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Feb 2024, Volume 18 Issue 1
    
  • Select all
    Perspective
  • PERSPECTIVE
    Haibo CHEN, Ning JIA, Jie YIN
  • Software
  • LETTER
    Zhuo ZHANG, Ya LI, Jianxin XUE, Xiaoguang MAO
  • RESEARCH ARTICLE
    Shiyuan LIU, Yunchun LI, Hailong YANG, Ming DUN, Chen CHEN, Huaitao ZHANG, Wei LI

    In recent years, the demand for real-time data processing has been increasing, and various stream processing systems have emerged. When the amount of data input to the stream processing system fluctuates, the computing resources required by the stream processing job will also change. The resources used by stream processing jobs need to be adjusted according to load changes, avoiding the waste of computing resources. At present, existing works adjust stream processing jobs based on the assumption that there is a linear relationship between the operator parallelism and operator resource consumption (e.g., throughput), which makes a significant deviation when the operator parallelism increases. This paper proposes a nonlinear model to represent operator performance. We divide the operator performance into three stages, the Non-competition stage, the Non-full competition stage, and the Full competition stage. Using our proposed performance model, given the parallelism of the operator, we can accurately predict the CPU utilization and operator throughput. Evaluated with actual experiments, the prediction error of our model is below 5%. We also propose a quick accurate auto-scaling (QAAS) method that uses the operator performance model to implement the auto-scaling of the operator parallelism of the Flink job. Compared to previous work, QAAS is able to maintain stable job performance under load changes, minimizing the number of job adjustments and reducing data backlogs by 50%.

  • Artificial Intelligence
  • LETTER
    Xiaoxu CAI, Jianwen LOU, Jiajun BU, Junyu DONG, Haishuai WANG, Hui YU
  • LETTER
    Fanyi YANG, Huifang MA, Wentao WANG, Zhixin LI, Liang CHANG
  • REVIEW ARTICLE
    Fengxia LIU, Zhiming ZHENG, Yexuan SHI, Yongxin TONG, Yi ZHANG

    Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw datasets. To enhance privacy in federated learning, multi-party computation can be leveraged for secure communication and computation during model training. This survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy, as well as the corresponding optimization techniques to improve model accuracy and training efficiency. We also pinpoint future directions to deploy federated learning to a wider range of applications.

  • RESEARCH ARTICLE
    Yang YANG, Jinyi GUO, Guangyu LI, Lanyu LI, Wenjie LI, Jian YANG

    Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent representations of heterogeneous modalities, thereby to search similar instances in one modality according to the query from another modality in result. The basic assumption behind these methods is that parallel multi-modal data (i.e., different modalities of the same example are aligned) can be obtained in prior. In other words, the image-sentence cross-modal retrieval task is a supervised task with the alignments as ground-truths. However, in many real-world applications, it is difficult to realign a large amount of parallel data for new scenarios due to the substantial labor costs, leading the non-parallel multi-modal data and existing methods cannot be used directly. On the other hand, there actually exists auxiliary parallel multi-modal data with similar semantics, which can assist the non-parallel data to learn the consistent representations. Therefore, in this paper, we aim at “Alignment Efficient Image-Sentence Retrieval” (AEIR), which recurs to the auxiliary parallel image-sentence data as the source domain data, and takes the non-parallel data as the target domain data. Unlike single-modal transfer learning, AEIR learns consistent image-sentence cross-modal representations of target domain by transferring the alignments of existing parallel data. Specifically, AEIR learns the image-sentence consistent representations in source domain with parallel data, while transferring the alignment knowledge across domains by jointly optimizing a novel designed cross-domain cross-modal metric learning based constraint with intra-modal domain adversarial loss. Consequently, we can effectively learn the consistent representations for target domain considering both the structure and semantic transfer. Furthermore, extensive experiments on different transfer scenarios validate that AEIR can achieve better retrieval results comparing with the baselines.

  • LETTER
    Zhen LIANG, Changyuan ZHAO, Wanwei LIU, Bai XUE, Wenjing YANG, Zhengbin PANG
  • RESEARCH ARTICLE
    Tao HU, Chengjiang LONG, Chunxia XIAO

    Generating photo-realistic images from a text description is a challenging problem in computer vision. Previous works have shown promising performance to generate synthetic images conditional on text by Generative Adversarial Networks (GANs). In this paper, we focus on the category-consistent and relativistic diverse constraints to optimize the diversity of synthetic images. Based on those constraints, a category-consistent and relativistic diverse conditional GAN (CRD-CGAN) is proposed to synthesize K photo-realistic images simultaneously. We use the attention loss and diversity loss to improve the sensitivity of the GAN to word attention and noises. Then, we employ the relativistic conditional loss to estimate the probability of relatively real or fake for synthetic images, which can improve the performance of basic conditional loss. Finally, we introduce a category-consistent loss to alleviate the over-category issues between K synthetic images. We evaluate our approach using the Caltech-UCSD Birds-200-2011, Oxford 102 flower and MS COCO 2014 datasets, and the extensive experiments demonstrate superiority of the proposed method in comparison with state-of-the-art methods in terms of photorealistic and diversity of the generated synthetic images.

