Reviewerly/exHarmony huggingface dataset:
Psychological and Pedagogical Training of Teachers of Engineering Universities in the Framework of Additional Professional Education

The article is devoted to problems of professional psychological and pedagogical training of teachers of engineering universities. A set of criteria for assessing the scientific and educational activities of teachers of technical disciplines is proposed. The authors describe the achievements of domestic and international practice of training teachers of technical universities; identified ways to improve the efficiency of this process. The presented scientific material reveals the relevance and importance of the problem of insufficient professional readiness of teachers of technical universities for teaching and research activities. The authors define the pedagogical professionalism of an engineering university teacher as an integrative personality trait, which is a combination of psychological and pedagogical competencies, manifested in readiness for the successful solution of educational and scientific tasks, as part of their professional and pedagogical competence in general. The following measures are identified as ways to increase the efficiency of the process of professional and pedagogical training of teachers of higher education: modernization and adaptation of the process to modern requirements; improving the quality of psychological and pedagogical support of beginning teachers; designing professional and personal self-development routes; attraction of all subjects of the educational process to joint activities; the use of rational innovative forms of organization of the educational process.
Focusing Visual Relation Detection on Relevant Relations with Prior Potentials

Understanding images relies on the understanding of how visible objects are linked to each other. Current approaches of Visual Relation Detection (VRD) are hindered by the high frequency of some relations: when an important focus is put on them, more meaningful ones are overlooked. We address this challenge by learning the relative relevance of relations, and integrating this term into a novel scene graph extraction scheme. We show that this allows our model to predict relations on fewer and more relevant object pairs. It outperforms MotifNet, a state of the art model, on the Visual Genome dataset. It increases the Class Macro recall, the metric we propose to use, from 38.1% to 44.4%. In addition, we propose a new split of Visual Genome, with a more balanced relation distribution, emphasizing on the detection of uncommon relations and validates the use of the previous metric. On this set, our model outperforms MotifNet on all metrics, e.g. from 39.6% to 44.0% at 10 predictions per image on the relation classification task.
Legal smart contracts in Ethereum Block chain: Linking the dots

Block chain technology provides a decentralized and secure platform for executing transactions. Smart contracts in Ethereum have been proposed as the mechanism to automate legal contracts securely without the involvement of third parties. Yet, there are still several issues to be resolved especially regarding the updating of smart contracts in blockchain as well as the use of blockchain as part of a legal smart contracts system. In this work we propose a methodology and an architecture for building and deploying legal contracts in the blockchain. As the blockchain is immutable, we cannot update the code of the smart legal contracts, but in real life applications updating of contracts is a requirement that cannot be ignored. In this paper we address the problem of contract update by introducing a new versioning system that keeps track of the changes and links the different versions using a linked list. Moreover, we propose a system architecture where the user interface, the application logic and the blockchain are smoothly integrated in a manner that each part of the system contributes for producing a flexible and transparent execution. We show the applicability of our approach by implementing a system for the case of a rental agreement.
An Image Cipher Based on Latin Cubes

An image cipher based on Latin cubes is presented in this paper. Latin cube is the high-dimensional form of Latin square. Although Latin squares have been widely used in image encryption, the applications of Latin cubes in image encryption are still relatively few. Analogous to Latin squares, Latin cubes also have some excellent cryptographic properties, such as discreteness and uniformity. Specifically, Latin cubes have 3D attribute. Based on these properties, a permutation scheme which is highly plaintext-related and a substitution scheme which has strong diffusivity have been constructed in this paper. Simulation results demonstrate that the proposed algorithm has high security. It is therefore very suitable for the insecure network environment.
Stochastic Convex Optimization

In this chapter, we focus on stochastic convex optimization problems which have found wide applications in machine learning. We will first study two classic methods, i.e., stochastic mirror descent and accelerated stochastic gradient descent methods.
Robust integrated covariance intersection fusion Kalman estimators for networked mixed uncertain time-varying systems

Abstract For the multisensor time-varying networked mixed uncertain systems with random one-step sensor delays and uncertain-variance multiplicative and linearly dependent additive white noises, a new augmented state method with fictitious noises is presented, by which the original system is transformed into a standard system without delays and with uncertain-variance fictitious white noises. According to the minimax robust estimation principle and the Kalman filtering theory, based on the worst-case system with the conservative upper bounds of uncertain noise variances, the local and integrated covariance intersection (ICI) fused robust time-varying Kalman estimators (filter, predictor and smoother) are presented respectively in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Their robustness is proved by the extended Lyapunov equation method, and their accuracy relations are compared based on the traces of the variance matrices and the covariance ellipsoids, respectively. Specially, a universal ICI fusion robust Kalman filtering method of integrating the local robust estimators and their conservative cross-covariances is presented. It overcomes the drawbacks of the original covariance intersection (CI) fusion method and improves robust accuracy of the original CI fuser. A simulation example applied to two-mass spring system shows the effectiveness of the proposed methods and results.
Semantics-preserving optimisation of mapping multi-column key constraints for RDB to RDF transformation

