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  • RESEARCH ARTICLE
    Yu OU, Lang LI
    Frontiers of Computer Science, 2022, 16(2): 162303. https://doi.org/10.1007/s11704-020-0209-4

    There has been a growing interest in the side-channel analysis (SCA) field based on deep learning (DL) technology. Various DL network or model has been developed to improve the efficiency of SCA. However, few studies have investigated the impact of the different models on attack results and the exact relationship between power consumption traces and intermediate values. Based on the convolutional neural network and the autoencoder, this paper proposes a Template Analysis Pre-trained DL Classification model named TAPDC which contains three sub-networks. The TAPDC model detects the periodicity of power trace, relating power to the intermediate values and mining the deeper features by the multi-layer convolutional net. We implement the TAPDC model and compare it with two classical models in a fair experiment. The evaluative results show that the TAPDC model with autoencoder and deep convolution feature extraction structure in SCA can more effectively extract information from power consumption trace. Also, Using the classifier layer, this model links power information to the probability of intermediate value. It completes the conversion from power trace to intermediate values and greatly improves the efficiency of the power attack.

  • RESEARCH ARTICLE
    Elisabetta DE MARIA, Abdorrahim BAHRAMI, Thibaud L’YVONNET, Amy FELTY, Daniel GAFFÉ, Annie RESSOUCHE, Franck GRAMMONT
    Frontiers of Computer Science, 2022, 16(3): 163404. https://doi.org/10.1007/s11704-020-0029-6

    Having a formal model of neural networks can greatly help in understanding and verifying their properties, behavior, and response to external factors such as disease and medicine. In this paper, we adopt a formal model to represent neurons, some neuronal graphs, and their composition. Some specific neuronal graphs are known for having biologically relevant structures and behaviors and we call them archetypes. These archetypes are supposed to be the basis of typical instances of neuronal information processing. In this paper we study six fundamental archetypes (simple series, series with multiple outputs, parallel composition, negative loop, inhibition of a behavior, and contralateral inhibition), and we consider two ways to couple two archetypes: (i) connecting the output(s) of the first archetype to the input(s) of the second archetype and (ii) nesting the first archetype within the second one. We report and compare two key approaches to the formal modeling and verification of the proposed neuronal archetypes and some selected couplings. The first approach exploits the synchronous programming language Lustre to encode archetypes and their couplings, and to express properties concerning their dynamic behavior. These properties are verified thanks to the use of model checkers. The second approach relies on a theorem prover, the Coq Proof Assistant, to prove dynamic properties of neurons and archetypes.

  • RESEARCH ARTICLE
    Hongbin XU, Weili YANG, Qiuxia WU, Wenxiong KANG
    Frontiers of Computer Science, 2022, 16(5): 165332. https://doi.org/10.1007/s11704-021-0475-9

    Finger vein biometrics have been extensively studied for the capability to detect aliveness, and the high security as intrinsic traits. However, vein pattern distortion caused by finger rotation degrades the performance of CNN in 2D finger vein recognition, especially in a contactless mode. To address the finger posture variation problem, we propose a 3D finger vein verification system extracting axial rotation invariant feature. An efficient 3D finger vein reconstruction optimization model is proposed and several accelerating strategies are adopted to achieve real-time 3D reconstruction on an embedded platform. The main contribution in this paper is that we are the first to propose a novel 3D point-cloud-based end-to-end neural network to extract deep axial rotation invariant feature, namely 3DFVSNet. In the network, the rotation problem is transformed to a permutation problem with the help of specially designed rotation groups. Finally, to validate the performance of the proposed network more rigorously and enrich the database resources for the finger vein recognition community, we built the largest publicly available 3D finger vein dataset with different degrees of finger rotation, namely the Large-scale Finger Multi-Biometric Database-3D Pose Varied Finger Vein (SCUT LFMB-3DPVFV) Dataset. Experimental results on 3D finger vein datasets show that our 3DFVSNet holds strong robustness against axial rotation compared to other approaches.

