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  • RESEARCH ARTICLE
    Yan JIANG, Youwen ZHU, Jian WANG, Xingxin LI
    Frontiers of Computer Science, 2023, 17(5): 175813. https://doi.org/10.1007/s11704-022-2370-4

    Identity-based threshold signature (IDTS) is a forceful primitive to protect identity and data privacy, in which parties can collaboratively sign a given message as a signer without reconstructing a signing key. Nevertheless, most IDTS schemes rely on a trusted key generation center (KGC). Recently, some IDTS schemes can achieve escrow-free security against corrupted KGC, but all of them are vulnerable to denial-of-service attacks in the dishonest majority setting, where cheaters may force the protocol to abort without providing any feedback. In this work, we present a fully decentralized IDTS scheme to resist corrupted KGC and denial-of-service attacks. To this end, we design threshold protocols to achieve distributed key generation, private key extraction, and signing generation which can withstand the collusion between KGCs and signers, and then we propose an identification mechanism that can detect the identity of cheaters during key generation, private key extraction and signing generation. Finally, we formally prove that the proposed scheme is threshold unforgeability against chosen message attacks. The experimental results show that the computation time of both key generation and signing generation is <1 s, and private key extraction is about 3 s, which is practical in the distributed environment.

  • LETTER
    Lili BO, Yue LI, Xiaobing SUN, Xiaoxue WU, Bin LI
    Frontiers of Computer Science, 2023, 17(3): 173207. https://doi.org/10.1007/s11704-022-1729-x
  • LETTER
    Zhuo ZHANG, Ya LI, Jianxin XUE, Xiaoguang MAO
    Frontiers of Computer Science, 2024, 18(1): 181205. https://doi.org/10.1007/s11704-023-2597-8
  • REVIEW ARTICLE
    Muning WEN, Runji LIN, Hanjing WANG, Yaodong YANG, Ying WEN, Luo MAI, Jun WANG, Haifeng ZHANG, Weinan ZHANG
    Frontiers of Computer Science, 2023, 17(6): 176349. https://doi.org/10.1007/s11704-023-2689-5

    Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e.g., GPT-3 and Swin Transformer. Although originally designed for prediction problems, it is natural to inquire about their suitability for sequential decision-making and reinforcement learning problems, which are typically beset by long-standing issues involving sample efficiency, credit assignment, and partial observability. In recent years, sequence models, especially the Transformer, have attracted increasing interest in the RL communities, spawning numerous approaches with notable effectiveness and generalizability. This survey presents a comprehensive overview of recent works aimed at solving sequential decision-making tasks with sequence models such as the Transformer, by discussing the connection between sequential decision-making and sequence modeling, and categorizing them based on the way they utilize the Transformer. Moreover, this paper puts forth various potential avenues for future research intending to improve the effectiveness of large sequence models for sequential decision-making, encompassing theoretical foundations, network architectures, algorithms, and efficient training systems.

  • RESEARCH ARTICLE
    Huifang YU, Ning WANG
    Frontiers of Computer Science, 2023, 17(5): 175810. https://doi.org/10.1007/s11704-022-2128-z

    Network coding can improve the information transmission efficiency and reduces the network resource consumption, so it is a very good platform for information transmission. Certificateless proxy signatures are widely applied in information security fields. However, certificateless proxy signatures based on classical number theory are not suitable for the network coding environment and cannot resist the quantum computing attacks. In view of this, we construct certificateless network coding proxy signatures from lattice (LCL-NCPS). LCL-NCPS is new multi-source signature scheme which has the characteristics of anti-quantum, anti-pollution and anti-forgery. In LCL-NCPS, each source node user can output a message vector to intermediate node and sink node, and the message vectors from different source nodes will be linearly combined to achieve the aim of improving the network transmission rate and network robustness. In terms of efficiency analysis of space dimension, LCL-NCPS can obtain the lower computation complexity by reducing the dimension of proxy key. In terms of efficiency analysis of time dimension, LCL-NCPS has higher computation efficiency in signature and verification.

