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The assessment of image quality provides the confidentiality, safety, quality and transparency of the obtained images. At the same time, there are many existing quality metric approaches that belong to the group of modification alteration procedures (pixel-based metric). These techniques have not been well-matched with perceptual image quality. The Perceptual Human Visual System (HVS) aspect drives our approach; The human visual system (HVS) is the best judge of image quality. This paper presents a new reduced reference image quality metric in the spatial domain that reduces the complexity of the image quality assessment. From the reference and distortion images, we extract four intrinsic features, namely, contrast, entropy, histogram and standard deviation. Then, we build the formal concept analysis matrix for reference and distortion images. Finally, we compare the obtained matrixes to evaluate the image quality. The performance of the proposed technique is assessed using LIVE, TID2013 and CSIQ datasets, and the obtained results are compared in terms of PSNR, SSIM and NCC metrics. Also, a comparison with more recent and relevant approaches is performed to highlight the superior performance of our proposed approach, the experimental results indicated that the proposed approach provides efficient performance among the compression, Gaussian blur, Contrast and add noise distortion types. [ABSTRACT FROM AUTHOR]

This paper defines a novel graph constructed on a residuated lattice using a specific definition of the fuzzy graph (called |$L$| -graph). These types of graphs also have applications in libraries, pharmacies and machine facilities. Among these applications, this research presents the |$L$| -graph automaton related to the |$L$| -graph constructed by a minimum zero forcing set. The |$L$| -graph automaton is used to find efficient searching for diseases that have the most similar symptoms. It has been demonstrated that if two |$L$| -graphs are isomorphic, then the related |$L$| -graph automata are isomorphic but not necessarily vice versa. New notions, |$L^{\prime\prime}$| -graph automata, pseudo-isomorphism and self-sufficiency are proved. Besides, by taking advantage of the properties of the residuated lattice, it has been proven that the two |$L$| -graph automata are equivalent if and only if the |$L^{\prime}$| -graph automata contracted of |$L$| -graph automata are equivalent. Ultimately, in light of the above, some related theorems are proved and several examples are provided to illustrate these new notions. [ABSTRACT FROM AUTHOR]

In order to solve the incalculability caused by the issue of inconsistent objective functions in multi-agent deep reinforcement learning, the concept of Nash equilibrium is introduced. However, a Marko game may have multiple equilibriums, how to filter out a stable and optimal one is worth studying. Besides solution concept, how to keep the balance between exploration and exploitation is another key issue in reinforcement learning. On basis of the methods, which can converge to Nash equilibrium, this paper makes improvement through Pareto optimization. In order to alleviate the problem of over fitting caused by Pareto optimization and non-convergence caused by strategy change, we use stratified sampling in place of random sampling as assistance. What's more, our methods are trained through fictitious self-play to make full of self-learning experiences. By analyzing the experiment carried out on MAgent platform, the proposed methods are not only far better than traditional methods, but also reaching or even surpassing the state of art MADRL methods. [ABSTRACT FROM AUTHOR]

With the rapid development of blockchains, blockchain systems are moving on from a stand-alone manner to cross-chain interactions, and achieving interoperability is emerging as one of the essential features of blockchains. Unfortunately, existing mechanisms such as XCLAIM mostly focus on exchanging assets between two blockchains and it is slow and expensive to process each cross-chain trade among more than two blockchains as multiple transactions are required. In this paper, we present Interopera, a decentralized and efficient cross-chain trading protocol among two or more blockchains. Interopera atomically processes each cross-chain trade faster and more cheaply with fewer transactions by a two-phase lock/unlock process. Interopera also achieves efficient cross-chain communication by our presented Partitioned-FlyClient and Tx-FlyClient. Partitioned-FlyClient is based on FlyClient but more efficient with smaller proof size, reducing the storage and bandwidth overheads. Tx-FlyClient maintains efficiency even when cross-chain trades become frequent, instead of other mechanisms only being effective under low cross-chain trades volumes. We also develop a proof-of-concept implementation and the results demonstrate high efficiency of our protocol. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of flying over an area affected by a natural disaster (e.g. an earthquake) with a fleet of self-piloting unmanned aerial vehicles with cameras or other kinds of sensors on board; the aim is to acquire knowledge of the situation before rescuers start working. We model this situation as a new graph theoretical problem; then, we study its complexity providing an approximation ratio that becomes constant in some special (though practically reasonable) cases; finally, we put in relation the approximability of this new problem and of a well-known one. To the best of our knowledge, no previous work has ever considered all together the constraints we take into account from a theoretical point of view, so this is the first very general graph theoretical model for this problem. [ABSTRACT FROM AUTHOR]

