Reviewerly/exHarmony huggingface dataset:
Formation of student youth value-orientations through competency approach in personality upbrining

The article highlights the relevance of the educational paradigm problem concerning the practical formation of competences based on the value-orientations of contemporary student youth in the university educational space, the content of the concepts of «competence», «competence approach», as the necessary condition for the upbringing of a highly culturally competent personality is outlined. The essence and content of the competent approach determine values, since the formation of value-orientations is the main goal of upbringing the personality in the university educational space. Optimization of the process of student youth value-orientations formation through the competence approach involves the continuous enrichment of the inner world of the individual. Changes in the value-orientations of student youth emphasize the importance of the whole system of educational work at the university, the search for and introduction of new forms and methods of working with student youth. An important task of the higher education institution is the creation of university educational space, which will promote the creation of conditions for the development of social and creative activity of student youth, their attraction to the values and meanings of social life, national and universal values formation and assimilation by students towards oneself, family, people, creative work, nature, society and the state. At the same time, special attention is given to solving the problems of the formation and development of the unity of the student group, the mutual understanding of students and teachers, the satisfaction of students cultural needs, especially the interest in the national and civic orientations of the student's personality, the needs of each student in constant self-improvement; a positive motivation for students to have a healthy lifestyle, and a desire to participate in community activities at the university. All this will allow more efficiently conduct the educational process at the university.
Development of cleaning viewpoints and evaluation of anonymity among Japanese Adverse Drug Event Report database

This paper evaluates and analyzes the data cleaning method and anonymity of the Japanese Adverse Drug Event Report database (JADER). JADER consists of multiple tables and more than three million reports of adverse events. Since there are several errors included in this database, it is hard to analyze its anonymity. There are more than two thousand type of drug names which are unique in JADER. We have cleaned the data one by one to develop data cleaning method and found error mode and cleaning procedure by utilizing some online database and general search engine, google. As a result, we found rules for data correction of drug names and there are still two hundred and forty-two records shown k=1 for its anonymity.
Estimation of the Distance Between Fingertips Using Silhouette and Texture Information of Dorsal of Hand

A three-dimensional virtual object can be manipulated by hand and finger movements with an optical hand tracking device which can recognize the posture of one’s hand. Many of the conventional hand posture recognitions are based on three-dimensional coordinates of fingertips and a skeletal model of the hand. It is difficult for the conventional methods to estimate the posture of the hand when a fingertip is hidden from an optical camera, and self-occlusion often hides the fingertip. Our study, therefore, proposes an estimation of the posture of a hand based on a hand dorsal image that can be taken even when the hand occludes its fingertips. Manipulation of a virtual object requires the recognition of movements like pinching, and many of such movements can be recognized based on the distance between the fingertips of the thumb and the forefinger. Therefore, we use a regression model to estimate the distance between the fingertips of the thumb and forefinger using hand dorsal images. The regression model was constructed using Convolution Neural Networks (CNN). Our study proposes Silhouette and Texture methods for estimation of the distance between fingertips using hand dorsal images and aggregates them into two methods: Clipping method and Aggregation method. The Root Mean Squared Error (RMSE) of estimation of the distance between fingertips was 1.98 mm or less by Aggregation method for hand dorsal images which does not contain any fingertips. The RMSE of Aggregation method is smaller than that of other methods. The result shows that the proposed Aggregation method could be an effective method which is robust to self-occlusion.
The concept of multiple variables

The article is devoted to the consideration of the multiple variables concept. The author emphasizes that mathematics is one of the most important disciplines for human functioning and mathematical principles are permeated through all spheres of life and activity, making the importance of understanding these principles even greater. For proper conclusion and solution of life's problems, mathematics is more than necessary. The author comes to the conclusion that by learning mathematics, it is actually possible to train your reasoning and logic so that you can respond properly to key life problems.
Mutual Information-Based Texture Spectral Similarity Criterion

Fast novel texture spectral similarity criterion, capable of assessing spectral modeling resemblance of color and Bidirectional Texture Functions (BTF) textures, is presented. The criterion reliably compares the multi-spectral pixel values of two textures, and thus it allows to assist an optimal modeling or acquisition setup development by comparing the original data with its synthetic simulations. The suggested criterion, together with existing alternatives, is extensively tested in a long series of thousands specially designed monotonically degrading experiments moreover, successfully compared on a wide variety of color and BTF textures.
Challenges and Approaches for Mining Astronomical Data and Complex Models

