Reviewerly/exHarmony huggingface dataset:
Organizational and pedagogical conditions for arranging educational practice in the process of vocational training of future managers

This article is dedicated to determination of organizational and pedagogical conditions for arranging educational practice in the process of vocational training of future managers in the university. The relevance of this topic is substantiated by dissatisfaction of the pedagogues, students and employers&amp;rsquo; representatives with the results of educational practice. The author compares the international models of management education, and highlights the key differences in the Russian and foreign approaches towards arranging educational practice for the bachelors in management. The author describes the basic organizational and pedagogical conditions for improving the effectiveness of educational activity, implemented in Herzen State Pedagogical University of Russia within the framework of the Bachelor&amp;rsquo;s Program in Management. The novelty of this research consists in the proposal of criteria and indicators for assessing the results of organization of educational practice from the perspective satisfaction of its subjects, as well in elucidation of the key mechanisms for improving the stages of training and conducting educational practice, such as individualization of the process of vocational training of future managers, establishment of long-term relations between the graduate departments and employers&amp;rsquo; representatives, continuous improvement of the content and mechanisms of its organization. Practical importance of this work consists in the possibility of application of the selected organizational and pedagogical conditions for arranging educational practice in development and implementation by the institutions of higher education of the basic programs of vocational education for training future specialists; development of &amp;nbsp;the programs for educational, industrial and other types of practices; as well as creation of the corporate internship programs.
A Cloud Data Storage Technology for Alliance Blockchain Technology

The rapid development of blockchain application technology promotes continuous exploration in the field of computer application science. Although it is still in the initial stage of development, the technical features of blockchain technology such as decentralization, identity verification, tamper resistance, data integrity, and security are regarded as excellent solutions to today's computer security technical problems. In this paper, we will analyze and compare blockchain data storage and cloud data processing technologies, focusing on the concept and technology of blockchain distributed data storage technology, and analyze and summarize the key issues. The results of this paper will provide a useful reference for the application and research of blockchain technology in cloud storage security.
Elliptic Curve Cryptography: A Software Implementation

The elliptic curve cryptography (ECC) has been a promising alternative to RSA algorithm particularly for resource constrained environment like Internet of things. Efficient implementation of ECC for low power applications has been a challenging task in wireless technologies. Computability and complexity of the realization process of ECC are the major performance parameters. In this paper, we present software implementation of ECC utilizing the Weierstrass curve. It offers insights on the implementation process and helps in identifying the important operations used for the ECC. Following the hierarchy of the operations and aiming to optimize the important operations including scalar multiplications and inversions, it presents an in-depth study toward achieving enhanced execution efficacy of the ECC.
Multi-Perspective Reasoning Transformers

Machine Reading Comprehension is defined as the ability of machines to read and understand unstructured text and answer questions about it. It is considered as a challenging task with wide range of enterprise applications. Wide range of natural language understanding and reasoning tasks are found embedded within machine reading comprehension datasets. This requires effective models with robust relational reasoning capabilities to answer complex questions. Reasoning in natural language is a long-term machine-learning goal and is critically needed for building intelligent agents. However, most papers heavily depend on underlying language modeling and thus pay little to no attention on creating effective reasoning models. This paper proposes a modified transformer architecture that effectively combines soft and hard attention to create multi-perspective reasoning model capable of tackling wide range of reasoning tasks. An attention mechanism that highlights the relational significance of input signals is considered as well. The result from this study shows performance gain as compared to its counterpart the transformer network on bAbI dataset, a natural language reasoning tasks.
Makeup removal for face verification based upon contrastive learning

Makeup, derived from human`s pursuit of beauty, is widely accepted by the public. Despite its popularization, there are little effect been made to tackle the makeup face verification challenges. Aiming to promote existing verification system to accept or reject the claimed identity of a person with makeup in an image, a makeup robust face verification framework is proposed based upon a generative adversarial network. The proposal synthesizes non-makeup face images from makeup images for further verification. Specifically, a patchwise contrastive loss is introduced in the generative model to narrow the distance between makeup and non-makeup images. The challenge in the state-of-the-art is the employment of a pre-specified and hand-designed loss function to measure the performance, which is not the case in the proposal. Experimental results demonstrate that the proposal generates non-makeup faces with few artifacts and achieves 96.3% accuracy on Dataset1 in face verification, which is at least 0.8% better than some well discussed models.
Frequent Elements with Witnesses in Data Streams