  • RESEARCH ARTICLE
    Abdelfettah MAATOUG, Ghalem BELALEM, Saïd MAHMOUDI
    Frontiers of Computer Science, 2023, 17(2): 172501. https://doi.org/10.1007/s11704-021-0375-z

    Nowadays, smart buildings rely on Internet of things (IoT) technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected objects. Fog is characterized by low latency with a wider spread and geographically distributed nodes to support mobility, real-time interaction, and location-based services. To provide optimum quality of user life in modern buildings, we rely on a holistic Framework, designed in a way that decreases latency and improves energy saving and services efficiency with different capabilities. Discrete EVent system Specification (DEVS) is a formalism used to describe simulation models in a modular way. In this work, the sub-models of connected objects in the building are accurately and independently designed, and after installing them together, we easily get an integrated model which is subject to the fog computing Framework. Simulation results show that this new approach significantly, improves energy efficiency of buildings and reduces latency. Additionally, with DEVS, we can easily add or remove sub-models to or from the overall model, allowing us to continually improve our designs.

  • RESEARCH ARTICLE
    Chunxi ZHANG, Yuming LI, Rong ZHANG, Weining QIAN, Aoying ZHOU
    Frontiers of Computer Science, 2023, 17(2): 172202. https://doi.org/10.1007/s11704-022-1056-2

    Massive scale of transactions with critical requirements become popular for emerging businesses, especially in E-commerce. One of the most representative applications is the promotional event running on Alibaba’s platform on some special dates, widely expected by global customers. Although we have achieved significant progress in improving the scalability of transactional database systems (OLTP), the presence of contention operations in workloads is still one of the fundamental obstacles to performance improving. The reason is that the overhead of managing conflict transactions with concurrency control mechanisms is proportional to the amount of contentions. As a consequence, generating contented workloads is urgent to evaluate performance of modern OLTP database systems. Though we have kinds of standard benchmarks which provide some ways in simulating contentions, e.g., skew distribution control of transactions, they can not control the generation of contention quantitatively; even worse, the simulation effectiveness of these methods is affected by the scale of data. So in this paper we design a scalable quantitative contention generation method with fine contention granularity control. We conduct a comprehensive set of experiments on popular opensourced DBMSs compared with the latest contention simulation method to demonstrate the effectiveness of our generation work.

  • RESEARCH ARTICLE
    Xin YOU, Hailong YANG, Zhongzhi LUAN, Depei QIAN
    Frontiers of Computer Science, 2022, 16(3): 163102. https://doi.org/10.1007/s11704-020-0169-8

    The cryo-electron microscopy (cryo-EM) is one of the most powerful technologies available today for structural biology. The RELION (Regularized Likelihood Optimization) implements a Bayesian algorithm for cryo-EM structure determination, which is one of the most widely used software in this field. Many researchers have devoted effort to improve the performance of RELION to satisfy the analysis for the ever-increasing volume of datasets. In this paper, we focus on performance analysis of the most time-consuming computation steps in RELION and identify their performance bottlenecks for specific optimizations. We propose several performance optimization strategies to improve the overall performance of RELION, including optimization of expectation step, parallelization of maximization step, accelerating the computation of symmetries, and memory affinity optimization. The experiment results show that our proposed optimizations achieve significant speedups of RELION across representative datasets. In addition, we perform roofline model analysis to understand the effectiveness of our optimizations.

  • RESEARCH ARTICLE
    Yujin CHAI, Yanlin WENG, Lvdi WANG, Kun ZHOU
    Frontiers of Computer Science, 2022, 16(3): 163703. https://doi.org/10.1007/s11704-020-0133-7

    In this paper, we present an efficient algorithm that generates lip-synchronized facial animation from a given vocal audio clip. By combining spectral-dimensional bidirectional long short-term memory and temporal attention mechanism, we design a light-weight speech encoder that learns useful and robust vocal features from the input audio without resorting to pre-trained speech recognition modules or large training data. To learn subject-independent facial motion, we use deformation gradients as the internal representation, which allows nuanced local motions to be better synthesized than using vertex offsets. Compared with state-of-the-art automatic-speech-recognition-based methods, our model is much smaller but achieves similar robustness and quality most of the time, and noticeably better results in certain challenging cases.