  • RESEARCH ARTICLE
    Miao ZHANG, Tingting HE, Ming DONG

    Commonsense question answering (CQA) requires understanding and reasoning over QA context and related commonsense knowledge, such as a structured Knowledge Graph (KG). Existing studies combine language models and graph neural networks to model inference. However, traditional knowledge graph are mostly concept-based, ignoring direct path evidence necessary for accurate reasoning. In this paper, we propose MRGNN (Meta-path Reasoning Graph Neural Network), a novel model that comprehensively captures sequential semantic information from concepts and paths. In MRGNN, meta-paths are introduced as direct inference evidence and an original graph neural network is adopted to aggregate features from both concepts and paths simultaneously. We conduct sufficient experiments on the CommonsenceQA and OpenBookQA datasets, showing the effectiveness of MRGNN. Also, we conduct further ablation experiments and explain the reasoning behavior through the case study.

  • RESEARCH ARTICLE
    Yanbin JIANG, Huifang MA, Xiaohui ZHANG, Zhixin LI, Liang CHANG

    Heterogeneous information network (HIN) has recently been widely adopted to describe complex graph structure in recommendation systems, proving its effectiveness in modeling complex graph data. Although existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths, they have the following major limitations. Existing works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths, which are not expressive enough to capture more complicated dependency relationships involved on the metapath. Besides, the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately considered. To tackle these limitations, we propose a novel social recommendation model MPISR, which models MetaPath Interaction for Social Recommendation on heterogeneous information network. Specifically, our model first learns the initial node representation through a pretraining module, and then identifies potential social friends and item relations based on their similarity to construct a unified HIN. We then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different metapaths. Extensive experiments on five real datasets demonstrate the effectiveness of our method.

  • RESEARCH ARTICLE
    Meimei YANG, Qiao LIU, Xinkai SUN, Na SHI, Hui XUE

    Data hierarchy, as a hidden property of data structure, exists in a wide range of machine learning applications. A common practice to classify such hierarchical data is first to encode the data in the Euclidean space, and then train a Euclidean classifier. However, such a paradigm leads to a performance drop due to distortion of data embedding in the Euclidean space. To relieve this issue, hyperbolic geometry is investigated as an alternative space to encode the hierarchical data for its higher ability to capture the hierarchical structures. Those methods cannot explore the full potential of the hyperbolic geometry, in the sense that such methods define the hyperbolic operations in the tangent plane, causing the distortion of data embeddings. In this paper, we develop two novel kernel formulations in the hyperbolic space, with one being positive definite (PD) and another one being indefinite, to solve the classification tasks in hyperbolic space. The PD one is defined via mapping the hyperbolic data to the Drury-Arveson (DA) space, which is a special reproducing kernel Hilbert space (RKHS). To further increase the discrimination of the classifier, an indefinite kernel is further defined in the Kreĭn spaces. Specifically, we design a 2-layer nested indefinite kernel which first maps hyperbolic data into the DA spaces, followed by a mapping from the DA spaces to the Kreĭn spaces. Extensive experiments on real-world datasets demonstrate the superiority of the proposed kernels.

  • Theoretical Computer Science
  • LETTER
    Guoxia NIE, Daoyun XU, Xi WANG, Zaijun ZHANG
  • Networks and Communication
  • LETTER
    Jinhong ZHANG, Xingwei WANG, Ruixia LI, Bo YI, Min HUANG, Dongxing SHUI
  • Information Systems
  • RESEARCH ARTICLE
    Daoliang HE, Pingpeng YUAN, Hai JIN

    Reachability query plays a vital role in many graph analysis tasks. Previous researches proposed many methods to efficiently answer reachability queries between vertex pairs. Since many real graphs are labeled graph, it highly demands Label-Constrained Reachability (LCR) query in which constraint includes a set of labels besides vertex pairs. Recent researches proposed several methods for answering some LCR queries which require appearance of some labels specified in constraints in the path. Besides that constraint may be a label set, query constraint may be ordered labels, namely OLCR (Ordered-Label-Constrained Reachability) queries which retrieve paths matching a sequence of labels. Currently, no solutions are available for OLCR. Here, we propose DHL, a novel bloom filter based indexing technique for answering OLCR queries. DHL can be used to check reachability between vertex pairs. If the answers are not no, then constrained DFS is performed. So, we employ DHL followed by performing constrained DFS to answer OLCR queries. We show that DHL has a bounded false positive rate, and it’s powerful in saving indexing time and space. Extensive experiments on 10 real-life graphs and 12 synthetic graphs demonstrate that DHL achieves about 4.8–22.5 times smaller index space and 4.6–114 times less index construction time than two state-of-art techniques for LCR queries, while achieving comparable query response time. The results also show that our algorithm can answer OLCR queries effectively.

