Reviewerly/exHarmony huggingface dataset:
Fast reconstruction of hyperspectral images from coded acquisitions using a separability assumption

We present a fast reconstruction algorithm for hyperspectral images, utilizing a small amount of data without the need for any training. The method is implemented with a dual disperser hyperspectral imager and makes use of spatial-spectral correlations by a so-called separability assumption that assumes that the image is made of regions of homogenous spectra. The reconstruction algorithm is simple and ready-to-use and does not require any prior knowledge of the scene. A simple proof-of-principle experiment is performed, demonstrating that only a small number of acquisitions are required, and the resulting compressed data-cube is reconstructed near instantaneously.
Two pointsets in $ \mathrm{PG}(2,q^n) $ and the associated codes

In this paper we consider two pointsets in PG(2, q n ) arising from a linear set L of rank n contained in a line of PG(2, q n ): the first one is a linear blocking set of Rédei type, the second one extends the construction of translation KM-arcs.We point out that their intersections pattern with lines is related to the weight distribution of the considered linear set L. We then consider the Hamming metric codes associated with both these constructions, for which we can completely describe their weight distributions.By choosing L to be an Fq-linear set with a short weight distribution, then the associated codes have few weights.We conclude the paper by providing a connection between the ΓL-class of L and the number of inequivalent codes we can construct starting from it.
The troubling future for facial recognition software

Considering the myriad perspectives of facial recognition technology.
Cognitive Development in Childhood

A pendulum swing in views of cognitive development has occurred over the past 20 years. The issue in the pendulum swing is the proper description of the child's cognitive capabilities – the proper description of how the child's conceptual system differs from the adult's. A commitment to stages is a commitment to there being a domain-general, i.e., conceptual content free, level of description of what changes in the course of cognitive development. The pendulum has swung away from a commitment to stages in the Piagetian sense. Essences are the ultimate reality, the real nature of the things in nature, and are usually deeply hidden. It is the business of science both to discover essences and to discover deeper realities underlying surface appearances. The discovery of the essences of the things in nature and the discovery of deeper realities underlying surface phenomena are two of the goals of theory development.
A classification of ECM-friendly families of elliptic curves using modular curves

In this work, we establish a link between the classification of ECM-friendly elliptic curves and Mazur’s program B, which consists in parameterizing all the families of elliptic curves with exceptional Galois image. Motivated by Barbulescu et al. [ANTS X–proceedings of the tenth algorithmic number theory symposium, Berkeley, CA, 2013], we say an elliptic curve is ECM-friendly if it does not have complex multiplication and if its Galois image is exceptional for some level. Building upon two recent works which treated the case of congruence subgroups of prime-power level which occur for infinitely many <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="j"> <mml:semantics> <mml:mi>j</mml:mi> <mml:annotation encoding="application/x-tex">j</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-invariants, we prove that there are exactly 1525 families of rational elliptic curves with distinct Galois images which are cartesian products of subgroups of prime-power level. This makes a complete list of rational families of ECM-friendly elliptic curves with cartesian Galois images, out of which less than 23 were known in the literature. We furthermore refine a heuristic of Montgomery to compare these families and conclude that the best 4 families which can be put in <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="a equals negative 1"> <mml:semantics> <mml:mrow> <mml:mi>a</mml:mi> <mml:mo>=</mml:mo> <mml:mo>−<!-- − --></mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:annotation encoding="application/x-tex">a=-1</mml:annotation> </mml:semantics> </mml:math> </inline-formula> twisted Edwards’ form are new.
Cross Validation Analysis of Convolutional Neural Network Variants with Various White Blood Cells Datasets for the Classification Task

White Blood Cells (WBCs) analysis is an important procedure to detect diseases is that closely related to human immunity system. Manual WBCs analysis is laborious and hence computer aided system (CAD) is a better option to alleviate the shortcoming. Since conventional segmentation-classification approach is tedious to configure, a Convolutional Neural Network (CNN) become recent trend for WBCs classification. Previously, there are many works proposed for WBCs identification. However, the models that can be generalised to works well among various datasets is remain vague. In this paper, an analysis of various CNN models which are simple Alexnet, embedded friendly Mobilenet, inception based Googlenet, systematic architecture VGG-16 and skip connection based model (Resnet &amp; Densenet), are tested with three major WBCs datasets (Kaggle, LISC and IDB-2). From the rigorous experiments, it can be concluded that simple CNN model of Alexnet performs well across all three datasets with 98.08% accuracy on Kaggle, 96.34% accuracy on IDB-2 and 84.52% on LISC. This outcome can be utilise as a basis to improve the CNN classification model that can be generalize to works under various WBCs datasets.
Creating a Service-Oriented Information and Educational Space

