformer-based semantic segmentation approaches, which divide the image into different regions by sliding windows and model the relation inside each window, have achieved outstanding success. However, since the relation modeling between windows was not the primary emphasis of previous work, it was not fully utilized. To address this issue, we propose a Graph-Segmenter, including a graph transformer
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BVDFed: Byzantine-resilient and verifiable aggregation for differentially private federated learning Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-23
Abstract Federated Learning (FL) has emerged as a powerful technology designed for collaborative training between multiple clients and a server while maintaining data privacy of clients. To enhance the privacy in FL, Differentially Private Federated Learning (DPFL) has gradually become one of the most effective approaches. As DPFL operates in the distributed settings, there exist potential malicious
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Discriminative explicit instance selection for implicit discourse relation classification Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-23 Wei Song, Hongfei Han, Xu Han, Miaomiao Cheng, Jiefu Gong, Shijin Wang, Ting Liu
Discourse relation classification is a fundamental task for discourse analysis, which is essential for understanding the structure and connection of texts. Implicit discourse relation classification aims to determine the relationship between adjacent sentences and is very challenging because it lacks explicit discourse connectives as linguistic cues and sufficient annotated training data. In this paper
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Empirically revisiting and enhancing automatic classification of bug and non-bug issues Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-23 Zhong Li, Minxue Pan, Yu Pei, Tian Zhang, Linzhang Wang, Xuandong Li
A large body of research effort has been dedicated to automated issue classification for Issue Tracking Systems (ITSs). Although the existing approaches have shown promising performance, the different design choices, including the different textual fields, feature representation methods and machine learning algorithms adopted by existing approaches, have not been comprehensively compared and analyzed
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Provable secure authentication key agreement for wireless body area networks Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-23 Yuqian Ma, Wenbo Shi, Xinghua Li, Qingfeng Cheng
Wireless body area networks (WBANs) guarantee timely data processing and secure information preservation within the range of the wireless access network, which is in urgent need of a new type of security technology. However, with the speedy development of hardware, the existing security schemes can no longer meet the new requirements of anonymity and lightweight. New solutions that do not require complex
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Delegable zk-SNARKs with proxies Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-23
Abstract In this paper, we propose the concept of delegable zero knowledge succinct non-interactive arguments of knowledge (zk-SNARKs). The delegable zk-SNARK is parameterized by (μ,k,k′,k″). The delegable property of zk-SNARKs allows the prover to delegate its proving ability to μ proxies. Any k honest proxies are able to generate the correct proof for a statement, but the collusion of less than k
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ARCHER: a ReRAM-based accelerator for compressed recommendation systems Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-23 Xinyang Shen, Xiaofei Liao, Long Zheng, Yu Huang, Dan Chen, Hai Jin
Modern recommendation systems are widely used in modern data centers. The random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and memory. ReRAM-based processing-in-memory (PIM) can resolve this problem by processing embedding vectors where they
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Optimizing B+-tree for hybrid memory with in-node hotspot cache and eADR awareness Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-23 Peiquan Jin, Zhaole Chu, Gaocong Liu, Yongping Luo, Shouhong Wan
The advance in Non-Volatile Memory (NVM) has changed the traditional DRAM-only memory system. Compared to DRAM, NVM has the advantages of non-volatility and large capacity. However, as the read/write speed of NVM is still lower than that of DRAM, building DRAM/NVM-based hybrid memory systems is a feasible way of adding NVM into the current computer architecture. This paper aims to optimize the well-known
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Contactless interaction recognition and interactor detection in multi-person scenes Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-23 Jiacheng Li, Ruize Han, Wei Feng, Haomin Yan, Song Wang
Human interaction recognition is an essential task in video surveillance. The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people. In this paper, we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of
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Blockchain based federated learning for intrusion detection for Internet of Things Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-23 Nan Sun, Wei Wang, Yongxin Tong, Kexin Liu
In Internet of Things (IoT), data sharing among different devices can improve manufacture efficiency and reduce workload, and yet make the network systems be more vulnerable to various intrusion attacks. There has been realistic demand to develop an efficient intrusion detection algorithm for connected devices. Most of existing intrusion detection methods are trained in a centralized manner and are
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Label distribution similarity-based noise correction for crowdsourcing Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-23 Lijuan Ren, Liangxiao Jiang, Wenjun Zhang, Chaoqun Li
