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In this paper, a linear attack model of SNOW 3G and SNOW-V based on automatic search technology is proposed. We first describe the linear approximation of Finite State Machine transformation, which allows a wider range of automatic search, then model it with the automatic search technology based on SAT/SMT program. Adopting this generic method, we seek out a binary linear approximation of SNOW 3G with correlation of |$2^{-21.92}$| which has been verified by test. Treating this binary approximation as a mask of an 8-bit distribution in a fixed field, we provide a method to obtain the 8-bit distribution. The binary approximation is used in a fast correlation attack with expected time and memory complexity |$2^{184.67}$|⁠ , given |$2^{173.96}$| key stream words. For the full version of SNOW-V, considering the linear relationship between Linear Feedback Shift Register parts at three successive moments, we search out a distinguisher with correlation of |$2^{-175.51}$|⁠ , which results in a distinguishing attack with an expected complexity of |$2^{351.02}$|⁠. [ABSTRACT FROM AUTHOR]

A graph |$G = (V,E)$| is called a pairwise compatibility graph (PCG) if it admits a tuple |$(T, d_{min},d_{max})$| of an edge-weighted tree |$T$| of non-negative edge weights with leaf set |$L$|⁠ , two non-negative real numbers |$d_{min} \leq d_{max}$| such that each vertex |$u^{\prime} \in V$| represents a leaf |$u \in L$| and |$G$| has an edge |$(u^{\prime},v^{\prime}) \in E$| if and only if the distance between the two leaves |$u$| and |$v$| in the tree |$T$| lies within interval |$[d_{min}, d_{max}]$|⁠. It has been proven that not all graphs are PCGs. A graph |$G$| is called a |$k$| -interval PCG if there exists an edge-weighted tree |$T$| and |$k$| mutually exclusive intervals of non-negative real numbers such that there is an edge between two vertices in |$G$| if and only if the distance between their corresponding leaves in |$T$| lies within any of the |$k$| intervals. It is known that every graph |$G$| is a |$k$| -interval PCG for |$k=|E|$|⁠ , where |$E$| is the set of edges of |$G$|⁠. It is thus interesting to know the smallest value of |$k$| for which |$G$| is a |$k$| -interval PCG. In this paper, we show that grid graphs and a subclass of |$3$| D grid graphs are |$2$| -interval PCGs. [ABSTRACT FROM AUTHOR]

Image hashing is an effective technology for extensive image applications, such as retrieval, authentication and copy detection. This paper designs a new image hashing scheme based on saliency map and sparse model. The major contributions are twofold. The first contribution is the construction of a weighted image representation by combining a visual attention model called Itti model and the matrix of color vector angle (CVA). Since the Itti model can efficiently detect saliency map and CVA fully captures color information of image, they contribute to a visually robust and discriminative image representation. The second contribution is the hash extraction from the weighted image representation via sparse model. A classical sparse model called robust principal component analysis is exploited to decompose the weighted image representation into a low-rank component and a sparse component. As the low-rank component can describe intrinsic structure of image, hash calculation with low-rank component can achieve good discrimination. The efficiencies of the proposed scheme are validated by extensive experiments with open databases. The results demonstrate that the proposed scheme is superior to some state-of-the-art schemes in terms of classification performance between robustness and discrimination. [ABSTRACT FROM AUTHOR]

Traffic encrypted technology enables Internet users to protect their data secrecy, but it also brings a challenge to malicious package detection. To tackle this issue, researchers have investigated into encrypted traffic analysis (ETA) in recent years. Existing works, however, only focus on the accuracy of malicious flow identification. Using ETA as a technical black box, they pay little attention to the internal details and explanation of models. In this paper, we, for the first time, introduce interpretable machine learning into ETA. We aim to provide a reasonable explanation for detection results, so as to enable one to understand and further trust network security analysts. We develop a complete analysis framework, named DEV-ETA (detection, explanation and verification of ETA). DEV-ETA applies post hoc interpretation methods to explain the detection results and verify the explanation using the joint distribution of support features on the dataset. We run thorough experiments to explain the detection result using three popular explanation approaches, namely SHAP, LIME and MSS, and we verify the explanation via the feature distribution plot. The experimental results show that our design can interpret the detection result of ETA model instead of just simply treating the model as a black box. [ABSTRACT FROM AUTHOR]

FOX block cipher was designed with a Lai–Massey scheme, in which the round function uses the Substitution-Permutation-Substitution structure. A meet-in-the-middle (MITM) attack is one of the most important issues for the security of the block cipher, which consists of a precomputation phase for constructing a distinguisher and an online phase for key recovery. This paper studies the MITM attacks against FOX. The first MITM distinguishers of 5-round FOX64, 7-round FOX64-256 and 5-round FOX128 are presented when using the differential enumeration technique with truncated differential characteristics. Then, based on these distinguishers, the attacks for key recovery on 7-round FOX64, 11-round FOX64-256 and 7-round FOX128 are presented with the state-test and state-search techniques. It is shown that the attack on 11-round FOX64-256 is proposed for the first time; attacks on 7-round FOX64 and 7-round FOX128 can be improved with lower time and memory complexities compared with the currently known attacks. [ABSTRACT FROM AUTHOR]