Detecting frequent elements is among the oldest and most-studied problems in the area of data streams. Given a stream of m data items in \1, 2, \dots, n\, the objective is to output items that appear at least d times, for some threshold parameter d, and provably optimal algorithms are known today. However, in many applications, knowing only the frequent elements themselves is not enough: For example, an Internet router may not only need to know the most frequent destination IP addresses of forwarded packages, but also the timestamps of when these packages appeared or any other meta-data that "arrived'' with the packages, e.g., their source IP addresses. In this paper, we introduce the witness version of the frequent elements problem: Given a desired approximation guarantee α \ge 1$ and a desired frequency $d łe Δ$, where Δ is the frequency of the most frequent item, the objective is to report an item together with at least $d / α$ timestamps of when the item appeared in the stream (or any other meta-data that arrived with the items). We give provably optimal algorithms for both the insertion-only and insertion-deletion stream settings: In insertion-only streams, we show that space $\tildeO (n + d \cdot n^\frac1 α )$ is necessary and sufficient for every integral $1 łe α łe łog n$. In insertion-deletion streams, we show that space $\tildeO (\fracn \cdot d α^2 )$ is necessary and sufficient, for every α łe \sqrtn $.
Pre-Service Teachers’ Attitude toward Internet Use in Learning and Their Academic Achievement

The internet has helped us spreading knowledge in a very swift manner and to make its accessibility available for almost everyone. It can offer the possibility to help learners, with the right attitude, to achieve better in their respective fields. This study tries to investigate the attitude of pre-service teachers in an English Education Study Program in South Sumatra toward internet use in their teaching and learning process; and to check its relationship with their academic achievement. All active students, 157 in total, in an English Education Study Program in South Sumatra, Indonesia were taken in as the subjects of the study. The data were collected using Attitude toward Internet Use in Language Learning Questionnaire and students’ cumulative GPA. The results revealed that most of the pre-service teachers showed a moderate level of positive attitude toward internet use (77.71%, 21.02% for high level, 1.27% for low level) with affective aspect as the dominant factors for the attitude (followed by behavior and cognitive aspects respectively). Furthermore, the results of the Pearson correlation indicated that there was a significant fair positive association between pre-service teachers’ attitude toward internet use in learning and academic achievement, (r(157) = .443, p = .001). This study also provided some implications and recommendations for further research.
Image Classification Using Convolutional Neural Networks

The term Deep Learning can be termed as the subset of artificial intelligence with multiple network layers forming neural patterns. People’s interest in having the knowledge of deep hidden layers have recently boosted and have begun to takeover various classical strategical performances in numerous fields; especially in pattern recognition and image recognition. One of the most talked-about and important part in deep learning and neural networks is about the Convolutional Neural Network (CNN). We have briefly described about how CNNs plays an integral role in the field of image recognition. Starting from the concepts of Deep Learning and Neural Networks we make our way for CNNs.We have given the idea of how CNN works along with convolutional layers and convolutional filters in a lay-man language and the simplest way possible. Further, how a convolutional filter classifies objects and shapes is also explained in the paper. Later in the paper we have also discussed the advantages of CNN over any other neural network technique.
意外の意味を表す｢さえ・まで・すら｣をめぐって －承接の類型及び傾向の実体調査－

Among contemporary Japanese “collecting particles”, “SAE・MADE・SURA”, which means “unexpected”, is a Japanese educational point of view, especially for learners who do not speak Japanese as their mother tongue. Distinguishing between points and differences is never easy. From this point of view, “SAE・MADE・ SURA” was examined with a focus on the tendency of connection relationships. More specifically, “SAE・MADE・SURA” through a large amount of materials (the Asahi Shimbun editorial and the novel of Shincho Bunko 100) is what element is combined with, or what kind of constraint when combined. It was described as to whether or not there is. First of all, when combining before “SAE-MADE-SURA”, “SAE” and “SURA” were often “ga” on the surface, but most of them are “ga” which means theme. It was a case. Next, we investigated the tendency of case particles in the form of “noun + case particle”.　As a result of the survey, it was confirmed that “SAE” is the one that can be contacted with the most elements among “SAE-MADE-SURA”. When dealing with the word, there were some examples of “SAE” and “MADE”, but since the quantity was small, it is thought that it is difficult to co-occur with the word.　And when joining after “SAE-MADE-SURA”, there are fewer types than the elements that join before, but among them, the particles “ga” and “mo” often co-occur. I understand that. In the case of “SAE” and “SURA”, only the “mo” rating is attached. In the case of “MADE”, there were many elements that were followed by “SAE” and “SURA”, but among them, the “ga” rating was the most confirmed.　 As mentioned above, it became clear that there is a certain difference between the judgment based on introspection and the actual data. In many cases, there were no cases in the actual data that could be judged based on introspection. Of course, just because it doesn’t exist in the actual data doesn’t mean it doesn’t exist in that format.
Improving the Transformer Translation Model with Back-Translation

