Online First

Online First Papers are peer-reviewed and accepted for publication. Note that the papers under this directory, they are posted online prior to technical editing and author proofing. Please use with caution.
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Labeled Multi-Bernoulli Maneuvering Target Tracking Algorithm via TSK Iterative Regression Model
WANG Xiaoli, XIE Weixin, LI Liangqun
, Available online  , doi: 10.1049/cje.2020.00.156
Abstract(96) HTML (45) PDF(12)
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Aiming at the problem that the existing labeled multi-Bernoulli (LMB) method has a single and fixed model set, a LMB maneuvering target tracking algorithm via Takagi-Sugeno-Kang (TSK) iterative regression multiple model is proposed. In the TSK iterative regression modeling, the feature information of the targets is analyzed and represented by multiple semantic fuzzy sets. Then the state is expanded to introduce model information, thereby the adaptive multi-model idea is incorporated into the framework of the LMB method to solve the uncertain maneuverability of moving targets. Finally, the simulation results show that the proposed algorithm can effectively achieve maneuvering target tracking in the nonlinear system.
Binary Image Steganalysis Based on Symmetrical Local Residual Patterns
LUO Junwei, YU Mujian, YIN Xiaolin, LU Wei
, Available online  , doi: 10.1049/cje.2020.00.414
Abstract(57) HTML (28) PDF(0)
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Residual computation is an effective method for gray-scale image steganalysis. For binary images, the residual computation calculated by the XOR operation is also employed in the Local residual patterns (LRP) model for steganalysis. In this paper, a binary image steganalytic scheme based on Symmetrical local residual patterns (SLRP) is proposed. The symmetrical relationships among residual patterns are introduced that make the features more compact while reducing the dimensionality of the features set. Multi-scale windows are utilized to construct three SLRP submodels which are further merged to construct the final features set instead of a single model. What's more, SLRPs with higher probability to be modified after embedding are emphasized and selected to construct the feature sets for training the SVM classifier. Finally, experimental results show that the proposed steganalytic scheme is effective for detecting binary image steganography.
Quantum Attacks on Type-3 Generalized Feistel Scheme and Unbalanced Feistel Scheme with Expanding Functions
ZHANG Zhongya, WU Wenling, SUI Han, WANG Bolin
, Available online  , doi: 10.1049/cje.2021.00.294
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Quantum algorithms are raising concerns in the field of cryptography all over the world. A growing number of symmetric cryptography algorithms have been attacked in the quantum setting. Type-3 generalized Feistel scheme (GFS) and unbalanced Feistel scheme with expanding functions (UFS-E) are common symmetric cryptography schemes, which are often used in cryptographic analysis and design. We propose quantum attacks on the two Feistel schemes. For $ d $-branch Type-3 GFS and UFS-E, we propose distinguishing attacks on $(d+1)$-round Type-3 GFS and UFS-E in polynomial time in the quantum chosen plaintext attack (qCPA) setting. We propose key recovery by applying Grover's algorithm and Simon's algorithm. For $ r $-round $ d $-branch Type-3 GFS with $ k $-bit length subkey, the complexity is $O({2^{(d - 1)(r - d - 1)k/2}})$ for $r\ge d + 2$. The result is better than that based on exhaustive search by a factor ${2^{({d^2} - 1)k/2}}$. For $ r $-round $ d $-branch UFS-E, the attack complexity is $O({2^{(r - d - 1)(r - d)k/4}})$ for $d + 2 \le r \le 2d$, and $O({2^{(d - 1)(2r - 3d)k/4}})$ for $r > 2d$. The results are better than those based on exhaustive search by factors ${2^{(4rd - {d^2} - d - {r^2} - r)k/4}}$ and ${2^{3(d - 1)dk/4}}$ in the quantum setting, respectively.
