Reviewerly/exHarmony huggingface dataset:
Possibilities and barriers for practical application of internationally recognized diplomas of remote education

Purpose The purpose of this paper is to determine the possibilities and barriers of the practical application of internationally recognized diplomas of higher education. Design/methodology/approach The authors used SWOT analysis for determining preconditions and barriers as well as possibilities and threats that are related to the provision and practical application of internationally recognized diplomas of higher education and the method of modeling of socio-economic processes and systems for compiling a conceptual model of the provision and practical application of internationally recognized diplomas of higher education. Demand for the practical application of internationally recognized diplomas in modern Russia is determined with the method of analysis of statistical data (method of economic statistics). The information and analytical basis of the research consists of materials of the Federal State Statistics Service of the Russian Federation. Findings It is shown by the example of modern Russia that the practical application of internationally recognized diplomas is popular, and universities that provide remote education possess wide possibilities in the sphere of their provision, as they are characterized by high flexibility that allows them to adapt to the international standards of university education. The barriers of the practical application of internationally recognized diplomas of higher education are caused by the lack of clear and generally recognized international standards of university education, absence of entrepreneurial culture of application of internationally recognized diplomas and absence of international organization that certifies activities of universities and issues licenses for internationally recognized diplomas. Originality/value In order to overcome these barriers, the authors created a conceptual model of the provision and practical application of internationally recognized diplomas of higher education.
A Note on Minimum Hamming Weights of Correlation-Immune Boolean Functions

It is known that correlation-immune (CI) Boolean functions used in the framework of side channel attacks need to have low Hamming weights. In this letter, we determine all unknown values of the minimum Hamming weights of d-CI Boolean functions in n variables, for d ≤ 5 and n ≤ 13.
Data Science in the Business Environment: Skills Analytics for Curriculum Development

Data science is an interdisciplinary field of methods, processes, algorithms and systems to extract knowledge or insights from data. University of Winchester Business School, UK is developing an undergraduate degree programme in Data Science which brings together student-centred and business-driven approaches: positioning the course for the interests of students and requirements of employers. The new programme follows the expectations of relevant subject benchmark statements and is built on activities which focus on different aspects of data science, drawing on some existing modules as a base. It integrates key themes in information management, data mining, machine learning and business intelligence. This paper presents the ongoing development of the Data Science programme through the key aspects in its conception and design. Understanding the employment market while defining specific skills sets associated with potential graduates is always important for courses in higher education. The Skills Framework for the Information Age (SFIA) has been adopted and a novel mapping proposed for the interpretation of employability skills related to data science. These are then linked to an adapted process model as well as the specialist modules across academic levels.
A Remark on the Economics of Quantifier Raising

Quantifier Raising is often considered to be a relatively local operation, though it has been argued that nonlocal applications are licensed by generating new scope interpretations ( Fox 2000 , Reinhart 2006 ) or resolving antecedent-contained deletions ( Fox 2002 , Wilder 2003 , Cecchetto 2004 ). In this article, an account of the restricted distribution of sloppy pronouns in antecedent-contained deletions leads to the conclusion that exceptional applications of Quantifier Raising are in fact not licensed purely by virtue of QR’s escaping the antecedent for ellipsis.
Person Reidentification by Deep Structured Prediction—A Fully Parameterized Approach

Existing efforts on person reidentification (re-ID) either ignore the structural interactions among person images or require a highly crafted re-ID structure as a priori information. In contrast, our approach formulates person re-ID as a deep structured prediction problem that outperforms the state-of-the-art methods by utilizing neural-style-transfer-based structure sampling and fully parameterized energy networks.
A Memetic Algorithm-Based Design Space Exploration for Datapath Resource Allocation During High-Level Synthesis

System designers have started adopting high-level synthesis (HLS) for architectural design because of the higher levels of abstraction offered. The HLS tools provide multiple design choices with tradeoff among different design parameters. Design Space Exploration (DSE) involves optimizing the synthesis options to achieve best tradeoffs among the metrics of interest. With the aim of exploring the design space in a feasible amount of time, we present a novel automated DSE approach. In particular, meeting the constraints presented by different parameters of interest is modeled as a multi-objective problem and solved using Memetic algorithm. The effectiveness of different variations of the Memetic algorithm in solving the DSE problem is studied and a Firefly algorithm-based solution is proposed with a novel probabilistic local search mechanism. The proposed approach is compared with existing solutions and the results prove that the proposed approach outperforms both existing solutions and other variations of Memetic algorithms in terms of convergence time and quality of results. In addition to that, a case study has been included to demonstrate the applicability of the approach. Results show that the proposed approach achieves a 33% improvement in cost, [Formula: see text] improvement in speed and [Formula: see text] improvement in hypervolume.
Text Line and Word Extraction of Arabic Handwritten Documents

