Reviewerly/exHarmony huggingface dataset:
The Eigenvalue Complexity of Sequences in the Real Domain

The eigenvalue is one of the important cryptographic complexity measures for sequences. However, the eigenvalue can only evaluate sequences with finite symbols—it is not applicable for real number sequences. Recently, chaos-based cryptography has received widespread attention for its perfect dynamical characteristics. However, dynamical complexity does not completely equate to cryptographic complexity. The security of the chaos-based cryptographic algorithm is not fully guaranteed unless it can be proven or measured by cryptographic standards. Therefore, in this paper, we extended the eigenvalue complexity measure from the finite field to the real number field to make it applicable for the complexity measurement of real number sequences. The probability distribution, expectation, and variance of the eigenvalue of real number sequences are discussed both theoretically and experimentally. With the extension of eigenvalue, we can evaluate the cryptographic complexity of real number sequences, which have a great advantage for cryptographic usage, especially for chaos-based cryptography.
Side Channel Attack on a Quantum Random Number Generator

The paper presents the results of applying an advanced attack on an external channel to an extractor of a quantum random number generator (QRNG). The attack is based on the verification of mathematical models of the interception channel and the EM signal of the extractor. Numerical simulation of the electromagnetic (EM) signal and the master key interception channel was performed based on the implementation of the DEMA algorithm of the attack on the AES-128 cipher. Measurements of the parameters of the EM signal and the interception channel were carried out on an experimental setup using a modern reference base in the field of electromagnetic compatibility.
Online Medical Data Stream Mining Based on Adaptive Neuro-Fuzzy Approaches

Introduction.Data mining approaches in medical diagnostics tasks have a number of special properties that do not allow the use of such approaches in a classical form.That's why adaptive neuro-fuzzy systems for online medical data stream processing tasks and its learning algorithms have been developed.Proposed systems can process medical data streams in three modes: supervised learning, unsupervised learning and active learning.The purpose of the paper is to develop approach, based on adaptive neuro-fuzzy systems to solve the tasks of medical data stream mining in online-mode.Methods.The methods of computational intelligence are used for medical data stream processing and, first of all, artificial neural networks, neuro-fuzzy systems, neo-fuzzy systems, their supervised, unsupervised and active learning approaches, gradient methods of optimization, methods of evolving system.Results.As a result, approbation of the developed approach in supervised learning mode using multidimensional neo-fuzzy neuron on medical data of patients with urological disease was investigated.Percentage of errors in system testingusing all feature space is 11.11 %, using the most informative features the error rate becomes 6.4 %.Also multidimensional neo-fuzzy neuron was used for diagnostic of the pharmacoresistant form of epilepsy, percentage of errors in system testing is 5.82 %.Approval of the developed approach in the mode of active training and association on the data of patients with pulmonary diseases was performed.For all approbation results performance criterion was calculated, its values are suitable for the tasks of medical diagnostics in data stream mode.Conclusions.The proposed neuro-fuzzy approaches allow obtaining additional information about patients diagnosis in conditions of limited a priori information about patient.
Interference Characterization in Wireless Networks: A Determinantal Learning Approach

Owing to its tractability, stochastic geometry (SG) has recently gained immense popularity for the modeling and analysis of wireless networks. However, given their simplicity, the state-of-the-art SG-based models are not well-equipped to capture complex interactions that exist in actual wireless networks. On the other hand, this problem of modeling interactions in actual data patterns has been explored in machine learning (ML) community from different perspectives. In particular, the idea of determinantal point process (DPP) from SG is used extensively in ML to solve a general class of subset selection problems with diversity. Since subset selection also arises naturally in wireless networks, the DPP learning (DPPL) framework can be leveraged to develop a new class of learning-enabled analytical models using SG. In this paper, we explore this idea through a case study. In particular, we use the DPPL framework for characterizing the interference distribution in a wireless local area network (WLAN) with carrier sense multiple access (CSMA) where a subset of the transmitters (Tx-s) are active in a given time instant. In particular, we model the locations of the active Tx-s as a DPP and learn its distribution from a sequence of network realizations. Numerical results demonstrate that our analytical characterization of interference based on DPP learning (DPPL) is remarkably accurate.
Fostering Design Patterns Education

