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The release of millions of financial documents, which has been known as the 'WikiLeaks' of the financial world (a.k.a. 'Panama Papers'), has dragged global attention in how highly structured means applied by some of the elite to conceal their financial assets. Consequently, significant financial corruption allegations were raised. We concentrate on a somewhat overlooked region, the Middle East and North Africa (MENA) region. This study aims to use social network analytics to study the information contained in these documents. We are checking the major players in the MENA's trends and patterns to determine if it matches the known economic powers. The analysis reveals that while the constructed network enjoys some typical characteristics, many interesting observations and properties are worth discussing. Specifically, using the extracted network consisting of 62 987 nodes and 84 692 edges, our social network analysis finding shows that, perhaps surprisingly, the nodes or the social network are not necessarily directly correlated with perceived economic influence. [ABSTRACT FROM AUTHOR]

The emergent technology has been increasingly incorporated with the tourism industry. In the digital world, social media plays an important role in identifying the most visited tourist places. More than 80% of people commonly used the social media platform called 'Twitter'. By using tweet reviews, tourists around the world know about the feelings, suggestions and opinions of most visited tourist places in the selected region. This paper recommends an approach for the development of trip planning in South India based on tourist's preferences. For the research, we use Twitter Application Programming Interface to collect information about the most visited tourist places in South India by using tweets reviews and store it as South India Tourism Tweet reviews database. This paper mainly focuses on analyzing Twitter reviews, which are very helpful to locate the most visited tourist place. In the tweets where reviews are mostly unstructured and heterogeneous, which are then classified into positive tweets, negative tweets and neutral tweets by using the preprocessing technique and machine learning algorithm called support vector machine. Performance measures are calculated by using the classification results. Finally, the results are plotted by using the classification results and performance measures as a wordcloud. This can be very useful for tourists to select the most visited tourist locations. The proposed system can satisfy individual tourist needs. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT), data gathered from dozens of devices are the base for creating business value and developing new products and services. If data are of poor quality, decisions are likely to be non-sense. Data quality is crucial to gain business value of the IoT initiatives. This paper presents a systematic literature review regarding IoT data quality from 2000 to 2020. We analyzed 58 articles to identify IoT data quality dimensions and issues and their categorizations. According to this analysis, we offer a classification of IoT data characterizations using the focus group method and clarify the link between dimensions and issues in each category. Manifesting a link between dimensions and issues in each category is incumbent, while this critical affair in extant categorizations is ignored. We also examine data security as an important data quality issue and suggest potential solutions to overcome IoT's security issues. The finding of this study proposes a new research discipline for additional examination for researchers and practitioners in determining data quality in the context of IoT. [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]

Completing the classification of tactics and techniques in cyber threat intelligence (CTI) is an important way to obtain tactics, techniques and procedures (TTPs) and portray the behavior of cyber attacks. However, the high level of abstraction of tactics and techniques information and their presence in CTI, usually in the form of natural language text, make it difficult for traditional manual analysis methods and feature engineering-based machine learning methods to complete the classification of tactics and techniques effectively. Meanwhile, flat deep learning methods do not perform well in classifying more fine-grained techniques due to their inability to exploit the hierarchical relationship between tactics and techniques. Therefore, this paper regards the tactics and techniques of TTPs defined in Adversarial Tactics, Techniques and Common Knowledge knowledge base as labels and proposes a Convolutional Neural Network (CNN) model based on hierarchical knowledge migration and attention mechanism for classifying tactics and techniques in CTI, named HM-ACNN (CNN based on hierarchical knowledge migration and attention mechanism). HM-ACNN classifies tactics and techniques into two phases, and the underlying network model for both phases is the Attention-based CNN network. The first step in HM-ACNN is converting the CTI text into a two-dimensional image based on the word embedding model, and then start training the classification of tactics through the CNN structure based on the attention mechanism before the classification of techniques. Secondly, after the tactics classification training is completed, the tactic-to-technique knowledge migration is then completed by transforming the parameters of the CNN layer and the attention layer in the tactics classification process based on the special hierarchical relationship between tactics and techniques. Then, the classification of techniques is finished by fine-tuning. The experimental results show that HM-ACNN performs well in the tactics and techniques classification tasks, and the metric F1 values reach 93.66% and 86.29%, which are better than other models such as CNN, Recurrent Neural Network and CRNN (Recurrent Convolutional Neural Networks). [ABSTRACT FROM AUTHOR]

With the rapid construction and implementation of smart grid, lots of studies have been conducted to explore how to ensure the security of information privacy. At present, most privacy-preserving data aggregation schemes in smart grid achieve privacy data protection through homomorphically encrypted data aggregation. However, these data aggregation schemes tend to rely on a trusted third party (TTP), and fail to efficiently handle the case of a meter failure. Besides, they are less flexible for overall user management, and resistance to collusion attacks needs to be improved. In this paper, we propose an efficient and robust privacy-preserving data aggregation scheme without TTP, called EFTA. Overall, the scheme eliminates the reliance on a TTP, combines with Shamir threshold secret sharing scheme to increase overall fault tolerance, supports flexible and dynamic user management, and effectively defends against entity initiated collusion attacks. According to security and performance analysis results, the scheme proposed in this paper meets the multiple security requirements of smart grid, and is more efficient in terms of overall overhead compared to the existing privacy-preserving data aggregation schemes. [ABSTRACT FROM AUTHOR]