The documents of Arabic handwritten contain text lines and words. Words are often a succession of sub-words (characters, connected components) separated by spaces, in Arabic handwritten its spaces are divided into two types: the first type represents the spaces that separate two connected components of the same word (within-word), the second type are spaces that separate two connected components from two consecutive words(between-words). We detect the second type for word extracting. Word extraction based on the classification of spaces detected and extracts between-words spaces to segment the text into words. In this paper, we present a method for segmenting Arabic handwritten text into lines and words, to make our method of word extraction more optimal, we compute the threshold of spaces for each line, the threshold is not fixed in the document, each line is associated its classification threshold spaces. Before segmenting the text into words, it is necessary to segment it into text lines in order to apply our method to each line. To extract the lines, the preprocessing is applied to the text images in order to apply the proposed method for the line segmentation step. Our system is applied on the benchmarking datasets of the Arabic handwriting database for text recognition (AHDB) and the experimental results are very promising as we achieved a success word extraction rate of 87.9%.
Adaptive Elman Model of Gene Regulation Network Based on Time Series Data

Background: Time series expression data of genes contain relations among different genes, which are difficult to model precisely. Slime-forming bacteria is one of the three major harmful bacteria types in industrial circulating cooling water systems. Objective: This study aimed at constructing gene regulation network(GRN) for slime-forming bacteria to understand the microbial fouling mechanism. Methods: For this purpose, an Adaptive Elman Neural Network (AENN) to reveal the relationships among genes using gene expression time series is proposed. The parameters of Elman neural network were optimized adaptively by a Genetic Algorithm (GA). And a Pearson correlation analysis is applied to discover the relationships among genes. In addition, the gene expression data of slime-forming bacteria by transcriptome gene sequencing was presented. Results: To evaluate our proposed method, we compared several alternative data-driven approaches, including a Neural Fuzzy Recurrent Network (NFRN), a basic Elman Neural Network (ENN), and an ensemble network. The experimental results of simulated and real datasets demonstrate that the proposed approach has a promising performance for modeling Gene Regulation Networks (GRNs). We also applied the proposed method for the GRN construction of slime-forming bacteria and at last a GRN for 6 genes was constructed. Conclusion: The proposed GRN construction method can effectively extract the regulations among genes. This is also the first report to construct the GRN for slime-forming bacteria.
Efficient and scalable outsourced data access control with user revocation in cloud: A comprehensive study

Data outsourcing into the third party cloud server has been radically increasing with the expanding popularity of cloud computing. Recently, researchers are more attentive on privacy preserved secure outsourced data access control due to data sensibility, gradually growth of data in size, dynamic n ature of data user, accessing the data in lightweight device etc. Dynamic and efficient revocable data access control using attribute based encryption (ABE) is most prominent technology for such situation. In this article, we are concentrating to summarize several existing approaches to identify the current scenario of outsourced data access control in cloud with respect to security, privacy and efficient revocation. We are also concern about significant analysis of numerous advanced technologies to achieve dynamic and efficient revocable outsourced data access control for further research with respect to reliability, scalability and efficiency. We have presented here a comprehensive study and analysis of several functionalities of ABE, revocation and privacy preservation.
Kernel-based template attacks of cryptographic circuits using static power

Side-channel attacks using static power have been shown to be successful against cryptographic circuits in different environments. This class of attacks exploits the power leakage when the circuit is in a static state, during which the power leakage is expected to be a fixed value. Due to the low signal-to-noise ratio of static power, usually more traces are needed for a static power attack to reach the same success rate as a dynamic power attack. The probabilistic distribution pattern of static power varies significantly in different devices, which further poses challenges to the accurate modeling of static power. In this paper we propose non-parametric template attacks which use a kernel methodology to improve the accuracy of modeling static power consumption. The proposed template attacks are tested using transistor-level simulations of circuits designed with a 45-nm standard cell library. Our test results show that our approach improves the success rate of template attacks using static power in cases where the distribution of static power consumption cannot be accurately modeled by Gaussian models.
Comparative Analysis of Symbolic Reasoning Models for Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) can be defined as recurrent neural networks that allow modeling complex systems using concepts and causal relations. While this Soft Computing technique has proven to be a valuable knowledge-based tool for building Decision Support Systems, further improvements related to its transparency are still required. In this paper, we focus on designing an FCM-based model where both the causal weights and concepts’ activation values are described by words like low, medium or high. Hybridizing FCMs and the Computing with Words paradigm leads to cognitive models closer to human reasoning, making it more comprehensible for decision makers. The simulations using a well-known case study related to simulation scenarios illustrate the soundness and potential application of the proposed model.
Experimental Investigations and Future Possibilities in Network-Mediated Folk Music Performance