Quantum Wolf Pack Evolutionary Algorithm of Weight Decision-Making Based on Fuzzy Control
LU Na, MA Long
, Available online  , doi: 10.1049/cje.2021.00.217
Abstract(42) HTML (19) PDF(15)
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In the traditional quantum wolf pack algorithm, the wolf pack distribution is simplified, and the leader wolf is randomly selected. This leads to the problems that the development and exploration ability of the algorithm is weak and the rate of convergence is slow. Therefore, a quantum wolf pack evolutionary algorithm of weight decision-making based on fuzzy control is proposed in this paper. First, to realize the diversification of wolf pack distribution and the regular selection of the leader wolf, a dual strategy method and sliding mode cross principle are adopted to optimize the selection of the quantum wolf pack initial position and the candidate leader wolf. Second, a new non-linear convergence factor is adopted to improve the leader wolf’s search direction operator to enhance the local search capability of the algorithm. Meanwhile, a weighted decision-making strategy based on fuzzy control and the quantum evolution computation method is used to update the position of the wolf pack and enhance the optimization ability of the algorithm. Then, a functional analysis method is adopted to prove the convergence of the quantum wolf pack algorithm, thus realizing the feasibility of the algorithm’s global convergence. The performance of the quantum wolf pack algorithm of weighted decision-making based on fuzzy control was verified through six standard test functions. The optimization results are compared with the standard wolf pack algorithm and the quantum wolf pack algorithm. Results show that the improved algorithm had a faster rate of convergence, higher convergence precision, and stronger development and exploration ability.
A Novel Robust Online Extreme Learning Machine for the Non-Gaussian Noise
GU Jun, ZOU Quanyi, DENG Changhui, WANG Xiaojun
, Available online  , doi: 10.1049/cje.2021.00.122
Abstract(76) HTML (36) PDF(7)
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Samples collected from most industrial processes have two challenges: one is contaminated by the non-Gaussian noise, and the other is gradually obsolesced. These features can obviously reduce the accuracy and generalization of models. To handle these challenges, a novel method, named the Robust Online Extreme Learning Machine (RO-ELM), is proposed in this paper. In the RO-ELM, the Least Mean $ p $-Power (LMP) criterion is employed as the cost function which is to boost the robustness of the ELM, and the forgetting mechanism is introduced to discard the obsolescence samples. To investigate the performance of the RO-ELM, experiments on artificial and real-world datasets with the non-Gaussian noise are performed, and the datasets are from regression or classification problems. Results show that the RO-ELM is more robust than the ELM, the OS-ELM and the FOS-ELM. The accuracy and generalization of the RO-ELM models are better than those of other models for online learning.
LaTLS: A Lattice-Based TLS Proxy Protocol
ZHANG Xinglong, CHENG Qingfeng, LI Yuting
, Available online  , doi: 10.1049/cje.2018.00.357
Abstract(58) HTML (29) PDF(5)
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The function of the Internet proxy is to check and convert the data exchanged between client and server. In fact, the two-party secure communication protocol with good security is turned into an unsafe multiparty protocol. At present, there are relatively few proxy protocols that can be applied in practice. This paper analyzes the classic agent protocol mcTLS and pointed out the security issues. We focus on the security of TLS 1.3 and proposed a lattice-based multi-party proxy protocol: LaTLS. LaTLS can be proved secure in the eCK model, it can resist key-sharing attacks,counterfeiting attacks, replay attacks, and achieve forward security. Compared with traditional DH and ECDH schemes, LaTLS is more effcient. At the same time, its security is based on the shortest vector problem, there for it has anti-quantum attack properties.
Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks: A Novel Defense Framework
XU Zhenyu, SUN Zhiguo, GUO Lili, Muhammad Zahid Hammad, Chintha Tellambura
, Available online  , doi: 10.1049/cje.2021.00.090
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Multiple secondary users (SUs) perform collaborative spectrum sensing (CSS) in cognitive radio networks to improve the sensing performance. However, this system severely degrades with spectrum sensing data falsification (SSDF) attacks from a large number of malicious secondary users, i.e., massive SSDF attacks. To mitigate such attacks, we propose a joint spectrum sensing and spectrum access framework. During spectrum sensing, each SU compares the decisions of CSS and Independent spectrum sensing (IndSS), and then the reliable decisions are adopted as its final decisions. Since the transmission slot is divided into several tiny slots, at the stage of spectrum access, each SU is assigned with a specific tiny time slot. In accordance with its independent final spectrum decisions, each node separately accesses the tiny time slot. Simulation results verify effectiveness of the proposed algorithm.