Back-translation has been proved to help improve the translation quality of statistical machine translation (SMT) systems and neural machine translation (NMT) systems. But in the previous work, Back-translation needed to use additional monolingual data to improve the quality of machine translation, translation models in these works are still independent. In this paper, we propose a novel approach using back-translation to improve the quality of NMT without additional monolingual data, and apply our approach to the Transformer model, which is the best NMT model in recent years. The specific method is to extend the Transformer model by using the same structure as Transformer model. The extended part is used for the backward translation of the predictive sentences obtained from forward translation to fit the source statement in each step of the training process. By optimizing both forward and back translation losses, the same effect can be achieved without the use of additional monolingual data. Experiments on the IWSLT2016 German-English datasets and show IWSLT2012 French-English datasets that our approach improves over Transformer significantly.
Efficient Medical Image Adaption via Axis-Aligned Mesh Deformation

Content-aware medical image adaptation can make medical images be well presented on different display devices. The existing adaption algorithms mainly consider the visual effect of salient regions, such as specific organ areas of the patient body, but either ignore the quality of unimportant areas or execute more slowly. In order to enhance the effect of adaption and accelerate the speed of adaptation, we propose an efficient medical image adaptation method via axis-aligned mesh deformation. With this method, importance map is firstly produced by combing the weighted edge map and saliency map. Then, integer programming is used to initialize and deform the axis-aligned mesh based on importance map. Finally, image adaptation is operated rapidly by bi-linear interpolation. With the proposed method, the real-time image adaptation can be realized, and not only the visual effect of the significant areas but also the contour integrity and continuity of the non significant areas can be maintained. Experiments on open data-sets show that the proposed method has high efficiency, better effect and strong stability, and is suitable for real-time image adaptation.
Combining Feature Selection with Neural Networks for Polish Sign Alphabet Recognition

Nowadays there are many researches on sign language recognition, but just a few with regards to Polish Sign Language. It is not a trivial topic, because there is still a barrier between the Hearing and Deaf people. Different techniques such as machine learning can be applied to recognise gestures and to help to overcome the problem. The purpose of this work is to ease the communication between the Deaf and Hearing. During the work, we focused on the letters of the Polish Sign Language alphabet and digits. In the research, we proposed some new feature extraction algorithms. To classify signs the neural networks have been used. The experimental results confirmed the effectiveness of our solution.
Bi-window based stereo matching using combined Siamese convolutional neural network

Estimating a scene's depth from image pairs is a problem for many computer vision applications such as autonomous vehicles and 3D object reconstruction. However, traditional methods propose dissimilarity functions that do not solve ambiguity problems. We present a combined siamese convolutional neural network (CSCNN) approach to calculate the costs of disparities in stereo images using patches with different sizes. This approach can learn the patches context, improving the accuracy of the estimated costs. We apply the semi-global matching method and the median filter to increase further the robustness of matching costs. We trained and evaluated our approach using the Middlebury database, and, through the bad pixel evaluation, we demonstrate that our approach achieved an accuracy improvement of approximately 22%, compared to the results obtained by single-window CNN-based approaches.
Raising Awareness for IT Security in Higher Education - A Teaching Experiment on SQL Injection for Non-Computer Science Majors

This is a preliminary case study examining the use of a hands-on IT security activity to raise awareness regarding IT security issues at undergraduate non-major students with little or basic knowledge in SQL and programming. In order to communicate IT security to this target group in an understandable and clear sense, the learning scenario focuses only on the specific threat caused by SQL injection. The evaluation of the scenario by questionnaires proved the potential of hands-on SQL injection exercises and simulating a hacker attack as a successful approach for IT-security education.
From Present to Future: Developing Personas Combining Scenario Thinking and LDA Model

Personas help design teams understand user expectations and set goals. Occasionally, personas lose effect due to a lack of foresight and extensive data. This paper provides a persona method combining scenario thinking and the LDA model. We optimize the traditional personas by integrating existing methods. The method enhances the ability to collect predictive information and analyze big data has a better value of foresight. The workflow of this method is introduced in a future mobile system that is a forecast scenario. Eight foresighted personas are obtained in this practice case, which shows the applicability of the method to gain insight into future user types.
Image-text discourse coherence relation discoveries on multi-image and multi-text documents