Reliability of a multiprocessor system becomes an important issue for parallel computing. Component diagnosability and component connectivity of a graph play crucial roles in assessing the vulnerability of an interconnection network, which are two significant indicators for the reliability and fault tolerance of a multiprocessor system. Until now, only a little knowledge of results have been known on |$r$| -component diagnosability and |$r$| -component connectivity. In this paper, we first propose the |$r$| -component diagnosability of |$n$| -dimensional alternating group graph |$AG_{n}$| under PMC model. And then we promote our research on |$AG_{n}$| by a fairly good construction for general |$r$| -component connectivity of |$AG_{n}$|⁠ , where |$6\leq r\leq n-1$|⁠. The theoretical analysis and simulation show that the general |$r$| -component connectivity of |$AG_{n}$| is larger than those of |$Q_{n}$|⁠ , |$D_n$| and |$FQ_{n}$|⁠. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to find high-probability truncated differentials using matrix muliplication. For Markov cipher with similar round function, suppose that the transition probability matrix of round function is |$\mathcal{D}$|⁠ , then |$\mathcal{D}^{r}$| contains all the differential probabilities of an |$r$| -round block cipher. To reduce the matrix dimension, we consider the word-oriented truncated differential and the truncated transition probability matrix |$\mathcal{T}$|⁠. Regardless of the effect of the |$S$| -box, we focus on whether there is a non-zero difference on one cell instead of the value of the difference. In this case, the matrix dimension reduces significantly and we can calculate |$\mathcal{T}^{r}$| using a workstation. Then all the |$r$| -round truncated differential probabilities can be found from |$\mathcal{T}^{r}$|⁠. And the probability in |$\mathcal{T}^{r}$| is the probability of the whole truncated differential hull but not a single or several truncated differential characteristics. Besides, we make a more accurate probability estimation of the truncated differential of lightweight block cipher. Combined with the truncated differential hull, we found some longer truncated differential distinguishers. And as |$\mathcal{T}^{r}$| stores all the truncated differential probabilities, we can also find all the impossible truncated differentials. [ABSTRACT FROM AUTHOR]

Educational Data Mining (EDM) has become a promising research field for improving the quality of students and the education system. Although EDM dates back to several years, there is still lack of works for measuring and enhancing the computer programming skills of tertiary students. As such, we, in this paper, propose an EDM system for evaluating and improving tertiary students' programming skills. The proposed EDM system comprises two key modules for (i) classification process and (ii) learning process,. The classification module predicts the current status of a student and the learning process module helps generate respective suggestions and feedback to enhance the student's quality. In particular, for the classification module, we prepare a real dataset related to this task and evaluate the dataset to investigate six key Machine Learning (ML) algorithms, Support Vector Machine (SVM), decision tree, artificial neural network, Random Forest (RF), k -nearest neighbor and naive Bayes classifier, using accuracy-related performance measure metrics and goodness of the fit. The experimental results manifest that RF and SVM can predict the students more accurately than the other models. In addition, critical factors analysis is accomplished to identify the critical features toward achieving high classification accuracy. At last, we design an improvement mechanism in the learning process module that helps the students enhance their programming skills. [ABSTRACT FROM AUTHOR]

The Capsule Reviews are intended to provide a short succinct review of each paper in the issue in order to bring it to a wider readership. The Capsule Reviews were compiled by Fairouz Kamareddine. Professor Kamareddine is an Associate Editor of The Computer Journal and is based in the Department of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh, UK. [ABSTRACT FROM AUTHOR]

Computing a Nash equilibrium for strategic multi-agent systems is challenging for black box systems. Motivated by the ubiquity of games involving exploitation of common resources, this paper considers the above problem for potential games. We use a Bayesian optimization framework to obtain novel algorithms to solve finite (discrete action spaces) and infinite (real interval action spaces) potential games, utilizing the structure of potential games. Numerical results illustrate the efficiency of the approach in computing a Nash equilibrium of static potential games and linear Nash equilibrium of dynamic potential games. [ABSTRACT FROM AUTHOR]

Information in the form of the image conveys more details than any other form of information. Several software packages are available to manipulate the images so that the authenticity of the images is being questioned. Several image processing approaches are available to create fake images without leaving any visual clue about the forging operation. So, proper image forgery detection tools are required to detect such forgery images. Over the past few years, several research papers were published in the digital image forensics domain for detecting fake images, thus escalating the legitimacy of the images. This survey paper attempts to review the recent approaches proposed for detecting image forgery. Accordingly, several research papers related to image forgery detection are reviewed and analyzed. The taxonomy of image forgery detection techniques is presented, and the algorithms related to each technique are discussed. The comprehensive analysis is carried out based on the dataset used, software used for the implementation and the performance achievement. Besides, the research issues associated with every approach were scrutinized together with the recommendation for future work. [ABSTRACT FROM AUTHOR]

Image hashing is an efficient technique of many multimedia systems, such as image retrieval, image authentication and image copy detection. Classification between robustness and discrimination is one of the most important performances of image hashing. In this paper, we propose a robust image hashing with singular values of quaternion singular value decomposition (QSVD). The key contribution is the innovative use of QSVD, which can extract stable and discriminative image features from CIE L*a*b* color space. In addition, image features of a block are viewed as a point in the Cartesian coordinates and compressed by calculating the Euclidean distance between its point and a reference point. As the Euclidean distance requires smaller storage than the original block features, this technique helps to make a discriminative and compact hash. Experiments with three open image databases are conducted to validate efficiency of our image hashing. The results demonstrate that our image hashing can resist many digital operations and reaches a good discrimination. Receiver operating characteristic curve comparisons illustrate that our image hashing outperforms some state-of-the-art algorithms in classification performance. [ABSTRACT FROM AUTHOR]

In view of the fact that the existing public opinion propagation aspects are mostly based on single-layer propagation network, these works rarely consider the double-layer network structure and the negative opinion evolution. This paper proposes a new susceptible-infected-vaccinated-susceptible negative opinion information propagation model with preventive vaccination by constructing double-layer network topology. Firstly, the continuous-time Markov chain is used to simulate the negative public opinion information propagation process and the nonlinear dynamic equation of the model is derived; secondly, the steady state condition of the virus propagation in the model is proposed and mathematically proved; finally, Monte Carlo method is applied in the proposed model. The parameters of simulation model have an effect on negative public opinion information propagation, the derivation results are verified by computer simulation. The simulation results show that the proposed model has a larger threshold of public opinion information propagation and has more effective control of the scale of negative public opinion; it also can reduce the density of negative public opinion information propagation and suppress negative public opinion information compared with the traditional susceptible infected susceptible model. It also can provide the scientific method and research approach based on probability statistics for the study of negative public opinion information propagation in complex networks. [ABSTRACT FROM AUTHOR]

