Reviewerly/exHarmony huggingface dataset:
Klasifikasi Kanker Kulit Berdasarkan Fitur Tekstur, Fitur Warna Citra Menggunakan SVM dan KNN

Skin cancer is one type of cancer that is quite serious that can not be controlled completely, so that many still result in death, disability and high medical costs. The diagnosis process carried out by dermatologists generally uses the Biopi process which is expensive, painful and requires a long recovery time for the wound, due to taking body tissue that scratches a small piece of tissue or by using a syringe to get a sample. Therefore we need a tool or system that can speed up helping to find out the type of skin cancer suffered, so that it can find out its treatment early by using digital image processing techniques. The purpose of this study is to classify the types of skin cancer based on texture and color image features using the SVM and KNN algorithm. The benefits are expected to help the skin medicine team in diagnosing skin cancer early. The features used are grayscale imagery taken by the average value, standard deviation, skewness, entropy, variance, contrast, energy, correlation, and homogeneity. Furthermore, the value of these features is trained and classified. The classification results using the SVM algorithm have an accuracy value of 69.85%. And accuracy using the KNN algorithm, with a value of K = 2 67.27%, K = 3 67.88%, K = 4 70.15%, K = 5 70.61%, K = 6 69.55%. Thus the best K on KNN is 5, the accuracy is 70.61%. Where the data used are 2637 training dataset images, and 660 test data images. And classified as a class of malignant, benign skin cancer.
Quantum Error Correction in the Presence of Initial System-Environment Correlations

Quantum error correction is studied in a framework consisting of an open quantum system and its environment, jointly subjected to a unitary action, and an interaction-free reference system. It has been shown that coherent information between the initially correlated open system and reference system is conserved in the transmission stage of any quantum communication process, provided that the tripartite input is any pure Markov state and the overall evolution preserves its form. This conservation constitutes the necessary and sufficient condition for accomplishment of perfect error correction by a recovery channel even in the presence of initial system-environment correlations. Explicit expressions of the recovery operators and examples of the joint unitary evolution preserving the form of inputs are given for all classes of pure Markov states.
TOGAF MODELS IMPLEMENTATION IN DESIGN E-CAMPAIGN PUBLIC RELATIONS KPU ASAHAN

KPU is one of the independent state institutions. In carrying out the KPU, the task is to carry out General Elections (Elections) for Members of the DPR, DPD, DPRD, the Elections of the President and Vice President, and the General Elections of Regional Heads and Deputy Regional Heads. General Election or Election is a means of implementing people's sovereignty which is held directly, publicly, freely, confidentially, honestly and fairly. Lack of socialization or news from the mass media about candidates from each legislative candidate from the KPU for the public makes people not know which candidates are good for them to choose. So that causes many people to be abstentions (non-voter) in the selection of legislative candidates, especially members of the Regional Parliament (DPRD). Therefore, researchers designed an e-campaign application using the ADM Togaf model in the design of the web-based system which contained information about candidates for Elections. This system uses the CodeIgniter framework. CodeIgniter is a PHP framework created based on the View Controlleer (MVC) model which aims to make it easier for the admin to develop the system and make the display more attractive so that people are interested in finding information.
Research on the Development Process of a Concept Model Based on Engineering Thought

The development process of the standard concept model can avoid some common mistakes of the concept model. In this paper, in the process of developing the simulation conceptual model, the engineering idea is introduced into it, and the development process of the simulation conceptual model based on the engineering idea is given. The main purpose is to construct the system simulation conceptual model more effectively and improve the development efficiency of the simulation system.
Интерактивные методы обучения математике в технических вузах: образовательный и воспитательный аспекты

The relevance of research aimed at identifying promising interactive methods of teaching mathematics to improve the effectiveness of the educational and upbringing process is proved. The need to systematize ideas about interactive methods of teaching mathematics that contribute to improving the quality of education and upbringing of students is determined. Both educational and upbringing resources of interactive teaching are shown, where education is possible through the purposeful formation of personal qualities due to the development of independent creative and critical thinking. It is noted that software products, information and communication technologies, interactive whiteboards, STEM classes, as components of interactive methods, should be used not only for information transfer, but also contribute to the process of accumulation and exchange of knowledge for the formation of the personality, creativity and abilities of the student. It is defined that the project methods, when the trainees interact to solve the set of tasks for a given period of time, individually or in groups, contribute to the development of internal motivation, independent thinking, self-esteem and social responsibility. At the same time, pedagogical efforts should be aimed at reducing interpersonal tension in the group in order to achieve synergistic effect from working in the team, as well as using the element of the game is necessary for the upbringing that contributes to increasing the confidence, motivation and effectiveness of students' education.
3-To-1 Pipeline: Restructuring Transfer Learning Pipelines for Medical Imaging Classification via Optimized GAN Synthetic Images