Abstract The occurrences of the image-text pairs are common in social media such as image captions, image annotations, and cooking instructions. However, the links between images and texts as well as the coherence relationship among them are always implicit. We developed a multi-modal approach by firstly establishing the links between image and text in an unsupervised way, then discovered their coherence relations through computational models of discourse to improve the consistency and quality of the images and their corresponding texts and also explore a dynamic and flexible image-text coherence relation.
Research on Hybrid Algorithm Based on Particle Swarm Optimization and Cuckoo Search Algorithm

For premature convergence and instability of particle Swarm Optimization (PSO) and Cuckoo Search algorithm (CS) in solving function optimization problem, a new hybrid algorithm which based on the combination of PSO and CS is presented. In this algorithm, all individuals are randomly divided into two populations, and the evolutions of two sub-groups are simultaneously performed with different optimization strategies. The information interaction mechanism with last out strategy is introduced to achieve complementary advantages between the algorithm. Finally, the algorithm is put through six benchmark functions and an application example, the results demonstrate that its a powerful global optimization algorithm with rapid convergence rate and high computational precision.
Speech2Phone: A Novel and Efficient Method for Training Speaker Recognition Models

In this paper we present an efficient method for training models for speaker recognition using small or under-resourced datasets. This method requires less data than other SOTA (State-Of-The-Art) methods, e.g. the Angular Prototypical and GE2E loss functions, while achieving similar results to those methods. This is done using the knowledge of the reconstruction of a phoneme in the speaker’s voice. For this purpose, a new dataset was built, composed of 40 male speakers, who read sentences in Portuguese, totaling approximately 3h. We compare the three best architectures trained using our method to select the best one, which is the one with a shallow architecture. Then, we compared this model with the SOTA method for the speaker recognition task: the Fast ResNet–34 trained with approximately 2,000 h, using the loss functions Angular Prototypical and GE2E. Three experiments were carried out with datasets in different languages. Among these three experiments, our model achieved the second best result in two experiments and the best result in one of them. This highlights the importance of our method, which proved to be a great competitor to SOTA speaker recognition models, with 500x less data and a simpler approach.
국어교육에서 이론적 지식과 실천적 지식 - 아리스토텔레스의 지식론을 중심으로 -

This study revisited the issue of language use, based on Aristotle''s theory of knowledge. Aristotle divided knowledge into episteme (theory), phronesis (wisdom), and techne (skills), which are aimed at describing truth, goodness, and beauty in human behavior. They are homogeneous and work integrally with each other to enable excellence in thinking, such as language use. Based on this, the “knowledge” of national Korean language arts curriculum (KLAC) is aligned with episteme, “skill” with techne, and “authenticity” with phronesis. While episteme provides theoretical knowledge of KLAC, both phronesis and techne are the basis of practical knowledge of the KLAC. The 6th national KLAC syllabus was best aligned with Aristotle''s perspective. Theoretical knowledge and practical knowledge of teachers should be interactively acquired during teacher education, such as pre-service education, practice teaching, and in-service education. The early viewpoint of the KLAC(from the 6th period to the 2009 period) was aligned with Aristotle''s theory. This means that the 6th curriculum viewpoint will be useful for the future design of KLAC.
Building a Mobile Application by Combining Third Party Platforms in Order to Reduce Time, Costs and Improve Functionality

The article explains the collaboration between two different programs like structure (web design and android programming) in order to shorten the time process in design, reduce costs and improve the graphical results. Exemplifying will be explained using Wordpress and Android studio demonstrating the effectiveness of the programs in their achievement.
A Method to Combine Multiple Recommendation Lists

Many studies have reported that combining multiple recommender systems improves their accuracy. In such a combination process, it is important to combine algorithms with different properties properly. However, many of the existing Hybrid Recommender Systems (HRS) can only combine specific algorithms. This study proposed a new HRS, Rescoring Hybrid Recommender System (RHRS), that integrate arbitrary recommendation lists. RHRS can integrate not only collaborative filtering but also popularity rankings and new arrivals lists into one list. It has the following features. (1) Unify the definition of the recommended score of an item in each recommendation list by scoring according to each list’s position. (2) Define the combined weight of the recommendation list as a function of the recommending situation. (3) Optimize the weight of the recommendation list according to the situation. We verified this RHRS with Netflix dataset and confirmed the following results. (1) RHRS has higher recommendation accuracy than existing HRS. (2) RHRS achieves both accuracy of the popularity rankings and diversity of the recommendation list. (3) RHRS recommends items to new users based on popularity ranking and uses collaborative filtering to reflect users’ usage history.