A Cartesian tree is associated with a string of numbers and is structured as a heap from which the original string can be recovered. Although Cartesian trees have been introduced 40 years ago, the Cartesian tree matching problem appeared very recently. It consists in finding all substrings of given text, which have the same Cartesian tree as that of a given pattern. In this paper, we address the problem of computing the longest common Cartesian substrings of two strings and present three methods for such problem. Our first method is based on a classical suffix tree construction and solves the problem in randomized linear time and linear space, although the space overhead is quite prohibitive in the case of large strings. Our second solution is based on classical dynamic programming, and our third solution is based on a constructive approach. Both of them run in quadratic worst case time but are more space economical in practice. From our experimental results, it turns out that our second solution runs faster than the standard suffix tree solution for short strings, whereas our third solution is more suitable for large strings, when storing a full suffix tree becomes prohibitive. [ABSTRACT FROM AUTHOR]

The most prominent challenges in compressive sensing are seeking the domain where an image is represented sparsely and hence be faithfully recovered to obtain high-quality results. This paper introduces an approach for image compression and recovery. The proposed approach involves two phases: the initial step is the compression phase, and the second step is the recovery phase. Initially, the medical image is subjected to the compression module wherein the self-similarity and the 3-dimensional (3D) transform are adapted for compressing the image. Then, in the recovery phase, the compressive sensing recovery is performed based on structural similarity index measure (SSIM)-based collaborative sparsity measure (S-CoSM), and the novel optimization algorithm, named Taylor-based Sunflower optimization (Taylor-SFO) algorithm. An effective S-CoSM measure is designed by modifying the CoSM using the SSIM metric. The proposed Taylor-SFO will be designed by integrating the Taylor series with the sunflower optimization (SFO) algorithm. The performance of the proposed Taylor-SFO approach is evaluated for matrices SSIM of 0.9412 and peak signal to noise ratio of 57.57 dB. [ABSTRACT FROM AUTHOR]

In this paper, we examined single and parallel machine scheduling problems with a learning effect and job rejection simultaneously. In real life, job processing times decrease when there is a learning effect. In some cases, producers cannot process all the jobs and pay the penalty cost for these jobs that they do not process. In our study, learning effect and job rejection are considered at the same time. We examined four different objective functions. Our objectives for single-machine scheduling problems are makespan and rejection cost minimization, total completion time and rejection cost minimization and total absolute deviation of completion times (TADC) and rejection cost minimization. Our objective for parallel machines is makespan and rejection cost minimization. The problems are solved by mathematical models, and four different algorithms are proposed for the problems. From these algorithms, the same results are obtained with single-machine makespan and rejection cost minimization, parallel machine makespan and rejection cost minimization and total completion time and rejection cost minimization. The accuracy for these models is obtained as 100%. The proposed algorithm for TADC and rejection cost minimization yielded close-to-optimal results. Mathematical model and algorithm results for 10 jobs, 20 jobs and 30 jobs are compared and the results are presented. The obtained solutions are obtained in polynomial time. [ABSTRACT FROM AUTHOR]

3D is an Advanced Encryption Standard (AES)-like cipher employed 3D structure proposed in 2008. The main innovation of 3D is the multi-dimensional state, generalizing the design of Rijndael and allowing block sizes beyond the 256-bit boundary. Saturnin, a lightweight block cipher has been selected as a second-round candidate in the National Institute of Standards and Technology standardization for lightweight cryptography. It also employs a 3D structure and provides high security against quantum and classic attacks. The exchange-equivalence attacks proposed by Bardeh and Rønjom consider how quadruples of plaintexts confirm distinguishable properties for AES. It is similar to the principle of yoyo attack, but it can find a longer number of rounds of distinguisher. In this paper, we investigate the exchange-equivalence attack on 3D and yoyo attack on Saturnin. Our new results turn out to be the first secret-key chosen plaintext distinguisher for 10-round 3D. The complexity of the distinguisher is about |$2^{364.2}$| in terms of data, memory and computational complexity. For Saturnin, we propose the first six-super-round impossible differential yoyo attack, which is suitable for the two-S-layer version. Compared with the previous impossible differential attacks in the design report of Saturnin, the attacks presented here are the best in terms of the complexity under the chosen-plaintext scenario. [ABSTRACT FROM AUTHOR]

To mitigate state exposure threats to long-lived instant messaging sessions, ratcheting was introduced, which is used in practice in protocols like Signal. However, existing ratcheting protocols generally come with a high cost. Recently, Caforio et al. proposed pragmatic constructions, which compose a weakly secure 'light' protocol and a strongly secure 'heavy' protocol, in order to achieve so-called ratcheting on-demand. The light protocol they proposed has still a high complexity. In this paper, we propose the lightest possible protocol we could imagine, which essentially encrypts and then hashes the secret key. We prove it secure in the standard model by introducing a new security notion, which relates symmetric encryption with key updates by hashing. Our protocol composes well with the generic transformation techniques by Caforio et al. to offer high security and performance at the same time. In a second step, we propose another protocol based on a newly defined integrated primitive, extending standard one-time authenticated encryption with an additional output block used as a secret key for the next message. We instantiate this primitive firstly from any authenticated encryption with associated data, and then we propose an efficient instantiation using advanced encryption standard (AES) encryption to update the key and AES-Galois/Counter mode of operation to encrypt and decrypt messages. [ABSTRACT FROM AUTHOR]