The difficulty of applying deep learning algorithms to biomedical imaging systems arises from a lack of training images. An existing workaround to the lack of medical training images involves pre-training deep learning models on ImageNet, a non-medical dataset with millions of training images. However, the modality of ImageNet's dataset samples consisting of natural images in RGB frequently differs from the modality of medical images, consisting largely of images in grayscale such as X-ray and MRI scan imaging. While this method may be effectively applied to non-medical tasks such as human face detection, it proves ineffective in many areas of medical imaging. Recently proposed generative models such as Generative Adversarial Networks (GANs) are able to synthesize new medical images. By utilizing generated images, we may overcome the modality gap arising from current transfer learning methods. In this paper, we propose a training pipeline which outperforms both conventional GAN-synthetic methods and transfer learning methods.
ПІДВИЩЕННЯ КВАЛІФІКАЦІЇ НАУКОВО-ПЕДАГОГІЧНИХ ПРАЦІВНИКІВ В УНІВЕРСИТЕТІ: АКТУАЛЬНІСТЬ ТРЕНІНГІВ

The article is devoted to the survey results of academic staff of the Borys Grinchenko University of Kyiv on identifying topical subjects for qualification training as part of the didactic, research, leadership modules and the module on ICT. The list of topics for training courses was formed in accordance with certain current trends of higher education institutions academic staff continuing professional development, as well as taking into account the European experience of organizing advanced training courses for lecturers at universities. The survey showed that the lecturers’ choice for continuing professional development is fully consistent with pan-European tendencies. The largest number of choices received trainings related to innovative teaching methods, as an improvement of teaching skills and an innovation establishment in teaching are a priority in the European higher education area. The respondents’ great interest was caused by trainings on internationalization in education, which is an important aspect of both European and Ukrainian educational policy. The research showed that lecturers were less interested in training on general and social competencies, motivation and leadership qualities. As part of the module on ICT, academic staff chose trainings, dedicated to teaching improvement with innovations in ICT, that is, didactic issues of introducing the new technological tools into an educational process. The results of the survey can become a basis for a modernization of universities academic staff qualification improvement program in order to acquire new and / or improve previously acquired competencies within professional activity. The perspectives of the further researches will be a justification of competency profiles within the lecturers training program modules.
A Decentralized Multi-authority ABE Scheme in Cooperative Medical Care System

In this paper, we propose an attribute-based encryption (ABE) scheme that can be used in cooperative medical care systems with multiple distrusted principles. Unlike prior multi-authority ABEs, this scheme distributes secret key generation to different medical care centers, which enables distributed authorization and access control. By separating the key generation process among medical care centers and data owners (DOs), our scheme is resilient to collusion between malicious their parties and users. This new Cooperative Medical Care System (CMCS) distinguishes between DO principal and medical care centers: DOs own the data but allows medical care centers to arbitrate access by providing attribute labels to users. The data is protected by access policy encryption over these attributes. Unlike prior systems, attributes generated by medical centers are not user-specific, and neither is the system susceptible to collusion between users who try to escalate their access by sharing keys. We prove our CMCS scheme correct under the Decisional Bilinear Diffie-Hellman (DBDH) assumption; we also include a complete end-to-end implementation that demonstrates the practical efficacy of our technique.
MTrill project: machine translation impact on language learning

Over the last decades, massive research investments have been made in the development of machine translation (MT) systems (Gupta and Dhawan, 2019). This has brought about a paradigm shift in the performance of these language tools, leading to widespread use of popular MT systems (Gaspari and Hutchins, 2007). Although the first MT engines were used for gisting purposes, in recent years, there has been an increasing interest in using MT tools, especially the freely available online MT tools, for language teaching and learning (Clifford et al., 2013). The literature on MT and Computer Assisted Language Learning (CALL) shows that, over the years, MT systems have been facilitating language teaching and also language learning (Nin o, 2006). It has been shown that MT tools can increase awareness of grammatical linguistic features of a foreign language. Research also shows the positive role of MT systems in the development of writing skills in English as well as in improving communication skills in English(Garcia and Pena, 2011). However, to date, the cognitive impact of MT on language acquisition and on the syntactic aspects of language processing has not yet been investigated and deserves further scrutiny. The MTril project aims at filling this gap in the literature by examining whether MT is contributing to a central aspect of language acquisition: the so-called language binding, i.e., the ability to combine single words properly in a grammatical sentence (Heyselaar et al., 2017; Ferreira and Bock, 2006). The project focus on the initial stages (pre-intermediate and intermediate) of the acquisition of English syntax by Brazilian Portuguese native speakers using MT systems as a support for language learning.
Design and Implementation of Four-Meter Reading Sharing System Based on Blockchain