A large event sequence can generate episode rules that are patterns which help to identify the possible dependencies existing among event types. Frequent episodes occurring in a simple sequence of events are commonly used for mining the episodes from a sequential database. Mining serial positioning episode rules (MSPER) using a fixed-gap episode occurrence suffers from unsatisfied scalability with complex sequences to test whether an episode occurs in a sequence. Large number of redundant nodes was generated in the MSPER-trie-based data structure. In this paper, forward and backward search algorithm (FBSA) is proposed here to detect minimal occurrences of frequent peak episodes. An extensive correlation of parameter settings and the generating procedure of fixed-gap episodes are carried out. To generate a fixed-gap episode and estimate the variance that decides the parameter selection in event sequences, Spearman's correlation coefficient is used for verifying the sequence of occurrences of the episodes. MFSPER with FBSA is developed to eliminate the frequent sequence scans and redundant event sets. The MFSPER–FBSA stores the minimal occurrences of frequent peak episodes from the event sequences. The experimental evaluation on benchmark datasets shows that the proposed technique outperforms the existing methods with respect to memory, execution time, recall and precision. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has been a globally dangerous crisis that causes an increasingly high death rate. Applying machine learning to the computed-tomography (CT)-based COVID-19 diagnosis is essential and attracts the attention of the research community. This paper introduces an approach for simultaneously identifying COVID-19 disease and segmenting its manifestations on lung images. The proposed method is an asymmetric U-Net-like model improved with skip connections. The experiment was conducted on a light-weighted feature extractor called CRNet with a feature enhancement technique called atrous spatial pyramid pooling. Classifying between COVID-19 and non-COVID-19 cases recorded the highest mean scores of 97.1, 94.4, and 97.0% for accuracy, dice similarity coefficient (DSC) and F1 score, respectively. Alternatively, the respective highest mean scores of the classification between COVID-19 and community-acquired pneumonia were 99.89, 99.79, and 99.97%. The lesion segmentation performance was with the highest mean of 99.6 and 84.7% for, respectively, accuracy and DSC. [ABSTRACT FROM AUTHOR]

Privacy and security in the medical field are major aspects to consider in the current era. This is due to the huge necessity for data among providers, payers and patients, respectively. Recently, more researchers are making their contributions in this field under different aspects. But, there need more enhancements concerning security. In this circumstance, this paper intends to propose a new IoT-dependent health care privacy preservation model with the impact of the machine learning algorithm. Here, the information or data from IoT devices is subjected to the proposed sanitization process via generating the optimal key. In this work, the utility of the machine learning model is the greatest gateway to generating optimal keys as it is already trained with the neural network. Moreover, identifying the optimal key is the greatest crisis, which is done by a new Improved Dragonfly Algorithm algorithm. Thereby, the sanitization process works, and finally, the sanitized data are uploaded to IoT. The data restoration is the inverse process of sanitization, which gives the original data. Finally, the performance of the proposed work is validated over state-of-the-art models in terms of sanitization and restoration analysis. [ABSTRACT FROM AUTHOR]

When generating primes |$p$| and |$q$| for an RSA key, the algorithm specifies that if |$p-1$| and |$q-1$| must be relatively prime to the public exponent |$e$|⁠. If this is not done, then the decryption exponent is not well defined. However, what if a software bug allows the generation of public parameters |$N$| and |$e$| of an RSA key with this property and then it is subsequently used for encryption? Though this may seem like a purely academic question, a software bug in a preview release of the Windows 10 operating system makes this question of more than purely theoretical. Without a well defined decryption exponent, plaintexts encrypted to such keys will be undecryptable thus potentially losing user data, a serious software defect. Though the decryption exponent is no longer well defined, it is in fact possible to recover the a small number of potential plaintexts, if the prime factors |$p$| and |$q$| of the public modulus |$N$| are known. This paper presents an analysis of what steps fail in the RSA algorithm and derives a plaintext recovery algorithm. The runtime of this algorithm is |$O(e)$| making it practical to use, and it has been implemented in python. [ABSTRACT FROM AUTHOR]