At CRYPTO 2021, Dong et al. proposed an automatic method of Meet-in-the-Middle (MITM) key-recovery attacks. In this paper, we further extend it to a new automatic model which can be used to find low-data complexity attacks. With the help of the automatic model, we propose MITM attacks against reduced-round versions of all the six members of the SKINNY family with extremely low-data complexity. More precisely, we present MITM attacks against 19-round SKINNY - |$n$| - |$3n$|⁠ , 15-round SKINNY - |$n$| - |$2n$|⁠ , 11-round SKINNY - |$n$| - |$n$| with three, two, one plaintext-ciphertext pairs, separately. In addition, we can attack two more rounds and three more rounds with no more than |$2^8$| and |$2^{32}$| data complexity, respectively. [ABSTRACT FROM AUTHOR]

In a competitive market, it is of great significance to divide customer groups to develop customer-centered personalized products. In this paper, we propose a customer segmentation method based on the K-means algorithm and the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm easily falls into local extremum, the improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to improve optimization accuracy. The full factorial design is used to determine the optimal parameter combination; the roulette operator is used to select excellent particles; then, the selected particles are crossed according to their adaptive crossover probabilities; when the population falls into a local optimum, the particles are mutated according to their adaptive mutation probabilities. Aimed at the K-means' sensitivity to selecting the initial cluster centers, IHPSO is used to optimize the cluster centers (IHPSO-KM). We compare IHPSO with the PSO, LDWPSO, GA, GA-PSO and ALPSO algorithms on nine benchmark functions. We also conduct comparative experiments to compare IHPSO-KM with several conventional and state-of-the-art approaches on five UCI datasets. All results show that the two proposed methods outperform existing models. Finally, IHPSO-KM is applied in customer segmentation. The experimental results also prove the rationality and applicability of IHPSO-KM for customer segmentation. [ABSTRACT FROM AUTHOR]

Although a large number of provably secure cryptographic primitives have been proposed in the literature, many of these schemes might be broken in practice because of various leakage attacks. Therefore, the leakage resilience should be considered in designing these primitives. However, in identity-based cryptography, most of the existing leakage-resilient identity-based encryption (IBE) schemes suffer some limitations: they either resist the leakage attacks in the selective identity security model or achieve the chosen-ciphertext attack (CCA) security based on a non-static assumption. In this paper, an IBE scheme with adaptive leakage-resilient CCA security is proposed, and its security is rigorously proved in the random oracle model under a classic static complexity assumption, e.g. decisional bilinear Diffie–Hellman assumption. In our construction, all elements of ciphertext are randomly distributed in the adversary's view. Hence, the adversary cannot obtain any useful information of the user's private key from the given ciphertexts. Moreover, a unique property of our construction is that the leakage parameter is independent of the plaintext space, which contributes a better leakage rate. [ABSTRACT FROM AUTHOR]

Attribute-based signature (ABS) allows signers with a set of attributes to sign messages anonymously using a specific signing policy. However, previous schemes suffer from some efficiency issues which are not widely applied on the blockchain. In this paper, we investigate ABS regarding its features and efficiency in the blockchain setting and provide our solution correspondingly. To solve the revocation problem of ABS in a more efficient manner, we introduce the update-free revocation function. Instead of the passive attribute expiration approaches, we take the active method to ensure that no parameter updates are required by users after the execution of the revocation function. In terms of efficiency, we first address the problem that the signer has to provide proof for all attributes in the predicate for privacy, which is one of the efficiency bottlenecks for ABS. By taking advantage of the blockchain architecture, we propose a new solution which can achieve the constant signature size and verification cost, while the signing cost can be greatly reduced. The corresponding security levels are satisfied according to their strict criteria. A generic construction as well as an instantiation are provided which is provably secure in the standard model satisfying the newly defined formal security definitions. Finally, a purer primitive is discussed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a human visual system (HVS) guided reversible data hiding method based on multiple histograms modification. The proposed method utilizes the texture features of an image to adaptively modify the pixels, for a lower HVS quality distortion. The HVS quality is taken as the optimization objective and a new expansion-bin-selection strategy is given to solve the optimization. For the same HVS quality distortion, the proposed method can embed more secret bits and give higher priority for the modifications in texture regions. Besides, a better optimization rule is proposed to accelerate the speed and then consider more available solutions. In this way, a trade-off between the embedding performance and time complexity can be achieved. Experimental results show that the proposed method can achieve a better HVS quality than the conventional methods. [ABSTRACT FROM AUTHOR]

In this paper, we present sasa , an open-source SimulAtor of Self-stabilizing Algorithms. Self-stabilization defines the ability of a distributed algorithm to recover after transient failures. sasa is implemented as a faithful representation of the atomic-state model (also called the locally shared memory model with composite atomicity). This model is the most commonly used one in the self-stabilizing area to prove both the correct operation of self-stabilizing algorithms and complexity bounds on them. sasa encompasses all features necessary to debug, test and analyze self-stabilizing algorithms. All these facilities are programmable to enable users to accommodate to their particular needs. For example, asynchrony is modeled by programmable stochastic daemons playing the role of input sequence generators. Properties of algorithms can be checked using formal test oracles. The sasa distribution also provides several facilities to easily achieve (batch-mode) simulation campaigns. We show that the lightweight design of sasa allows to efficiently perform huge such campaigns. Following a modular approach, we have aimed at relying as much as possible the design of sasa on existing tools, including ocaml , dot and several tools developed in the Synchrone Group of the VERIMAG laboratory. [ABSTRACT FROM AUTHOR]