The four-meter reading refers to the unified collection and treatment of the energy data of water, electricity, gas and heat, and is a part of the national energy strategy. Although the state has already proposed four-meter reading, it has developed slowly and has little effect. The main reason is the problem of the reliability of the transmission of data in the channel and the collaboration problems caused by the industrial barriers of various energy companies. In this paper, we explore the problem of using the blockchain technology to solve the problem of the application of the four-meter reading application scenario. The research content of this paper is based on the fabric alliance chain, design data format and data management model, guarantee data security isolation, and realize a set of four-meter reading sharing system based on blockchain. For the research content of the four-meter reading scenario, firstly we investigate the knowledge of the four-meter reading application scenario, and then learn the blockchain technology and the fabric blockchain open source project. After that, the system is analyzed and designed, including requirements analysis, data isolation design, and system design. Next, the implementation of the system, including the construction of the fabric blockchain network, the preparation of smart contracts, the development of the client interface. Finally, the system is tested to verify that the system is functioning properly.
On the Separability Problem of String Constraints

We address the separability problem for straight-line string constraints. The separability problem for languages of a class C by a class S asks: given two languages A and B in C, does there exist a language I in S separating A and B (i.e., I is a superset of A and disjoint from B)? The separability of string constraints is the same as the fundamental problem of interpolation for string constraints. We first show that regular separability of straight line string constraints is undecidable. Our second result is the decidability of the separability problem for straight-line string constraints by piece-wise testable languages, though the precise complexity is open. In our third result, we consider the positive fragment of piece-wise testable languages as a separator, and obtain an ExpSpace algorithm for the separability of a useful class of straight-line string constraints, and a Pspace-hardness result.
Toward the use of quantile fuzzy transforms for the construction of fuzzy association rules

This paper analyzes the possibility of defining fuzzy association rules by means of direct quantiles F-transforms. The set of fuzzy association rules is used in a fuzzy inference system, defined by means of the inverse quantile F-transform. The obtained inference system reminds the Takagi-Sugeno one due to the use of a weighted sum to perform the inference. However, there is an important difference: the output is a fuzzy set and, as a result, we require the use of a defuzzification procedure. In addition, in this paper we prove experimentally that the fuzzy set obtained as the output of the proposed inference system is related to a probability distribution.
Self-reconfigurable Field Programmable Photonic Gate Arrays Using First-order Optimization Techniques

We demonstrate a novel technique for the automated synthesis of photonic structures in a field-programmable photonic gate array using first-order optimization techniques and defining customized cost functions as per their main figures of merit.
Research on Dual Anti Duplication and Anti-counterfeiting Technology of QR Code Based on Metamerism Characteristics

Ordinary QR code does not have the function of anti duplication, and it is easy to be copied and counterfeited. In order to overcome this loophole, an anti-counterfeiting method using CMYK color printing metamerism characteristics + embedded pseudo-random noise is adopted to generate a QR code with dual anti duplication and anti-counterfeiting (secure QR code for short). Firstly, the anti-counterfeiting information is encoded and embedded into the ordinary QR code in the form of noise to generate the first-level anti-copying and anti-counterfeiting QR code with information. Then the CMYK color ratio is modulated with the second-level anti-counterfeiting information to satisfy the metamerism characteristic, and the second-level anti-counterfeiting is realized. Experimental tests show that the secure QR code designed in this paper has good information hiding performance, anti-replication performance and multi-code integration function, and can be used in anti-counterfeiting of trademarks, books, labels, etc.
Multiview Active Learning for Scene Classification with High-Level Semantic-Based Hypothesis Generation

Multiview active learning (MVAL) is a technique which can result in a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. This paper made research on MVAL-based scene classification for helping the computer accurately understand diverse and complex environments macroscopically, which has been widely used in many fields such as image retrieval and autonomous driving. The main contribution of this paper is that different high-level image semantics are used for replacing the traditional low-level features to generate more independent and diverse hypotheses in MVAL. First, our algorithm uses different object detectors to achieve local object responses in the scenes. Furthermore, we design a cascaded online LDA model for mining the theme semantic of an image. The experimental results demonstrate that our proposed theme modeling strategy fits the large-scale data learning, and our MVAL algorithm with both high-level semantic views can achieve significant improvement in the scene classification than traditional active learning-based algorithms.
A methodology for encoding regulatory rules