Abstract In crowdsourcing scenarios, we can obtain each instance’s multiple noisy labels from different crowd workers and then infer its integrated label via label aggregation. In spite of the effectiveness of label aggregation methods, there still remains a certain level of noise in the integrated labels. Thus, some noise correction methods have been proposed to reduce the impact of noise in recent
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How graph convolutions amplify popularity bias for recommendation? Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-23
Abstract Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall accuracy, GCNs unfortunately amplify popularity bias — tail items are less likely to be recommended. This effect prevents the GCN-based RS from making precise and fair recommendations, decreasing the effectiveness of
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$$\cal{Y}$$ -Tuning: an efficient tuning paradigm for large-scale pre-trained models via label representation learning Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-18 Yitao Liu, Chenxin An, Xipeng Qiu
With current success of large-scale pre-trained models (PTMs), how efficiently adapting PTMs to downstream tasks has attracted tremendous attention, especially for PTMs with billions of parameters. Previous work focuses on designing parameter-efficient tuning paradigms but needs to save and compute the gradient of the whole computational graph. In this paper, we propose \(\cal{Y}\)-Tuning, an efficient
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A prompt-based approach to adversarial example generation and robustness enhancement Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-18 Yuting Yang, Pei Huang, Juan Cao, Jintao Li, Yun Lin, Feifei Ma
Abstract Recent years have seen the wide application of natural language processing (NLP) models in crucial areas such as finance, medical treatment, and news media, raising concerns about the model robustness and vulnerabilities. We find that prompt paradigm can probe special robust defects of pre-trained language models. Malicious prompt texts are first constructed for inputs and a pre-trained language
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Quantum speedup and limitations on matroid property problems Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-18 Xiaowei Huang, Jingquan Luo, Lvzhou Li
Matroid theory has been developed to be a mature branch of mathematics and has extensive applications in combinatorial optimization, algorithm design and so on. On the other hand, quantum computing has attracted much attention and has been shown to surpass classical computing on solving some computational problems. Surprisingly, crossover studies of the two fields seem to be missing in the literature
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Route selection for opportunity-sensing and prediction of waterlogging Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-18
Abstract Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’ daily travel. However, due to feedback delays or high costs, existing methods make large-scale, fine-grained waterlogging monitoring impossible. A common method is to forecast the city’s global waterlogging status using its partial waterlogging data. This method has two challenges:
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Multi-user reinforcement learning based task migration in mobile edge computing Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-18 Yuya Cui, Degan Zhang, Jie Zhang, Ting Zhang, Lixiang Cao, Lu Chen
Mobile Edge Computing (MEC) is a promising approach. Dynamic service migration is a key technology in MEC. In order to maintain the continuity of services in a dynamic environment, mobile users need to migrate tasks between multiple servers in real time. Due to the uncertainty of movement, frequent migration will increase delays and costs and non-migration will lead to service interruption. Therefore
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On the upper bounds of (1,0)-super solutions for the regular balanced random (k,2s)-SAT problem Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-18 Yongping Wang, Daoyun Xu, Jincheng Zhou
Abstract This paper explores the conditions which make a regular balanced random (k,2s)-CNF formula (1,0)-unsatisfiable with high probability. The conditions also make a random instance of the regular balanced (k − 1,2(k − 1)s)-SAT problem unsatisfiable with high probability, where the instance obeys a distribution which differs from the distribution obeyed by a regular balanced random (k − 1,2(k −
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Learning group interaction for sports video understanding from a perspective of athlete Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-18 Rui He, Zehua Fu, Qingjie Liu, Yunhong Wang, Xunxun Chen
Learning activities interactions between small groups is a key step in understanding team sports videos. Recent research focusing on team sports videos can be strictly regarded from the perspective of the audience rather than the athlete. For team sports videos such as volleyball and basketball videos, there are plenty of intra-team and inter-team relations. In this paper, a new task named Group Scene
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Semantic-aware entity alignment for low resource language knowledge graph Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-18 Junfei Tang, Ran Song, Yuxin Huang, Shengxiang Gao, Zhengtao Yu
Entity alignment (EA) is an important technique aiming to find the same real entity between two different source knowledge graphs (KGs). Current methods typically learn the embedding of entities for EA from the structure of KGs for EA. Most EA models are designed for rich-resource languages, requiring sufficient resources such as a parallel corpus and pre-trained language models. However, low-resource
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HACAN: a hierarchical answer-aware and context-aware network for question generation Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-18 Ruijun Sun, Hanqin Tao, Yanmin Chen, Qi Liu
Question Generation (QG) is the task of generating questions according to the given contexts. Most of the existing methods are based on Recurrent Neural Networks (RNNs) for generating questions with passage-level input for providing more details, which seriously suffer from such problems as gradient vanishing and ineffective information utilization. In fact, reasonably extracting useful information
