Reviewerly/exHarmony huggingface dataset:
The UU-test for statistical modeling of unimodal data

Deciding on the unimodality of a dataset is an important problem in data analysis and statistical modeling. It allows to obtain knowledge about the structure of the dataset, i.e. whether data points have been generated by a probability distribution with a single or more than one peaks. Such knowledge is very useful for several data analysis problems, such as for deciding on the number of clusters and determining unimodal projections. We propose a technique called UU-test (Unimodal Uniform test) to decide on the unimodality of a one-dimensional dataset. The method operates on the empirical cumulative density function (ecdf) of the dataset. It attempts to build a piecewise linear approximation of the ecdf that is unimodal and models the data sufficiently in the sense that the data corresponding to each linear segment follows the uniform distribution. A unique feature of this approach is that in the case of unimodality, it also provides a statistical model of the data in the form of a Uniform Mixture Model. We present experimental results in order to assess the ability of the method to decide on unimodality and perform comparisons with the well-known dip-test approach. In addition, in the case of unimodal datasets we evaluate the Uniform Mixture Models provided by the proposed method using the test set log-likelihood and the two-sample Kolmogorov-Smirnov (KS) test.
Smooth Trajectory Generation for Predefined Path With Pseudo Spectral Method

In view of smooth trajectory generation for a 3-axis machine tool, many methods have been presented. Among them, the optimal control based method is increasingly concerned, because it is considered to be able to make full use of kinematic abilities of machine tools. Under the unified framework of optimal control, the feedrate can be adjusted flexibly by adding or removing axial constraints and tangential constraints. But the problem of smooth trajectory generation (PSTG) based on optimal control for a machine tool is not easily to be solved. In this article, to efficiently solve the PSTG, it is divided into two sub-problems: the problem of minimum time trajectory planning (PMTTP) and the pseudo problem of smooth trajectory generation (PPSTG). Since both sub-problems are convex, the existence of unique solutions can be guaranteed. Then, the PMTTP and the PPSTG are transformed into nonlinear programming (NLP) problems with radau-pseudo-spectral (RPM) method successively. Due to convexity, the two NLP problems can be efficiently solved with mature optimization methods. In addition, the RPM method allows two sub-problems to have different Legendre-Gauss-Radau (LGR) points, thereby further saving computational costs. Finally, three different predefined paths are employed to test the proposed method, and simulation results show the effectiveness of proposed method.
A New Clustering Algorithm for Live Road Surveillance on Highways based on DBSCAN and Fuzzy Logic

Video streaming over Vehicular Ad Hoc Networks is a promising technique (VANETs), and it has gained great importance in the last few years.The highly dynamic topology of VANETs makes high-quality video streaming very challenging.In order to provide the most useful camera views to the vehicles, clustering and cluster head selection techniques are used.Too frequent camera view changes can be annoying; therefore, we propose a new stable clustering algorithm to ensure a stable live road surveillance service without interruptions for vehicles that do not have enough vision area.In the proposed solution, we integrated Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with Fuzzy Logic Control (FLC).DBSCAN is used to form the clusters, while FLC is used to find the best cluster head for the cluster.Different parameters are utilized like density parameters for DBSCAN, and relative speed, acceleration, leadership degree and vision area for fuzzy logic.Our proposed algorithm showed better results in terms of cluster lifetime and vehicle status change.Our proposed algorithm has been compared with another clustering scheme to prove the effectiveness of our proposed algorithm.
Cyber Security Defence Policies: A Proposed Guidelines for Organisations Cyber Security Practices

Many organisations have been struggling to defend their cyberspace without a specific direction or guidelines to follow and they have described and identified cyber attack as a devastating potential on business operation in a broader perspective.Since then, researchers in cyber security have come out with numerous reports on threats and attack on organisations.This study is conducted to develop and propose a Cyber Security Defence Policies (CSDP) by harmonising and synthesizing the existing practices identified from the literature review.Observation and questionnaire were adopted to evaluate, review and collect data under ethical agreement from 10 organisations.The validation is based on the principal components for the proposed CSDP and the proposed CSDP, using SPSS as the statistical tool.The result shows that, the validation of the proposed CSDP by 20 experts reveals a standard deviation of 0.607, 0.759, 0.801, 0.754, 0.513, 0.587 and 0.510 on each of the principal components without a missing value respectively.While the correlation matrix and the reproduced correlation matrix for the proposed CSDP indicated 61% and the percentage of acceptance on the principal components for the proposed CSDP are higher than 50%.Therefore, from the outcome, it has shown that the acceptance responds towards the proposed CSDP and the result from the principal components analysis (eigenvalue analysis) are significant enough for implementation and can be adopted by organisations as a guidelines for organisation cyber security practices.
Lung Cancer Prediction and Detection Using Image Processing Mechanisms: An Overview