  • REVIEW ARTICLE
    Zeli WANG, Hai JIN, Weiqi DAI, Kim-Kwang Raymond CHOO, Deqing ZOU
    Frontiers of Computer Science, 2021, 15(2): 152802. https://doi.org/10.1007/s11704-020-9284-9

    Blockchain has recently emerged as a research trend, with potential applications in a broad range of industries and context. One particular successful Blockchain technology is smart contract, which is widely used in commercial settings (e.g., high value financial transactions). This, however, has security implications due to the potential to financially benefit froma security incident (e.g., identification and exploitation of a vulnerability in the smart contract or its implementation). Among, Ethereum is the most active and arresting. Hence, in this paper, we systematically review existing research efforts on Ethereum smart contract security, published between 2015 and 2019. Specifically, we focus on how smart contracts can be maliciously exploited and targeted, such as security issues of contract program model, vulnerabilities in the program and safety consideration introduced by program execution environment. We also identify potential research opportunities and future research agenda.

  • RESEARCH ARTICLE
    Dunbo ZHANG, Chaoyang JIA, Li SHEN
    Frontiers of Computer Science, 2022, 16(3): 163104. https://doi.org/10.1007/s11704-020-9485-2

    GPUs are widely used in modern high-performance computing systems. To reduce the burden of GPU programmers, operating system and GPU hardware provide great supports for shared virtual memory, which enables GPU and CPU to share the same virtual address space. Unfortunately, the current SIMT execution model of GPU brings great challenges for the virtual-physical address translation on the GPU side, mainly due to the huge number of virtual addresses which are generated simultaneously and the bad locality of these virtual addresses. Thus, the excessive TLB accesses increase the miss ratio of TLB. As an attractive solution, Page Walk Cache (PWC) has received wide attention for its capability of reducing the memory accesses caused by TLB misses.

    However, the current PWC mechanism suffers from heavy redundancies, which significantly limits its efficiency. In this paper, we first investigate the facts leading to this issue by evaluating the performance of PWC with typical GPU benchmarks. We find that the repeated L4 and L3 indices of virtual addresses increase the redundancies in PWC, and the low locality of L2 indices causes the low hit ratio in PWC. Based on these observations, we propose a new PWC structure, namely Compressed Page Walk Cache (CPWC), to resolve the redundancy burden in current PWC. Our CPWC can be organized in either direct-mapped mode or set-associated mode. Experimental results show that CPWC increases by 3 times over TPC in the number of page table entries, increases by 38.3% over PWC in L2 index hit ratio and reduces by 26.9% in the memory accesses of page tables. The average memory accesses caused by each TLB miss is reduced to 1.13. Overall, the average IPC can improve by 25.3%.

  • RESEARCH ARTICLE
    Yunhao SUN, Guanyu LI, Jingjing DU, Bo NING, Heng CHEN
    Frontiers of Computer Science, 2022, 16(3): 163606. https://doi.org/10.1007/s11704-020-0360-y

    The problem of subgraph matching is one fundamental issue in graph search, which is NP-Complete problem. Recently, subgraph matching has become a popular research topic in the field of knowledge graph analysis, which has a wide range of applications including question answering and semantic search. In this paper, we study the problem of subgraph matching on knowledge graph. Specifically, given a query graph q and a data graph G, the problem of subgraph matching is to conduct all possible subgraph isomorphic mappings of q on G. Knowledge graph is formed as a directed labeled multi-graph having multiple edges between a pair of vertices and it has more dense semantic and structural features than general graph. To accelerate subgraph matching on knowledge graph, we propose a novel subgraph matching algorithm based on subgraph index for knowledge graph, called as F G q T-Match. The subgraph matching algorithm consists of two key designs. One design is a subgraph index of matching-driven flow graph ( F G q T), which reduces redundant calculations in advance. Another design is a multi-label weight matrix, which evaluates a near-optimal matching tree for minimizing the intermediate candidates. With the aid of these two key designs, all subgraph isomorphic mappings are quickly conducted only by traversing F G q T. Extensive empirical studies on real and synthetic graphs demonstrate that our techniques outperform the state-of-the-art algorithms.