Intrusion detection has become a research focus in internet information security, with deep learning algorithms playing a crucial role in its development. Typically, intrusion detection data are transformed into a two-dimensional matrix by segmenting, stacking and padding them with zeros for input into deep learning models. However, this method consumes computational resources and fails to consider the correlation between features. In this paper, we transform the data into images through visualization operations and propose an information entropy weighted scheme to optimize the collision element problem during the transformation process. This method enhances the correlation between pixel frame features, leading to approximately 2% improvement in accuracy of the classification model when using the generated image samples for detection in experiments. To address the issues of insensitivity to target feature locations and incomplete feature extraction in traditional neural networks, this paper introduces a new network model called CBAM-CapsNet, which combines the advantages of the lightweight Convolutional Block Attention Module and capsule networks. Experimental results on the UNSW-NB15 and IDS-2017 datasets demonstrate that the proposed model achieves accuracies of 92.94% and 99.72%, respectively. The F1 scores obtained are 91.83% and 99.56%, indicating a high level of detection. [ABSTRACT FROM AUTHOR]

The accurate representation of objects holds pivotal significance in the realm of panoptic segmentation. Presently, prevalent object representation methodologies, including box-based, keypoint-based and query-based techniques, encounter a challenge known as the 'representation confusion' issue in specific scenarios, often resulting in the mislabeling of instances. In response, this paper introduces Convex Object Representation (COR), a straightforward yet highly effective approach to address this problem. COR leverages a CNN-based Euclidean Distance Transform to convert the target instance into a convex heatmap. Simultaneously, it offers a parallel embedding method for encoding the object. Subsequently, COR characterizes objects based on the distinctive embedding vectors of their convex vertices. This paper seamlessly integrates COR into a state-of-the-art query-based panoptic segmentation framework. Experimental findings validate that COR successfully mitigates the representation confusion predicament, enhancing segmentation accuracy. The COR-augmented methods exhibit notable improvements of +1.3 and +0.7 points in PQ on the Cityscapes validation and MS COCO panoptic 2017 validation datasets, respectively. [ABSTRACT FROM AUTHOR]

Parameter servers (PSs) placement is one of the most important factors for global model training on distributed deep learning. This paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training time of the distributed deep learning under the constraints of storage capacity and the number of local PSs. Then, we provide the proof for the NP-hardness of the proposed problem. The whole training epochs are divided into two parts, i.e. the first epoch and the other epochs. For the first epoch, an approximation algorithm and a rounding algorithm are proposed in this paper, to solve the proposed problem. For the other epochs, an adjustment algorithm is proposed, by continuously adjusting the decisions for placement strategy of PSs to decrease the training time of the global model. Simulation results show that the proposed approximation algorithm and rounding algorithm perform better than existing works for all cases, in terms of the training time of global model. Meanwhile, the training time of global model for the proposed approximation algorithm is very close to that for optimal solution generated by the brute-force approach for all cases. Besides, the integrated algorithm outperforms the existing works when the available storage capacity varies during the training. [ABSTRACT FROM AUTHOR]

We consider two finite and disjoint sets of homogeneous robots deployed at the nodes of an infinite grid graph. The grid graph also comprises two finite and disjoint sets of prefixed meeting nodes located over the nodes of the grid. The objective of our study is to design a distributed algorithm that gathers all the robots belonging to the first team at one of the meeting nodes belonging to the first type, and all the robots in the second team must gather at one of the meeting nodes belonging to the second type. The robots can distinguish between the two types of meeting nodes. However, a robot cannot identify its team members. This paper assumes the strongest adversarial model, namely the asynchronous scheduler. We have characterized all the initial configurations for which the gathering problem is unsolvable. For the remaining initial configurations, the paper proposes a distributed gathering algorithm. Assuming the robots are capable of global-weak multiplicity detection , the proposed algorithm solves the problem within a finite time period. The algorithm runs in |$\Theta (dn)$| moves and |$O(dn)$| epochs, where |$d$| is the diameter of the minimum enclosing rectangle of all the robots and meeting nodes in the initial configuration, and |$n$| is the total number of robots in the system. [ABSTRACT FROM AUTHOR]

Ridesharing is an effective approach to alleviate traffic congestion. In most existing works, drivers and passengers are assigned prices without considering the constraints of desired benefits. This paper investigates ridesharing by formulating a matching and pricing problem to maximize the total payoff of drivers, with the constraints of desired benefit and quality of experience. An efficient algorithm is proposed to solve the formulated problem based on coalitional double auction. Secondary pricing based strategy and sacrificed minimum bid based strategy are proposed to support the algorithm. This paper also proves that the proposed algorithm can achieve a Nash-stable coalition partition in finite steps, and the proposed two strategies guarantee truthfulness, individually rational and budget balance. Extensive simulation results on the real-world dataset of taxi trajectory in Beijing city show that the proposed algorithm outperforms the existing ones, in terms of average total payoff of drivers while meeting the benefits of passengers. [ABSTRACT FROM AUTHOR]