Nowadays, cancer has counted as a hazardous disease that many people suffered from especially Lung-Cancer. Cancer is the disease that cell has grown rapidly and abnormally that is why treating it is somehow tough in some cases but it can be controlled if it is detected in the initial stage. Image Processing Mechanisms have a vital role in predicting and recognizing both benign and malignant cells with the help of classifier mechanisms such as Decision-Tree (D-Tree), A-NN, Support-Vector-Machine, and Naïve-Bayes classifier which are widely utilized in the biomedical field. These classifiers are available to classify the usual and unusual cells. This study aims to review the most well-known Image Processing Mechanisms for Lung-Cancer Detection and Prediction. Brief information about the main steps of proposing an effective system by using Image Processing stages like Image Acquisition, Pre-processing of the image which includes noise elimination and enhancement, Segmentation, Extracting Feature, and Binarization had been demonstrated. In the literature, several researchers' work had been reviewed. A comparison had been done among various reviewed research papers that proposed various models for recognizing and estimating the Lung-Cancer nodule. The comparison based on the Image Processing Mechanisms, accuracy, and classifier used in each reviewed research paper.
Using Online Resources Technology for Foreign Language Learning: Strategies, Impact, and Challenges

This paper explored students’ perceptions of strategies, impact, and challenges using online resources for foreign language learning. The study also identified challenges faced by learners in applying online resources for their language learning. The study employed a qualitative interview with four students. This case study was conducted in one public university. The participants in this study were chosen purposively due to their investment and commitment using online resources for foreign language learning. The study identified that there were several strategies used students to maximize the use of online resources, i.e., searching meaning and synonyms, testing language proficiency, writing practice, grammar exercise, and gaining teaching materials. Students found it very interactive and enhanced their learning, not only contents and language learning, but also how to use the technologies. However, some students encountered challenges, including technical problems, skill issues, and irrelevant with their interests. The study applied an in-depth exploration of the phenomenon, and the results can contribute to perceptive academic and applied knowledge to the public principally on the idea of online foreign language learning. Future study wishes to examine a significant number of sampling; the broader sample is expected to enhance the findings of this study. The prospective research also demands to be conducted within numerous academic settings. Within multiple parameters, it is expected to contribute to a broader population and encompasses our conclusion by providing a more wide-ranging spot to reinforce the findings of this study.
Atomized Scan Method for High-Definition Silicon-Based OLED Microdisplays

Silicon-based organic light-emitting diode (OLED) microdisplays are widely used in the head-mounted devices. This article proposes a novel atomized scan method for the digital driving silicon-based OLED microdisplays. By eliminating the time redundancy in the digital driving, the proposed method generates the high transmission efficiency, high lighting efficiency, high grayscale efficiency, and good linearity compared with the traditional pulsewidth modulation (PWM) scan methods. Four scan characteristics of the high-efficiency scan sequence are analyzed. The high efficient atomized scan can be obtained by implanting the low grayscale scan into the high grayscale scan. A 1.6 k × 1.6 k full color silicon-based OLED microdisplay, together with the hardware driving platform, is designed and implemented to verify the proposed atomized scan method. By measuring the brightness values of 256 gray levels, the atomized scan method with the blank operation can achieve high scan efficiency with the low clock frequency (57.6 MHz).
Bag-of-Attributes Representation: A Vector Space Model for Electronic Health Records Analysis in OMOP

Several studies have been performed worldwide to improve health services using data generated by digital medical systems. The increasing volume of data generated by these systems is making the use of knowledge discovery and data analysis techniques essential to improve the quality of the health services, which are offered by the medical facilities. However, it is possible to observe a gap, in the literature, about generic and flexible vector space models (VSM) that are well adapted to handle electronic health records (EHR), requiring that each knowledge discovery effort develop their own VSM or other representation model. This restriction can turn a knowledge discovery task over clinical pathways nonviable for comparative evaluations among different methods. Targeting such scenario, we propose the Bag-of-Attributes Representation (BOAR). BOAR represents an EHR as an n-dimensional vector space. Since BOAR takes advantage of the OMOP (Observational Medical Outcomes Partnership) standard, BOAR is able to represent records retrieved from different data models. The experimental results show that BOAR is flexible and robust to representing EHR from several sources, and allows the execution and evaluation of several clustering algorithms.
Business Process of Library Electronic Catalog Integration and Samara University Repository