The current stage of the digitalization of education is associated with the transition to professional standards as a basis for practice-oriented training of competent professionals who can actively function given the constant improvement of various kinds of technology and have a high degree of professional qualification and competitiveness. At present, a vast number of new professions and activities related to digital transformation and re-engineering of traditional business processes are emerging in the labor market. As a response to such a challenge of our time, the highest priority becomes the task of even more in-depth practice-oriented training of highly qualified personnel for the digital economy based on a variety of methods and tools to support learning. The goal of the present study is to develop fundamentally new methods of creating educational programs with consideration of individual requirements and characteristics of students. In this area, the standards developed by the World Wide Web Consortium, such as WSDL, SAWSDL, SOAP, UDDI, WSMO, are used. However, their application in the process of composition and interaction of information and educational services leaves several unresolved issues, which are considered in this article. As a result of the conducted research, the authors attempt to conceptually define the composition and principles of creating ontologies to provide for the search for and subsequent design of services of the information and educational space. Keywords—concept, information and educational environment, ontology, semantic interoperability, serviceoriented architecture.
The Impact of University Leadership and the University Organizational Environment on the Performance of staff: A Case Study at Al Maaqal University (Private Sector)

This study aims to explore the impact of the interaction of the university leadership and the university organizational environment on the performance level of the staff at Al-Maaqal university. This university was chosen as a field of study because it is a new university and needs studies that develops and contributes to improve its overall performance. The problem defined by the following question: to what extent does the interaction of the university leadership and the university organizational environment affect the performance level of the teaching staff at Al Maaqal University?). The method of this study adopted a major hypothesis in which there is a statistically significant effect of university leadership and university environment on the performance of university staff. A questionnaire was designed for the purpose of testing the relationship and the impact between university leadership and university environment and the performance of university staff. The results of the study indicated that the building of university leadership was appropriate environment for staff, which is reflected in improving their teaching performance.
Comparison of Algorithms on Machine Learning For Spam Email Classification

The rapid development of email use and the convenience provided make email as the most frequently used means of communication. Along with its development, many parties are abusing the use of email as a means of advertising promotion, phishing and sending other unimportant emails. This information is called spam email. One of the efforts in overcoming the problem of spam emails is by filtering techniques based on the content of the email. In the first study related to the classification of spam emails, the Naïve Bayes method is the most commonly used method. Therefore, in this study researchers will add Random Forest and K-Nearest Neighbor (KNN) methods to make comparisons in order to find which methods have better accuracy in classifying spam emails. Based on the results of the trial, the application of Naïve bayes classification algorithm in the classification of spam emails resulted in accuracy of 83.5%, Random Forest 83.5% and KNN 82.75%
Brute Force Algorithm Implementation Of Dictionary Search

In the manual dictionary the more words are accommodated, the heavier the dictionary. Dictionaries that are generally book-shaped are difficult to carry anywhere because they are thick and heavy. With this mobile-based computer dictionary application, users no longer need to carry heavy dictionaries and the search process will also be faster and easier. This mobile-based kmputer dictionary application is created using the java programming language with the NetBeans 7.0 editor and using an RMS (Record Management System) based database. So it can be installed on various types of mobile phones that already support Java. String Matching is one of the algorithms used to speed up the search process for the desired word. String matching algorithms have been used frequently before as examples in the process of matching strings based on the equation of text data namely Brute Force. Because this Brute Force algorithm can be used to perform string or text searches. Brute force algorithm is an algorithm to match a pattern with all text between 0 and n-m to find the presence of a pattern in text.
Influence of Highly Inflected Word Forms and Acoustic Background on the Robustness of Automatic Speech Recognition for Human–Computer Interaction

Automatic speech recognition is essential for establishing natural communication with a human–computer interface. Speech recognition accuracy strongly depends on the complexity of language. Highly inflected word forms are a type of unit present in some languages. The acoustic background presents an additional important degradation factor influencing speech recognition accuracy. While the acoustic background has been studied extensively, the highly inflected word forms and their combined influence still present a major research challenge. Thus, a novel type of analysis is proposed, where a dedicated speech database comprised solely of highly inflected word forms is constructed and used for tests. Dedicated test sets with various acoustic backgrounds were generated and evaluated with the Slovenian UMB BN speech recognition system. The baseline word accuracy of 93.88% and 98.53% was reduced to as low as 23.58% and 15.14% for the various acoustic backgrounds. The analysis shows that the word accuracy degradation depends on and changes with the acoustic background type and level. The highly inflected word forms’ test sets without background decreased word accuracy from 93.3% to only 63.3% in the worst case. The impact of highly inflected word forms on speech recognition accuracy was reduced with the increased levels of acoustic background and was, in these cases, similar to the non-highly inflected test sets. The results indicate that alternative methods in constructing speech databases, particularly for low-resourced Slovenian language, could be beneficial.
Uncovering Personal Histories: A Technology-Mediated Approach to Eliciting Reflection on Identity Transitions