  • RESEARCH ARTICLE
    Song SUN, Bo ZHAO, Muhammad MATEEN, Xin CHEN, Junhao WEN
    Frontiers of Computer Science, 2022, 16(3): 163311. https://doi.org/10.1007/s11704-020-0400-7

    Recent studies have shown remarkable success in face image generation task. However, existing approaches have limited diversity, quality and controllability in generating results. To address these issues, we propose a novel end-to-end learning framework to generate diverse, realistic and controllable face images guided by face masks. The face mask provides a good geometric constraint for a face by specifying the size and location of different components of the face, such as eyes, nose and mouse. The framework consists of four components: style encoder, style decoder, generator and discriminator. The style encoder generates a style code which represents the style of the result face; the generator translate the input face mask into a real face based on the style code; the style decoder learns to reconstruct the style code from the generated face image; and the discriminator classifies an input face image as real or fake. With the style code, the proposed model can generate different face images matching the input face mask, and by manipulating the face mask, we can finely control the generated face image. We empirically demonstrate the effectiveness of our approach on mask guided face image synthesis task.

  • RESEARCH ARTICLE
    Jingya FENG, Lang LI
    Frontiers of Computer Science, 2022, 16(3): 163813. https://doi.org/10.1007/s11704-020-0115-9

    In this paper, we propose a new lightweight block cipher called SCENERY. The main purpose of SCENERY design applies to hardware and software platforms. SCENERY is a 64-bit block cipher supporting 80-bit keys, and its data processing consists of 28 rounds. The round function of SCENERY consists of 8 4 × 4 S-boxes in parallel and a 32 × 32 binary matrix, and we can implement SCENERY with some basic logic instructions. The hardware implementation of SCENERY only requires 1438 GE based on 0.18 um CMOS technology, and the software implementation of encrypting or decrypting a block takes approximately 1516 clock cycles on 8-bit microcontrollers and 364 clock cycles on 64-bit processors. Compared with other encryption algorithms, the performance of SCENERY is well balanced for both hardware and software. By the security analyses, SCENERY can achieve enough security margin against known attacks, such as differential cryptanalysis, linear cryptanalysis, impossible differential cryptanalysis and related-key attacks.

  • RESEARCH ARTICLE
    Yamin HU, Hao JIANG, Zongyao HU
    Frontiers of Computer Science, 2023, 17(6): 176214. https://doi.org/10.1007/s11704-022-2313-0

    The maintainability of source code is a key quality characteristic for software quality. Many approaches have been proposed to quantitatively measure code maintainability. Such approaches rely heavily on code metrics, e.g., the number of Lines of Code and McCabe’s Cyclomatic Complexity. The employed code metrics are essentially statistics regarding code elements, e.g., the numbers of tokens, lines, references, and branch statements. However, natural language in source code, especially identifiers, is rarely exploited by such approaches. As a result, replacing meaningful identifiers with nonsense tokens would not significantly influence their outputs, although the replacement should have significantly reduced code maintainability. To this end, in this paper, we propose a novel approach (called DeepM) to measure code maintainability by exploiting the lexical semantics of text in source code. DeepM leverages deep learning techniques (e.g., LSTM and attention mechanism) to exploit these lexical semantics in measuring code maintainability. Another key rationale of DeepM is that measuring code maintainability is complex and often far beyond the capabilities of statistics or simple heuristics. Consequently, DeepM leverages deep learning techniques to automatically select useful features from complex and lengthy inputs and to construct a complex mapping (rather than simple heuristics) from the input to the output (code maintainability index). DeepM is evaluated on a manually-assessed dataset. The evaluation results suggest that DeepM is accurate, and it generates the same rankings of code maintainability as those of experienced programmers on 87.5% of manually ranked pairs of Java classes.

  • RESEARCH ARTICLE
    Sijing CHENG, Chao CHEN, Shenle PAN, Hongyu HUANG, Wei ZHANG, Yuming FENG
    Frontiers of Computer Science, 2022, 16(5): 165327. https://doi.org/10.1007/s11704-021-0568-5

    Most current crowdsourced logistics aim to minimize systems cost and maximize delivery capacity, but the efforts of crowdsourcers such as drivers are almost ignored. In the delivery process, drivers usually need to take long-distance detours in hitchhiking rides based package deliveries. In this paper, we propose an approach that integrates offline trajectory data mining and online route-and-schedule optimization in the hitchhiking ride scenario to find optimal delivery routes for packages and drivers. Specifically, we propose a two-phase framework for the delivery route planning and scheduling. In the first phase, the historical trajectory data are mined offline to build the package transport network. In the second phase, we model the delivery route planning and package-taxi matching as an integer linear programming problem and solve it with the Gurobi optimizer. After that, taxis are scheduled to deliver packages with optimal delivery paths via a newly designed scheduling strategy. We evaluate our approach with the real-world datasets; the results show that our proposed approach can complete citywide package deliveries with a high success rate and low extra efforts of taxi drivers.