An Improved Navigation Pseudolite Signal Structure Based on the Kasami Sequences and the Pulsing Scheme
TAO Lin, SUN Junren, LI Guangchen, ZHU Bocheng
, Available online  , doi: 10.1049/cje.2020.00.403
Abstract(147) HTML (62) PDF(11)
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Pseudolites (PLs) are ground-based satellites, providing users with navigation solutions. However, implementation of the PL system leads to the near-far problem. In this paper, we proposed an improved navigation PL signal structure of combing kasami sequences and the pulsing scheme to mitigate the near-far effect. The pulse modulation method is adopted to ensure that the PLs transmit signals at different timeslots and reduce the PL signals’ mutual interference. Additionally, we employ the small set of kasami sequences with good cross-correlation properties to improve the anti-interference ability. A simulation test based on software is carried out to evaluate the performance of the proposed signal. The simulation proves that the improved PL signal has an impulsive power spectral density (PSD), makes it a feasible solution to mitigate the near-far effect, and performs better in the capture.
Adaptive Simplified Chicken Swarm Optimization Based on Inverted S-Shaped Inertia Weight
GU Yanchun, LU Haiyan, XIANG Lei, SHEN Wanqiang
, Available online  , doi: 10.1049/cje.2020.00.233
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Considering the issues of premature convergence and low solution accuracy in solving high-dimensional problems with the basic chicken swarm optimization algorithm (CSO), an Adaptive simplified chicken swarm optimization algorithm based on inverted S-shaped inertia weight (ASCSO-S) is proposed. Firstly, a simplified chicken swarm optimization algorithm (SCSO) is presented by removing all the chicks from the chicken swarm. Secondly, an inverted S-shaped inertia weight is designed and introduced into the updating process of the roosters and hens to dynamically adjust their moving step size and thus to improve the convergence speed and solution accuracy of the algorithm. Thirdly, in order to enhance the exploration ability of the algorithm, an adaptive updating strategy is added to the updating process of the hens. Simulation experiments on 21 classical test functions show that ASCSO-S is superior to the other comparison algorithms in terms of convergence speed, solution accuracy, and solution stability. In addition, ASCSO-S is applied to the parameter estimation of Richards model, and the test results indicate that ASCSO-S has the best fitting results compared with other three algorithms.
Knowledge Graph Completion Based on GCN of Multi-Information Fusion and High-Dimensional Structure Analysis Weight
NIU Haoran, HE Haitao, FENG Jianzhou, NIE Junlan, ZHANG Yangsen, REN Jiadong
, Available online  , doi: 10.1049/cje.2021.00.080
Abstract(89) HTML (41) PDF(7)
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Knowledge graph completion (KGC) can solve the problem of data sparsity in the knowledge graph. A large number of models for the KGC task have been proposed in recent years. However, the underutilisation of the structure information around nodes is one of the main problems of the previous KGC model, which leads to relatively single encoding information. To this end, a new KGC model that encodes and decodes the feature information is proposed. First, we adopt the subgraph sampling method to extract node structure. Moreover, the graph convolutional network (GCN) introduced the channel attention convolution encode node structure features and represent them in matrix form to fully mine the node feature information. Eventually, the high-dimensional structure analysis weight decodes the encoded matrix embeddings and then constructs the scoring function. The experimental results show that the model performs well on the datasets used.