With the rapid increase in the practical applications of secure computation protocols, increasingly more research is focused on the efficiency of the symmetric-key primitives underlying them. Whereas traditional block ciphers have evolved to be efficient with respect to certain performance metrics, secure computation protocols call for a different efficiency metric: arithmetic complexity. Arithmetic complexity is viewed through the number and layout of nonlinear operations in the circuit implemented by the protocol. Symmetric-key algorithms that are optimized for this metric are said to be algebraic ciphers. It has been shown that recently proposed algebraic ciphers are greatly efficient in ZK and MPC protocols. However, there has not been many algebraic ciphers proposed targeting Fully Homomorphic Encryption (FHE). In this paper, we evaluate the behavior of Vision when implemented as a circuit in an FHE protocol. To this end, we present a state-of-the-art comparison of AES and Vision implemented using HElib. Counterintuitively, Vision does not deliver a better performance than AES in this setting. Then, by attempting to improve a bottleneck of the FHE implementation evaluating Vision we present a new cipher: Seljuk. Despite the improvement with respect to Vision , Seljuk does not deliver the expected performance. [ABSTRACT FROM AUTHOR]

The Pilsung cipher is part of the North Korean Red Star operating system, which was leaked to the West in 2014. Initial analysis by Kryptos Logic reported a possibility of a class of weak keys due to the use of pseudo-random diffusion. Following this lead, we analyzed the cipher and identified a small class of such weak keys. We developed techniques for searching for a key that belongs to the class. After spending thousands of CPU hours, we found a supposedly weak key for a slightly weaker version of Pilsung, but the key did not behave as we expected. On further investigation we found out a crucial misunderstanding in a critical part of the cipher and that no such class of weak keys exists in Pilsung. Thus, this paper makes two main contributions to the art of cryptanalysis. First, it identifies and shows how to investigate a potential weakness in randomizing diffusion, which although does not exist in Pilsung, may affect future designs. Second, it highlights the need for early verification of results in order to identify errors before expending significant resources. [ABSTRACT FROM AUTHOR]

Horizontal and vertical scalability have been widely studied in the context of computational resources. However, with the exponential growth in the number of connected objects, functional scalability (in terms of the size of software systems) is rapidly becoming a central challenge for building efficient service-oriented Internet of Things (IoT) systems that generate huge volumes of data continuously. As systems scale up, a centralized approach for moving data between services becomes infeasible because it leads to a single performance bottleneck. A distributed approach avoids such a bottleneck, but it incurs additional network traffic as data streams pass through multiple mediators. Decentralized data exchange is the only solution for realizing totally efficient IoT systems, since it avoids a single performance bottleneck and dramatically minimizes network traffic. In this paper, we present a functionally scalable approach that separates data and control for the realization of decentralized data flows in service-oriented IoT systems. Our approach is evaluated empirically, and the results show that it scales well with the size of IoT systems by substantially reducing both the number of data flows and network traffic in comparison with distributed data flows. [ABSTRACT FROM AUTHOR]

Neural-aided cryptanalysis is a challenging topic, in which the neural distinguisher (⁠|$\mathcal{ND}$|⁠) is a core module. In this paper, we propose a new |$\mathcal{ND}$| considering multiple ciphertext pairs simultaneously. Besides, multiple ciphertext pairs are constructed from different keys. The motivation is that the distinguishing accuracy can be improved by exploiting features derived from multiple ciphertext pairs. To verify this motivation, we have applied this new |$\mathcal{ND}$| to five different ciphers. Experiments show that taking multiple ciphertext pairs as input indeed brings accuracy improvement. Then, we prove that our new |$\mathcal{ND}$| applies to two different neural-aided key recovery attacks. Moreover, the accuracy improvement is helpful for reducing the data complexity of the neural-aided statistic attack. The code is available at https://github.com/AI-Lab-Y/ND_mc. [ABSTRACT FROM AUTHOR]