© 2020 CEUR-WS. All rights reserved. This paper introduces a methodology for the encoding of rules into a semantic logical format to facilitate the automated reasoning process. We demonstrate how to identify, capture, combine, and thus formulate all the components from rules into a computationally-oriented formalism. The need for the methodology is motivated by the desire for automated reasoning of automated vehicle information regarding traffic rules. We use Defeasible Deontic Logic as a formal foundation of our methodology. The overtaking traffic rule is our use-case to illustrate the usefulness of our methodology. Through this use-case, it is seen that the logical semantic representation of the traffic rules seems conceivable to support automated reasoning. This paper includes the source materials, the use cases, proposed methodology, and the example of encoding.
GlobalSearchRegression.jl: \ Building bridges between Machine Learning and Econometrics in Fat-Data scenarios

The aim of this paper is twofold.The first one is to describe a novel research-project designed for building bridges between machine learning and econometric worlds (ModelSelection.jl).The second one is to introduce the main characteristics and comparative performance of the first Julia-native all-subset regression algorithm included in GlobalSearchRegression.jl (v1.0.5).As other available alternatives, this algorithm allows researchers to obtain the best model specification among all possible covariate combinations -in terms of user defined information criteria-, but up to 3165 and 197 times faster than STATA and R alternatives, respectively.
Modia3D: Modeling and Simulation of 3D-Systems in Julia

Modia3D is an experimental Julia package to model and simulate 3D mechanical systems.Ideas from modern game engines are used to achieve a highly flexible setup and features of multi-body algorithms are used to get a rigid mathematical formulation and support, for example, of closed kinematic loops.Collision handling is performed on convex geometries with elastic response calculation.A Modia3D model is solved with a variable-step solver.This is important to combine Modia3D with the equation-based modeling system Modia in the future.
Improving Students’ Reading Comprehension By Using Numbered Head Together (NHT) Strategy in SD IT Fadilah Pekanbaru

This action research is aimed at improving the students‘ reading comprehension by using Numbered Head Together (NHT) strategy in five (A) grade at SD IT Fadilah in Pekanbaru. The researcher applies the discussion method in learning English at SD IT Fadilah Pekanbaru. Numbered Head Together (NHT) or numbering thinking together is a type of cooperative learning that is designed to influence patterns of interaction and as an alternative to classroom. The result shows that the use of NHT strategy has successfully made the students able to comprehend the reading. It can be seen from student learning outcomes obtained a value of 54.60 with a low category, in UH I increased learning outcomes with the acquisition of value of 65.76 in the medium category, and in UH II student learning outcomes increased again with the acquisition of a value of 79.25 with high category. Based on the above research data, it appears that student learning outcomes in English have improved after the Number Head Together (NHT) strategy was applied. The Numbered Head Together (NHT) strategy generally involves students in reviewing the material in the lesson and understanding of the material provided.
НЕПЕРЕРВНИЙ ЕКСПЕРТНИЙ КОУЧИНГ ДЛЯ ПІДВИЩЕННЯ КОМПЕТЕНТНОСТІ УЧАСНИКІВ ІНКЛЮЗИВНОГО ПРОЦЕСУ В ЗАКЛАДАХ ОСВІТИ

In the article, the authors analyze ways to increase the professional competence of teachers of multidisciplinary IEP teams. This allows the conclusion that that the use of coaching is appropriate for mastering support specialists with inclusive technologies, as well as the ability to work in a team interaction mode. Despite the obvious advantages of teamwork, in Ukraine this format of work is still not mastered in an inclusive educational environment. In the article the authors present the structure of the training program based on the team management model for significant achievements (R. Hackman). We conducted a training program for 10 IEP teams, aimed at increasing the level of team interaction and inclusive competence of specialists. Formative influence was done directly during the professional activity of teachers and was accompanied by prolonged expert coaching. In their study, the authors applied a special design scheme for teamwork. This scheme was concretized in the fact that the participants of the IEP teams used the experience gained in the training sessions in the practice of their professional activity, critically comprehended and analyzed their new experience, sought to comprehend the factors of both success and failure. Together with the coordinator and experts, all participants of the IEP teams developed rules for the team, established feedback, sought to influence the focus on mastering the modern approaches of the inclusive process at the level of the entire educational institution. In turn, this maintained the necessary level of motivation and joint intentions to introduce a competently constructed educational environment. To assess the state of inclusive competence formation before and after the training program, we used teacher self-assessment method «Professional Development Tool for Improving the Quality of Practice in primary school». As a result, all teachers have achieved a significant increase in the effectiveness of interdisciplinary support for children with special needs, which have reflected in the positive changes that have occurred in teachers at the professional, interpersonal and personal levels.