The synchronous stream ciphers RCR-64 and RCR-32 designed by Sekar, Paul and Preneel are strengthened variants of the ciphers TPy and TPypy (designed by Biham and Seberry), respectively. The RCR ciphers have remained unbroken since they were published in 2007. In this paper, we present arguments that not only support the designers' security claims but suggest, in general, that the ciphers are secure against several classes of cryptanalytic attacks. We find that the ciphers are best used with 256-bit keys and 384-bit IVs. We also suggest ways to protect software implementations of the RCR ciphers against (cache-)timing and processor flag attacks. Our performance evaluation suggests that the protected implementation of the RCR-64 encrypts long messages at speeds comparable to some of the fastest stream ciphers available today. Consequently, we find that the RCR ciphers may be well suited for PC-based applications in general and streaming audio / video applications in particular. This is the first paper to present a detailed study on the security and performance of the RCR ciphers. [ABSTRACT FROM AUTHOR]

With the development of NAND flash, the storage density has become larger and larger, but the reliability has become lower and lower. To address this problem, stronger error correction codes, low-density parity-check (LDPC) codes, are widely used in flash memory. However, the direct use of LDPC codes for error correction has a great impact on the read and write performance of SSDs. Given this, this paper proposes a new mentality that combines replica and LDPC decoding methods to improve the read performance of solid state drives (SSDs). Considering the characteristics of the quantization level of LDPC soft-decision decoding, this paper proposes the IDSD (LDPC soft-decision decoding and duplication method) and IDHSD (a method combining duplication and LDPC hybrid decoding)schemes. These schemes are mainly for hot data. When reading the data, the IDSD scheme needs to first judge whether it is soft-decision decoding. Then, according to the number of quantization levels recorded, it needs to select whether to use duplication or soft-decision decoding to correct the error data. The IDHSD scheme does not need to judge the soft-decision decoding but directly uses the duplication after the hard-decision decoding fails. Both schemes reduce the average response time to a certain extent and optimize the read performance of the SSD. The experimental results show that compared with the Native Scheme, the average response delay time of the IDSD scheme decreases by 5–30% and the IDHSD scheme slashes by 5–38%. Compared with the Traditional Refresh Scheme, the average response delay time of the IDSD scheme cuts down by 1–30% and the IDHSD scheme lowers by 1–29%. [ABSTRACT FROM AUTHOR]

As code is now an inextricable part of science it should be supported by competent Software Engineering, analogously to statistical claims being properly supported by competent statistics. If and when code avoids adequate scrutiny, science becomes unreliable and unverifiable because results — text, data, graphs, images, etc — depend on untrustworthy code. Currently, scientists rarely assure the quality of the code they rely on, and rarely make it accessible for scrutiny. Even when available, scientists rarely provide adequate documentation to understand or use it reliably. This paper proposes and justifies ways to improve science using code: 1.   Professional Software Engineers can help, particularly in critical fields such as public health, climate change and energy. 2.   'Software Engineering Boards,' analogous to Ethics or Institutional Review Boards, should be instigated and used. 3.   The Reproducible Analytic Pipeline (RAP) methodology can be generalized to cover code and Software Engineering methodologies,        in a generalization this paper introduces called RAP +. RAP + (or comparable interventions) could be supported and or even required       in journal, conference and funding body policies. The paper's Supplemental Material provides a summary of Software Engineering best practice relevant to scientific research, including further suggestions for RAP + workflows. 'Science is what we understand well enough to explain to a computer.' Donald E. Knuth in |$A=B$| [ 1 ] 'I have to write to discover what I am doing.' Flannery O'Connor, quoted in Write for your life [ 2 ] 'Criticism is the mother of methodology.' Robert P. Abelson in Statistics as Principled Argument [ 3 ] 'From its earliest times, science has operated by being open and transparent about methods and evidence, regardless of which technology has been in vogue.' Editorial in Nature [ 4 ] [ABSTRACT FROM AUTHOR]

The brain is regarded as the central part of the human body and has a very complicated structure. The abnormal growth of tissue inside the brain is called a brain tumour. Tumour detection at an early stage is the most difficult task in the discipline of health. In this review article, the authors have deeply analysed and reviewed the brain tumour detection mechanisms which include manual, semi- and fully automated techniques. Today, fully automated mechanisms apply deep learning (DL) methods for tumour detection in brain magnetic resonance images (MRIs). This paper deals with previously published research articles relevant to various brain tumour detection techniques. Review of various types of tumours, MRI modalities, datasets, filters, segmentation methods and DL techniques like long short-term memory, gated recurrent unit network, convolution neural network, auto encoder, deep belief network, recurrent neural network, generative adverse network and deep stacking networks have been included in this paper. It has been observed from the analysis that the use of DL techniques in the detection of brain tumours improves accuracy. Finally, this paper reveals research gaps, limitations of existing methods, challenges in tumour detection and contributions of the proposed article. [ABSTRACT FROM AUTHOR]