  • RESEARCH ARTICLE
    Bo WANG, Zitong KANG, Pengwei DONG, Fan WANG, Peng MA, Jiajing BAI, Pengwei LIANG, Chongyi LI
    Frontiers of Computer Science, 2023, 17(2): 172702. https://doi.org/10.1007/s11704-022-1205-7

    Underwater images often exhibit severe color deviations and degraded visibility, which limits many practical applications in ocean engineering. Although extensive research has been conducted into underwater image enhancement, little of which demonstrates the significant robustness and generalization for diverse real-world underwater scenes. In this paper, we propose an adaptive color correction algorithm based on the maximum likelihood estimation of Gaussian parameters, which effectively removes color casts of a variety of underwater images. A novel algorithm using weighted combination of gradient maps in HSV color space and absolute difference of intensity for accurate background light estimation is proposed, which circumvents the influence of white or bright regions that challenges existing physical model-based methods. To enhance contrast of resultant images, a piece-wise affine transform is applied to the transmission map estimated via background light differential. Finally, with the estimated background light and transmission map, the scene radiance is recovered by addressing an inverse problem of image formation model. Extensive experiments reveal that our results are characterized by natural appearance and genuine color, and our method achieves competitive performance with the state-of-the-art methods in terms of objective evaluation metrics, which further validates the better robustness and higher generalization ability of our enhancement model.

  • RESEARCH ARTICLE
    Ashish SINGH, Abhinav KUMAR, Suyel NAMASUDRA
    Frontiers of Computer Science, 2024, 18(1): 181801. https://doi.org/10.1007/s11704-022-2193-3

    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.

  • RESEARCH ARTICLE
    Yaoqi YANG, Xianglin WEI, Renhui XU, Weizheng WANG, Laixian PENG, Yangang WANG
    Frontiers of Computer Science, 2023, 17(3): 173704. https://doi.org/10.1007/s11704-022-1550-6

    Recently revealed beam stealing attacks could greatly threaten the security and privacy of IEEE 802.11ad communications. The premise to restore normal network service is detecting and locating beam stealing attackers without their cooperation. Current consistency-based methods are only valid for one single attacker and are parameter-sensitive. From the viewpoint of image processing, this paper proposes an algorithm to jointly detect and locate multiple beam stealing attackers based on RSSI (Received Signal Strength Indicator) map without the training process involved in deep learning-based solutions. Firstly, an RSSI map is constructed based on interpolating the raw RSSI data for enabling high-resolution localization while reducing monitoring cost. Secondly, three image processing steps, including edge detection and segmentation, are conducted on the constructed RSSI map to detect and locate multiple attackers without any prior knowledge about the attackers. To evaluate our proposal’s performance, a series of experiments are conducted based on the collected data. Experimental results have shown that in typical parameter settings, our algorithm’s positioning error does not exceed 0.41 m with a detection rate no less than 91%.

  • RESEARCH ARTICLE
    Muazzam MAQSOOD, Sadaf YASMIN, Saira GILLANI, Maryam BUKHARI, Seungmin RHO, Sang-Soo YEO
    Frontiers of Computer Science, 2023, 17(4): 174329. https://doi.org/10.1007/s11704-022-2050-4

    Innovations on the Internet of Everything (IoE) enabled systems are driving a change in the settings where we interact in smart units, recognized globally as smart city environments. However, intelligent video-surveillance systems are critical to increasing the security of these smart cities. More precisely, in today’s world of smart video surveillance, person re-identification (Re-ID) has gained increased consideration by researchers. Various researchers have designed deep learning-based algorithms for person Re-ID because they have achieved substantial breakthroughs in computer vision problems. In this line of research, we designed an adaptive feature refinement-based deep learning architecture to conduct person Re-ID. In the proposed architecture, the inter-channel and inter-spatial relationship of features between the images of the same individual taken from nonidentical camera viewpoints are focused on learning spatial and channel attention. In addition, the spatial pyramid pooling layer is inserted to extract the multiscale and fixed-dimension feature vectors irrespective of the size of the feature maps. Furthermore, the model’s effectiveness is validated on the CUHK01 and CUHK02 datasets. When compared with existing approaches, the approach presented in this paper achieves encouraging Rank 1 and 5 scores of 24.6% and 54.8%, respectively.