The relational database (RDB) to resource description framework (RDF) transformation is a major semantic information extraction method because most web data are managed by RDBs. Existing automatic RDB-to-RDF transformation methods generate RDF data without losing the semantics of original relational data. However, two major problems have been observed during the mapping of multi-column key constraints: repetitive data generation and semantic information loss. In this article, we propose an improved RDB-to-RDF transformation method that ensures mapping without the aforementioned problems. Optimised rules are defined to generate an accurate semantic data structure for a multi-column key constraint and to reduce repetitive constraint data. Experimental results show that the proposed method achieves better accuracy in transforming multi-column key constraints and generates compact semantic results without repetitive data.
Analogue ontology and digital disruption

Pervasive digitality reveals us as analogue creatures that are unprepared for a world and a logic generated increasingly through automation. Promulgated by capitalism, digitality has created a new form of alienation, one far more powerful and comprehensive than that envisaged by either Marx or Lukács in the analogue-industrial age. Digital alienation-through-automation is the central process in our digital post-modernity. The effects reach increasing registers and spheres of culture, economy and politics. This essay considers the effects within the production of knowledge within higher education. Through a critique of Clayton Christensen’s concept of ‘disruptive technologies’, it develops the argument that knowledge produced in our higher institutions of learning suffer from two forms of ‘knowledge capture’. The first and most comprehensive is knowledge increasingly oriented toward instrumental outcomes, generated in vocationally oriented and market-pressured contexts, and produced by the general-sector institutions; second is knowledge, much of it profound and potentially world-changing—in economics and climate science in the cases discussed here. These are generated in both prestigious general-sector universities, and in the elite Ivy League and Russell Group universities but are unable to gain traction with an ascendant and alien post-Westphalian, post-modern and post-Enlightenment political context—created by a globalising digitality. I argue that unless we recognise our analogue essence, and that this has been alienated by digital automation, then the aporias of digitality, with knowledge production an important element, then our institution of higher learning will continue to produce much valuable knowledge that has diminishing practical (political) effect.
Machine Learning Models with Optimization for Clothing Recommendation from Personal Wardrobe

In the present-day scenario, several clothing recommender systems have been developed for the online e-commerce industry. However, when it comes to recommending clothes that a person already possesses, i.e, from their personal wardrobe, there are very few systems that have been proposed to perform the task. In this paper, we tackle the latter issue, and perform experimental analysis of the various Machine Learning techniques that can be used for carrying out the task. Since the recommendations must be made from a user's personal wardrobe, the recommender system doesn't follow a traditional approach. This is explained in detail in the following sections. Further, the paper contains a complete description of the results obtained from the experiments conducted, and the best approach is specified, with appropriate justification for the same.
Semi-Quantum Private Query Protocol Without Invoking the Measurement Capability of Classical User

Recently, Yang et al. suggested a semi-quantum private query (SQPQ) protocol (Quantum Inf Process (2015)14:1017) using one quantum state. Later, Yu et al. (Quantum Inf Process (2015)14:4201) pointed out that Yang et al.’s SQPQ protocol runs the risk of user privacy being invaded, i.e., the database owner can manipulate the conclusiveness of bits definitely by launching a special kind of fake initial state attack, and then constructed an improved quantum private query (QPQ) protocol to avoid this risk. However, Yu et al.’s QPQ protocol is not semi-quantum. In this paper, we successfully design the SQPQ protocol without running the risk of user privacy being invaded. The proposed SQPQ protocol has the following features: (1) it only employs one kind of quantum state as the initial quantum state; (2) it doesn’t require the classical user to perform the measurement operation; and (3) it is cheat-sensitive, which means that if the database privacy or the user privacy is invaded, the attack behavior can be detected.
Development of an android-based math equation editor