Cloud data center serves tremendous workload demand due to the ever-increasing usage of internet services. Scheduling of these workloads over the physical servers is the combinatorial problem that resembles to an NP-complete problem. Furthermore, the workload is dynamic and changes at each scheduling interval result in high power consumption and Service Level Agreement (SLA) violation. Virtual Machine (VM) migrations provide the opportunity to balance this dynamic workload. However, it results in additional power consumption and performance loss. Therefore, this paper aims to find the optimal VM scheduling with minimum VM migration to minimize power consumption and ensure the SLA. This paper proposes a Residual Optimum Power Efficiency (ROPE) aware Improved Clonal Selection Algorithm (ICSA) for dynamic VM scheduling. ICSA-ROPE algorithm finds optimal VM schedules at each scheduling interval guided by two optimization functions Total Datacenter Residual Optimum Power-Efficiency (TDCROPE) and VM Migration Cost (VMC). TDCROPE ensures that servers operate at optimum power efficiency as they consume less power and are less prone to SLA violation, while VMC ensures to find optimal VM schedule with minimum VM migrations. The proposed approach is implemented on a CloudSim simulator, and results show that the ICSA-ROPE is 95.54 %,90.34% and 88.49% more significant in terms of performance efficiency than the LrMmt, DthMf and VMS-MCSA. [ABSTRACT FROM AUTHOR]

Whirlpool is a hash function that has been standardized by ISO/IEC. In this paper, we develop a new type of distinguishing property for its underlying permutation |$ W $|⁠. Division property proposed by Todo at EUROCRYPT 2015 was initially used in the integral cryptanalysis of symmetric-key algorithms. This work for the first time utilizes the MILP method to search for the integral distinguishers of |$ W $| in both the forward and backward directions while concentrating on word-based division property. Under the known-key model, the fact that the permutation used in the hash function does not depend on any secret parameters allows the previous properties to be exploited from the middle, i.e. from an intermediate internal state. Therefore, we apply the inside-out strategy which is the essential step in the zero-sum property to connect the trails in opposite directions. Consequently, we obtain new distinguishers up to full rounds for the |$ W $|⁠. To further reduce the complexity of the integral distinguishers, we add one round in the middle with the help of subspace trails. Finally, we succeed in extending the length and improving the complexity of the integral distinguishers. To the best of our knowledge, all the results in this paper are competitive with the previous work in both computational cost and memory complexity. It is worth mentioning that the methods presented in this paper are applicable to a broad class of hash functions. [ABSTRACT FROM AUTHOR]

Emails are often used to illegal cybercrime today, so it is important to verify the identity of the email author. This paper proposes a general model for solving the problem of anonymous email author attribution, which can be used in email authorship identification and email authorship verification. The first situation is to find the author of an anonymous email among the many suspected targets. Another situation is to verify if an email was written by the sender. This paper extracts features from the email header and email body and analyzes the writing style and other behaviors of email authors. The behaviors of email authors are extracted through a statistical algorithm from email headers. Moreover, the author's writing style in the email body is extracted by a sequence-to-sequence bidirectional long short-term memory (BiLSTM) algorithm. This model combines multiple factors to solve the problem of anonymous email author attribution. The experiments proved that the accuracy and other indicators of proposed model are better than other methods. In email authorship verification experiment, our average accuracy, average recall and average F1-score reached 89.9%. In email authorship identification experiment, our model's accuracy rate is 98.9% for 10 authors, 92.9% for 25 authors and 89.5% for 50 authors. [ABSTRACT FROM AUTHOR]

The use of cloud computing and mobile devices is increasing in healthcare service delivery primarily because of the huge storage capacity of cloud, the heterogeneous structure of health data and the user-friendly interfaces on mobile devices. We propose a healthcare delivery scheme where a large knowledge base is stored in the cloud and user responses from mobile devices are input to this knowledge base to reach a preliminary diagnosis of diseases based on patients' symptoms. However, instead of sending every response to the cloud and getting data from cloud server, it may often be desirable to prune a portion of the knowledge base that is stored in a graph form and download in to the mobile devices. Downloading data from cloud depends on the storage, battery power, processor of a mobile device, wireless network bandwidth and cloud processor capacity. In this paper, we focus on developing mathematical expressions involving the above mentioned criteria and show how these parameters are dependent on each other. The expressions built in this paper can be used in real-life scenarios to take decisions regarding the amount of data to be pruned in order to save energy as well as time. [ABSTRACT FROM AUTHOR]