As a fundamental metric, the connectivity to assess fault tolerance and reliability of interconnection networks has been extensively explored. However, classical connectivity is not very effective at evaluating large-scale networking systems. To overcome this deficiency, two new indices, cluster connectivity and super cluster connectivity, have been proposed to characterize the robustness of interconnection networks. This paper focuses on investigating |$\mathcal{H} (\mathcal{H}^{*})$| -cluster connectivity and super |$\mathcal{H} (\mathcal{H}^{*})$| -connectivity of composition graph |$DQ_{n}$|⁠ , based on disc-ring and hypercube, for |$\mathcal{H}\in \{K_{1,r}\ |\ 0\leq r\leq n+1\}$|⁠ , respectively. In detail, we show that |$\kappa (DQ_{n}|K_{1,1} (K_{1,1}^{*}))=\kappa ^{\prime}(DQ_{n}|K_{1,1}(K_{1,1}^{*})) =n+1 (n\geq 3)$|⁠ , |$\kappa (DQ_{n}|K_{1,r}(K_{1,r}^{*})) =\left\lceil \frac{n}{2}\right\rceil +1 (2\leq r\leq 4)$| for |$n\geq 3$|⁠ , |$\kappa ^{\prime}(DQ_{n}|K_{1}(K_{1}^{*})) =\kappa ^{\prime}(DQ_{n})=2n (n\geq 3)$|⁠ , and for |$2\leq r\leq 3$| and |$k\geq 2$|⁠ , $$\begin{align*} \kappa^{\prime}(DQ_{n}|K_{1,r}(K_{1,r}^{*}))=\left\{\begin{array}{@{}ll} n+1, & if\ n=2k+1; \\ n, & if\ n=2k. \end{array}\right. \end{align*}$$ As by-products, we show that DQcube is super |$K_{1,r}-\ (K_{1,r}^{*}-)$| connected |$(2\leq r\leq 3)$|⁠ , and derive the 4-extra connectivity of DQcube |$\kappa _{4}(DQ_{n}) = 5n-9 (n\geq 4)$|⁠. [ABSTRACT FROM AUTHOR]

Federated learning (FL), a variant of distributed learning (DL), supports the training of a shared model without accessing private data from different sources. Despite its benefits with regard to privacy preservation, FL's distributed nature and privacy constraints make it vulnerable to data poisoning attacks. Existing defenses, primarily designed for DL, are typically not well adapted to FL. In this paper, we study such attacks and defenses. In doing so, we start from the perspective of DL and then give consideration to a real-world FL scenario, with the aim being to explore the requisites of a desirable defense in FL. Our study shows that (i) the batch size used in each training round affects the effectiveness of defenses in DL, (ii) the defenses investigated are somewhat effective and moderately influenced by batch size in FL settings and (iii) the non-IID data makes it more difficult to defend against data poisoning attacks in FL. Based on the findings, we discuss the key challenges and possible directions in defending against such attacks in FL. In addition, we propose detect and suppress the potential outliers(DSPO), a defense against data poisoning attacks in FL scenarios. Our results show that DSPO outperforms other defenses in several cases. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel cryptographic primitive named reusable group fuzzy extractor (RGFE) allowing any member of a group to extract and reproduce random strings from a fuzzy and non-uniform source of high entropy (called fingerprint). Any group member can anonymously generate a random string for the group using his fingerprint and can be traced when needed, whereas other members can reproduce the string using their own fingerprints. Moreover, a fingerprint can be repeatedly used to generate multiple random strings. Basing on RGFE, we present group-shared Bitcoin wallet, which can be used by a group of users to receive or spend coins via biometrics in a traceable way. [ABSTRACT FROM AUTHOR]

Intensional sets are sets given by a property rather than by enumerating their elements. In a previous work, we have proposed a decision procedure for a first-order logic language which provides restricted intensional sets (RISs), i.e. a sub-class of intensional sets that are guaranteed to denote finite—though unbounded—sets. In this paper, we show how RIS can be exploited as a convenient programming tool also in a conventional setting, namely the imperative O-O language Java. We do this by considering a Java library, called JSetL, that integrates the notions of logical variable, (set) unification and constraints that are typical of constraint logic programming languages into the Java language. We show how JSetL is naturally extended to accommodate for RIS and RIS constraints and how this extension can be exploited; on the one hand, to support a more declarative style of programming and, on the other hand, to effectively enhance the expressive power of the constraint language provided by the library. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) is an architecture of connected physical objects; these objects can communicate with each other and transmit and receive data. Also, fog-based IoT is a distributed platform that provides reliable access to virtualized resources based on various technologies such as high-performance computing and service-oriented design. A fog recommender system is an intelligent engine that suggests suitable services for fog users with less answer time and more accuracy. With the rapid growth of files and information sharing, fog recommender systems' importance is also increased. Besides, the resource management problem appears challenging in fog-based IoT because of the fog's unpredictable and highly variable environment. However, many current methods suffer from the low accuracy of fog recommendations. Due to this problem's Non-deterministic Polynomial-time (NP)-hard nature, a new approach is presented for resource recommendation in the fog-based IoT using a hybrid optimization algorithm. To simulate the suggested method, the CloudSim simulation environment is used. The experimental results show that the accuracy is optimized by about 1–8% compared with the Cooperative Filtering method utilizing Smoothing and Fusing and Artificial Bee Colony algorithm. The outcomes of the present paper are notable for scholars, and they supply insights into subsequent study domains in this field. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-document summarization model using an optimization algorithm named CAVIAR Sun Flower Optimization (CAV-SFO). In this method, two classifiers, namely: Generative Adversarial Network (GAN) classifier and Deep Recurrent Neural Network (Deep RNN), are utilized to generate a score for summarizing multi-documents. Initially, the simHash method is applied for removing the duplicate/real duplicate contents from sentences. Then, the result is given to the proposed CAV-SFO based GAN classifier to determine the score for individual sentences. The CAV-SFO is newly designed by incorporating CAVIAR with Sun Flower Optimization Algorithm (SFO). On the other hand, the pre-processing step is done for duplicate-removed sentences from input multi-document based on stop word removal and stemming. Afterward, text-based features are extracted from pre-processed documents, and then CAV-SFO based Deep RNN is introduced for generating a score; thereby, the internal model parameters are optimally tuned. Finally, the score generated by CAV-SFO based GAN and CAV-SFO based Deep RNN is hybridized, and the final score is obtained using a multi-document compression ratio. The proposed TaylorALO-based GAN showed improved results with maximal precision of 0.989, maximal recall of 0.986, maximal F-Measure of 0.823, maximal Rouge-Precision of 0.930, and maximal Rouge-recall of 0.870. [ABSTRACT FROM AUTHOR]