The fast correlation attack (FCA) is one of the most important cryptanalytic techniques against LFSR-based stream ciphers. In CRYPTO 2018, Todo et al. found a new property for the FCA and proposed a novel algorithm which was successfully applied to the Grain family of stream ciphers. Nevertheless, these techniques cannot be directly applied to Grain-like small state stream ciphers with keyed update, such as Plantlet, Fruit-v2 and Fruit80. In this paper, we study the security of Grain-like small state stream ciphers by the FCA. We first observe that the number of required parity-check equations can be reduced when there are multiple different parity-check equations. With exploiting the Skellam distribution, we introduce a sufficient condition to identify the correct LFSR initial state and derive a new relationship between the number and bias of the required parity-check equations. Then, a modified algorithm is presented based on this new relationship, which can recover the LFSR initial state no matter what the round key bits are. Under the condition that the LFSR initial state is known, an algorithm is given against the degraded system and to recover the NFSR state at some time instant, along with the round key bits. As cases study, we apply our cryptanalytic techniques to Plantlet, Fruit-v2 and Fruit-80. As a result, for Plantlet, our attack takes |$ 2^{73.75} $| time complexity and |$ 2^{73.06} $| keystream bits to recover the full 80-bit key. Regarding Fruit-v2, |$ 2^{55.34} $| time complexity and |$ 2^{55.62} $| keystream bits are needed to determine the secret key. As for Fruit-80, |$2^{64.47}$| time complexity and |$2^{62.82}$| keystream bits are required to recover the secret key. More flexible attacks can be obtained with lower data complexity at the cost of increasing the attack time. Especially, for Fruit-v2, a key recovery attack can be launched with data complexity of |$2^{42.38}$| and time complexity of |$2^{73.31}$|⁠. Moreover, we have implemented our attack methods on a toy version of Fruit-v2. The attack matches the expected complexities predicted by our theoretical analysis quite well, which proves the validity of our cryptanalytic techniques. [ABSTRACT FROM AUTHOR]

With the development of heterogeneous systems, the demand for high-level programming methods that ease heterogeneous programming and produce portable applications has become more urgent. This paper proposes DACL, the data associated computing language. DACL introduces data partition patterns to achieve architecture-independent parallelism expression. Meanwhile, DACL provides simplified language extensions, as well as programming features such as serialization of the computing process, parameterization of data attributes and modularity, thus reducing the difficulty of heterogeneous programming and improving programming productivity. The operational semantics show that DACL enables different levels of parallelism degree calculation and retains data access patterns, reserving optimization potential. To support cross-platform execution, the currently implemented source-to-source compilers employ OpenMP and OpenCL as the backend. We reconstructed multiple benchmarks selected from the Parboil and Rodinia benchmark suits with DACL and conducted a comparison test on CPU, GPU and MIC platforms. The code size of each rebuilt benchmark is roughly equivalent to that of the serial code, which is only 13%–64% of the benchmark OpenCL code. With the support of the compilation system, the reconstructed code can execute on different processors without modification, yielding a competitive or better performance to that of the manually written benchmark code. [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]

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]

A web shell is a backdoor used by hackers to control Web servers and perform privilege escalation, and thus it is crucial to detect web shells effectively. However, the detection of obfuscated web shells has always been a challenge. Inspired by adversarial training methods in the field of computer vision, this paper proposes a generative adversarial network (GAN)-based web shell detection model training framework. Since there has been no method that can generate obfuscated web shells effectively, a generator based on the genetic algorithm, which combines and optimizes the pre-set obfuscation methods, is used to obtain new obfuscation combinations and generate obfuscated samples. The whole proposed framework is named the CWSOGG. When training the detection model, the generator generates web shells that can bypass the discriminator, and the discriminator catches the features of obfuscated samples. Through the adversarial training of the discriminator and generator, the detection model improves its ability to detect obfuscated web shells. To verify the proposed framework is flexible to different models, the discriminator based on four main neural networks has been implemented. Meanwhile, to build complete feature extraction models, both statistical and semantic features are extracted. Due to the lack of web shell data, a clean dataset containing 4,375 web shells is constructed and used to evaluate the CWSOGG. The results have shown that the detection accuracy of each model increases by 86.71% on the generated obfuscated web shells on average and by 7.50% on the simulated real-world obfuscated web shells on average. [ABSTRACT FROM AUTHOR]