Community structure over social media (SM) is the collaborative group of globally spread users with identical characteristics and ideologies. The collective features of SM are inherent with both the implicit and explicit nature of end-users. This paper presents an analytical and methodological community detection framework to bind passive users' implicit and explicit nature after scrutinizing graphical data to identify seed nodes and communities. Moreover, this work provides the concept of the unsupervised machine learning approach over the graphical perspective of SM to identify the trade-off between similarity of nodes attributes and density of connections for social theories. Subsequently, this paper evaluates a comprehensive analysis of the benchmark community detection algorithm (CDA) Label Propagation Algorithm (LPA), Clique Percolation Method (CPM), Democratic Estimate of the Modular Organization of a Network (DEMON) and Non-Negative Matrix Factorization (NMF). The evaluation has been carried out over modularity and normalized mutual information of resultant structured community on six real-time SM data set. The performance of benchmark CDAs is significantly increased after incorporating social theories. NMF, DEMON, CPM and LPA gained the highest improvement over Zachary's Karate Club data sets, i.e. approximate 26.91%, 21.68%, 18.79%, 19.96%, respectively. [ABSTRACT FROM AUTHOR]

On-line gambling is the key illegal behaviour of public security department in most countries due to the potential threat to cyberspace security and social stability. Hence, the research on gambling domain names (GDN) classification is quite important and in great demand for academia and industry. Till now, there is very little research work on this topic. Most of the GDN training datasets in previous work were chosen from GDN blacklists provided by publicly available data sources, and the authors did not verify the authenticity and accuracy of these datasets, and the classification results are not particularly satisfactory. In this paper, certificated and textual analysis-based classification method CT-GDNC is proposed to get GDN training data set with an accuracy of 0.9776 and significantly improve the classification results of GDN. The exhaustive comparative experiments on 10K GDN obtained via Bert fine-tuning model and 10K benign data collected from Alex Top 1 million list show that the proposed method achieves new baseline result for GDN classification with classification accuracy 0.9936, precision 0.9936, F1 0.9936 and recall 0.9939. [ABSTRACT FROM AUTHOR]

In current century, big number of people adopting social media very fastly because social media is very reliable, fast and easy to use. So, many big industries adopting social media also, like Facebook to analyze their customers' reviews, analyze customers' emotions and to understand their behavior, which is very helpful in providing facilities to customers. Nowadays bulk of data available is on social media. To increase competitive benefits and assess competitive environment of industries, companies need to analyze social media data as well as customer generated data. To guide large industries in understanding how they may use social media to understand customer emotion and use social media marketing approaches into their marketing strategies to get customer engagement. This paper describes in-depth analysis of pizza industry of United Kingdom to understand their customers' emotions and get data for analysis from Facebook. We are using January 2018–December 2018 data of five largest pizza chains in United Kingdom: Papa John, Prezzo, Pizza Express, Pizza Hut and Dominos. The results disclose the power of sentimental analysis and social media competitive analysis will put positive impact on business value. At the end of this paper we will provide some recommendations to enhance marketing strategies. [ABSTRACT FROM AUTHOR]

The presence of a schema for eXtensible Markup Language (XML) documents has numerous advantages. Unfortunately, many XML documents in practice are not accompanied by a (valid) schema. Therefore, it is essential to devise algorithms to infer schemas from XML documents, where the fundamental task is learning regular expressions. In this paper, we focus on the learning of disjunctive multiplicity expressions (DMEs), a subclass of regular expressions that are particularly suitable to specify unordered models and have been used as the foundation of the schemas for unordered XML. Previous work for learning DME lacks inference algorithms that support positive and negative examples. Further, presently there has been no algorithm can learn DMEs extended with numeric occurrences. We address these challenges in the present paper and first propose a novel algorithm to learn DMEs from positive and negative examples by using genetic algorithms and parallel techniques. Then we extend DMEs to disjunctive generalized multiplicity expressions (DGMEs), which allow numeric occurrences and develop an algorithm to learn DGMEs from positive and negative examples. Finally, experimental results show that with only positive examples, our algorithm can generate a DME with an acceptable learning time, which can accept all positive examples, and when given both positive and negative examples, we can learn DMEs or DGMEs with high accuracy. [ABSTRACT FROM AUTHOR]

Graph partitioning is an NP-hard combinatorial optimization problem, and is a fundamental step in distributing workloads on parallel compute systems, circuit placement, and sparse matrix reordering. The proposed heuristic algorithms such as streaming graph partitioning provide solutions to large-scale graph in a reasonable amount of time. However, the ability of breaking out of local minima in existing these methods is very limited as they are simple in reflecting the connectivity between vertices in real graphs with power-law distribution characteristic. As hill climbing algorithm is a local search method, it can be adopted to improve the result of graph partitioning. However, directly adopting the existing hill climbing algorithm to graph partitioning will result in local minima and poor convergence speed during the iterative process. In this paper, we propose an improved hill climbing graph partitioning algorithm based on clustering. Instead of taking a single vertex as a basic unit, the proposed method considers a cluster consisting of a series of vertices as a hill to move during each iteration. The method uses a new metric that considers both balance and edgecuts to look for the most beneficial cluster as the hill. With these improvements, the method provides a strong power to break out of local minima and achieve an adaptive tradeoff between balance and edgecuts. Experimental results on real-world graphs show that the proposed algorithm substantially reduces edgecuts within a controlled imbalance range. [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]