With the rapid growth of the number of web services on the Internet, various service providers provide many similar services with the same function but different quality of service (QoS) attributes. It is a key problem to be solved urgently to select the service composition quickly, meeting the users' QoS requirements from many candidate services. Optimization of web service composition is an NP-hard issue and intelligent optimization algorithms have become the mainstream method to solve this complex problem. This paper proposed a hybrid strategy improved whale optimization algorithm, which is based on the concepts of chaos initialization, nonlinear convergence factor and mutation. By maintaining a balance between exploration and exploitation, the problem of slow or early convergence is overcome to a certain extent. To evaluate its performance more accurately, the proposed algorithm was first tested on a set of standard benchmarks. After, simulations were performed using the real quality of web service dataset. Experimental results show that the proposed algorithm is better than the original version and other meta-heuristic algorithms on average, as well as verifies the feasibility and stability of web service composition optimization. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is an emerging privacy-preserving technology for machine learning, which enables end devices to cooperatively train a global model without uploading their local sensitive data. Because of limited network bandwidth and considerable communication overhead, communication efficiency has become an essential bottleneck for FL. Existing solutions attempt to improve this situation by reducing communication rounds while usually come with more computation resource consumption or model accuracy deterioration. In this paper, we propose a parameter Prediction-Based DL (PBFL). In which an extended Kalman filter-based prediction algorithm, a practical prediction error threshold setting mechanism and an effective global model updating strategy are included. Instead of collecting all updates from participants, PBFL takes advantage of predicting values to aggregate the model, which substantially reduces required communication rounds while guaranteeing model accuracy. Inspired by the idea of prediction, each participant checks whether its prediction value is out of the tolerance threshold limits and only uploads local updates that have an inaccurate prediction value. In this way, no additional local computational resources are required. Experimental results on both multilayer perceptrons and convolutional neural networks show that PBFL outperforms the state-of-the-art methods and improves the communication efficiency by >66% with 1% higher model accuracy. [ABSTRACT FROM AUTHOR]

Taxi flow forecast is significant for planning transportation and allocating basic transportation resources. The flow forecast in the urban adjacent area is different from the fixed-point flow forecast. Their data are more complex and diverse, which make them more challenging to forecast. This paper introduces a deep spatial–temporal forecast (DeepSTF) model for the flow forecasting of urban adjacent area, which divides the urban into grids and makes it have a graph structure. The model builds a spatial–temporal calculation block, which uses graph convolutional network to extract spatial correlation feature and uses two-layer temporal convolutional networks to extract time-dependent feature. Based on the theory of dilation convolution and causal convolution, the model overcomes the under-fitting phenomenon of other models when calculating with rapidly changing data. In order to improve the accuracy of prediction, we take weather as an implicit factor and let it participate in the feature calculation process. A comparison experiment is set between our model and the seven existing traffic flow forecast models. The experimental results prove that the model has better the capabilities of long-term traffic prediction and performs well in various evaluation indicators. [ABSTRACT FROM AUTHOR]

As there are countless significant works done for handwritten character recognition, very meager effort has been reported for inscription characters especially for Tamil stone inscriptions. The real challenge faced in handling stone inscription is dataset collection and foreground and background discrimination. Till present days, the archeological department follows traditional way of capturing, preserving and deciphering stone inscriptions which is manual, more time consuming and need expert assistance. Hence digitized recognition is essential and efficient pattern matching algorithm is needed to be developed to deal with variations in shape and size of complex structured characters present in Tamil stone inscriptions. In this paper, an automated character recognition by pattern matching approach is developed, where character features were extracted by using pattern matching algorithm that helps achieving good recognition rate. Recognition of ancient Tamil stone inscriptions characters and finding their corresponding contemporary Tamil character is done by Image-based Character Pattern Identification (ICPI) system. Modified Speeded Up Robust Feature with Bag of Grapheme (MSURF-BoG) algorithm is implemented to detect the strongest key points from the input character with different orientations. These key point features were created for training the image as a model called Bag of Grapheme (BoG) with code word creation. Hence unsupervised key point features were extracted and pattern matching is performed. 11th century Tamil stone inscriptions were taken as samples which has 7 vowels and 17 consonants, totally 24 characters were used. Here samples with different orientation from each 24 character were used for training the system. The proposed system is evaluated by recognition accuracy which is reported for character wise at the maximum of 96%. [ABSTRACT FROM AUTHOR]

Liver cancer is the fourth common cancer in the world and the third leading reason of cancer mortality. The conventional methods for detecting liver cancer are blood tests, biopsy and image tests. In this paper, we propose an automated computer-aided diagnosis technique for the classification of multi-class liver cancer i.e. primary, hepatocellular carcinoma, and secondary, metastases using computed tomography (CT) images. The proposed algorithm is a two-step process: enhancement of CT images using contrast limited adaptive histogram equalization algorithm and extraction of features for the detection and the classification of the different classes of the tumor. The overall achieved accuracy, sensitivity and specificity with the proposed method for the classification of multi-class tumors are 97%, 94.3% and 100% with experiment 1 and 84% all of them with experiment 2, respectively. By automatic feature selection scheme accuracy is deviated maximum by 10.5% from the overall and the ratio features accuracy decreases linearly by 5.5% with 20 to 5 selected features. The proposed methodology can help to assist radiologists in liver cancer diagnosis. [ABSTRACT FROM AUTHOR]