  • RESEARCH ARTICLE
    Shiwei PAN, Yiming MA, Yiyuan WANG, Zhiguo ZHOU, Jinchao JI, Minghao YIN, Shuli HU
    Frontiers of Computer Science, 2023, 17(4): 174326. https://doi.org/10.1007/s11704-022-2023-7

    The minimum independent dominance set (MIDS) problem is an important version of the dominating set with some other applications. In this work, we present an improved master-apprentice evolutionary algorithm for solving the MIDS problem based on a path-breaking strategy called MAE-PB. The proposed MAE-PB algorithm combines a construction function for the initial solution generation and candidate solution restarting. It is a multiple neighborhood-based local search algorithm that improves the quality of the solution using a path-breaking strategy for solution recombination based on master and apprentice solutions and a perturbation strategy for disturbing the solution when the algorithm cannot improve the solution quality within a certain number of steps. We show the competitiveness of the MAE-PB algorithm by presenting the computational results on classical benchmarks from the literature and a suite of massive graphs from real-world applications. The results show that the MAE-PB algorithm achieves high performance. In particular, for the classical benchmarks, the MAE-PB algorithm obtains the best-known results for seven instances, whereas for several massive graphs, it improves the best-known results for 62 instances. We investigate the proposed key ingredients to determine their impact on the performance of the proposed algorithm.

  • 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
    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
    Jian ZHANG, Fazhi HE, Yansong DUAN, Shizhen YANG
    Frontiers of Computer Science, 2023, 17(2): 172703. https://doi.org/10.1007/s11704-022-1523-9

    The haze phenomenon seriously interferes the image acquisition and reduces image quality. Due to many uncertain factors, dehazing is typically a challenge in image processing. The most existing deep learning-based dehazing approaches apply the atmospheric scattering model (ASM) or a similar physical model, which originally comes from traditional dehazing methods. However, the data set trained in deep learning does not match well this model for three reasons. Firstly, the atmospheric illumination in ASM is obtained from prior experience, which is not accurate for dehazing real-scene. Secondly, it is difficult to get the depth of outdoor scenes for ASM. Thirdly, the haze is a complex natural phenomenon, and it is difficult to find an accurate physical model and related parameters to describe this phenomenon. In this paper, we propose a black box method, in which the haze is considered an image quality problem without using any physical model such as ASM. Analytically, we propose a novel dehazing equation to combine two mechanisms: interference item and detail enhancement item. The interference item estimates the haze information for dehazing the image, and then the detail enhancement item can repair and enhance the details of the dehazed image. Based on the new equation, we design an anti-interference and detail enhancement dehazing network (AIDEDNet), which is dramatically different from existing dehazing networks in that our network is fed into the haze-free images for training. Specifically, we propose a new way to construct a haze patch on the flight of network training. The patch is randomly selected from the input images and the thickness of haze is also randomly set. Numerous experiment results show that AIDEDNet outperforms the state-of-the-art methods on both synthetic haze scenes and real-world haze scenes.

  • RESEARCH ARTICLE
    Hongsheng XU, Zihan CHEN, Yu ZHANG, Xin GENG, Siya MI, Zhihong YANG
    Frontiers of Computer Science, 2023, 17(2): 172309. https://doi.org/10.1007/s11704-022-1154-1

    Temporal localization is crucial for action video recognition. Since the manual annotations are expensive and time-consuming in videos, temporal localization with weak video-level labels is challenging but indispensable. In this paper, we propose a weakly-supervised temporal action localization approach in untrimmed videos. To settle this issue, we train the model based on the proxies of each action class. The proxies are used to measure the distances between action segments and different original action features. We use a proxy-based metric to cluster the same actions together and separate actions from backgrounds. Compared with state-of-the-art methods, our method achieved competitive results on the THUMOS14 and ActivityNet1.2 datasets.