Cryptanalysis of AEGIS-128
SHI Tairong, HU Bin, GUAN Jie, WANG Senpeng
, Available online  , doi: 10.1049/cje.2020.00.231
Abstract(84) HTML (40) PDF(9)
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AEGIS, an authenticated encryption algorithm designed by Wu and Preneel, is one of the six winners of the CAESAR competition. In this paper, we comprehensively investigate the existence of collision in the initialization of AEGIS-128 and evaluate the number of AES round functions involved in initialization, which reflects the resistance to differential attack. As a result, we find that there are 40 AES round functions, which is less than 50 ones claimed in the design document. We also prove that AEGIS-128 is strong enough to resist adversary who has access to partial state. In particular, we present a collision-based distinguisher and exploit it to recover the key of 4-step and 5-step (out of the full 10) AEGIS-128. The time and memory complexities are about ${2}^{58}$ and ${2}^{26}$ respectively. Specifically, we quantize the attack of 4-step AEGIS-128, in which we solve the technical issue of dealing with the function that does not fulfill Simon's promise. It is noted that the nonce is not reused in our work. Although we present some results of AEGIS-128 that exceed the existed analysis, the security margin of AEGIS-128 remains large.
An Edge-Cloud Collaborative Cross-Domain Identity-Based Authentication Protocol with Privacy Protection
SUN Haipeng, TAN Yu-an, LI Congwu, LEI Lei, ZHANG Qikun, HU Jingjing
, Available online  , doi: 10.1049/cje.2021.00.269
Abstract(91) HTML (43) PDF(14)
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Edge-cloud collaborative computing has a wide range of application scenarios. Resource sharing is one of the key technologies to realize various application scenarios. Identity authentication is an important means to ensure the security of resource sharing in various application scenarios. Because the edge-cloud collaborative application scenario is more complex, it involves collaborative operations among different security domains, frequently access and exit application system of mobile terminals. Traditional identity authentication is no longer suitable for complex application scenarios of edgecloud collaborative computing. Therefore, a cross-domain identity authentication protocol based on privacy protection is proposed. The main advantages of the protocol are as follows. 1) Self-certified key generation algorithm: the public/private key pair of the mobile terminal is generated by the terminal members themselves. The identity registration is realized through the correspondence between the self-authenticating public key and the identity to protect the privacy of the individual. It avoids security risks caused by third-party key distribution and key escrow; 2) Crossdomain identity authentication: the alliance keys are calculated among edge servers through blockchain technology. Each edge server uses the alliance keys to sign the identity information of terminals in its domain. Cross-domain identity authentication is realized through the signature authentication of the alliance domain. The cross-domain authentication process is simple and efficient; 3) Revocability of identity authentication: When the mobile terminal has logged off or exited the system, the legal identity of the terminal in the system will also become invalid immediately, so as to ensure the forward and backward security of accessing system resources. Under the hardness assumption of discrete logarithm problem (DLP) and computational Diffie-Hellman(CDH) problem, the security of the protocol is proven, and the efficiency of the protocol is verified.
Efficient 3D Hilbert Curve Encoding and Decoding Algorithms
JIA Lianyin, LIANG Binbin, LI Mengjuan, LIU Yong, CHEN Yinong, DING Jiaman
, Available online  , doi: 10.1049/cje.2020.00.171
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Hilbert curve describes a one-to-one mapping between multidimensional space and 1D space. Most traditional 3D Hilbert encoding and decoding algorithms work on order-wise manner and are not aware of the difference between different input data and spend equivalent computing costs on them, thus resulting in a low efficiency. To solve this problem, in this paper we design efficient 3D state views for fast encoding and decoding. Based on the state views designed, a new encoding algorithm (JFK-3HE) and a new decoding algorithm (JFK-3HD) are proposed. JFK-3HE and JFK-3HD can avoid executing iteratively encoding or decoding each order by skipping the first 0s in input data, thus decreasing the complexity and improving the efficiency. Experimental results show that JFK-3HE and JFK-3HD outperform the state-of-the-arts algorithms for both uniform and skew-distributed data.