Design patterns aim at easing the reuse of design solutions, saving time in solving recurring problems and supporting code maintainability. However, teaching design patterns is challenging. One of the reasons lie in the students' difficulties, not only in understanding this concept in a high level of abstraction, but also in realizing how to effectively use design patterns. The use of games in software engineering education has been successfully enabling students to understand some contents more easily, besides acting as a motivational factor. However, the few existing approaches for teaching design patterns using game-oriented methods focus mostly in the pattern implementation, ignoring the fact that students need to first recognize the scenarios in which patterns can be used. In this sense, this paper proposes and evaluates an educational material that illustrates the use of design patterns using game scenarios from the Angry Birds franchise. An evaluation with students indicates an overall positive feedback, also pointing out some difficulties and barriers in abstraction levels and knowledge transferring between domains. There were also some suggestions for improvements.
Laplacian using Abstract State Transition Graphs: A Framework for Skill Acquisition

Automatic definition of macro-actions for Reinforcement Learning (RL) is a way of breaking a large problem into smaller sub-problems. Macro-actions are known to boost the agent's learning process, leading to a better performance. One recent approach, called Laplacian Framework, uses the Proto-Value Functions of the State Transition Graph (STG) associated with a RL problem in order to create options. For larger problems, however, the STG is unavailable. In this context, we propose an improvement upon the Laplacian Framework for large problems, called Laplacian using Abstract State Transition Graphs (LAST-G), which uses an Abstract State Transition Graph (ASTG), a reduced version of the original STG. This approach allows the creation of intra-policies for the discovered options by using the ASTG as a model of the environment. Our experimental results show that the proposed framework is capable of: (i) effectively creating purposeful options; and (ii) successfully executing the identified options.
Graph Transliterator: A graph-based transliteration tool

information sciences, as well as other fields.It enables text to be read by those who do not know the original alphabet, and also makes languages or
Text Sentiment Analysis Based on Parallel Recursive Constituency Tree-LSTM

With the rapid development of mobile Internet, online platforms such as social entertainment and e-commerce have brought a large number of unstructured texts containing sentiment polarity. Sentiment analysis of these texts can be used for public opinion analysis, product review and other tasks. Recent researches show great interest and success in using deep learning methods for sentiment analysis tasks. Among these contributions, Constituency Tree-LSTM has achieved good results because of its superior ability and tree structure to preserve sequence information over time. However, the model calculates the sentiment label of the node based mainly on its vector representation, while missing the sentiment information of its children. This paper proposes a parallel recursive Constituency Tree-LSTM (PRCT-LSTM), which combines recursive neural network with Constituency Tree-LSTM to achieve better performance. The node sentiment information propagates to the global evaluation through recursive neural networks. Therefore, the sentiment information of each node in the PRCT-LSTM structure consists of sentiment information generated by Constituency Tree-LSTM and the recursive model. In this way, we take more full usage of sentiment information. Experimental results manifest that the proposed model achieves a higher performance than the Constituency Tree-LSTM.
The combined method of textual information analysis for the content of destructive indicators

Abstract Modern society is characterized by increased progress in science and technology, accompanied by rapid uncontrolled growth and a variety of textual information, including of destructive orientation, in the information space of the Internet. In this regard, as well as in the context of the potential creation of a “closed” Internet space in the Russian Federation, solving the problem of analyzing text content by searching and automatically identifying destructive information is a priority trend. Within the framework of the paper task of identifying destructive information is reduced to the task of classifying the analyzed textual data based on the presence or absence of destructive indicators. The article describes the universal three-stage combined method, including blocks for normalizing input data, the modified dictionary search of destructive indicators and the Bayesian classifier, which allows staged search of various indicators in the text and automatically classifies it as a “dangerous”.
PWD: A Petrological Workspace and Database tool

Abstract The Petrology Workspace and Database (PWD; https://petro.wovodat.org ) is a web‐based data repository and interface that allows researchers to access, share, store, and manage petrological, mineralogical, and whole rock data in a contextualized manner. The uniqueness of the PWD is that it links images to different types of images and to compositional data providing a powerful visualization and framework of information at a wide range of scales, from the meter‐sized outcrop to a few micrometers of a thin rock section. The PWD archives various data types and formats, and it includes multilevel data sets with an interactive online interface. The database is linked with other databases for volcanic eruptions (Smithsonian' Global Volcanism Program (GVP)) and for whole rock chemistry (EarthChem). The tool has four main features: (1) storage and management of spatial‐referenced data (e.g., from fieldwork notes to lab geochemical analysis); (2) a hierarchical relationship between different types of data using the Workspace interactive tool; (3) graph plots to visualize the data; and (4) the possibility of data sharing in a database structure that is managed by authorization levels. The PWD is a practical and efficient database management system that facilitates effective contextualized data preservation and sharing among scientists. It also can serve as a Data Management Plan and provide a framework for auditing research integrity both of which are becoming the new standards of most funding agencies and journals.©2019. The Authors.
Attention-Based Deep Q-Network in Complex Systems