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Representation learning: serial-autoencoder for personalized recommendation Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-16 Yi Zhu, Yishuai Geng, Yun Li, Jipeng Qiang, Xindong Wu
Nowadays, the personalized recommendation has become a research hotspot for addressing information overload. Despite this, generating effective recommendations from sparse data remains a challenge. Recently, auxiliary information has been widely used to address data sparsity, but most models using auxiliary information are linear and have limited expressiveness. Due to the advantages of feature extraction
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LMR-CBT: learning modality-fused representations with CB-Transformer for multimodal emotion recognition from unaligned multimodal sequences Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-16 Ziwang Fu, Feng Liu, Qing Xu, Xiangling Fu, Jiayin Qi
Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion recognition. Existing approaches use directional pairwise attention or a message hub to fuse language, visual, and audio modalities. However, these fusion methods are often quadratic in complexity with respect to the modal sequence length, bring redundant information
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A robust optimization method for label noisy datasets based on adaptive threshold: Adaptive-k Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-16 Enes Dedeoglu, Himmet Toprak Kesgin, Mehmet Fatih Amasyali
The use of all samples in the optimization process does not produce robust results in datasets with label noise. Because the gradients calculated according to the losses of the noisy samples cause the optimization process to go in the wrong direction. In this paper, we recommend using samples with loss less than a threshold determined during the optimization, instead of using all samples in the mini-batch
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Gria: an efficient deterministic concurrency control protocol Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-16 Xinyuan Wang, Yun Peng, Hejiao Huang
Deterministic databases are able to reduce coordination costs in a replication. This property has fostered a significant interest in the design of efficient deterministic concurrency control protocols. However, the state-of-the-art deterministic concurrency control protocol Aria has three issues. First, it is impractical to configure a suitable batch size when the read-write set is unknown. Second
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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-16 Hongru Gao, Xiaofei Liao, Zhiyuan Shao, Kexin Li, Jiajie Chen, Hai Jin
Graphs that are used to model real-world entities with vertices and relationships among entities with edges, have proven to be a powerful tool for describing real-world problems in applications. In most real-world scenarios, entities and their relationships are subject to constant changes. Graphs that record such changes are called dynamic graphs. In recent years, the widespread application scenarios
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Communication-robust multi-agent learning by adaptable auxiliary multi-agent adversary generation Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-16 Lei Yuan, Feng Chen, Zongzhang Zhang, Yang Yu
Communication can promote coordination in cooperative Multi-Agent Reinforcement Learning (MARL). Nowadays, existing works mainly focus on improving the communication efficiency of agents, neglecting that real-world communication is much more challenging as there may exist noise or potential attackers. Thus the robustness of the communication-based policies becomes an emergent and severe issue that
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Minimizing the cost of periodically replicated systems via model and quantitative analysis Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-16 Chenhao Zhang, Liang Wang, Limin Xiao, Shixuan Jiang, Meng Han, Jinquan Wang, Bing Wei, Guangjun Qin
Geographically replicating objects across multiple data centers improves the performance and reliability of cloud storage systems. Maintaining consistent replicas comes with high synchronization costs, as it faces more expensive WAN transport prices and increased latency. Periodic replication is the widely used technique to reduce the synchronization costs. Periodic replication strategies in existing
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Density estimation-based method to determine sample size for random sample partition of big data Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-16
Abstract Random sample partition (RSP) is a newly developed big data representation and management model to deal with big data approximate computation problems. Academic research and practical applications have confirmed that RSP is an efficient solution for big data processing and analysis. However, a challenge for implementing RSP is determining an appropriate sample size for RSP data blocks. While
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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-16
Abstract Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for battery management systems. Deep learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series data. However, the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs
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Robust AUC maximization for classification with pairwise confidence comparisons Front. Comput. Sci. (IF 4.2) Pub Date : 2023-12-16 Haochen Shi, Mingkun Xie, Shengjun Huang
Supervised learning often requires a large number of labeled examples, which has become a critical bottleneck in the case that manual annotating the class labels is costly. To mitigate this issue, a new framework called pairwise comparison (Pcomp) classification is proposed to allow training examples only weakly annotated with pairwise comparison, i.e., which one of two examples is more likely to be