  • RESEARCH ARTICLE
    Yajing GUO, Xiujuan LEI, Lian LIU, Yi PAN
    Frontiers of Computer Science, 2023, 17(5): 175904. https://doi.org/10.1007/s11704-022-2151-0

    Circular RNAs (circRNAs) are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins (RBPs). Existing methods for predicting these interactions have limitations in feature learning. In view of this, we propose a method named circ2CBA, which uses only sequence information of circRNAs to predict circRNA-RBP binding sites. We have constructed a data set which includes eight sub-datasets. First, circ2CBA encodes circRNA sequences using the one-hot method. Next, a two-layer convolutional neural network (CNN) is used to initially extract the features. After CNN, circ2CBA uses a layer of bidirectional long and short-term memory network (BiLSTM) and the self-attention mechanism to learn the features. The AUC value of circ2CBA reaches 0.8987. Comparison of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.

  • RESEARCH ARTICLE
    Zhong JI, Jingwei NI, Xiyao LIU, Yanwei PANG
    Frontiers of Computer Science, 2023, 17(2): 172312. https://doi.org/10.1007/s11704-022-1250-2

    Although few-shot learning (FSL) has achieved great progress, it is still an enormous challenge especially when the source and target set are from different domains, which is also known as cross-domain few-shot learning (CD-FSL). Utilizing more source domain data is an effective way to improve the performance of CD-FSL. However, knowledge from different source domains may entangle and confuse with each other, which hurts the performance on the target domain. Therefore, we propose team-knowledge distillation networks (TKD-Net) to tackle this problem, which explores a strategy to help the cooperation of multiple teachers. Specifically, we distill knowledge from the cooperation of teacher networks to a single student network in a meta-learning framework. It incorporates task-oriented knowledge distillation and multiple cooperation among teachers to train an efficient student with better generalization ability on unseen tasks. Moreover, our TKD-Net employs both response-based knowledge and relation-based knowledge to transfer more comprehensive and effective knowledge. Extensive experimental results on four fine-grained datasets have demonstrated the effectiveness and superiority of our proposed TKD-Net approach.

  • LETTER
    Zhongqiu WANG, Shixiong XIA, Fengrong ZHANG
    Frontiers of Computer Science, 2023, 17(2): 172803. https://doi.org/10.1007/s11704-021-0550-2
  • RESEARCH ARTICLE
    Peng YANG, Zhiguo FU
    Frontiers of Computer Science, 2023, 17(2): 172401. https://doi.org/10.1007/s11704-022-1231-5

    Local holographic transformations were introduced by Cai et al., and local affine functions, an extra tractable class, were derived by it in #CSP2. In the present paper, we not only generalize local affine functions to #CSPd for general d, but also give new tractable classes by combining local holographic transformations with global holographic transformations. Moreover, we show how to use local holographic transformations to prove hardness. This is of independent interests in the complexity classification of counting problems.

  • RESEARCH ARTICLE
    Xiaoling HUANG, Youxia DONG, Guodong YE, Yang SHI
    Frontiers of Computer Science, 2023, 17(3): 173804. https://doi.org/10.1007/s11704-022-1419-8

    A new meaningful image encryption algorithm based on compressive sensing (CS) and integer wavelet transformation (IWT) is proposed in this study. First of all, the initial values of chaotic system are encrypted by RSA algorithm, and then they are open as public keys. To make the chaotic sequence more random, a mathematical model is constructed to improve the random performance. Then, the plain image is compressed and encrypted to obtain the secret image. Secondly, the secret image is inserted with numbers zero to extend its size same to the plain image. After applying IWT to the carrier image and discrete wavelet transformation (DWT) to the inserted image, the secret image is embedded into the carrier image. Finally, a meaningful carrier image embedded with secret plain image can be obtained by inverse IWT. Here, the measurement matrix is built by both chaotic system and Hadamard matrix, which not only retains the characteristics of Hadamard matrix, but also has the property of control and synchronization of chaotic system. Especially, information entropy of the plain image is employed to produce the initial conditions of chaotic system. As a result, the proposed algorithm can resist known-plaintext attack (KPA) and chosen-plaintext attack (CPA). By the help of asymmetric cipher algorithm RSA, no extra transmission is needed in the communication. Experimental simulations show that the normalized correlation (NC) values between the host image and the cipher image are high. That is to say, the proposed encryption algorithm is imperceptible and has good hiding effect.