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]

Global efforts such as the National Institute of Standards and Technology (NIST)'s post-quantum standardization center on cryptographic primitives like public-key encryption and signature schemes that are secure even in the presence of quantum adversaries. In addition, one must also consider efficient certificate management as new technologies like the Internet of Things and 5G wireless networks rely on them. For example, the IEEE Standard for vehicle-to-vehicle communication depends on implicit certificates. However, the only efficient construction available is over elliptic curves, and hence not quantum-secure. This paper investigates approaches to construct implicit certificate schemes from lattices, employing the NIST Round 3 signature schemes Dilithium and Falcon. We consider emulation of the existing implicit certificate scheme and proceed to more innovative techniques like combining the two schemes or pairing them with encryption. Unfortunately, we encounter problems with each design, due to recurring causes like conflicting secret key and signature sizes, unique sampler requirements and the rigidity of the parameter sets. By explaining each of these issues, this paper will hopefully spark ideas for more successful constructions. [ABSTRACT FROM AUTHOR]

A self-stabilizing distributed algorithm is guaranteed eventually to reach and stay at a legitimate configuration regardless of the initial configuration of a distributed system. In this paper, we propose the generalized dominating set problem, which is a generalization of the dominating set and |$k$| -redundant dominating set problems. In the generalized dominating set we propose in this paper, each node |$P_{i}$| is given its set of domination wish sets, and a generalized dominating set is a set of nodes such that each node is contained in the set or has a wish set in which all its members are in the set. We propose a self-stabilizing distributed algorithm for finding a minimal generalized dominating set in an arbitrary network under the unfair distributed daemon. The proposed algorithm converges in |$O(n^{3}m)$| steps and |$O(n)$| rounds, where |$n$| (resp. |$m$|⁠) is the number of nodes (resp. edges). Furthermore, it has the safe convergence property with safe convergence time in |$O(1)$| rounds. The space complexity of the proposed algorithm is |$O(\Delta \log n)$| bits per node, where |$\Delta $| is the maximum degree of nodes. [ABSTRACT FROM AUTHOR]

Cybersecurity has gradually become the public focus between common people and countries with the high development of Internet technology in daily life. The cybersecurity knowledge analysis methods have achieved high evolution with the help of knowledge graph technology, especially a lot of threat intelligence information could be extracted with fine granularity. But named entity recognition (NER) is the primary task for constructing security knowledge graph. Traditional NER models are difficult to determine entities that have a complex structure in the field of cybersecurity, and it is difficult to capture non-local and non-sequential dependencies. In this paper, we propose a cybersecurity entity recognition model CyberEyes that uses non-local dependencies extracted by graph convolutional neural networks. The model can capture both local context and graph-level non-local dependencies. In the evaluation experiments, our model reached an F1 score of 90.28% on the cybersecurity corpus under the gold evaluation standard for NER, which performed better than the 86.49% obtained by the classic CNN-BiLSTM-CRF model. [ABSTRACT FROM AUTHOR]

Blockchain has attracted tremendous attention from both the academia and the capital market. The insertion of data in a blockchain known as anchoring makes data verifiable and independently auditable and has many potential applications. The objective of this paper is to introduce a dynamic cross-chain anchoring framework for snapshotting users' data into multiple public blockchains with the help of smart contracts. This paper starts by proposing the framework and depicting the components and workflow. Second, we design a target chain selection algorithm that combines the states of different blockchains. Using the algorithm, users can dynamically select a suitable public blockchain for anchoring tasks. Third, the virtual chain protocol is presented and achieves data anchoring orderly and integrally. Analyzation and evaluation show that the framework can enhance user experience, robustness and performance. [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]

Social computing is a disruptive technology that is changing how we do business and building the enterprises. Mining consumer insights and consumer segmentation are the new drivers that are likely to be the cornerstone of developing new service innovations and social interactions. This special issue was designed to stimulate research on the data mining tools and methods for social computing. In this editorial, we are outlining the broad research framework on social computing, including the papers in this special issue and areas for future research. [ABSTRACT FROM AUTHOR]

This paper studies adversarial attacks on network intrusion detection systems (IDSs) based on deep or machine learning algorithms. Adversarial attacks on network IDSs must maintain the functional logic of the attack flow. To prevent the produced adversarial examples from violating the attack behavior, most solutions define some limited modification actions. The result limits the production of adversarial examples, and the produced adversarial examples are not guaranteed to find the attack packets. This paper proposes the concept of flow containers to model packets in a flow. Then, we propose a generative adversarial network framework with dual adversarial training to train the generator to produce adversarial flow containers. Flow containers can correlate attack packets and feature vectors of attack flows. We test the evasion rate of the produced adversarial examples using 12 deep and machine learning algorithms. For experiments on the CTU42 data set, the proposed adversarial examples have the highest evasion rates among all 12 classifiers, with the highest evasion rate as high as 1.00. For experiments on the CIC-IDS2017 data set, the proposed adversarial examples have the highest evasion rate among the five classifiers, and the highest evasion rate is also up to 1.00. [ABSTRACT FROM AUTHOR]