Bitcoin is a popular decentralized cryptocurrency, and the Bitcoin network is essentially an unstructured peer-to-peer (P2P) network that can synchronize distributed database of replicated ledgers through message broadcasting. In the Bitcoin network, the average clustering coefficient of nodes is very high, resulting in low message propagation efficiency. In addition, average node degree in the Bitcoin network is also considerably large, causing high message redundancy when nodes use the gossip protocol to broadcast messages. These may affect message propagation speed, hindering Bitcoin from being applied to scenarios of high transactional throughputs. To illustrate, we have collected single-hop propagation data of transactions of 366 blocks from Bitcoin Core. The analysis results show that transaction verification and network delay are two major causes of low transaction propagation efficiency. In this paper, we propose a novel P2P network structure, called local clique network (LCN), for message broadcasting in the Bitcoin network. Specifically, to reduce transaction validation latency and message redundancy, in LCN local nodes (logically) form cliques, and only a few nodes in a clique broadcast messages to the other cliques, instead of each node sending messages to its neighboring nodes. We have conducted extensive experiments, and the results show that message redundancy is low in LCN, and message propagation speed increases significantly. Meanwhile, LCN exhibits excellent robustness when average node degree remains high in the Bitcoin network. [ABSTRACT FROM AUTHOR]

3D, an advanced encryption standard-like cipher employed three-dimensional structure, was proposed in 2008. Its recommended number of rounds is 22. Although the longest key recovery attack can currently reach 13 rounds, the complexity of existing attacks for >6 rounds seems to exceed the practically feasible complexity. Thus, a practical attack for 7-round 3D has yet to be developed. Recently, a lightweight block cipher called Saturnin has been selected as a second-round candidate in the National Institute of Standards and Technology standardization for lightweight cryptography. Saturnin also employs a three-dimensional structure and provides high security against quantum and classic attacks. In this paper, we investigate the yoyo attack on these two ciphers. Combined with the meet-in-the-middle technique, we apply the yoyo trick to 7-round 3D and recover the whole 512-bit secret key with |$2^{15}$| plaintexts and adaptively chosen ciphertexts and |$2^{16.5}$| complexity of full encryptions. To our best knowledge, it is the first practical key recovery attack for 7-round 3D to date. For Saturnin, we found a minor typo in its design report. The designers intended to make a super round containing two S-layers, but one was inadvertently omitted in the algorithm description. We propose a 5-super-round key recovery attack, which is suitable for both one-S-layer version and two-S-layer version. Since the round function of Saturnin has better diffusion, which leads that the meet-in-the-middle technique cannot be applied to this cipher directly. For the one-S-layer version, we address this problem by proposing a new technique called reducing key sets. This technique will fail on the other version, which proves the necessity of containing two S-layers in one-super-round. Finally, our attack requires |$2^{39.1}$| plaintext pairs and adaptively chosen ciphertext pairs and |$2^{46}$| one-round encryptions. [ABSTRACT FROM AUTHOR]

As the scale of the system expands, processor failures are inevitable. Fault diagnosis has great significance in analyzing the reliability of multiprocessing systems. Probabilistic fault diagnosis is a method that attempts to diagnose nodes correctly with high probability. In this paper, we extend the threshold |$t \leq 2$| to threshold |$t=3$| for regular networks based on probabilistic diagnosis algorithm and determine the status of a cluster of nodes by analyzing the local performance. Moreover, we evaluate the global performance based on the Poisson distribution and the Binomial distribution and show that the achievement in terms of correctness demonstrates a good performance. Finally, we employ the probabilistic diagnosis scheme to explore some well-known networks, including complete cubic networks, dual cubes and hierarchical hypercubes as well. [ABSTRACT FROM AUTHOR]

A bipartite graph is Hamiltonian laceable if any two of its vertices in different partite sets are connected by a Hamiltonian path. A Hamiltonian laceable graph |$G$| is called strongly Hamiltonian laceable if any two of its vertices in the same partite set are connected by a path of length |$|V(G)|-2$|⁠. A Hamiltonian laceable graph |$G$| (with two partite sets |$V_0, V_1$|⁠) is called hyper-Hamiltonian laceable, if for any vertex |$v \in V_{i}$| for |$i \in \{0,1\}$|⁠ , there is a Hamiltonian path of |$G-\{v\}$| between any two vertices in |$V_{1-i}$|⁠. In this paper, we focus on the edge-fault-tolerant strongly Hamiltonian laceability and hyper-Hamiltonian laceability on the class of Cayley graphs generated by transposition trees, which are a generalization of star graph and bubble-sort graph. For every |$n$| -dimensional Cayley graph generated by a transposition tree |$\Gamma _n$|⁠ , we show that |$\Gamma _{n}-F$| is strongly Hamiltonian laceable for any |$F \subseteq E(\Gamma _{n})$| with |$|F|\leq n-3$|⁠ , which generalizes results in [ 1 , 11 ], and show that |$\Gamma _{n}-F$| is hyper-Hamiltonian laceable for any |$F \subseteq E(\Gamma _{n})$| with |$|F|\leq n-4$|⁠. [ABSTRACT FROM AUTHOR]