  • Image and Graphics
  • LETTER
    Chuan LI, Enping HU, Xinyu ZHANG, Hao ZHOU, Hailing XIONG, Yun LIU
  • REVIEW ARTICLE
    Hanadi AL-MEKHLAFI, Shiguang LIU

    Super-resolution (SR) is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the decades. The main concept of SR is to reconstruct images from low-resolution (LR) to high-resolution (HR).It is an ongoing process in image technology, through up-sampling, de-blurring, and de-noising. Convolution neural network (CNN) has been widely used to enhance the resolution of images in recent years. Several alternative methods use deep learning to improve the progress of image super-resolution based on CNN. Here, we review the recent findings of single image super-resolution using deep learning with an emphasis on distillation knowledge used to enhance image super-resolution., it is also to highlight the potential applications of image super-resolution in security monitoring, medical diagnosis, microscopy image processing, satellite remote sensing, communication transmission, the digital multimedia industry and video enhancement. Finally, we present the challenges and assess future trends in super-resolution based on deep learning.

  • RESEARCH ARTICLE
    Qi LI, Xingli WANG, Luoyi FU, Xinde CAO, Xinbing WANG, Jing ZHANG, Chenghu ZHOU

    As a carrier of knowledge, papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and technology. With the booming development of science and technology, the number of papers has been growing exponentially. Just like the fact that Internet of Things (IoT) allows the world to be connected in a flatter way, how will the network formed by massive academic papers look like? Most existing visualization methods can only handle up to hundreds of thousands of node size, which is much smaller than that of academic networks which are usually composed of millions or even more nodes. In this paper, we are thus motivated to break this scale limit and design a new visualization method particularly for super-large-scale academic networks (VSAN). Nodes can represent papers or authors while the edges means the relation (e.g., citation, coauthorship) between them. In order to comprehensively improve the visualization effect, three levels of optimization are taken into account in the whole design of VSAN in a progressive manner, i.e., bearing scale, loading speed, and effect of layout details. Our main contributions are two folded: 1) We design an equivalent segmentation layout method that goes beyond the limit encountered by state-of-the-arts, thus ensuring the possibility of visually revealing the correlations of larger-scale academic entities. 2) We further propose a hierarchical slice loading approach that enables users to observe the visualized graphs of the academic network at both macroscopic and microscopic levels, with the ability to quickly zoom between different levels. In addition, we propose a “jumping between nebula graphs” method that connects the static pages of many academic graphs and helps users to form a more systematic and comprehensive understanding of various academic networks. Applying our methods to three academic paper citation datasets in the AceMap database confirms the visualization scalability of VSAN in the sense that it can visualize academic networks with more than 4 million nodes. The super-large-scale visualization not only allows a galaxy-like scholarly picture unfolding that were never discovered previously, but also returns some interesting observations that may drive extra attention from scientists.

  • Information Security
  • LETTER
    Qingjun YUAN, Gaopeng GOU, Yuefei ZHU, Yongjuan WANG
  • RESEARCH ARTICLE
    Yunbo YANG, Xiaolei DONG, Zhenfu CAO, Jiachen SHEN, Ruofan LI, Yihao YANG, Shangmin DOU

    Multiparty private set intersection (PSI) allows several parties, each holding a set of elements, to jointly compute the intersection without leaking any additional information. With the development of cloud computing, PSI has a wide range of applications in privacy protection. However, it is complex to build an efficient and reliable scheme to protect user privacy.

    To address this issue, we propose EMPSI, an efficient PSI (with cardinality) protocol in a multiparty setting. EMPSI avoids using heavy cryptographic primitives (mainly rely on symmetric-key encryption) to achieve better performance. In addition, both PSI and PSI with the cardinality of EMPSI are secure against semi-honest adversaries and allow any number of colluding clients (at least one honest client). We also do experiments to compare EMPSI with some state-of-the-art works. The experimental results show that proposed EMPSI(-CA) has better performance and is scalable in the number of clients and the set size.