A Fine-Grained Flash-Memory Fragment Recognition Approach for Low-Level Data Recovery
ZHANG Li, HAO Shengang, ZHANG Quanxin
, Available online  , doi: 10.1049/cje.2020.00.206
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Data recovery from flash memory in the mobile device can effectively reduce the loss caused by data corruption. Type recognition of data fragment is an essential prerequisite to the low-level data recovery. Previous works in this field classify data fragment based on its file type. Still, the classification efficiency is low, especially when the data fragment is a part of a composite file. We propose a fine-grained approach to classifying data fragment from the low-level flash memory to improve the classification accuracy and efficiency. The proposed method redefines flash-memory-page data recognition problem based on the encoding format of the data segment, and applies a hybrid machine learning algorithm to detect the data type of the flash page. The hybrid algorithm can significantly decompose the given data space and reduce the cost of training. The experimental results show that our method achieves better classification accuracy and higher time performance than the existing methods.
Probe Machine Based Computing Model for Maximum Clique Problem
CUI Jianzhong, YIN Zhixiang, TANG Zhen, YANG Jing
, Available online  , doi: 10.1049/cje.2020.00.293
Abstract(67) HTML (31) PDF(8)
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Probe Machine (PM) is a recently reported mathematic model with massive parallelism. Herein, we presented searching the maximum clique of an undirected graph with six vertices. We constructed data library containing n sublibraries, each sublibrary corresponded to a vertex in the given graph. Then, probe library according to the induced subgraph was designed in order to search and generate all maximal cliques. Subsequently, we performed probe operation, and all maximal cliques were generated in parallel. The advantages of the proposed model lie in two aspects. On one hand, solution to NP-complete problem is generated in just one step of probe operation rather than found in vast solution space. On the other hand, the proposed model is highly parallel. The work demonstrates that PM is superior to TM in terms of searching capacity when tackling NP-complete problem.
An Efficient Algebraic Solution for 3-D Moving Source Localization Using Quadruple Hybrid Measurements
DING Ting, ZHAO Yongsheng, ZHAO Yongjun
, Available online  , doi: 10.1049/cje.2020.00.410
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In this paper, we address the 3-D moving source localization using time difference of arrival (TDOA), frequency difference of arrival (FDOA), angle of arrival (AOA) and AOA rate measurements, gathered from an array of spatially distributed receivers. The TDOA, FDOA, AOA and AOA rate measurement equations were firstly established according to the space geometric relationship of the source relative to the receivers. Then an efficient closed-form algorithm for moving source localization was proposed, using the quadruple hybrid measurements. The proposed algorithm transforms the nonlinear measurement equations to a set of linear equations, from which the source position and velocity estimate can be obtained by applying weighted least square (WLS) minimization. In contrast to existing two-stage WLS algorithms, the proposed algorithm does not introduce any nuisance parameters and requires only one-stage, which allows for source localization with minimum number of receivers. Theoretical accuracy analysis indicates that the proposed algorithm reaches the Cramer-Rao lower bound, and simulation studies corroborate the efficiency and superiority of the proposed algorithm over existing algorithms.
Secure Beamforming in Downlink MISO Non-orthogonal Multiple Access Symbiotic Radio Systems
LI Yiqing, JIANG Miao
, Available online  , doi: 10.1049/cje.2020.00.187
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Symbiotic radio (SR) networks are possible solutions to the future low-power wireless communications for massive Internet of Things devices. In this paper, we investigate a multiple-input-single-output Non-orthogonal multiple access (NOMA) Backscatter device (BD) aided SR network with a potential eavesdropper. In the network, a Base station (BS) broadcasts signals to a central user and a cell-edge user using the NOMA protocol. With ambient backscatter modulation, the BD transmits its own messages to the central user over incident signals from the BS. We propose a constrained concave-convex procedure-based algorithm which maximizes the outage secrecy rate from the BD to the central user under the achievable secrecy rate constraints from the BS to the central and cell-edge users. Simulation results illustrate that our proposed network achieves a much larger secrecy rate region than the orthogonal multiple access network.