This chapter is intended to acquaint music ethnologists with the paradigm of Networked Music Performance (NMP). NMP facilitates computer networks to allow musicians from distant geographic locations to synchronously collaborate during performance, improvisation or more generally music-making. The chapter comprises two parts. The first part is devoted to providing an overview of research approaches in NMP and elaborates on the technical and perceptual impediments restricting the wide availability of this type of technology. The second part presents an experiment involving three musicians performing folk music over the network. The experiment serves to reveal not only technical and perceptual difficulties in the communication of performers, but more importantly their attitude towards engaging in this novel practice. The chapter concludes by discussing future perspectives on the use of NMP technology in the context of ethnic and folk music.
Inferring Human Knowledgeability from Eye Gaze in Mobile Learning Environments

What people look at during a visual task reflects an interplay between ocular motor functions and cognitive processes. In this paper, we study the links between eye gaze and cognitive states to investigate whether eye gaze reveal information about an individual’s knowledgeability. We focus on a mobile learning scenario where a user and a virtual agent play a quiz game using a hand-held mobile device. To the best of our knowledge, this is the first attempt to predict user’s knowledgeability from eye gaze using a noninvasive eye tracking method on mobile devices: we perform gaze estimation using front-facing camera of mobile devices in contrast to using specialised eye tracking devices. First, we define a set of eye movement features that are discriminative for inferring user’s knowledgeability. Next, we train a model to predict users’ knowledgeability in the course of responding to a question. We obtain a classification performance of 59.1% achieving human performance, using eye movement features only, which has implications for (1) adapting behaviours of the virtual agent to user’s needs (e.g., virtual agent can give hints); (2) personalising quiz questions to the user’s perceived knowledgeability.
Generalising the Dining Philosophers Problem: Competitive Dynamic Resource Allocation in Multi-agent Systems

We consider a new generalisation of the Dining Philosophers problem with a set of agents and a set of resource units which can be accessed by them according to a fixed graph of accessibility between agents and resources. Each agent needs to accumulate a certain (fixed for the agent) number of accessible resource units to accomplish its task, and once it is accomplished the agent releases all resources and starts accumulating them again. All this happens in succession of discrete ‘rounds’ and yields a concurrent game model of ‘dynamic resource allocation’. We use the Alternating time Temporal Logic (ATL) to specify important properties, such as goal achievability, fairness, deadlock, starvation, etc. These can be formally verified using the efficient model checking algorithm for ATL. However, the sizes of the resulting explicit concurrent game models are generally exponential both in the number of resources and the number of agents, which makes the ATL model checking procedure generally intractable on such models, especially when the number of resources is large. That is why we also develop an abstract representation of the dynamic resource allocation models and develop a symbolic version of the model checking procedure for ATL. That symbolic procedure reduces the time complexity of model checking to polynomial in the number of resources, though it can take a worst-case double exponential time in the number of agents.
Nonlinear system predictive control using a multi-dimensional Taylor network

A predictive control scheme on the basis of multi-dimensional Taylor network (MTN), named as MTN predictive compensation control, is proposed for single-input single-output nonlinear systems in this paper. We consider the MTN model as a one-step-ahead predictive model and train it by back-propagation (BP) algorithm with a momentum term, and then control the system by the predictive control law. Furthermore, to improve the anti-disturbance performance of the system, another MTN model is considered as the compensator and trained by a recursive least-squares algorithm to counteract the disturbance. An example is used to verify the effectiveness of the proposed scheme, in which the noise disturbance is considered. For comparison, the scheme that combines a radial basis function (RBF) neural network with a proportional-integral-derivative (PID) controller (RBF-PID), the predictive control scheme that combines a predictive neural network with a control neural network based on a BP neural network, and MTN predictive control are introduced. In addition, the difference in computational complexity between the MTN predictive compensation control scheme and the RBF-PID scheme is also considered. The experimental results show that the proposed scheme is effective, has good anti-disturbance performance in predictive control for the nonlinear system and is superior to the other comparison schemes.
On Convergence of Controlled Snowball Sampling for Scientific Abstracts Collection