The capacity is an important parameter in multidimensional linear attack. In this paper, we firstly explore the distribution of the key-dependent capacity. Based on the magnitude of the correlation contributions, we divide the linear approximations subspace into two sets: one set consists of the strong linear approximations, and the other set consists of the weak linear approximations. We construct two statistics using the linear approximations in the two sets, respectively. Under reasonable assumptions, both of the two statistics follow Gamma distribution. Thus, the capacity is the sum of two statistics that follow Gamma distribution. Secondly, the accuracy of the model is verified by experiments on SMALLPRESENT[4]. Our experimental results show that this model can estimate the variance of the key-dependent capacity more accurately. Thus, we obtain more precise knowledge of the data complexity of the multidimensional linear attack. We derive the upper bound of the data complexity for multidimensional linear attack. Finally, based on our theoretical results, we explore the data complexity of Cho's multidimensional linear attack on PRESENT. Our results are the smallest data complexity for the same round attack so far. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) co-operative systems are complex cyber-physical systems that integrate a high-level control algorithm with pre-existing closed implementations of lower-level vehicle kinematics. In model-driven development, simulation is one of the techniques that are usually applied, together with testing, in the analysis of system behaviours. This work proposes a method and tools to validate the design of UAV co-operative systems based on co-simulation and formal verification. The method uses the Prototype Verification System, an interactive theorem prover based on a higher-order logic language, and the Functional Mock-up Interface, a widely accepted standard for co-simulation. In this paper, results on the co-simulation and proofs of safety requirements of a representative co-ordination algorithm are shown and discussed in a scenario where quadcopters are deployed and perform space-coverage operations. [ABSTRACT FROM AUTHOR]

Magnetic resonance image (MRI) is extensively adapted in clinical diagnosis due to its improved representation of changes with soft tissue. The current innovations in compressive sensing (CS) showed that it is viable to precisely reconstruct MRI with K-space data with reduced scanning duration and it takes more time for computation and it is complex to capture the fine details. The CS is capable to minimize the imaging time and it uses compressibility for reconstructing the images. In this paper, a novel deep-learning method is devised for reconstructing images using a set of MRI. Here, MRI acquisition and MRIsub sampling is performed for reducing the size of images to perform improved analysis. Here, a convolution layer is utilized for imitating the process of compressed sampling that can learn the sampling matrix to prevent complex designs. In addition, a convolution layer is utilized to execute the initial reconstruction. In addition, Deep Convolutional Neural Network (Deep CNN) is adapted for image reconstruction. The training of Deep CNN is performed using proposed Fractional Sailfish Optimizer (FSO) algorithm. The proposed FSO is designed by incorporating fractional concepts in Sail fish optimization for tuning the optimal weights of Deep CNN. The proposed FSO-based CSNet outperformed other methods with minimal MSE of 1.877 and maximal PSNR of 45.396 dB. [ABSTRACT FROM AUTHOR]

The data science technologies of artificial intelligence (AI), Internet of Things (IoT), big data and behavioral/predictive analytics, and blockchain are poised to revolutionize government and create a new generation of GovTech start-ups. The impact from the 'smartification' of public services and the national infrastructure will be much more significant in comparison to any other sector given government's function and importance to every institution and individual. Potential GovTech systems include Chatbots and intelligent assistants for public engagement, Robo-advisors to support civil servants, real-time management of the national infrastructure using IoT and blockchain, automated compliance/regulation, public records securely stored in blockchain distributed ledgers, online judicial and dispute resolution systems, and laws/statutes encoded as blockchain smart contracts. Government is potentially the major 'client' and also 'public champion' for these new data technologies. This review paper uses our simple taxonomy of government services to provide an overview of data science automation being deployed by governments world-wide. The goal of this review paper is to encourage the Computer Science community to engage with government to develop these new systems to transform public services and support the work of civil servants. [ABSTRACT FROM AUTHOR]

The article presents a letter made by Donald Davis. The author suggests that the letter was intended for the benefit of historians and other scholars who study the progress of computer networking. It also provides an analysis of various documents that have been cited and publicized in the media as well as on the Internet pages claiming for invention of the packet-switching.

Aspect sentiment classification is an important research topic in natural language processing and computational linguistics, assisting in automatically review analysis and emotional tendency judgement. Different from extant methods that focus on text sequence representations, this paper presents a network framework to learn representation from concurrence-words relation graph (LRCWG), so as to improve the Macro-F1 and accuracy. The LRCWG first employs the multi-head attention mechanism to capture the sentiment representation from the sentences which can learn the importance of text sequence representation. And then, it leverages the priori sentiment dictionary information to construct the concurrence relations of sentiment words with Graph Convolution Network (GCN). This assists in that the learnt context representation can keep both the semantics integrity and the features of sentiment concurrence-words relations. The designed algorithm is experimentally evaluated with all the five benchmark datasets and demonstrated that the proposed aspect sentiment classification can significantly improve the prediction performance of learning task. [ABSTRACT FROM AUTHOR]

Task allocation plays a vital role in crowd computing by determining its performance. The power of crowd computing stems from a large number of workers potentially available to provide high quality of service and reduce costs. An important challenge in the crowdsourcing market today is the task allocation of crowdsourcing workflows. Task allocation aims to maximize the completion quality of the entire workflow and minimize its total cost. Trust can affect the quality of the produced results and costs. Selecting workers with high levels of trust could provide better solution to the workflow and increase the budget. Crowdsourcing workflow needs to balance the two conflicting objectives. In this paper, we propose an alternative greedy approach with four heuristic strategies to address the issue. In particular, the proposed approach aims to monitor the current status of workflow execution and use heuristic strategies to adjust the parameters of task allocation. We design a two-phase allocation model to accurately match the tasks with workers. T-Aware allocates each task to the worker that maximizes the trust level, while minimizing the cost. We conduct extensive experiments to quantitatively evaluate the proposed algorithms in terms of running time, task failure ratio, trust and cost using a customer objective function on WorkflowSim, a well-known cloud simulation tool. Experimental results based on real-world workflows show that T-Aware outperforms other optimal solutions on finding the tradeoff between trust and cost, which is 3 to 6% better than the best competitor algorithm. [ABSTRACT FROM AUTHOR]