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]

Aspect-based sentiment analysis aims to identify the sentiment polarity of aspects in a given sentence. Although existing neural network models show promising results, they cannot meet the expectations in the case of a single network structure and limited dataset. When an aspect term composes more than one word, many models use the coarse-grained attention mechanism but lead to the unsatisfactory results. Besides, the relative distance between words in a sentence is always out of consideration. In this paper, we propose a model based on the interaction matrix and global attention mechanism to improve the ability of aspect-based sentiment analysis. First of all, the relative distance features of words in a sentence are initialized to enrich word embedding. Second, classic neural networks are applied to extract the essential features of word embedding in a sentence, such as long short-term memory and convolutional neural network. Third, an interaction matrix and global attention mechanism are combined to calculate weighted scores and measure relationships between aspect terms and context words. Finally, sentiment polarity is represented through a softmax layer. Experimental results on restaurant, laptop and twitter datasets show that the performance of the proposed model is superior to other methods. [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]

By introducing the BHT algorithm into the slide attack on 1K-AES and the related-key attack on PRINCE, we present the corresponding quantum attacks in this paper. In the proposed quantum attacks, we generalize the BHT algorithm to the situation where the number of marked items is unknown ahead of time. Moreover, we give an implementation scheme of classifier oracle based on Quantum Phase Estimation algorithm in presented quantum attacks. The complexity analysis shows that the query complexity, time complexity and memory complexity of the presented quantum attacks are all |$\mathcal{O}(2^{n/3})$| when the success probability is about |$63\%$|⁠ , where |$n$| is the block size. Compared with the corresponding classical attacks, the proposed quantum attacks can achieve subquadratic speed-up under the same success probability no matter on query complexity, time complexity or memory complexity. Furthermore, the query complexity of the proposed quantum slide attack on 1K-AES is less than Grover search on 1K-AES by a factor of |$2^{n/6}.$| When compared with the Grover search on PRINCE, the query complexity of the presented quantum attack on PRINCE is reduced from |$\mathcal{O}(2^{n})$| to |$\mathcal{O}(2^{n/2}).$| When compared with the combination of Grover and Simon's algorithms on PRINCE, the query complexity of our quantum attack on PRINCE is reduced from |$\mathcal{O}(n\cdot 2^{n/2})$| to |$\mathcal{O}(2^{n/2}).$| Besides, the proposed quantum slide attack on 1K-AES indicates that the quantum slide attack could also be applied on Substitution-Permutation Network construction, apart from the iterated Even-Mansour cipher and Feistel constructions. [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]

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]

Decentralized storage of data is one of the typical applications in the blockchain network. However, most of the existing works neglected the storage balancing problem in the blockchain network, which has an immediate impact on the availability and stability of the network. Therefore, this paper proposes a storage balancing problem for non-local data storage in the blockchain network and proves that the problem is non-deterministic polynomial (NP)-hard. The criterion of the storage balance is established by a balanced coefficient in the proposed scheme. A heuristic matching algorithm (HMA), a genetic algorithm (GA) and a tabu search algorithm (TSA) are customized to solve the problem of imbalanced storage formalized in this paper. Compared with our previous algorithm fast matching algorithm (FMA), experimental results demonstrate that HMA achieves better performance in terms of accuracy, computation overhead and storage overhead. Specifically, the computation overhead of HMA is lower than that of FMA by 84.45% on average, whereas the storage overhead of HMA is lower than that of FMA by 32.26% on average. By using the initial solution of HMA, TSA achieves the highest accuracy among GA, TSA and moth-flame optimization (MFO). Meanwhile, by using the initial solution of FMA, TSA achieves the highest accuracy among GA, TSA and MFO. [ABSTRACT FROM AUTHOR]