Abstract The long-term goal of this research is to develop Android-based learning as an alternative to mathematics learning. Meanwhile, the specific objective to be achieved in this research is to examine the effect of Mathematics learning media in the form of an Android-based Math Equation Editor on student achievement in high school. This research is a Research and Development (R&amp;D) research. There are 8 stages in this study, namely (1) Potential and problems, (2) Data collection, (3) Product design, (4) Design validation, (5) Design revision, (6) Product testing, (7) Design revision, and (8) Trial usage. The conclusions obtained from the development of the Android-based Mathematical Equation Editor as an exponential learning media are valid, practical and effective. The conclusions of this study are: (1) Android-based Math Equation Editor Products are valid, practical and effective. Its validity can be seen from the results of the validator assessment analysis of the Mathematics learning media in the form of an Android-based Math Equation Editor obtained by Pearson correlation of 0.322; (2) Its practicality is seen from the results of user assessment analysis through limited trials of Mathematics Equation Editor-shaped learning media in the form of Android which shows an average score of 3.958 with practical criteria; (3) Its effectiveness is seen from the results of the analysis of student assessments through field trials of Mathematics Equation Editor-shaped learning media based on Android can improve student mathematics learning achievement in Mathematic.
P4Mobi: A Probabilistic Privacy-Preserving Framework for Publishing Mobility Datasets

The large-scale collection of individuals' mobility data poses serious privacy concerns. Instead of perturbing data by adding noise to the raw location data to preserve privacy of individuals, we propose an approach that achieves privacy-preservation at the statistics level of aggregating mobility datasets with the probabilistic data structure Count-Min Sketch (CMS) [1], which has been widely used to provide efficient statistic functions with a tunable error bound. We use CMS to estimate the population density distributions in the mobility datasets, where the error bound determines utility guarantees. We develop P4Mobi, a novel Probabilistic Privacy-Preserving Publishing framework for Mobility datasets that protects individuals' privacy while complying to a specific utility requirement. We empirically validate the performance of P4Mobi in terms of utility and privacy-preservation by demonstrating its resilience against a recently proposed reconstruction attack model using two real-world datasets. We compare P4Mobi to two state-of-the-art methods and show that with the same level of privacy achieved against our attack model, P4Mobi significantly improves the utility of the published mobility datasets by up to $20\%$. We also provide a theoretical estimate of the utility achieved by P4Mobi. We found a very consistent match between the estimated and empirical utility of P4Mobi as evaluated on two datasets.
On the Complex Event Identification Based on Cognitive Classification Process

The concept of Complex Event Processing (CEP) is excessively utilized to detect bearable knowledge from the implicit data flow. CEP has stood out as a consolidating scope for the application of scientific knowledge that needs processing and linking raw sensory data. In this paper, we offer an innovative method of CEP in order to reach a mature level of processing patterns or events depending on the cognitive computing. The Cognitive Knowledge-based Event (CKE) model is an event processing and identification approach with cognitive aptness. There is a mounting necessity for methodologies to provide an interactive response for data and events stream around us at the time that CEP approach has scarcity in some scopes. We integrate cognitive reasoning with CEP and put forward the concept of classification by our proposed model and we test this method on the multidimensional Big Data set captured during thirteen years of the Cassini-Huygens space exploratory project. There is a pivotal necessity to develop an identification and training method for all scales in any functional systems that include a part of cognitive reasoning and learning process.
Business Process Execution From the Alignment Between Business Processes and Web Services: A Semantic and Model-Driven Modernization Process

Many companies have implemented their business processes in Web applications which must be frequently adapted so as to stay aligned with new business process requirements. Service-oriented architectures (SOAs) constitute an appropriate option to manage the continuous changes in those processes by facilitating their alignment with the changing underlying system services. In this context, firms are trying to migrate their Web applications to new software architectures such as SOAs. However, this migration is usually carried out ad-hoc by means of non-reusable and error-prone manual processes. Similarly, the alignment between the business processes and the underlying services identified is usually done by hand. This work presents a model-driven semiautomatic approach to modernize legacy Web applications to SOAs. The approach is focused on an automatic semantic process aimed at discovering the services that can be used to implement the business processes (defined by the companies), then aligning these processes with the underlying services. A semantic algorithm is provided to aid the migration architect during the alignment process. The case study carried out shows that the alignment process results obtained by the semantic algorithm presented in this paper are similar to those obtained by the experts manually. Finally, SOA orchestration artifacts are generated from the semantic algorithm results.
Quantum Codes from Constacyclic Codes over the Ring Fq[u1,u2]/〈&#x0D; &#x0D; &#x0D; &#x0D; u&#x0D; 1&#x0D; 2&#x0D; &#x0D; &#x0D; &#x0D; -u1,&#x0D; &#x0D; &#x0D; &#x0D; u&#x0D; 2&#x0D; 2&#x0D; &#x0D; &#x0D; &#x0D; -u2,u1u2-u2u1〉