Learning to Combine Answer Boundary Detection and Answer Re-ranking for Phrase-Indexed Question Answering
WEN Liang, SHI Haibo, ZHANG Xiaodong, SUN Xin, WEI Xiaochi, WANG Junfeng, CHENG Zhicong, YIN Dawei, WANG Xiaolin, LUO Yingwei, WANG Houfeng
, Available online  , doi: 10.1049/cje.2021.00.079
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Phrase-indexed question answering (PIQA) seeks to improve the inference speed of question answering (QA) models by enforcing complete independence of the document encoder from the question encoder, and it shows that the constrained model can achieve significant efficiency at the cost of its accuracy. In this paper, we aim to build a model under the PIQA constraint while reducing its accuracy gap with the unconstrained QA models. We propose a novel framework—AnsDR, which consists of an answer boundary detector (AnsD) and an answer candidate ranker (AnsR). More specifically, AnsD is a QA model under the PIQA architecture and it is designed to identify the rough answer boundaries; and AnsR is a lightweight ranking model to finely re-rank the potential candidates without losing the efficiency. We perform the extensive experiments on public datasets. The experimental results show that the proposed method achieves the state of the art on the PIQA task.
Predicting Microbe-disease Association Based on Heterogeneous Network and Global Graph Feature Learning
WANG Yueyue, LEI Xiujuan, PAN Yi
, Available online  , doi: 10.1049/cje.2020.00.212
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Numerous microbes inhabit human body, making a vast difference in human health and disease. Therefore, understanding associations between microbes and diseases is beneficial to disease prevention and treatment. In this study, we develop a prediction method called HNGFL by Learning global graph feature on the heterogeneous network. Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple similarities. For microbe similarity network, on the basis of Gaussian interaction profile (GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, we use an embedding algorithm called GraRep to learn global structural information for this network. According to vector feature of every node, we utilize Support Vector Machine (SVM) classifier to calculate the relevance for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.
Backdoor Attacks on Image Classification Models in Deep Neural Networks
ZHANG Quanxin, MA Wencong, WANG Yajie, ZHANG Yaoyuan, SHI Zhiwei, LI Yuanzhang
, Available online  , doi: 10.1049/cje.2021.00.126
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Deep neural network (DNN) is applied widely in many applications and achieves state-of-the-art performance. However, DNN lacks transparency and interpretability for users in structure. Attackers can use this feature to embed trojan horses in the DNN structure, such as inserting a backdoor into the DNN, so that DNN can learn both the normal main task and additional malicious tasks at the same time. Besides, DNN relies on data set for training. Attackers can tamper with training data to interfere with DNN training process, such as attaching a trigger on input data. Because of defects in DNN structure and data, the backdoor attack can be a serious threat to the security of DNN. The DNN attacked by backdoor performs well on benign inputs while it outputs an attacker-specified label on trigger attached inputs. Backdoor attack can be conducted in almost every stage of the machine learning pipeline. Although there are a few researches in the backdoor attack on image classification, a systematic review is still rare in this field. This paper is a comprehensive review of backdoor attacks. According to whether attackers have access to the training data, we divide various backdoor attacks into two types: poisoning-based attacks and non-poisoning-based attacks. We go through the details of each work in the timeline, discussing its contribution and deficiencies. We propose a detailed mathematical backdoor model to summary all kinds of backdoor attacks. In the end, we provide some insights about future studies.
Internet of Brain, Thought, Thinking, and Creation
ZHANG Zhimin, YIN Rui, NING Huansheng
, Available online  , doi: 10.1049/cje.2021.00.236
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Thinking space came into being with the emergence of human civilization. With the emergence and development of cyberspace, the interaction between those two spaces began to take place. In the collision of thinking and technology, new changes have taken place in both thinking space and cyberspace. To this end, this paper divides the current integration and development of thinking space and cyberspace into three stages, namely Internet of brain (IoB), Internet of thought (IoTh), and Internet of thinking (IoTk). At each stage, the contents and technologies to achieve convergence and connection of spaces are discussed. Besides, the Internet of creation (IoC) is proposed to represent the future development of thinking space and cyberspace. Finally, a series of open issues are raised, and they will become thorny factors in the development of the IoC stage.