  • RESEARCH ARTICLE
    Xingxin LI, Youwen ZHU, Rui XU, Jian WANG, Yushu ZHANG

    Secure k-Nearest Neighbor (k-NN) query aims to find k nearest data of a given query from an encrypted database in a cloud server without revealing privacy to the untrusted cloud and has wide applications in many areas, such as privacy-preserving machine learning and secure biometric identification. Several solutions have been put forward to solve this challenging problem. However, the existing schemes still suffer from various limitations in terms of efficiency and flexibility. In this paper, we propose a new encrypt-then-index strategy for the secure k-NN query, which can simultaneously achieve sub-linear search complexity (efficiency) and support dynamical update over the encrypted database (flexibility). Specifically, we propose a novel algorithm to transform the encrypted database and encrypted query points in the cloud. By indexing the transformed database using spatial data structures such as the R-tree index, our strategy enables sub-linear complexity for secure k-NN queries and allows users to dynamically update the encrypted database. To the best of our knowledge, the proposed strategy is the first to simultaneously provide these two properties. Through theoretical analysis and extensive experiments, we formally prove the security and demonstrate the efficiency of our scheme.

  • RESEARCH ARTICLE
    Huiqiang LIANG, Jianhua CHEN

    A threshold signature is a special digital signature in which the N-signer share the private key x and can construct a valid signature for any subset of the included t-signer, but less than t-signer cannot obtain any information. Considering the breakthrough achievements of threshold ECDSA signature and threshold Schnorr signature, the existing threshold SM2 signature is still limited to two parties or based on the honest majority setting, there is no more effective solution for the multiparty case. To make the SM2 signature have more flexible application scenarios, promote the application of the SM2 signature scheme in the blockchain system and secure cryptocurrency wallets. This paper designs a non-interactive threshold SM2 signature scheme based on partially homomorphic encryption and zero-knowledge proof. Only the last round requires the message input, so make our scheme non-interactive, and the pre-signing process takes 2 rounds of communication to complete after the key generation. We allow arbitrary threshold tn and design a key update strategy. It can achieve security with identifiable abort under the malicious majority, which means that if the signature process fails, we can find the failed party. Performance analysis shows that the computation and communication costs of the pre-signing process grows linearly with the parties, and it is only 1/3 of the Canetti’s threshold ECDSA (CCS'20).

  • RESEARCH ARTICLE
    Ashish SINGH, Abhinav KUMAR, Suyel NAMASUDRA

    The Internet of Everything (IoE) based cloud computing is one of the most prominent areas in the digital big data world. This approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the cloud. The IoE-based cloud computing services are located at remote locations without the control of the data owner. The data owners mostly depend on the untrusted Cloud Service Provider (CSP) and do not know the implemented security capabilities. The lack of knowledge about security capabilities and control over data raises several security issues. Deoxyribonucleic Acid (DNA) computing is a biological concept that can improve the security of IoE big data. The IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol (StS KAP) and Feistel cipher algorithms. This paper proposed a DNA-based cryptographic scheme and access control model (DNACDS) to solve IoE big data security and access issues. The experimental results illustrated that DNACDS performs better than other DNA-based security schemes. The theoretical security analysis of the DNACDS shows better resistance capabilities.

  • Interdisciplinary
  • RESEARCH ARTICLE
    Yan LIN, Jiashu WANG, Xiaowei LIU, Xueqin XIE, De WU, Junjie ZHANG, Hui DING

    Fertility is the most crucial step in the development process, which is controlled by many fertility-related proteins, including spermatogenesis-, oogenesis- and embryogenesis-related proteins. The identification of fertility-related proteins can provide important clues for studying the role of these proteins in development. Therefore, in this study, we constructed a two-layer classifier to identify fertility-related proteins. In this classifier, we first used the composition of amino acids (AA) and their physical and chemical properties to code these three fertility-related proteins. Then, the feature set is optimized by analysis of variance (ANOVA) and incremental feature selection (IFS) to obtain the optimal feature subset. Through five-fold cross-validation (CV) and independent data tests, the performance of models constructed by different machine learning (ML) methods is evaluated and compared. Finally, based on support vector machine (SVM), we obtained a two-layer model to classify three fertility-related proteins. On the independent test data set, the accuracy (ACC) and the area under the receiver operating characteristic curve (AUC) of the first layer classifier are 81.95% and 0.89, respectively, and them of the second layer classifier are 84.74% and 0.90, respectively. These results show that the proposed model has stable performance and satisfactory prediction accuracy, and can become a powerful model to identify more fertility related proteins.

  • RESEARCH ARTICLE
    Yameng ZHAO, Yin GUO, Limin LI

    Single-cell RNA sequencing reveals the gene structure and gene expression status of a single cell, which can reflect the heterogeneity between cells. However, batch effects caused by non-biological factors may hinder data integration and downstream analysis. Although the batch effect can be evaluated by visualizing the data, which actually is subjective and inaccurate. In this work, we propose a quantitative method cKBET, which considers the batch and cell type information simultaneously. The cKBET method accesses batch effects by comparing the global and local fraction of cells of different batches in different cell types. We verify the performance of our cKBET method on simulated and real biological data sets. The experimental results show that our cKBET method is superior to existing methods in most cases. In general, our cKBET method can detect batch effect with either balanced or unbalanced cell types, and thus evaluate batch correction methods.