When studying identity transitions, interview participants can find it difficult to reflect on their transitions and recall specific details related to past experiences. We present a new approach to enable participant reflection on past identity transitions, and a means to fill in blanks by eliciting data that may not otherwise come up: showing participants sentiment visualizations of their social media data. After detailing our methods of constructing sentiment visualizations, we discuss our experiences using them in a study on gender transition. For most participants, the visualizations elicited substantial reflection, and enabled recalling forgotten data and new interpretations of transition experiences. We guide researchers on how to use this method when studying other identity transitions; this may be especially powerful for marginalized people who undergo substantial identity changes. This article proposes a way to uncover participants’ personal histories, which can help HCI researchers to better understand and support marginalized people’s experiences.
Deep Understanding Based Multi-Document Machine Reading Comprehension

Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore the following two kinds of understandings. First, to understand the semantic meaning of words in the input question and documents from the perspective of each other. Second, to understand the supporting cues for a correct answer from the perspective of intra-document and inter-documents. Ignoring these two kinds of important understandings would make the models overlook some important information that may be helpful for finding correct answers. To overcome this deficiency, we propose a deep understanding based model for multi-document machine reading comprehension. It has three cascaded deep understanding modules which are designed to understand the accurate semantic meaning of words, the interactions between the input question and documents, and the supporting cues for the correct answer. We evaluate our model on two large scale benchmark datasets, namely TriviaQA Web and DuReader. Extensive experiments show that our model achieves state-of-the-art results on both datasets.
Student Evaluation of Online Teaching Quality, Their Own Engagement and Success Expectancy in the Future Profession

The aim of this research was to examine students’ perceptions of online teaching, their own engagement and abilities, and their success expectancy in the future profession. 713 students attending the University of Zagreb, Croa-tia, took part in the research. The results indicate that students spend an aver-age of 6 hours studying per day. Statistically significant differences were identified in students’ perceptions of the way in which the teaching process was conducted, support provided by professors and the engagement of stu-dents in terms of the time they spend in organized and independent studying. Students who study less than 5 hours per day tend to give lower rating to the quality of the teaching process and their own engagement. Students who study more than 6 hours per day tend to give lower rating to the support pro-vided by professors. Regression analysis revealed that the perceptions of the way in which the online teaching process was organized and conducted, of the abilities, engagement and hope significantly contribute to the interpreta-tion of success expectancy in the future profession. Students’ age was not found to be a significant predictor.
Spatial Ability and Geometric Thinking of the Students of Teacher Training for Primary Education

In this article, we focus on geometric thinking and the solution of three spatial geometric tasks of future primary school teachers. The research sample consisted of 78 Bachelor´s students at Constantine the Philosopher University in Nitra, Slovakia. The research findings show that future primary school teachers have a problem with geometrical thinking at higher levels and with solving spatial ability tasks, too. Using the statistical implicit analysis we revealed the connections between the students´ solution of spatial ability tasks and the achieved level of geometric thinking according to van Hiele theory.
A Novel Animated CAPTCHA Technique based on Persistence of Vision

Image-based CAPTCHA challenges have been successfully used to distinguish between humans and bots for a long time.However, image-based CAPTCHA techniques are constantly broken by hackers, forcing web developers to implement more robust security features and new approaches in CAPTCHA images.Modern-day bots can use many techniques and technologies to break CAPTCHA images automatically.These techniques include OCR, Segmentation, erosion, threshold, flood fill, etc.This led to innovative CAPTCHA systems, including those based on drag and drop, image recognition, fingerprint, mathematical problems, etc. Animated image CAPTCHAs have also been designed to show moving characters and objects and require users to recognize the characters or objects in the animation.Unfortunately, these CAPTCHA systems have also been broken successfully.This research proposes a novel animated CAPTCHA technique based on the persistence of vision, which shows text characters in multiple layers in an animated image.The proposed CAPTCHA technique has been implemented in PHP using GD library functions and tested using various popular CAPTCHA breaking tools.Further, the proposed CAPTCHA challenge has also been tested against the frame separation based breaking technique.The security analysis and usability study have demonstrated user-friendliness, vast accessibility, and robustness.
MR-DBIFOA: a parallel Density-based Clustering Algorithm by Using Improve Fruit Fly Optimization