This paper describes the main challenges encountered when implementing new approaches for handling very large data sets in the area of Deep Cosmological Fields.The ultimate goal pursuit is the knowledge discovery in the area of galaxies surface brightness and by that the improvement of our current knowledge regarding galaxies and their role in the Universe. The specific problem addressed here is the de-blending of very faint and blurred galaxies in crowded images.We introduce a set of artificial intelligence methodologies for the effective implementation of a proof-of-concept, which can then be imported into Big Data Computing platforms, such as XSEDE. This paper is structured as follows: chapter 1 - Introduction, describing the current state-of-the-art and motivation related to the field of Big Data Mining, focussing on astronomical data; chapter 2 - Description of the difficulty of de-blending blurred and faint galaxies, one of the main challenges in the area of complex computing and very large data sets; chapter 3 - New approach, detailing the methodologies used for the problem of de blending blurred and overlapped sources; chapter 4 - Results and Discussion presenting preliminary results, advantages, disadvantages and future lines of work in this area.
Learning k-Occurrence Regular Expressions from Positive and Negative Samples

Deterministic regular expressions (DREs) are a core part of XML schema languages such as DTD/XSD and are used in different kinds of applications. Presently the most powerful model to learn DREs is k-occurrence regular expressions (k-OREs for short). However, there has been no algorithms can learn k-OREs from positive and negative samples. In this paper, we propose an efficient and effective algorithm to learn k-OREs from positive and negative samples. Our algorithm proceeds as follows: (1) learning deterministic k-OA from positive and negative samples based on genetic algorithm; (2) converting the k-OA into optimum deterministic k-OREs.
Dead-beat control synthesis for multi-tank system

The paper presents the authors' approach to dead-beat control synthesis for a multi-tank system. The control algorithm has been provided on the demand d-step response of closed-loop system addicted to model rank. An implementation of dead-beat control has been applied to a non-square model of the multi-tank system in state-space description. This solution requires the inverse method of non-square matrix, additionally. The effectiveness of the presented approach is confirmed in simulation example.
The Vulnerability Analysis of the Multi-layer Air Transport System

The problem of aviation safety is the eternal theme in the air traffic system. With the recent advancement of network theorem and big data science, as well as taking into account of the unique operation procedures, more and more researchers have begun to take into account the air traffic system with interdependent complex networks. Firstly, in this paper, we propose a multi-layer network model which constituted by the airport network, the air routes network and the air traffic control sector network, and in which the operation relationships between every two networks can also been explained clearly. Second, based on this model, we analysis the rules of fault propagation among nodes and the different layers. Then, from the view of entropy, the structural entropy and traffic flow entropy are used to illustrate the structure vulnerability and the operation vulnerability of the air traffic system respectively. At last, the data of air traffic system in the North China area are chosen to prove the validity of the theoretic results.
3D Visual Object Detection from Monocular Images

3D visual object detection is a fundamental requirement for autonomous vehicles. However, accurately detecting 3D objects was until recently a quality unique to expensive LiDAR ranging devices. Approaches based on cheaper monocular imagery are typically incapable of identifying 3D objects. In this paper, we propose a novel approach to predict accurate 3D bounding box locations on monocular images. We first train a generative adversarial network (GAN) to perform monocular depth estimation. The ground truth training depth data is obtained via depth completion on LiDAR scans. Next, we combine both depth and appearance data into a birds-eye-view representation with height, density and grayscale intensity as the three feature channels. Finally, We train a convolutional neural network (CNN) on our feature map leveraging bounding boxes annotated on corresponding LiDAR scans. Experiments show that our method performs favorably against baselines.
Defeating Fake Food Labels Using Watermarking and Biosequence Analysis

Fake food label is one of the leading ways to distribute a low quality food item as a high quality branded product. For example, under fake labels, significantly higher amount of fake Manuka honey is sold than what is actually being produced. In this paper, we propose a scheme to combat the spread of such low quality food items by identifying fake food labels. In our scheme, a watermarking is inserted to a genuine food label and biosequence analysis is used to detect this watermark. The proposed biosequence analysis is such that it can detect duplicate labels, for example a photocopy of the genuine label. The proposed method works by converting a label image into biological amino acid form (e.g., to A, C, D, G, H, etc. form) and then extracting a signature from the label (which is represented in amino acid form) using biological tools. These signatures are then matched against a query label image to find out its originality. Experiment with honey food labels (honey watermarked dataset created by us) shows that the proposed method has true positive rate of 91:67%.
Information System for Analysis of the Need of Doctors Using K-Means Clustering and Chi-Square Test