In this paper, we study the structural properties of ( α + u 1 β + u 2 γ + u 1 u 2 δ ) -constacyclic codes over R = F q [ u 1 , u 2 ] / ⟨ u 1 2 − u 1 , u 2 2 − u 2 , u 1 u 2 − u 2 u 1 ⟩ where q = p m for odd prime p and m ≥ 1 . We derive the generators of constacyclic and dual constacyclic codes. We have shown that Gray image of a constacyclic code of length n is a quasi constacyclic code of length 4 n . Also we have classified all possible self dual linear codes over this ring R . We have given the applications by computing non-binary quantum codes over this ring R .
Automatic Document Classification using Deep Feature Selection and Knowledge Transfer

Documents in an ERP system flow from different sources (customer, supplier, etc.) and can have different layouts, sizes and subjects (invoices, delivery forms, checks, etc.). The classification of these documents is usually done manually before being saved in the ERP system or processed by an Optical Character Recognition (OCR) engine. In this paper, we investigate using different deep convolutional neural networks (CNN) to extract deep features from images of scanned documents. The extracted features are further processed using various machine learning classifiers such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Gaussian Naive Bayes (GNB). Different metrics were used (accuracy, precision, etc.) and examined to compare all models performances, while cross-validation approach at different folds sizes (4, 6, 8 and 10) was used to assess their generalization ability. The effect of dimensionality reduction techniques on overall performances was also explored. The best classification rate was 96.1%, which was achieved by combining LR and the VGG19 model. This very good performance despite the small dataset used (200 images) can allow using this approach in an ERP system as a preprocessing step in document manipulation for ERP users.
Multi-label Classifier to Deal with Misclassification in Non-functional Requirements

Automatic classification of software requirements is an active research area; it can alleviate the tedious task of manual labeling and improves transparency in the requirements engineering process. Several attempts have been made towards the identification and classification by type of functional requirements (FRs) as well as non-functional requirements (NFRs). Previous work in this area suffers from misclassification. This study investigates issues with NFRs in particular the limitations of existing methods in the classification of NFRs. The goal of this work is to minimize misclassification and help stakeholders consider NFRs in early phases of development through automatically classifying requirements. In this study, we have proposed an improved requirement detection and classification technique. The following summarizes the proposed approach:(a) A newly created labelled corpus,(b) Textual semantics to augment user requirements by word2vec for automatically extracting features, and(c) A convolution neural network-based multi-label requirement classifier that classifies NFRs into five classes: reliability, efficiency, portability, usability, and maintainability.
Blended Learning on Physics Using Augmented Reality

Abstract this study aimed to determine the increase in student activity through a blended learning approach in physics using augmented reality. This type of research is a classroom action research with a population of all students in Deli Serdang Regency. The research instrument used was a student observation questionnaire and Student Activity Sheet. The results of data analysis explained that the blended learning approach in physics using augmented reality can improve student learning activities.
Toward Effective Cybersecurity Education in Saudi Arabia

Securing the cyberspace is a challenging task that needs well educated and trained professionals. Developing a workforce that can hold the burden of monitoring and ensure cyberspace security is becoming prominent nowadays. Accordingly, developing effective cybersecurity programs is gaining more focus in academia and industry. This paper examines the current state of various cybersecurity programs in Saudi universities and provides some recommendations.
A Sketch of a Deep Learning Approach for Discovering UML Class Diagrams from System’s Textual Specification

Drafting a formal or semi-formal model describing the functional requirements of a system from a textual specification is a prerequisite in the context of a model-driven engineering approach, such as the model-driven architecture initiative proposed by OMG. This model, called a platform-independent model (PIM), is used to derive automatically or semi-automatically the source code of a system. Different knowledge-based approaches have been proposed to extract a PIM from a textual specification automatically. These approaches use a predefined set of rules to perform this discovery. These approaches impose several restrictions on the way a specification is written. The emergence of machine learning techniques and more specifically of deep learning and their obvious success among others in several tasks in automatic language processing, such as speech recognition and translation, suggests the possibility of using these techniques to reach our objective. In this paper, we review state of the art in the domain and we sketch a rough deep learning approach to achieve our objective of extracting a PIM from the textual specification of a system.
On the Effect of Standing and Seated Viewing of 360° Videos on Subjective Quality Assessment

In this paper, we compare the impact that standing and seated viewing of 360° videos on head-mounted displays has on subjective quality assessment. The statistical analysis of the data gathered in a pilot study is reported in terms of average rating times, mean opinion scores, and simulator sickness scores. The results indicate: (1) Average rating times consumed for 360° video quality assessment are similar for standing and seated viewing, (2) Higher resolving power among different quality levels is obtained for seated viewing, (3) Simulator sickness is kept significantly lower when seated.