Institutional repositories (IR) help to increase public value, rating, prestige and visibility of both individual researchers and the whole universities. Repositories are filled with their own content and provide access to other researchers around the world. The number of IRs is growing due to the involvement in the work on their creation of university libraries. Libraries want all materials in the university is IR to be in demand by users and used in the educational and scientific process. Therefore, in addition to the repository’s own search engine and the search indexes ‘Google’ and ‘Yandex’, IR resources should be in the electronic catalog, that brings them as close as possible to the reader. The article describes the business processes introduced by Samara University library into the practice of working with electronic resources of the university repository.
Towards better performance: phase congruency based face recognition

Phase congruency is an edge detector and measurement of the significant feature in the image. It is a robust method against contrast and illumination variation. In this paper, two novel techniques are introduced for developing alow-cost human identification system based on face recognition. Firstly, the valuable phase congruency features, the gradient-edges and their associate dangles are utilized separately for classifying 130 subjects taken from three face databases with the motivation of eliminating the feature extraction phase. By doing this, the complexity can be significantly reduced. Secondly, the training process is modified when a new technique, called averaging-vectors is developed to accelerate the training process and minimizes the matching time to the lowest value. However, for more comparison and accurate evaluation,three competitive classifiers: Euclidean distance (ED),cosine distance (CD), and Manhattan distance (MD) are considered in this work. The system performance is very competitive and acceptable, where the experimental results show promising recognition rates with a reasonable matching time.
A Consensus Mechanism Based on an Improved Genetic Algorithm

An important feature of blockchain technology is that all participants jointly maintain transaction data and can achieve mutual trust relationships without integrated control, which relies on distributed consensus algorithms.Practical Byzantine Fault Tolerant algorithm (PBFT) is a fault-tolerant algorithm based on state machine replication, which solves the Byzantine error, that is, the malicious behavior of nodes.In PBFT, all participating nodes are divided into the primary node and backup nodes.When this primary node commits evil or fails, it will elect a primary node again for message communication.The genetic algorithm (GA) is a computer simulation study inspired by the natural biological genetic evolution criterion "natural selection, survival of the fittest".Genetic algorithm is actually a method to find the optimal solution.According to it, the best primary node is selected in the PBFT algorithm to improve consensus efficiency.The consensus algorithm is the guarantee of the decentralization feature in blockchain technology.The PBFT algorithm is a commonly used consensus algorithm.However, this algorithm has the following problems: when the primary node fails, it must be selected again, which leads to a decrease in consensus efficiency.This paper proposes a consensus mechanism based on an improved genetic algorithm, which uses an improved genetic algorithm to select the primary node.According to the genetic algorithm, the best primary node is selected, and it meets the minimum number of errors or evils and the highest transaction efficiency with other backup nodes.The improved consensus algorithm can effectively reduce system delay and improve consensus efficiency.
Skeleton based Human Action Recognition using a Structured-Tree Neural Network

The ability for automated technologies to correctly identify a human’s actions provides considerable scope for systems that make use of human-machine interaction. Thus, automatic3D Human Action Recognition is an area that has seen significant research effort. In work described here, a human’s everyday 3D actions recorded in the NTU RGB+D dataset are identified using a novel structured-tree neural network. The nodes of the tree represent the skeleton joints, with the spine joint being represented by the root. The connection between a child node and its parent is known as the incoming edge while the reciprocal connection is known as the outgoing edge. The uses of tree structure lead to a system that intuitively maps to human movements. The classifier uses the change in displacement of joints and change in the angles between incoming and outgoing edges as features for classification of the actions performed
Performance Evaluation of Scalar Multiplication in Elliptic Curve Cryptography Implementation using Different Multipliers Over Binary Field GF (2233)