Doctor is one of the medical staff who is needed by the community in implementing health in Indonesia. Some provinces in Indonesia have different populations and doctors, an analysis is needed to maximize the number of doctors to the population. In this research, the K-means method was used to divide the cluster into the number of doctors needed and the chi-square method to final testing for the result of clustering. This research will provide results on areas that need to increase the number of doctors or do not need to add additional doctors in Indonesia. Using of the K-means clustering method and chi-square test for doctor analysis, giving results of accuracy which is 78%.
A Cooperative Particle Swarm Optimization Algorithm Based on Greedy Disturbance

In this paper, an improved Particle Swarm Optimization Algorithm (GCPSO) is proposed to solve the shortcomings of the existing Particle Swarm Optimization Algorithm (PSO) which has low convergence precision, slow convergence rate and is easy to fall into local optimum when performing high-dimensional optimization in the late iteration. First, the whole particle swarm of the algorithm was divided into three sub-groups, and different ranges of inertia weight ω are set for balances global search and local search in each sub-group, which improves the algorithm’s ability to explore. Then we add Gaussian perturbation with the greedy strategy to PSO to avoid the algorithm falling into local optimum and improve the convergence speed. And finally, the proposed algorithm is compared with Genetic Algorithm (GA), PSO and Grasshopper Optimization Algorithm (GOA) to analyse its performance and speed. Through experimental analysis, GCPSO has a significant improvement at convergence speed, convergence accuracy and stability.
Model of Encryption System with Genetic Algorithm

One of the security conditions of Vernam's cipher is that the encryption key must be greater than or equal to the open text we want to encrypt. At the same time, this key must not be repeated in another encryption. Then, each change of the encryption key adds security to the encryption process. If a cipher is changed several times while encrypting a single open text, it becomes very difficult to decrypt the message. Therefore, our goal will be to design a mechanism to generate an encryption key using a Tree Parity Machine and a Genetic Algorithm that will be able to create the same encryption keys on both sides that enter the encryption process. While these keys should change during encryption.
Content-enhanced Bayesian Personalized Ranking

With the popularization of Knowledge Payment Products (KPP), more accurate recommendations are in great need to alleviate information overload of users. Bayesian Personalized Ranking (BPR) is one of the most representative pairwise ranking methods for recommendation, the performances of which greatly depend on the selection of negative feedback. However, traditional negative samplers may suffer from bias and noises. Therefore, in this paper, we focus on improving negative sampling strategy of BPR by incorporating side information of the knowledge products. We locate negative samples by calculating the cosine similarity among items by the textual features of KPP, under the assumption that a user shall have similar perceptions on items with similar content. We union our sampler strategy and the original one with different ratios. Compared to the original BPR that applies a uniform sampler on all the products, the join of our content-based sampler enhances BPR with a relative improvement over 4% on the ZhiHu Live dataset, which demonstrates the effectiveness of considering side information when capturing users' preferences on different items.
Are We Referring to the Same x86 64?: Detection of Cache Events in AMD's Zen Micro-architecture

The disclosure of attacks based on flawed speculative execution mechanisms exposed cache side-channels as an apparatus to violate process isolation guarantees. Previous work has made Intel's SGX trust model the critical target of state-of-the-art techniques that leverage shared caches to shatter Enclave's safeguard. We demonstrate that porting attack implementations between micro-architectures can become a complex task, dismantling the concept of x86_64 as a whole when talking about covert cache side-channels. Our work describes the shortcomings and alternatives of assessing low-level events within Zen's cache hierarchy. We disclose the effects of the implemented Second Level cache pre-fetcher and how it tampers the ability of algorithms to discover minimal eviction sets, for which we coin the term ZenAccess.
Fast and Secure Unified Field Multiplier for ECC Based on the 4-Segment Karatsuba Multiplication

In this paper we introduce an accelerator for the ECC kP operation in two different types of Galois fields i.e. GF(p) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ). In order to ensure fast execution of the multiplication in both cases we integrated the carry bit separation technique to speed-up the multiplication in GF(p). The two most important contributions of this paper are that the partial multiplier applied is used for both types of Galois field and the second one is that our design is resistant against Horizontal Collision Correlation Analysis. The latter was verified in 20 test runs per supported elliptic curve.
Malticriteria design of a large-scale mechanical systems