&lt;p&gt;Clustering is an important technique for data analysis and knowledge discovery. In the context of big data, the density-based clustering algorithm faces three challenging problems: unreasonable division of data gridding, poor parameter optimization ability and low efficiency of parallelization. In this study, a density-based clustering algorithm by using improve fruit fly optimization based on MapReduce (MR-DBIFOA) is proposed to tackle these three problems. Firstly, based on KD-Tree, a division strategy (KDG) is proposed to divide the cell of grid adaptively. Secondly, an improve fruit fly optimization algorithm (IFOA) which use the step strategy based on knowledge learn (KLSS) and the clustering criterion function (CFF) is designed. In addition, based on IFOA algorithm, the optimal parameters of local clustering are dynamically selected, which can improve the clustering effect of local clustering. Meanwhile, in order to improve the parallel efficiency, the density-based clustering algorithm using IFOA (MR-QRMEC) are proposed to parallel compute the local clusters of clustering algorithm. Finally, based on QR-Tree and MapReduce, a cluster merging algorithm (MR-QRMEC) is proposed to get the result of clustering algorithm more quickly, which improve the core clusters merging efficiency of density-based clustering algorithm. The experimental results show that the MR-DBIFOA algorithm has better clustering results and performs better parallelization in big data.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
A Dual CNN for Image Super-Resolution

High-quality images have an important effect on high-level tasks. However, due to human factors and camera hardware, digital devices collect low-resolution images. Deep networks can effectively restore these damaged images via their strong learning abilities. However, most of these networks depended on deeper architectures to enhance clarities of predicted images, where single features cannot deal well with complex screens. In this paper, we propose a dual super-resolution CNN (DSRCNN) to obtain high-quality images. DSRCNN relies on two sub-networks to extract complementary low-frequency features to enhance the learning ability of the SR network. To prevent a long-term dependency problem, a combination of convolutions and residual learning operation is embedded into dual sub-networks. To prevent information loss of an original image, an enhanced block is used to gather original information and obtained high-frequency information of a deeper layer via sub-pixel convolutions. To obtain more high-frequency features, a feature learning block is used to learn more details of high-frequency information. The proposed method is very suitable for complex scenes for image resolution. Experimental results show that the proposed DSRCNN is superior to other popular in SR networks. For instance, our DSRCNN has obtained improvement of 0.08 dB than that of MemNet on Set5 for ×3.
Secure Data Deduplication and Data Portability in Distributed Cloud Server Using Hash Chaining and Lf-Wdo

An application of Cloud Computing (CC) technologies for interconnecting data as well as applications served as of manifold geographic locations is the distributed cloud. For distributed CC, managing a vast measure of data along with maintaining security are vital aspects. Though lots of techniques were introduced recently to manage and protect data on the distributed cloud, those have not rendered any desirable security. Thus, this paper proposes a secure Data Deduplication (DD) and Data Portability (DP) for a distributed cloud environment. To enhance the user data’s security, primarily, the inputted files are bifurcated into blocks and after that, an appropriate Cloud Server (CS) is chosen utilizing Hybrid Forest Genetic Algorithm (HFGA) centred upon split file features together with CS features. In a DD phase, the Whirlpool algorithm converts the split data into Hash Code (HC), and then, the hash chaining technique securely removes the duplicated data. Then, that split data is comp ressed, encrypted, together with amassed in the selected CS. Next, the Levy Flight – Wind Driven Optimization (LF-WDO) performs the DP to enhance the cloud data’s security. Investigational outcomes exhibit the proposed work's potential.
Design and Implementation of a DEVS-Based Cyber-Attack Simulator for Cyber Security

The necessity of conducting business processes of institutions and individuals with information technologies has brought risks and threats.Cyber-attacks may lead to hard-to-recover results.Although many security systems have been developed against to these attacks, attacks and security breaches of information systems are increasing rapidly.In this study, it is aimed to understand the security weaknesses and vulnerabilities, which is one of the most important issues at the point of providing cyber security, and to detect cyber-attacks.Using physical networks to test cyber-attack methods is a very costly and time consuming process.In this paper, as a different method, a cyber-attack simulation model has been developed using the DEVS modelling approach to simulate and test cyber-attack scenarios and evaluate the results.An application has been developed that simulates an attack scenario in a virtual network and evaluates detector alerts by generating appropriate intrusion detection system signals.The DEVS-Suite simulation environment was used as a development environment.Comparisons were made with different cyber-attack simulation applications and their differences were revealed.