A Comprehensive Study on the Theory of Graphene Solution-Gated Field Effect Transistor: Simulations and Experiments
HU Shihui, ZHANG Jizhao, WANG Zhongrong, JIA Yunfang
, Available online  , doi: 10.1049/cje.2021.00.032
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Graphene solution-gated field effect transistors (G-SgFETs) have been widely developed in the field of biosensors, but deficiencies in their theories still exist. A theoretical model for G-SgFET, including the three-terminal equivalent circuit model and the numerically calculating method, is proposed by the comprehensive analyses of the graphene-liquid interface and the FET principle. Not only the applied voltages on the electrode-pairs of gate-source and drain-source, but also the nature of graphene and its derivatives are considered by analysing their influences on the Fermi level, the carriers’ concentration and mobility, which may consequently affect the output drain-source current. To verify whether it is available for G-SgFETs based on different method prepared graphene, three kinds of graphene materials which are liquid-phase exfoliated graphene (LEG), reduced graphene oxide (rGO), and tetra (4-Aminophenyl) porphyrin hybridized rGO (TAP/rGO) are used as examples. The coincidences of calculated output and transfer feature curves with the measured ones are obtained to confirm its adaptivity for simulating the basic G-SgFETs’ electric features, by modulating Fermi level and mobility. Furthermore, the model is exploited to simulate G-SgFETs’ current responding to the biological functionalization with aptamer and the detections for circulating tumor cells, as a proof-of-concept. The calculated curren t changes are compared with the experimental results, to verify the proposed G-SgFETs’ model is also suitable for mimicking the bio-electronic responding, which may give a preview of some conceived G-SgFETs’ biosensors and improve the design efficiency.
Statistical Model on CRAFT
WANG Caibing, GUO Hao, YE Dingfeng, WANG Ping
, Available online  , doi: 10.1049/cje.2021.00.092
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Many cryptanalytic techniques for symmetric-key primitives rely on specific statistical analysis to extract some secrete key information from a large number of known or chosen plaintext-ciphertext pairs. For example, there is a standard statistical model for differential cryptanalysis that determines the success probability and complexity of the attack given some predefined configurations of the attack. In this work, we investigate the differential attack proposed by Guo et al. at Fast Software Encryption Conference 2020 and find that in this attack, the statistical behavior of the counters for key candidates deviate from standard scenarios, where both the correct key ${\boldsymbol{k}}$ and ${\boldsymbol{k \oplus XXX}}$ are expected to receive the largest number of votes. Based on this bimodal behavior, we give three different statistical models for truncated differential distinguisher on CRAFT (a cryptographic algorithm proposed by Beierle et al. in IACR Transactions on Symmetric Cryptology in 2019) for bimodal phenomena. Then, we provide the formulas about the success probability and data complexity for different models under the condition of a fixed threshold value. Also, we verify the validity of our models for bimodal phenomena by experiments on round-reduced of the versions distinguishers on CRAFT. We find that the success probability of theory and experiment are close when we fix the data complexity and threshold value. Finally, we compare the three models using the mathematical tool Matlab and conclude that Model 3 has better performance.
Based on Weight and User Feedback: A Novel Trustworthiness Measurement Model
ZHOU Wei, MA Yanfang, PAN Haiyu
, Available online  , doi: 10.1049/cje.2020.00.391
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Software trustworthiness is an important criterion for evaluating software quality. In component-based software, different components play different roles and different users give different grades of trustworthiness after using the software. These elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.
WCM-WTrA: A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning
LEI Tianwei, XUE Jingfeng, WANG Yong, NIU Zequn, SHI Zhiwei, ZHANG Yu
, Available online  , doi: 10.1049/cje.2021.00.119
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Cross-project defect prediction is a hot topic in the field of defect prediction. How to reduce the difference between projects and make the model have better accuracy is the core problem. This paper starts from two perspectives: feature selection and distance-weight instance transfer. We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WtrA and multi-source model Multi-WCM-WTrA. We have tested on AEEEM and ReLink datasets, and the results show that our method has an average improvement of 23% compared with TCA + algorithm on AEEEM datasets, and an average improvement of 5% on ReLink datasets.