This paper presents evidences concerned to convergence of controlled snowball sampling iterations applied to collecting seminal papers in a selected domain of research. Iterations start from the seed paper selection, plain snowball sampling and probabilistic topic modelling, then greedy controlled snowball sampling and analysis of the collected citation network are performed in rotation until the list of seminal papers becomes stable. The topic model is built on the base of word-word co-occurrence probability with combination of sparse symmetric nonnegative matrix factorization and principal component approximation. Experiments show that the number of topics in the model is determined in natural way and the Kullback-Leibler (KL) divergence provides the upper bound of the cosine similarity calculated from keywords assigned by publication authors. Several citation networks are collected and analysed. The analysis shows that all networks are “small worlds” and therefore the observed saturation of the controlled snowball sampling can provide the complete set of publications in domains of interest. Experiments with KL-divergence, symmetric KL-divergence and Jensen-Shannon divergence show that KL-divergence produces less connected citation network but provides better convergence of snowball iterations. Multiple runs of the sampling confirm the hypothesis that the set of seminal publications is stable with respect to variations of the seed papers. The modified main path analysis allows to distinguish the seminal papers including new publications following main stream of research. The comparison of different ranking criterion is made. It shows that Search Path Count provides better lists of seminal papers than citation index, PageRank and indegree.
The benefit of using chaos in key schedule algorithm

Many ciphers suffer from weak-key issues. This problem is due to the simplicity of the key schedule algorithm. Unfortunately, the cipher designers have not paid much attention to the sub-keys generation as the cipher itself. Thus, any attacker can find many attractive properties between sub-keys such as statistical pattern, linearity, low avalanche criteria, etc. Many ciphers use a large key during encryption (e.g. 128 bits). This makes it practically impossible to test all keys, allowing all of today’s ciphers to be potentially vulnerable to the weak-key issue, including AES. Accordingly, this paper sheds light on the advantage of using chaos in sub-keys generation by developing a new key schedule algorithm based on the logistic maps. We name this algorithm CKSA. Compared to several existing key schedule algorithm, CKSA provides a good avalanche effect. It is a one-way function, and its size is flexible, i.e. the size of the sub-keys is not fixed, which allows to many ciphers the possibility of using it. Further, CKSA ensures confusion and diffusion. It resists differential attacks and avoids linear key correlation between sub-keys while ensuring a high degree of randomization among them.
Sodium Image Denoising Based on a Convolutional Denoising Autoencoder

Sodium Magnetic Resonance Imaging (sodium MRI) is an imaging modality that has gained momentum over the past decade, because of its potential ability to become a biomarker for several diseases, ranging from cancer to neurodegenerative pathologies, along with monitoring of tissues metabolism. One of the most important limitation to the exploitation of this imaging modality is its characteristic low resolution and signal-to-noise-ratio as compared to the classical proton MRI, which is due to the notably lower concentration of sodium than water in the human body. Therefore, denoising is a central aspect with respect to the clinical use of sodium MRI. In this work, we introduce a Convolutional Denoising Autoencoder that is trained on a training database of thirteen training subjects with three sodium MRI images each. The results illustrate that the denoised images show a strong improvement after application in comparison to the state-of-the-art Non Local Means denoising algorithm. This effect is demonstrated based on different noise metrics and a qualitative evaluation.
Rice Production Monitoring System in the Philippines

Objectives: To develop an automated system that will monitor the rice production of the different Barangay in Borongan City and create a graphical statistics of rice production every season. Methods/Statistical Analysis: A software development methodology called Rapid Application Development (RAD) was utilized to develop an automated system. Software evaluation test were conducted using ISO 9126 software quality standard. Findings: The system were tested by experts and as a result, the software is in compliant with the ISO software quality standard with an average weighted mean of 4.60 with an adjectival rating as strongly acceptable. Application/Improvements: It is recommended that the developed system will be implemented in the Agriculture office in Borongan City, Philippines. Keywords: Monitoring System, Philippines Rice Production, Rice Monitoring, Rice Production, Rice Production Monitoring System
Research on Digital Watermarking Algorithm for Anti-geometric Attack

For the problem that the traditional digital watermarking algorithm is less robustness against geometric attacks, this paper introduces the related content of digital watermarking technology, which combs digital watermarking technology, digital watermarking attack technology and digital watermarking evaluation method, and summarizes the improved algorithms proposed in recent years. Next, the traditional wavelet transform algorithm and the improved algorithm based on DCT transform are selected for comparison experiments. The latter combines Arnold scrambling and SVD decomposition, which has better shear resistance. Finally, combined with the research status, the future research focus of digital watermarking algorithm is prospected.