  • RESEARCH ARTICLE
    Huiqiang LIANG, Jianhua CHEN
    Frontiers of Computer Science, 2024, 18(1): 181802. https://doi.org/10.1007/s11704-022-2288-x

    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
    Yongquan LIANG, Qiuyu SONG, Zhongying ZHAO, Hui ZHOU, Maoguo GONG
    Frontiers of Computer Science, 2023, 17(6): 176613. https://doi.org/10.1007/s11704-022-2324-x

    Session-based recommendation is a popular research topic that aims to predict users’ next possible interactive item by exploiting anonymous sessions. The existing studies mainly focus on making predictions by considering users’ single interactive behavior. Some recent efforts have been made to exploit multiple interactive behaviors, but they generally ignore the influences of different interactive behaviors and the noise in interactive sequences. To address these problems, we propose a behavior-aware graph neural network for session-based recommendation. First, different interactive sequences are modeled as directed graphs. Thus, the item representations are learned via graph neural networks. Then, a sparse self-attention module is designed to remove the noise in behavior sequences. Finally, the representations of different behavior sequences are aggregated with the gating mechanism to obtain the session representations. Experimental results on two public datasets show that our proposed method outperforms all competitive baselines. The source code is available at the website of GitHub.

  • RESEARCH ARTICLE
    Yang CHANG, Huifang MA, Liang CHANG, Zhixin LI
    Frontiers of Computer Science, 2022, 16(5): 165324. https://doi.org/10.1007/s11704-021-0482-x

    Community detection methods based on random walks are widely adopted in various network analysis tasks. It could capture structures and attributed information while alleviating the issues of noises. Though random walks on plain networks have been studied before, in real-world networks, nodes are often not pure vertices, but own different characteristics, described by the rich set of data associated with them. These node attributes contain plentiful information that often complements the network, and bring opportunities to the random-walk-based analysis. However, node attributes make the node interactions more complicated and are heterogeneous with respect to topological structures. Accordingly, attributed community detection based on random walk is challenging as it requires joint modelling of graph structures and node attributes.

    To bridge this gap, we propose a Community detection with Attributed random walk via Seed replacement (CAS). Our model is able to conquer the limitation of directly utilize the original network topology and ignore the attribute information. In particular, the algorithm consists of four stages to better identify communities. (1) Select initial seed nodes in the network; (2) Capture the better-quality seed replacement path set; (3) Generate the structure-attribute interaction transition matrix and perform the colored random walk; (4) Utilize the parallel conductance to expand the communities. Experiments on synthetic and real-world networks demonstrate the effectiveness of CAS.

  • RESEARCH ARTICLE
    Yanbin JIANG, Huifang MA, Xiaohui ZHANG, Zhixin LI, Liang CHANG
    Frontiers of Computer Science, 2024, 18(1): 181302. https://doi.org/10.1007/s11704-022-2438-1

    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
    Shuo TAN, Lei ZHANG, Xin SHU, Zizhou WANG
    Frontiers of Computer Science, 2023, 17(6): 176338. https://doi.org/10.1007/s11704-022-2126-1

    Attention mechanism has become a widely researched method to improve the performance of convolutional neural networks (CNNs). Most of the researches focus on designing channel-wise and spatial-wise attention modules but neglect the importance of unique information on each feature, which is critical for deciding both “what” and “where” to focus. In this paper, a feature-wise attention module is proposed, which can give each feature of the input feature map an attention weight. Specifically, the module is based on the well-known surround suppression in the discipline of neuroscience, and it consists of two sub-modules, Minus-Square-Add (MSA) operation and a group of learnable non-linear mapping functions. The MSA imitates the surround suppression and defines an energy function which can be applied to each feature to measure its importance. The group of non-linear functions refines the energy calculated by the MSA to more reasonable values. By these two sub-modules, feature-wise attention can be well captured. Meanwhile, due to the simple structure and few parameters of the two sub-modules, the proposed module can easily be almost integrated into any CNN. To verify the performance and effectiveness of the proposed module, several experiments were conducted on the Cifar10, Cifar100, Cinic10, and Tiny-ImageNet datasets, respectively. The experimental results demonstrate that the proposed module is flexible and effective for CNNs to improve their performance.