In recent years, Deep Reinforcement Learning (DRL) has achieved great successes in many large scale applications, e.g., the Deep Q-Network (DQN) surpasses the level of professional human players in most of the challenging Atari 2600 games. As DQN transforms the whole input frames into some feature vectors by using convolutional neural networks (CNNs) at each decision step, all objects in the system are treated equally in the process of the feature extraction. However, in reality, for complex systems where many objects exist, the optimal action taken by the agent may only be affected by some important objects, which may lead to inefficiency or poor performance of DQN. In order to alleviate this problem, in this paper, we introduce two approaches that integrate global and local attention mechanisms respectively into the DQN model. For the approach with global attention, the agent is able to focus on all objects to varying degrees; for the approach with local attention, the agent is allowed to focus only on a few objects of great importance with the result that a better strategy can be learned by the agent. The performance of our proposed approaches are demonstrated on some benchmark domains. Source code is available at https://github.com/DMU-XMU/Attention-based-DQN .
Dynamic texture analysis using networks generated by deterministic partially self-avoiding walks

Dynamic textures are sequences of images (video) with the concept of texture patterns extended to the spatiotemporal domain. This research field has attracted attention due to the range of applications in different areas of science and the emergence of a large number of multimedia datasets. Unlike the static textures, the methods for dynamic texture analysis also need to deal with the time domain, which increases the challenge for representation. Thus, it is important to obtain features that properly describe the appearance and motion properties of the dynamic texture. In this paper, we propose a new method for dynamic texture analysis based on Deterministic Partially Self-avoiding Walks (DPSWs) and network science theory. Here, each pixel of the video is considered a vertex of the network and the edges are given by the movements of the deterministic walk between the pixels. The feature vector is obtained by calculating network measures from the networks generated by the DPSWs. The modeled networks incorporate important characteristics of the DPSWs transitivity and their spatial arrangement in the video. In this paper, two different strategies are tested to apply the DPSWs in the video and capture appearance and motion characteristics. One strategy applies the DPSWs in three orthogonal planes and the other is based on spatial and temporal descriptors. We validate our proposed method in Dyntex++ and UCLA (and their variants) databases, which are two well-known dynamic texture databases. The results have demonstrated the effectiveness of the proposed approach using a small feature vector for both strategies. Also, the proposed method improved the performance when compared to the previous DPSW-based method and the network-based method.
Pruning Convolutional Neural Network with Distinctiveness Approach

Convolutional Neural Network (CNN) is one of the widely used deep learning frameworks in image classification, target detection and object recognition domains. Because of the complexity of the network structures, CNNs usually contains a large number of layers and channels, which makes algorithm time-consuming. Using existing CNN architectures to solve specific problems usually leads to many redundant parameters. This paper introduces an approach based on the distinctiveness rules to prune both fully connected layers and filters of the baseline network. We experiment with different pruning means to prune multiple layer neurons and filters by the distinctiveness rules and evaluate the performance of the approach. We also discuss the influence of threshold on the selection of redundant neurons in pattern space and trade-off network scale and accuracy. The result shows using the approach will not change the structure of the baseline network and 32 filters and 161 neurons are removed. The accuracy of the pruned network reaches 80.5% compared with 87% of the original model.
Mental Health App Reviews Analyzer (MHARA) Using Logistic Regression and Tri-Gram

This study is related to assist for finding the mental health Apps on Google Play Store using text reviews and rating. The apps are related to Anxiety, Bipolar disorder, Epilepsy, Migraine and Mental retarded. A prediction system has been proposed using Tri-Gram features and Logistic Regression. The system is trained using reviews as features and rating as labels/classes. The model has been evaluated using accuracy, precision, recall and f1-score. It is also compared with other state-of-the-art classifiers, but the proposed approach provides the good accuracy. The accuracy, precision, recall and f1-score of the proposed model respectively are 0.69, 0.67, 0.69 and 0.66. The results exhibited that the Logistic Regression out-performs as compare to other state-of-the-art algorithms.
Two Quantum Attack Algorithms Against NTRU When the Private Key and Plaintext Are Codified in Ternary Polynomials

Our cryptanalysis is focused on the NTRU second round candidate submitted to National Institute of Standards and Technology (NIST) competition. The NTRU domain is the ring $$\mathbf {R_q}=\mathbb {Z}_q[X]/\mathbf (X^n -1)$$ with the private keys and the plaintext are codified in ternary polynomials, that means all their coefficients are in {−1, 0, 1}. Our two quantum attack algorithms namely KA_NTRU and PA_NTRU, inspired from Grover’s Algorithm, targeted respectively to find Private Keys and Plaintext. To test the proposed algorithms, we create a test release named NTRU_Attacks that integrate the principal cryptographic functions and the two attacks functions. In the general case, the quantum algorithms can break a system of dimension n in $$2^{n/2}$$ times.
DOES THE BUSINESS CLIMATE AFFECT PRIVATE DOMESTIC AND FOREIGN INVESTMENT? EMPIRICAL EVIDENCE FROM THE MENA REGION