In this era, the exchanging of multimedia content has become much comfortable. Since the distribution and the illegal copying of digital multimedia content have become much easier, an author's intellectual property copyrights have suffered from the violations that led to many damage to their benefits in many applications. This paper intends to propose a novel digital watermarking model for multimedia copyright protection that is based on 3D wavelet transformation. Initially, histogram-based motion frame extraction is conducted on the color video for which keyframes are extracted based on the extracted motion frames. To embed the watermark in the keyframes, 3D wavelet coefficients are selected followed by applying the spread spectrum technique. To conduct precise and effective embedding, it is planned to introduce fine-tuning aspects in the embedding process. As the intensity factor plays a major role in the embedding process, this paper tunes it rather than selecting randomly. Moreover, the Mersenne-Twister algorithm is known for spreading the power spectrum of watermark data to keep invisible. Hence, it is also intended to tune the seed factor of the respective algorithm to further effectuate the spreading process. These tuning processes can be solved as an optimization problem for which this paper proposes a new hybrid optimization algorithm named Spiral Updated Rider Optimization Algorithm (SU-ROA). SU-ROA is a hybrid variant of the Rider Optimization Algorithm (ROA) and Whale Optimization Algorithm (WOA). The performance of the proposed work is compared and proved over the state-of-the-art models in terms of both embedding and extracting effectiveness. [ABSTRACT FROM AUTHOR]

This paper considers the area convergence problem, which requires a group of robots to gather in a small area not defined a priori. While it is known that robots can gather at a point if they can precisely measure distances, we, in this paper, show that without any agreement on the coordinate system, it is impossible for robots to converge to an area if they cannot measure distances or angles. We denote these robots without the ability to measure distances or angles as monoculus robots. We present a counterexample showing that monoculus robots fail in area convergence even with the capability of measuring angles. However, monoculus robots with a weak notion of distance or minimal agreement on the coordinate system are sufficient to achieve area convergence. In particular, we present area convergence algorithms in asynchronous model for such monoculus robots with one of the two following simple additional capabilities: (1) locality detection (⁠|$\mathcal{L}\mathcal{D}$|⁠), a notion of distance or (2) orthogonal line agreement (⁠|$\mathcal{O}\mathcal{L}\mathcal{A}$|⁠), a notion of direction. We discuss extensions corresponding to multiple dimensions and the termination. Additionally, we validate our findings using simulation and show the robustness of our algorithms in the presence of errors in observation or movement. [ABSTRACT FROM AUTHOR]

This paper addresses the anomaly detection problem in feature-evolving systems such as server machines, cyber security, financial markets and so forth where in every millisecond, |$N$| -dimensional feature-evolving heterogeneous time series are generated. However, due to stochasticity and uncertainty in evolving heterogeneous time series coupled with temporal dependencies, their anomaly detection are extremely challenging. Furthermore, it is practically impossible to train an anomaly detection model per single time series across millions of metrics, leave alone memory space required to maintain the model and evolving data points in memory for timely processing in feature-evolving data streams. Thus, this paper proposes |$\underline{o}$| ne sketch |$\underline{f}$| its all |$\underline{a}$| lgorithm (OFA), which is a real-time stochastic recurrent deep neural network anomaly detector built on assumption-free probabilistic conditional quantile regression with well-calibrated predictive uncertainty estimates. The proposed framework is capable of detecting anomalies robustly, accurately and efficiently in real time while handling randomness and variabilities in feature-evolving heterogeneous time series. Extensive experiments and rigorous evaluation on large-scale real-world data sets showcase that OFA outperforms other competitive state-of-the-art anomaly detector methods. [ABSTRACT FROM AUTHOR]