  • RESEARCH ARTICLE
    Dongming HAN, Jiacheng PAN, Rusheng PAN, Dawei ZHOU, Nan CAO, Jingrui HE, Mingliang XU, Wei CHEN
    Frontiers of Computer Science, 2022, 16(2): 162701. https://doi.org/10.1007/s11704-020-0013-1

    Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along time. They are common in multimedia applications. Anomaly detection is one of the essential tasks in analyzing these networks though it is not well addressed. In this paper, we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic networks. We conclude features of rare categories and two types of anomalies of rare categories. Then we present a novel rare category detection method, called DIRAD, to detect rare category candidates with anomalies. We develop a prototype system called iNet, which integrates two major visualization components, including a glyph-based rare category identifier, which helps users to identify rare categories among detected substructures, a major view, which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex attributes. Evaluations, including an algorithm performance evaluation, a case study, and a user study, are conducted to test the effectiveness of proposed methods.

  • REVIEW ARTICLE
    Jiaqi LI, Ming LIU, Bing QIN, Ting LIU
    Frontiers of Computer Science, 2022, 16(5): 165329. https://doi.org/10.1007/s11704-021-0500-z

    Discourse parsing is an important research area in natural language processing (NLP), which aims to parse the discourse structure of coherent sentences. In this survey, we introduce several different kinds of discourse parsing tasks, mainly including RST-style discourse parsing, PDTB-style discourse parsing, and discourse parsing for multiparty dialogue. For these tasks, we introduce the classical and recent existing methods, especially neural network approaches. After that, we describe the applications of discourse parsing for other NLP tasks, such as machine reading comprehension and sentiment analysis. Finally, we discuss the future trends of the task.

  • RESEARCH ARTICLE
    Chaofan WANG, Xiaohai DAI, Jiang XIAO, Chenchen LI, Ming WEN, Bingbing ZHOU, Hai JIN
    Frontiers of Computer Science, 2022, 16(4): 164505. https://doi.org/10.1007/s11704-021-0221-3

    Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart contracts. These massive accounts can be divided into diverse categories, such as miners, tokens, and exchanges, which is termed as account diversity in this paper. The benefit of investigating diversity are multi-fold, including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal activities. Unfortunately, the exploration of blockchain account diversity remains scarce. Even the most relevant studies, which focus on the deanonymization of the accounts on Bitcoin, can hardly be applied on Ethereum since their underlying protocols and user idioms are different. To this end, we present the first attempt to demystify the account diversity on Ethereum. The key observation is that different accounts exhibit diverse behavior patterns, leading us to propose the heuristics for classification as the premise. We then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation (MLE). We collect real-world data through extensive efforts to evaluate our proposed method and show its effectiveness. Furthermore, we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage actions. As for the former, we validate two sweeping statements reliably: (1) standalone miners are gradually replaced by the mining pools and cooperative miners; (2) transactions related to the mining pool and exchanges take up a large share of the total transactions. The latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.

  • RESEARCH ARTICLE
    Peng LI, Junzuo LAI, Yongdong WU
    Frontiers of Computer Science, 2023, 17(1): 171802. https://doi.org/10.1007/s11704-021-0593-4

    We introduce a new notion called accountable attribute-based authentication with fine-grained access control (AccABA), which achieves (i) fine-grained access control that prevents ineligible users from authenticating; (ii) anonymity such that no one can recognize the identity of a user; (iii) public accountability, i.e., as long as a user authenticates two different messages, the corresponding authentications will be easily identified and linked, and anyone can reveal the user’s identity without any help from a trusted third party. Then, we formalize the security requirements in terms of unforgeability, anonymity, linkability and traceability, and give a generic construction to fulfill these requirements. Based on AccABA, we further present the first attribute-based, fair, anonymous and publicly traceable crowdsourcing scheme on blockchain, which is designed to filter qualified workers to participate in tasks, and ensures the fairness of the competition between workers, and finally balances the tension between anonymity and accountability.