The debate on the impact of business climate on private investment is still ongoing today. This paper contributes to the existing literature by examining the impact of a wide range of dimensions of the business climate on domestic investment and foreign direct investment in a sample of Middle East and North African economies between 2000 and 2015. Findings of the paper add new evidence and shed interesting insights into the debate. While almost all areas matter for domestic and foreign investors, the common and most important dimensions for both of them are regulations, macroeconomic management and infrastructure. Moreover, the control of corruption and labor market regulation are found to exert opposite effects on domestic investment and foreign direct investment. We conclude that setting up a good business climate is an overall process that should touch simultaneously and gradually all dimensions.
Reducing the Effects of Time Cheating on the Performance of Divisible Load Scheduling Using Analytical Hierarchy Process

The divisible load theory (DLT) is a paradigm in the area of distributed and parallel computing. As a matter of fact, the computations and communications can be divided into some independent part in which each part can be executed separately by a processor. The problem that the processors may cheat the algorithm has examined the divisible load theory. However, the computation rate cheating issue may appear if the processors accomplish their fraction of loads with various rates. According to the literature, if the processors do not report their true computation rates, they can not obtain optimal performance. This paper focuses on this problem. This paper proposes an AHP-based divisible load scheduling method aiming to decrease the impacts of cheating on the efficiency of divisible load scheduling. The experimental results indicate the proposed method considerably reduce the impacts of cheating on the startup time, speedup, and makespan specially when a huge number of processors cheat the algorithm.
On Blockchain-Based Dynamic Resource Allocation for Concurrent Industrial Wireless Premises Networks

The Blockchain technology has become one of the most groundbreaking discoveries of the last decade, encompassing a secure and distributed exchange of digital resources. By now, a large variety of applications and topics have been identified which have proven its feasibility. In this paper we discuss the deployment of Blockchains to manage concurrent industrial wireless premises networks. We propose a simple example of a Smart Contract protocol for distributed resource allocation, forcing the participants to reach an agreement on the wireless channel usage. A simple simulation model is set up highlighting Blockchain-based consensus findings as a promising approach for autonomous resource allocation within this application.
Text2SQLNet: Syntax Type-Aware Tree Networks for Text-to-SQL

Building natural language interfaces to relational databases is an important and challenging problem in natural language processing (NLP), it requires a system that is able to understand natural language questions and generate corresponding SQL queries. In this paper, we present our idea of using type information and database content to better understand rare entities and numbers in natural language questions, in order to improve the model SyntaxSQLNet as the state of the art in Text-to-SQL task. We also present the global architecture and techniques that can be used in the implementation of our Neural Network (NN) model Text2SQLNet, with the integration of our idea that consists of using type information to better understand rare entities and numbers in natural language questions. We can also use the database content to better understand the user query if it is not well-formed. The implementation of this idea can further improve performance in the Text-to-SQL task.
The Extended Generalized Neo-Fuzzy Network and Its Online Learning in Image Recognition Problem

Real-time pattern recognition is required when solving many important practical problems. This paper proposes an extended generalized neo-fuzzy network that provides high recognition accuracy. The network learning algorithm based on the use of the cross-entropy criterion makes it possible to achieve the required speed important for real-time applications. The effectiveness of the network is confirmed for the human emotions recognition.
Vision-Based Assistive Systems for Deaf and Hearing Impaired People

Sign language is the way of communication among deaf and hearing impaired community and consist of a combination of hand movements and facial expressions. Successful efforts in computer vision-based research within the last years paved the path for first automatic sign language recognition systems. However, unresolved challenges, such as cultural differences in the sign languages of the world, lack of the representative databases for model training, relatively small size of the region-of-interest, issues due to occlusion, etc. keep automatic sign language recognition reliability still far from human-level performance, especially for the Russian sign language. To address this issue, we present a framework and an automatic system for one-handed gestures of Russian Sign Language (RSL) recognition. The developed system supports both online and offline modes and is able to recognize 44 classes of RSL one-handed gestures with almost 70% of accuracy. The system is based on color-depth Kinect v2 sensor and trained on TheRuSLan database using a combination of state-of-the-art deep learning approaches. The future research will focus on extracting additional features, expanding the data set, and increasing the amount of recognizable gestures with two-handed gestures. The developed vision-based RSL recognition system is meant as an auxiliary system for deaf and hearing impaired people.