Keyword extraction is one of the most important aspects of text mining. Keywords help in identifying the document context. Many researchers have contributed their work to keyword extraction. They proposed approaches based on the frequency of occurrence, the position of words or the similarity between two terms. However, these approaches have shown shortcomings. In this paper, we propose a method that tries to overcome some of these shortcomings and present a new algorithm whose efficiency has been evaluated against widely used benchmarks. It is found from the analysis of standard datasets that the position of word in the document plays an important role in the identification of keywords. In this paper, a fuzzy logic-based automatic keyword extraction (FLAKE) method is proposed. FLAKE assigns weights to the keywords by considering the relative position of each word in the entire document as well as in the sentence coupled with the total occurrences of that word in the document. Based on the above data, candidate keywords are selected. Using WordNet, a fuzzy graph is constructed whose nodes represent candidate keywords. At this point, the most important nodes (based on fuzzy graph centrality measures) are identified. Those important nodes are selected as final keywords. The experiments conducted on various datasets show that proposed approach outperforms other keyword extraction methodologies by enhancing precision and recall. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous development of internet of things and cloud computing technologies, data intensive applications have gotten more and more attention. In the distributed cloud environment, the access of massive data is often the bottleneck of its performance. It is very significant to propose a suitable data deployment algorithm for improving the utilization of cloud server and the efficiency of task scheduling. In order to reduce data access cost and data deployment time, an optimal data deployment algorithm is proposed in this paper. By modeling and analyzing the data deployment problem, the problem is solved by using the improved genetic algorithm. After the data are well deployed, aiming at improving the efficiency of task scheduling, a task progress aware scheduling algorithm is proposed in this paper in order to make the speculative execution mechanism more accurate. Firstly, the threshold to detect the slow tasks and fast nodes are set. Then, the slow tasks and fast nodes are detected by calculating the remaining time of the tasks and the real-time processing ability of the nodes, respectively. Finally, the backup execution of the slow tasks is performed on the fast nodes. While satisfying the load balancing of the system, the experimental results show that the proposed algorithms can obviously reduce data access cost, service-level agreement (SLA) default rate and the execution time of the system and optimize data deployment for improving scheduling efficiency in distributed clouds. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common kind of cancer, which is the cause of death among the women worldwide. There is evidence that shows that the early detection and treatment can increase the survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and parameter optimization of an Multi Layer Perceptron (MLP) neural network. The aim of this paper is to propose a hybrid classification algorithm based on Multi-stage Weights Adjustment in the MLP (MWAMLP) neural network in two parts to improve the breast cancer diagnosis. In the first part, the three classifiers are trained simultaneously on the learning dataset. The output of the first part classifier together with the learning dataset is placed in a new dataset. This dataset uses a hybrid classifier method to model the mapping between the outputs of each ordinary classifier of the first part with real output labels. The proposed algorithm is implemented with three different variations of the backpropagation (BP) technique, namely the Levenberg–Marquardt, resilient BP and gradient descent with momentum for fine tuning of the weight of MLP neural network and their performances are compared. Interestingly, one of the proposed algorithms titled MWAMLP-RP produces the best and on average, 99.35% and 98.74% correct classification, respectively, on the Wisconsin Breast Cancer Database dataset, which is comparable with the obtained results from the methods titled GP-DLNN, GAANN and CAFS and other works found in the literature. [ABSTRACT FROM AUTHOR]

Short text or sentence similarity is crucial in various natural language processing activities. Traditional measures for sentence similarity consider word order, semantic features and role annotations of text to derive the similarity. These measures do not suit short texts or sentences with negation. Hence, this paper proposes an approach to determine the semantic similarity of sentences and also presents an algorithm to handle negation. In sentence similarity, word pair similarity plays a significant role. Hence, this paper also discusses the similarity between word pairs. Existing semantic similarity measures do not handle antonyms accurately. Hence, this paper proposes an algorithm to handle antonyms. This paper also presents an antonym dataset with 111-word pairs and corresponding expert ratings. The existing semantic similarity measures are tested on the dataset. The results of the correlation proved that the expert ratings are in order with the correlation obtained from the semantic similarity measures. The sentence similarity is handled by proposing two algorithms. The first algorithm deals with the typical sentences, and the second algorithm deals with contradiction in the sentences. SICK dataset, which has sentences with negation, is considered for handling the sentence similarity. The algorithm helped in improving the results of sentence similarity. [ABSTRACT FROM AUTHOR]