  • RESEARCH ARTICLE
    Tianning ZHANG, Miao CAI, Diming ZHANG, Hao HUANG
    Frontiers of Computer Science, 2022, 16(4): 164818. https://doi.org/10.1007/s11704-021-0342-8

    Currently, security-critical server programs are well protected by various defense techniques, such as Address Space Layout Randomization(ASLR), eXecute Only Memory(XOM), and Data Execution Prevention(DEP), against modern code-reuse attacks like Return-oriented Programming(ROP) attacks. Moreover, in these victim programs, most syscall instructions lack the following ret instructions, which prevents attacks to stitch multiple system calls to implement advanced behaviors like launching a remote shell. Lacking this kind of gadget greatly constrains the capability of code-reuse attacks. This paper proposes a novel code-reuse attack method called Signal Enhanced Blind Return Oriented Programming(SeBROP) to address these challenges. Our SeBROP can initiate a successful exploit to server-side programs using only a stack overflow vulnerability. By leveraging a side-channel that exists in the victim program, we show how to find a variety of gadgets blindly without any pre-knowledges or reading/disassembling the code segment. Then, we propose a technique that exploits the current vulnerable signal checking mechanism to realize the execution flow control even when ret instructions are absent. Our technique can stitch a number of system calls without returns, which is more superior to conventional ROP attacks. Finally, the SeBROP attack precisely identifies many useful gadgets to constitute a Turing-complete set. SeBROP attack can defeat almost all state-of-the-art defense techniques. The SeBROP attack is compatible with both modern 64-bit and 32-bit systems. To validate its effectiveness, We craft three exploits of the SeBROP attack for three real-world applications, i.e., 32-bit Apache 1.3.49, 32-bit ProFTPD 1.3.0, and 64-bit Nginx 1.4.0. Experimental results demonstrate that the SeBROP attack can successfully spawn a remote shell on Nginx, ProFTPD, and Apache with less than 8500/4300/2100 requests, respectively.

  • REVIEW ARTICLE
    Miao CAI, Hao HUANG
    Frontiers of Computer Science, 2021, 15(4): 154207. https://doi.org/10.1007/s11704-020-9395-3

    Emerging persistent memory technologies, like PCM and 3D XPoint, offer numerous advantages, such as higher density, larger capacity, and better energy efficiency, compared with the DRAM. However, they also have some drawbacks, e.g., slower access speed, limited write endurance, and unbalanced read/write latency. Persistent memory technologies provide both great opportunities and challenges for operating systems. As a result, a large number of solutions have been proposed. With the increasing number and complexity of problems and approaches, we believe this is the right moment to investigate and analyze these works systematically.

    To this end, we perform a comprehensive and in-depth study on operating system support for persistent memory within three steps. First, we present an overview of how to build the operating system on persistent memory from three perspectives: system abstraction, crash consistency, and system reliability. Then, we classify the existing research works into three categories: storage stack, memory manager, and OS-bypassing library. For each category, we summarize the major research topics and discuss these topics deeply. Specifically, we present the challenges and opportunities in each topic, describe the contributions and limitations of proposed approaches, and compare these solutions in different dimensions. Finally, we also envision the future operating system based on this study.