Edge connectivity is an important parameter for the reliability of the inter-connection network. A graph |$G$| is strong Menger edge-connected (⁠|$SM$| - |$\lambda $| for short) if there exist min |$\{\deg _{G}(u),\deg _{G}(v)\}$| edge-disjoint paths between any pair of vertices |$u$| and |$v$| of |$G$|⁠. The conditional edge-fault-tolerance strong Menger edge connectivity of |$G$|⁠ , denoted by |$sm_{\lambda }^{r}(G)$|⁠ , is the maximum integer |$m$| such that |$G-F$| remains |$SM$| - |$\lambda $| for any edge set |$F$| with |$|F|\leq m$| and |$\delta (G-F)\geq r$|⁠ , where |$\delta (G-F)\geq r$| is the minimum degree of |$G-F$|⁠. Most of the previous papers discussed |$sm_{\lambda }^{r}(G)$| in the case of |$r\leq 2$|⁠. In this paper, we show that |$sm_{\lambda }^{r}(FQ_{n})=2^{r}(n-r+1)-(n+1)$| for |$1\leq r\leq n-2$|⁠ , where |$n\geq 4$|⁠. [ABSTRACT FROM AUTHOR]

This paper presents an artificial intelligence deep learning model for an energy-aware task scheduling algorithm based on learning automata (LA) in the Fog Computing (FC) Applications. FC is a distributed computing model that serves as an intermediate layer between the cloud and Internet of Things (IoT) to improve the quality of service. The IoT is the closest model to the wireless sensor network (WSN). One of its important applications is to create a global approach to health care system infrastructure development that reflects recent advances in WSN. The most influential factor in energy consumption is task scheduling. In this paper, the issue of reducing energy consumption is investigated as an important challenge in the fog environment. Also, an algorithm is presented to solve the task scheduling problem based on LA and measure the makespan (MK) and cost parameters. Then, a new artificial neural network deep model is proposed, based on the presented LA task scheduling fog computing algorithm. The proposed neural model can predict the relation among MK, energy and cost parameters versus VM length for the first time. The proposed model results show that all of the desired parameters can be predicted with high precision. [ABSTRACT FROM AUTHOR]

Security faces huge challenges in Internet of Things (IoT) environments. In particular, conventional access control standards and models tend to be less tailored for IoT due to the constrained nature of smart objects. Usually, a powerful third party is used to handle the access control logic. However, this third party is lacking in transparency and could harm user privacy. Therefore, providing a distributed access control solution, while considering transparency and privacy-preserving awareness in IoT smart systems, is of paramount importance. The described issue can be addressed using the emergent Blockchain technology that provides a promising choice to build a new generation of decentralized and transparent access control solutions. This paper proposes a smart contract-based access control framework for IoT smart healthcare systems, which is based on smart contracts to provide a distributed and trustworthy access control, combined with the GTRBAC model to express fine-grained access control policies while considering temporal authorization constraints. To prove the feasibility and validity of the proposed framework, this paper also provides a detailed technical description and an initial implementation and execution. An experimental evaluation shows that security properties' analyses on smart contracts achieved the best possible evaluation with no vulnerabilities found, and the cost of access control operations increases linearly as the number of policy constraints increases. Besides, a comparative analysis reveals that the proposed approach can achieve good results with low gas costs and latency. [ABSTRACT FROM AUTHOR]

BORON is a lightweight substitution–permutation network cipher proposed in 2017. We reduce the number of guessed key bits by key-bridging technology and first utilize Fast Walsh Transform on BORON to minimize the time complexity. Finally, this paper gives the better key-recovery attack against block cipher BORON than previously proposed by 2 rounds: we realize a 11-round key-recovery attack on BORON-80 and 13-round key-recovery attack on BORON-128. The attacks proposed in this paper are the best attacks against BORON-80/128 to date. [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]

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]

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]

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]

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]

Two-party signing can be used to provide a high level of key protection especially in the blockchain systems where the safety of money relies on the safety of the signing key. With a two-party signing protocol, the signing key is distributed among two devices, thus the funds are safe as long as one device remains uncorrupted. In this paper, we study the two-party signing protocols for all ISO/IEC signature standards. The mechanisms based on elliptic curve discrete logarithm in ISO/IEC can be divided into three types: Schnorr-type, Elliptic Curve Digital Signature Algorithm (ECDSA)-type and SM2-type. There have already been efficient two-party protocols based on Schnorr signature scheme which can be easily extended into all Schnorr-type standards. However, it is particularly hard to construct efficient distributed SM2-type and ECDSA-type protocols due to their nonlinear signing equations. In this paper, we present the first secure and efficient two-party protocol over SM2-type signature standard. We prove its security in the generic group model. We then construct a more efficient two-party ECDSA protocol that is secure in the generic group model and outperforms all previous works. [ABSTRACT FROM AUTHOR]