  • RESEARCH ARTICLE
    Mingdi HU, Long BAI, Jiulun FAN, Sirui ZHAO, Enhong CHEN
    Frontiers of Computer Science, 2023, 17(3): 173321. https://doi.org/10.1007/s11704-022-1389-x

    Vehicle Color Recognition (VCR) plays a vital role in intelligent traffic management and criminal investigation assistance. However, the existing vehicle color datasets only cover 13 classes, which can not meet the current actual demand. Besides, although lots of efforts are devoted to VCR, they suffer from the problem of class imbalance in datasets. To address these challenges, in this paper, we propose a novel VCR method based on Smooth Modulation Neural Network with Multi-Scale Feature Fusion (SMNN-MSFF). Specifically, to construct the benchmark of model training and evaluation, we first present a new VCR dataset with 24 vehicle classes, Vehicle Color-24, consisting of 10091 vehicle images from a 100-hour urban road surveillance video. Then, to tackle the problem of long-tail distribution and improve the recognition performance, we propose the SMNN-MSFF model with multi-scale feature fusion and smooth modulation. The former aims to extract feature information from local to global, and the latter could increase the loss of the images of tail class instances for training with class-imbalance. Finally, comprehensive experimental evaluation on Vehicle Color-24 and previously three representative datasets demonstrate that our proposed SMNN-MSFF outperformed state-of-the-art VCR methods. And extensive ablation studies also demonstrate that each module of our method is effective, especially, the smooth modulation efficiently help feature learning of the minority or tail classes. Vehicle Color-24 and the code of SMNN-MSFF are publicly available and can contact the author to obtain.

  • REVIEW ARTICLE
    Jinyang GUO, Lu ZHANG, José ROMERO HUNG, Chao LI, Jieru ZHAO, Minyi GUO
    Frontiers of Computer Science, 2023, 17(5): 175106. https://doi.org/10.1007/s11704-022-2127-0

    Cloud vendors are actively adopting FPGAs into their infrastructures for enhancing performance and efficiency. As cloud services continue to evolve, FPGA (field programmable gate array) systems would play an even important role in the future. In this context, FPGA sharing in multi-tenancy scenarios is crucial for the wide adoption of FPGA in the cloud. Recently, many works have been done towards effective FPGA sharing at different layers of the cloud computing stack.

    In this work, we provide a comprehensive survey of recent works on FPGA sharing. We examine prior art from different aspects and encapsulate relevant proposals on a few key topics. On the one hand, we discuss representative papers on FPGA resource sharing schemes; on the other hand, we also summarize important SW/HW techniques that support effective sharing. Importantly, we further analyze the system design cost behind FPGA sharing. Finally, based on our survey, we identify key opportunities and challenges of FPGA sharing in future cloud scenarios.

  • RESEARCH ARTICLE
    Huize LI, Hai JIN, Long ZHENG, Yu HUANG, Xiaofei LIAO
    Frontiers of Computer Science, 2023, 17(2): 172103. https://doi.org/10.1007/s11704-022-1322-3

    With the increasing amount of data, there is an urgent need for efficient sorting algorithms to process large data sets. Hardware sorting algorithms have attracted much attention because they can take advantage of different hardware’s parallelism. But the traditional hardware sort accelerators suffer “memory wall” problems since their multiple rounds of data transmission between the memory and the processor. In this paper, we utilize the in-situ processing ability of the ReRAM crossbar to design a new ReCAM array that can process the matrix-vector multiplication operation and the vector-scalar comparison in the same array simultaneously. Using this designed ReCAM array, we present ReCSA, which is the first dedicated ReCAM-based sort accelerator. Besides hardware designs, we also develop algorithms to maximize memory utilization and minimize memory exchanges to improve sorting performance. The sorting algorithm in ReCSA can process various data types, such as integer, float, double, and strings.