The design and optimization of a large-scale systems are the most difficalt problems. A large-scale system consists of a number of subsystems. For example, in a harvest for harvesting one can separate the following subsystems: the frame, driver's cab, platform, engine, transmission, and steering system. Different departments of the design office engaged in creating a machine optimize their ‘own’ subsystems, while ignoring others. A machine assembled from ‘autonomously optimal’ subsystems turns out to be far from perfect. A machine is a single whole. When improving one of its subsystems, we can unwittingly worsen others. This implies that it is not always possible to solve optimization problems directly even for determination of the feasible solution set. The correct determination of the feasible solution set was a major challenge in engineering optimization problems. In order to construct the feasible solution set, a method called the Parameter Space Investigation (PSI) has been created and successfully integrated into various fields of industry, science, and technology. The methods of approximation of the feasible solution and Pareto optimal sets and the regularization of the Pareto optimal set are described in our paper. These methods are applied to solving the multicriteria optimization problems of large- scale systems. For example, they were applied in an agricultural engineering to a harvester for harvesting design.
An improved kernel correlation filtered image target tracking algorithm

The kernel correlation filtering (KCF) tracking algorithm cannot solve the target tracking mesoscale variation and target loss problem. For this, an improved kernel correlation filtering (IKCF) target tracking algorithm is proposed in this paper. A scale filter is added to the training displacement filter to improve the target scale change problem. In order to solve the problem of target loss, the occlusion processing mechanism is combined, when the target is affected by a small occlusion area, the support vector machine (SVM) is used to train the sample online; when the target is occluded, the re-detection classifier is used for detection. The experimental results show that the tracking accuracy of this method is significantly improved compared with other excellent tracking algorithms.
Adaptive Learning Method in Facial Expression Recognition Model Using Fuzzy-ART

In a facial expression recognition model that we previously proposed, additional learning of facial expression patterns is performed based on a feature space generated by initial learning; therefore, properly setting initial learning data is necessary. This paper defines an index for measuring dispersion of facial expression patterns and analyzes the relationship between the dispersion and the accuracy of facial expression recognition using classifiers generated through additional learning.
Location Privacy Protection Method Based on a Proxy in Un-Trusted Mobile Social Networks

In location-based services of mobile social networks, queries including users' locations are usually passed through multiple un-trusted/trusted users to reach location providers. These users may put themselves at risk if an un-trusted (malicious) user comes to know any sensitive information. To address this issue, we propose a Location Privacy Protection based on a Proxy (L3P) method for mobile social networks. This method uses a proxy who is a trustworthy user far away from an original sender, to request a location-aware query, and thus it could hide the original sender's information. Simulations show that our method improves the success ratio compared to the existing method, while still maintaining a very high level of location privacy protection.
Data-driven learning in a Greek secondary education setting

Although data-driven learning (DDL) has gained considerable attention over the last 30 years, it is still under-researched in secondary education EFL settings. We address this gap by investigating the implementation of a “blended” DDL approach for the teaching of English modal verbs to high school students in Greece which involves students’ conducting online queries and working with handouts of printed concordances from various sources: a) an English course book corpus (ECCo) compiled specifically for our purposes, b) free-to-access materials from the Web, and c) the Corpus of Contemporary American English (COCA). We present the step-by-step application of DDL training sessions and DDL-based lessons on modal verbs, with a particular focus on the adaptation of existing textbook materials. We present students’ impressions of this treatment after each DDL session, as well as the implications of our study for further research and relevant pedagogical practice.
Improving Visual Reasoning with Attention Alignment

Since attention mechanisms were introduced, they have become an important component of neural network architectures. This is because they mimic how humans reason about visual stimuli by focusing on important parts of the input. In visual tasks like image captioning and visual question answering (VQA), networks can generate the correct answer or a comprehensible caption despite attending to wrong part of an image or text. This lack of synchronization between human and network attention hinders the model’s ability to generalize. To improve human-like reasoning capabilities of the model, it is necessary to align what the network and a human will focus on, given the same input. We propose a mechanism to correct visual attention in the network by explicitly training the model to learn the salient parts of an image available in the VQA-HAT dataset. The results show an improvement in the visual question answering task across different types of questions.
Large-Scale Network Traffic Analysis and Retrieval System Using CFS Algorithm

Retrieving information from network traffic is a fundamental task for network administrators to deeply analyze network traffic, and can be used in many applications such as network monitoring, anomaly detection, and network system optimization. However, it remains a challenging issue toefficiently retrieve information from large-scale network traffic. Moreover, many existing methods and software such as NetFlow and WireShark don't support to retrieve the content information of the network traffic or lack the power of handling the large-scale network traffic. To solve this problem, we proposed a novel framework and built a system named LTARS on this framework. In addition, we designed an algorithm called CFS algorithm and utilized by our system to improve the efficiency of retrieving. We conduct the experiments on many real-world datasets and the experimental results demonstrate that our method outperforms many existing state-of-the-art methods especially for extremely large-scale information data.