This paper presents a point multiplication processor over the binary field GF (2233) with internal registers integrated within the point-addition architecture to enhance the Performance Index (PI) of scalar multiplication. The proposed design uses one of two types of finite field multipliers, either the Montgomery multiplier or the interleaved multiplier supported by the additional layer of internal registers. Lopez Dahab coordinates are used for the computation of point multiplication on Koblitz Curve (K-233bit). In contrast, the metric used for comparison of the implementations of the design on different types of FPGA platforms is the Performance Index.&#x0D; The first approach attains a performance index of approximately 0.217610202 when its realization is over Virtex-6 (6vlx130tff1156-3). It uses an interleaved multiplier with 3077 register slices, 4064 lookup tables (LUTs), 2837 flip-flops (FFs) at a maximum frequency of 221.6Mhz. This makes it more suitable for high-frequency applications. The second approach, which uses the Montgomery multiplier, produces a PI of approximately 0.2228157 when its implementation is on Virtex-4 (6vlx130tff1156-3). This approach utilizes 3543 slices, 2985 LUTs, 3691 FFs at a maximum frequency of 190.47MHz. Thus, it is found that the implementation of the second approach on Virtex-4 is more suitable for applications with a low frequency of about 86.4Mhz and a total number of slices of about 12305.
Deep-Learning Steganalysis for Removing Document Images on the Basis of Geometric Median Pruning

The deep-learning steganography of current hotspots can conceal an image secret message in a cover image of the same size. While the steganography secret message is primarily removed via active steganalysis. The document image as the secret message in deep-learning steganography can deliver a considerable amount of effective information in a secret communication process. This study builds and implements deep-learning steganography removal models of document image secret messages based on the idea of adversarial perturbation removal: feed-forward denoising convolutional neural networks (DnCNN) and high-level representation guided denoiser (HGD). Further—considering the large computation cost and storage overheads of the above model—we use the document image-quality assessment (DIQA) as threshold, calculate the importance of filters using geometric median and prune redundant filters as extensively as possible through the overall iterative pruning and artificial bee colony (ABC) automatic pruning algorithms to reduce the size of the network structure of the existing vast and over-parameterized deep-learning steganography removal model, while maintaining the good removal effects of the model in the pruning process. Experiment results showed that the model generated by this method has better adaptability and scalability. Compared with the original deep-learning steganography removal model without pruning in this paper, the classic indicators params and flops are reduced by more than 75%.
Real-time coding method and tool for artefact-centric interaction analysis in co-design situations assisted by augmented reality

This paper presents and discusses a fast and efficient method to study artefact-centric interactions in co-design sessions. The method is particularly useful for exploring the introduction of augmented reality (AR) environments since AR application representations combine both digital content and physical content, transforming the way users interact with the design object. Although protocol analysis is extensively used in cognitive studies of design, it is a time-consuming and cumbersome method and hence unsuitable for extensive analysis in industrial environments. Our real-time coding method makes it possible to perform “on-the-fly” coding of physical interactions in co-design sessions. Focusing on quantifying interaction occurrences, our results are consistent with those obtained with post-session coding. Internal validity was assessed using relevant statistical tests. Based on the data collected in co-design sessions we show how aggregate results, especially timelines and interaction densities, can be displayed rapidly at the end of each session. This research paves the way for a more general implementation of real-time coding of collaborative work sessions in industrial situations.
A Study on the Correlations of Self-Esteem, Self-Efficacy, and Learning Motivations of Underachieving Elementary School Students

This study aims to identify the differences in self-esteem, self-efficacy, and learning motivations of underachievement children and to identify the variables that affect learning motivations.The participants of this study were 90 underachievement children and 90 general children in fourth, fifth, and sixth grades of elementary school from three elementary schools of G Metropolitan City.The underachievement children are those who showed underachievement in Korean, English, and Mathematics.The tools used in this study are 40 items from the self-esteem scale, 25 items from the self-efficacy scale, and 25 items from the learning motivation scale.The statistical processes used in this study are descriptive statistics, reliability analysis, t-tests, correlation analysis, and regression analysis.The results are as follows.First, underachievement children showed lower performances in self-esteem, self-efficacy, and learning motivations than general children.Second, the learning motivations of underachievement children were highly correlated with self-esteem and self-efficacy.Third, the predictor of learning motivations from both underachievement children and general children was preferences in task difficulties.Therefore, with bad academic performance, negative self-esteem in particular, was formed in the group of underachieving children.The underachieving children make less effort to complete given tasks.In conclusion, it seems necessary to figure out a proper way to improve the confidence of underachieving children in general academic circumstances, such as through developing their self-esteem, encouraging their expectations on successfully finishing tasks, and supporting them to study according to their own internal motives.Moreover, it is necessary to provide various educational programs appropriately.