    We also present experiments to evaluate the performance and energy efficiency against the state-of-the-art sort accelerators. The experimental results show that ReCSA has 90.92×, 46.13×, 27.38×, 84.57×, and 3.36× speedups against CPU-, GPU-, FPGA-, NDP-, and PIM-based platforms when processing numeric data sets. ReCSA also has 24.82×, 32.94×, and 18.22× performance improvement when processing string data sets compared with CPU-, GPU-, and FPGA-based platforms.

  • 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.

  • RESEARCH ARTICLE
    Zhihui YANG, Juan LIU, Xuekai ZHU, Feng YANG, Qiang ZHANG, Hayat Ali SHAH
    Frontiers of Computer Science, 2023, 17(5): 175903. https://doi.org/10.1007/s11704-022-2163-9

    Prediction of drug-protein binding is critical for virtual drug screening. Many deep learning methods have been proposed to predict the drug-protein binding based on protein sequences and drug representation sequences. However, most existing methods extract features from protein and drug sequences separately. As a result, they can not learn the features characterizing the drug-protein interactions. In addition, the existing methods encode the protein (drug) sequence usually based on the assumption that each amino acid (atom) has the same contribution to the binding, ignoring different impacts of different amino acids (atoms) on the binding. However, the event of drug-protein binding usually occurs between conserved residue fragments in the protein sequence and atom fragments of the drug molecule. Therefore, a more comprehensive encoding strategy is required to extract information from the conserved fragments.

    In this paper, we propose a novel model, named FragDPI, to predict the drug-protein binding affinity. Unlike other methods, we encode the sequences based on the conserved fragments and encode the protein and drug into a unified vector. Moreover, we adopt a novel two-step training strategy to train FragDPI. The pre-training step is to learn the interactions between different fragments using unsupervised learning. The fine-tuning step is for predicting the binding affinities using supervised learning. The experiment results have illustrated the superiority of FragDPI.

  • RESEARCH ARTICLE
    Junxiao XUE, Hao ZHOU
    Frontiers of Computer Science, 2023, 17(2): 172318. https://doi.org/10.1007/s11704-022-2121-6

    Biometric speech recognition systems are often subject to various spoofing attacks, the most common of which are speech synthesis and speech conversion attacks. These spoofing attacks can cause the biometric speech recognition system to incorrectly accept these spoofing attacks, which can compromise the security of this system. Researchers have made many efforts to address this problem, and the existing studies have used the physical features of speech to identify spoofing attacks. However, recent studies have shown that speech contains a large number of physiological features related to the human face. For example, we can determine the speaker’s gender, age, mouth shape, and other information by voice. Inspired by the above researches, we propose a spoofing attack recognition method based on physiological-physical features fusion. This method involves feature extraction, a densely connected convolutional neural network with squeeze and excitation block (SE-DenseNet), and feature fusion strategies. We first extract physiological features in audio from a pre-trained convolutional network. Then we use SE-DenseNet to extract physical features. Such a dense connection pattern has high parameter efficiency, and squeeze and excitation blocks can enhance the transmission of the feature. Finally, we integrate the two features into the classification network to identify the spoofing attacks. Experimental results on the ASVspoof 2019 data set show that our model is effective for voice spoofing detection. In the logical access scenario, our model improves the tandem decision cost function and equal error rate scores by 5% and 7%, respectively, compared to existing methods.

  • RESEARCH ARTICLE
    Xumeng WANG, Ziliang WU, Wenqi HUANG, Yating WEI, Zhaosong HUANG, Mingliang XU, Wei CHEN
    Frontiers of Computer Science, 2023, 17(6): 176709. https://doi.org/10.1007/s11704-023-2691-y

    Visualization and artificial intelligence (AI) are well-applied approaches to data analysis. On one hand, visualization can facilitate humans in data understanding through intuitive visual representation and interactive exploration. On the other hand, AI is able to learn from data and implement bulky tasks for humans. In complex data analysis scenarios, like epidemic traceability and city planning, humans need to understand large-scale data and make decisions, which requires complementing the strengths of both visualization and AI. Existing studies have introduced AI-assisted visualization as AI4VIS and visualization-assisted AI as VIS4AI. However, how can AI and visualization complement each other and be integrated into data analysis processes are still missing. In this paper, we define three integration levels of visualization and AI. The highest integration level is described as the framework of VIS+AI, which allows AI to learn human intelligence from interactions and communicate with humans through visual interfaces. We also summarize future directions of VIS+AI to inspire related studies.