  • RESEARCH ARTICLE
    Cairui YAN, Huifang MA, Qingqing LI, Fanyi YANG, Zhixin LI
    Frontiers of Computer Science, 2023, 17(5): 175335. https://doi.org/10.1007/s11704-022-2220-4

    Community search is an important problem in network analysis, which has attracted much attention in recent years. As a query-oriented variant of community detection problem, community search starts with some given nodes, pays more attention to local network structures, and gets personalized resultant communities quickly. The existing community search method typically returns a single target community containing query nodes by default. This is a strict requirement and does not allow much flexibility. In many real-world applications, however, query nodes are expected to be located in multiple communities with different semantics. To address this limitation of existing methods, an efficient spectral-based Multi-Scale Community Search method (MSCS) is proposed, which can simultaneously identify the multi-scale target local communities to which query node belong. In MSCS, each node is equipped with a graph Fourier multiplier operator. The access of the graph Fourier multiplier operator helps nodes to obtain feature representations at various community scales. In addition, an efficient algorithm is proposed for avoiding the large number of matrix operations due to spectral methods. Comprehensive experimental evaluations on a variety of real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

  • RESEARCH ARTICLE
    Xiaoheng JIANG, Hao LIU, Li ZHANG, Geyang LI, Mingliang XU, Pei LV, Bing ZHOU
    Frontiers of Computer Science, 2022, 16(3): 163314. https://doi.org/10.1007/s11704-021-0387-8

    In recent years, crowd counting has increasingly drawn attention due to its widespread applications in the field of computer vision. Most of the existing methods rely on datasets with scarce labeled images to train networks. They are prone to suffer from the over-fitting problem. Further, these existing datasets usually just give manually labeled annotations related to the head center position. This kind of annotation provides limited information. In this paper, we propose to exploit virtual synthetic crowd scenes to improve the performance of the counting network in the real world. Since we can obtain people masks easily in a synthetic dataset, we first learn to distinguish people from the background via a segmentation network using the synthetic data. Then we transfer the learned segmentation priors from synthetic data to real-world data. Finally, we train a density estimation network on real-world data by utilizing the obtained people masks. Our experiments on two crowd counting datasets demonstrate the effectiveness of the proposed method.

  • REVIEW ARTICLE
    Yingjie LIU, Tiancheng ZHANG, Xuecen WANG, Ge YU, Tao LI
    Frontiers of Computer Science, 2023, 17(1): 171604. https://doi.org/10.1007/s11704-022-1128-3

    Cognitive diagnosis is the judgment of the student’s cognitive ability, is a wide-spread concern in educational science. The cognitive diagnosis model (CDM) is an essential method to realize cognitive diagnosis measurement. This paper presents new research on the cognitive diagnosis model and introduces four individual aspects of probability-based CDM and deep learning-based CDM. These four aspects are higher-order latent trait, polytomous responses, polytomous attributes, and multilevel latent traits. The paper also sorts on the contained ideas, model structures and respective characteristics, and provides direction for developing cognitive diagnosis in the future.

  • RESEARCH ARTICLE
    Qi LI, Xingli WANG, Luoyi FU, Xinde CAO, Xinbing WANG, Jing ZHANG, Chenghu ZHOU
    Frontiers of Computer Science, 2024, 18(1): 181701. https://doi.org/10.1007/s11704-022-2078-5

    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.

  • RESEARCH ARTICLE
    Yunbo YANG, Xiaolei DONG, Zhenfu CAO, Jiachen SHEN, Shangmin DOU
    Frontiers of Computer Science, 2023, 17(5): 175811. https://doi.org/10.1007/s11704-022-2236-9

    Oblivious Cross-Tags (OXT) [1] is the first efficient searchable encryption (SE) protocol for conjunctive queries in a single-writer single-reader framework. However, it also has a trade-off between security and efficiency by leaking partial database information to the server. Recent attacks on these SE schemes show that the leakages from these SE schemes can be used to recover the content of queried keywords. To solve this problem, Lai et al. [2] propose Hidden Cross-Tags (HXT), which reduces the access pattern leakage from Keyword Pair Result Pattern (KPRP) to Whole Result Pattern (WRP). However, the WRP leakage can also be used to recover some additional contents of queried keywords. This paper proposes Improved Cross-Tags (IXT), an efficient searchable encryption protocol that achieves access and searches pattern hiding based on the labeled private set intersection. We also prove the proposed labeled private set intersection (PSI) protocol is secure against semi-honest adversaries, and IXT is L-semi-honest secure (L is leakage function). Finally, we do experiments to compare IXT with HXT. The experimental results show that the storage overhead and computation overhead of the search phase at the client-side in IXT is much lower than those in HXT. Meanwhile, the experimental results also show that IXT is scalable and can be applied to various sizes of datasets.