Predicting the bike demand can help rebalance the bikes and improve the service quality of a bike-sharing system. A lot of works focus on predicting the bike demand for all the stations, which is unnecessary as the travel cost of rebalance operations increases sharply as the number of stations increases. In this paper, we propose a framework for predicting the hourly bike demand based on the central stations we define. Firstly, we propose Two-Stage Station Clustering Algorithm to assign central stations and common stations into each cluster. Secondly, we propose a hierarchical prediction model to predict the hourly bike demand for every cluster and each central station progressively. Thirdly, we use a well-studied queuing model to determine the target initial inventory for each central station. The most innovative contribution of this paper is proposing the concept of central station, the use of a novel algorithm to cluster the central stations and present a hierarchical model, containing the Time and Weather Similarity Weighted K-Nearest Neighbor Algorithm and a linear model to predict the bike demand for central stations. The experimental results on the New York citi bike system demonstrate that our proposed method is more accurate than other methods in solving existing problems. [ABSTRACT FROM AUTHOR]

Palmprint is an important key player in biometric family and also informs some extra basic personality details of an individual. In this paper, we utilize these extra information and designed an automated mobile vision (MV) system to extract principal lines from human palm and analyze them for behavioral significances. Hence, the main concern of this paper is to come up with a simple yet powerful low-level MV solution to extract the complex challenging features from palmprint. In the proposed system, the computational tasks are offloaded to a dedicated palmistry server and efficiently minimizes the energy consumption of mobile device after performing some preliminary computational low-level tasks. The implementation is divided into four major phases: (i) hand-image acquisition and pre-processing, (ii) region-of-interest extraction from the palm images, (iii) post-processing to extract principal lines and (iv) features computation for behavior analysis. The basic palmistry uses line lengths, angles, curves and branches to identify a person's behavior. The exhaustive experiments show that the proposed system achieves an average accuracy of 96%, 92% and 84% for heart, life and head line detection and personality prediction, respectively. Finally, mapping the extracted results with the original palmprint is augmented back to the use for better visualization. [ABSTRACT FROM AUTHOR]

Medical imaging shows the internal structures hidden in the skin and bone to diagnose the disease. Diabetes mellitus (DM) is a metabolic disorder that causes high blood sugar levels due to the inadequate secretion of insulin or the body does not respond properly to the secreted insulin. This paper proposes a non-invasive method to detect DM at an early stage based on the physiognomy extracted from tongue images. The tongue extends to identify the disease of a human body. However, unpredictable response of the human body parts such as the stomach, pancreas, liver and intestines revert in the tongue. The changes in the tongue ensure the dereliction of the internal organs of the human being. The changes are difference in the color and surface of the tongue. Processing of tongue image is done by fractional order Darwinian particle swarm optimization (FODPSO) algorithm. The system framework involves obtaining the image, alluring of the image, identifying the texture and color feature and finally classified as normal or diabetic. In this paper, the authors propose to diagnose DM at an early stage from tongue digital image. The tongue image is acquired and processed with FODPSO to extract edge and texture features. Tongue reflects and diagnoses diabetes in a person. [ABSTRACT FROM AUTHOR]

Big data in medical diagnosis can provide abundant value for clinical diagnosis, decision support and many other applications, but obtaining a large number of labeled medical data will take a lot of time and manpower. In this paper, a classification model based on semi-supervised learning algorithm using both labeled and unlabeled data is proposed to process big data in medical diagnosis, which includes structured, semi-structured and unstructured data. For the medical laboratory data, this paper proposes a self-training algorithm based on repeated labeling strategy to solve the problem that mislabeled samples weaken the performance of classifiers. Aiming at medical record data, this paper extracts features with high correlation of classification results based on domain expert knowledge base first, and then chooses the unlabeled medical record data with the highest confidence to expand the training set and optimizes the performance of the classifiers of tri-training algorithm, which uses supervised learning algorithm to train three basic classifiers. The experimental results show that the proposed medical diagnosis data classification model based on semi-supervised learning algorithm has good performance. [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]

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]

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]

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]

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]

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]