'Laser Beam Welding (LBW) is a welding technique used to join pieces of metal or thermoplastics with the aid of laser'. The beam offers a concerted heat source, which enabled higher, deeper welds and narrower welding rates. The procedure is commonly exploited in higher volume appliances using mechanization. It is dependent on penetration or keyhole mode welding. This paper intends to design a novel prediction model on LBW using the Optimized Neural Network (NN) framework. The input to the optimized NN is the welding properties like 'Laser power, welding speed, offset, shielding gas, flow/pressure, focal distance and frequency (where power, speed and offset gets varied)' that directly predict the hardness and tensile strength of welds since the NN is already trained with the provided data. In order to make the prediction model more accurate, this paper aims to train the NN using a new improved Trial Integer-based Whale Optimization Algorithm (TI-WOA) via updating the weight. Finally, the betterment of the suggested scheme is validated with respect to error analysis. Accordingly, from the analysis, it is observed that the proposed methods are 50%, 13.33%, 6.67% and 4% better than ANN-BP, RBF, ANN-GA and NN-WOA models, respectively, at 70th learning percentage. [ABSTRACT FROM AUTHOR]

In recent years, deep neural networks have been extensively applied in various fields, and face recognition is one of the most important applications. Artificial intelligence has reached or even surpassed human capabilities in many fields. However, while artificial intelligence application provides convenience to the human lives, it also leads to the risk of privacy leaking. At present, the privacy protection technology for human faces has received extensive attention. Research goals of face privacy protection technology mainly include providing face anonymization and data availability protection. Existing methods usually have insufficient anonymity and they are not easy to control the degree of image distortion, which makes it difficult to achieve the purpose of privacy protection. Moreover, they do not explicitly perform diversity preservation of attributes such as emotions, expressions and ethnicities, so they cannot perform data analysis tasks on non-identity attributes. This paper proposes a diverse privacy face image generation algorithm based on machine learning, called DIVFGEN. This algorithm comprehensively considers image distortion, identity mapping distance loss and emotion classification loss; transforms the privacy protection target into the problem of generating adversarial examples based on the recognition model; and uses an adaptive optimization algorithm to generate anonymity and diversity of privacy images. The experimental results show that on the Cohn-Kanade+ dataset, our algorithm can reduce the probability of facial recognition by the neural network when it accurately classifies sentiment, from 98.6% to 4.8%. [ABSTRACT FROM AUTHOR]

The visible blood vein in sclera has unique characteristics for each person, and it can be captured in visible light conditions. Therefore, the sclera vessels have been used as an important feature to improve the performance of identification recognition systems, for example, mobile payment and phone encryption system. But recent studies show that unsatisfactory sclera segmentation and tedious matching process are the two critical issues to degrade the performance of sclera identification system. In this paper, we propose a robust sclera recognition system with high accuracy and efficiency. First, a novel sclera segmentation method that provides adaptive threshold is proposed. Second, we have designed an improved vessels enhancement method based on the local grey distribution and local texture information, and least square linearization and moment invariants are utilized to extract features. Finally, an efficient matching strategy is put forward based on the detected significant vessels. The experimental results on one database with images captured in colour illumination prove that the proposed sclera recognition method has an obvious improvement regarding efficiency and accuracy over other sclera recognition systems. [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]

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]

A number of text compression algorithms have been proposed in the past decades, which have been very effective and usually operate on conventional character/word-based approaches. A novel data compression perspective of data mining is explored in this research and the paper focuses on novel frequent sequence/pattern mining approach to text compression. This work attempts to make use of longer-range correlations between words in languages for achieving better text compression. We propose a novel and efficient method by making the compression of any word-level text in a universal manner for corpora across domains referred as Universal Huffman Tree-based encoding. The major contribution of this work is in terms of avoidance of code table communication to the decoder. Simulation results over benchmark datasets indicate that Universal Huffman encoding employing frequent sequence mining (achieves [20%, 89%] improvement in compression in reduced time. The paper also contributes a usable interface for Data compression that employs the proposed frequent sequent mining-based data compression algorithm. The interface supports features such as feedback, consistency, usability, navigation, visual appeal, performance and accessibility on par with existing compression softwares. The work results in an intelligent data compression software employing knowledge engineering perspective. [ABSTRACT FROM AUTHOR]

The stream ciphers RCR-64 and RCR-32 designed by Sekar et al. are the most recent additions to the Py-family of stream ciphers, originally designed by Biham et al. The ciphers are among the fastest stream ciphers on software. To the best of our knowledge, the only reported attacks on the ciphers are due to Ding et al. , published in the Journal of Universal Computer Science. In this paper, we review these alleged attacks on the RCR ciphers and show that they are based on non-existent keystream biases stemming from flawed probability calculations. [ABSTRACT FROM AUTHOR]