  • LETTER
    Jiajia WANG, Weizhong ZHAO, Xinhui TU, Tingting HE
    Frontiers of Computer Science, 2023, 17(4): 174609. https://doi.org/10.1007/s11704-022-2041-5
  • RESEARCH ARTICLE
    Liuyan YAN, Lang LI, Ying GUO
    Frontiers of Computer Science, 2023, 17(3): 173805. https://doi.org/10.1007/s11704-022-1677-5

    IoT devices have been widely used with the advent of 5G. These devices contain a large amount of private data during transmission. It is primely important for ensuring their security. Therefore, we proposed a lightweight block cipher based on dynamic S-box named DBST. It is introduced for devices with limited hardware resources and high throughput requirements. DBST is a 128-bit block cipher supporting 64-bit key, which is based on a new generalized Feistel variant structure. It retains the consistency and significantly boosts the diffusion of the traditional Feistel structure. The SubColumns of round function is implemented by combining bit-slice technology with subkeys. The S-box is dynamically associated with the key. It has been demonstrated that DBST has a good avalanche effect, low hardware area, and high throughput. Our S-box has been proven to have fewer differential features than RECTANGLE S-box. The security analysis of DBST reveals that it can against impossible differential attack, differential attack, linear attack, and other types of attacks.

  • RESEARCH ARTICLE
    Yao ZHANG, Liangxiao JIANG, Chaoqun LI
    Frontiers of Computer Science, 2023, 17(5): 175331. https://doi.org/10.1007/s11704-022-2225-z

    Crowdsourcing provides an effective and low-cost way to collect labels from crowd workers. Due to the lack of professional knowledge, the quality of crowdsourced labels is relatively low. A common approach to addressing this issue is to collect multiple labels for each instance from different crowd workers and then a label integration method is used to infer its true label. However, to our knowledge, almost all existing label integration methods merely make use of the original attribute information and do not pay attention to the quality of the multiple noisy label set of each instance. To solve these issues, this paper proposes a novel three-stage label integration method called attribute augmentation-based label integration (AALI). In the first stage, we design an attribute augmentation method to enrich the original attribute space. In the second stage, we develop a filter to single out reliable instances with high-quality multiple noisy label sets. In the third stage, we use majority voting to initialize integrated labels of reliable instances and then use cross-validation to build multiple component classifiers on reliable instances to predict all instances. Experimental results on simulated and real-world crowdsourced datasets demonstrate that AALI outperforms all the other state-of-the-art competitors.

  • RESEARCH ARTICLE
    Qingyang LI, Zhiwen YU, Huang XU, Bin GUO
    Frontiers of Computer Science, 2023, 17(2): 172317. https://doi.org/10.1007/s11704-022-1270-y

    Anomaly detectors are used to distinguish differences between normal and abnormal data, which are usually implemented by evaluating and ranking the anomaly scores of each instance. A static unsupervised streaming anomaly detector is difficult to dynamically adjust anomaly score calculation. In real scenarios, anomaly detection often needs to be regulated by human feedback, which benefits adjusting anomaly detectors. In this paper, we propose a human-machine interactive streaming anomaly detection method, named ISPForest, which can be adaptively updated online under the guidance of human feedback. In particular, the feedback will be used to adjust the anomaly score calculation and structure of the detector, ideally attaining more accurate anomaly scores in the future. Our main contribution is to improve the tree-based streaming anomaly detection model that can be updated online from perspectives of anomaly score calculation and model structure. Our approach is instantiated for the powerful class of tree-based streaming anomaly detectors, and we conduct experiments on a range of benchmark datasets. The results demonstrate that the utility of incorporating feedback can improve the performance of anomaly detectors with a few human efforts.