Reviewerly/exHarmony huggingface dataset:
Method of Topological Roughness in Tasks of Research and Control of Synergetic Systems

The method of a research of roughness of dynamic systems based on a concept of roughness according to Andronov—Pontryagin and named with "method of topological roughness" is considered. The concepts "roughness" and "bifurcation" of dynamic systems formulated at a dawn of formation of the scientific direction of mathematics — topology, are given by the great French scientist A. Poincare. Also the concept of roughness according to Andronov—Pontryagin is formulated and conditions of accessibility of required roughness of a dynamic system are defined. The definitions of concepts of the maximum roughness and minimum non roughness of dynamic systems entered by the author earlier are given. The corresponding theorems of necessary and sufficient conditions of accessibility of the maximum roughness and the minimum non roughness and also emergence of bifurcations of topological structures of dynamic systems, which were proved in the fundamental works of the author given in the list of references are formulated. At the same time it is claimed that sets of rough and not rough systems make roughnesses of a set, continuous on an indicator. As a roughness indicator in a method the number of conditionality of a matrix of reduction to a diagonal (quasidiagonal) type of a matrix of Jacobi in special points of phase space of a system is used. The method allows to controling roughness of control systems on the basis of the theorem formulated with use of the matrix equation of Sylvester and proved in works of the author which is also provided in this work. The main stages of researches of roughness and bifurcations of systems by means of the considered method are formulated in the form of the corresponding algorithm. In work questions of synergetic systems and chaos (strange attractors) in them, founders of science of synergetics — H. Haken, I. Prigozhin are briefly stated. The method can be used for researches of roughness and bifurcations of dynamic systems and also synergetic systems and chaos of the different physical nature. In works of the author the method is approved on examples of many synergetic systems, such as Lorenz attractors and R ö ssler, Belousov-Zhabotinsky’s systems, Chua, "predator-prey", Henon, Hopf’s bifurcations, etc. In this work of a possibility of a method are illustrated on examples of synergetic system Chua and also a technical system in the form of the nonlinear servomechanism.
학교에서 학생의 종교의 자유에 관한 연구

The purpose of this study is to understand students’ religious freedom in schools. First, this study discusses the philosophical justification of religious freedom, and then discusses how to understand the religious freedom of students, especially in the context of Korean schools. This study is concluded with a suggestion for a desirable direction in relation to the religious freedom of students in schools.Needless to say, it is clear that student religious freedom should be respected in schools. In a democratic society like Korea, respecting student religious freedom will give students an important opportunity to choose the way they want to live, express it, and learn about their rights and responsibilities as citizens. On the other hand, the ‘special environment’ of schools may limit or encourage student religious freedom in schools.Especially in the case of Korea where private schools play the role of public education, there is a high possibility of conflict between student religious freedom and freedom of religious education at private schools. In this circumstance where the private school is serving as a public education with enormous economic support from the Korean government, the school should ensure students’ freedom of choice in religious classes or activities and allow students to express their religious beliefs freely.
A Meta-Model for Strategic Educational Goals

Metamodeling technique is adopted widely in different fields related to software and system engineering.A meta-model represents the abstraction of a detailed design at multiple level.It is used in any structured environment ruled by a certain constraints and obligations and instantiate different platform specific domains from a single platform independent domain.This paper proposes a new model-driven approach for generating and analyzing automatically the outcome measures of strategic educational goals model.A new meta-model augmented with arithmetic semantics is created for Strategic Educational Goals where a set of outlines defines the enhancement framework of an academic organization.The vision, mission, program educational objectives and student outcomes are the four common strategic educational goals.These Goals support the performance roadmap to measures the institution situation and progress.The proposed meta-model is used to evaluate the strategic educational goals in a formal way, improve the continuous improvement process in academic organizations and allows the assessment at different level of management.
The Architecture of Fuzzy Logic Automat of Parallel Action for the Intelligent Smart Grid Networks

Some technology introduction problems of Smart Grid in the electric networks of Ukraine are considered in the article. It is offered to apply the automats of parallel action in control system by a power supply. It is offered to make alteration in the structure of parallel action classical automat for the decision of problem vagueness detains. The general strategy is set for the construction of parallel action automats with fuzzy logic.
The Promise of Data Science for the Technosignatures Field

This paper outlines some of the possible advancements for the technosignatures searches using the new methods currently rapidly developing in computer science, such as machine learning and deep learning, as well as a couple of case studies of large research programs where such methods have been already successfully implemented with notable results.
Multi-task pre-training of deep neural networks

In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology. It is motivated by the fact that many small and medium-size datasets have been released by the community over the years whereas there is no large scale dataset similar to ImageNet in the domain. We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images. Then, we propose a simple architecture and training scheme for creating a transferable model and a robust evaluation and selection protocol in order to evaluate our method. Depending on the target task, we show that our models used as feature extractors either improve significantly over ImageNet pre-trained models or provide comparable performance. Fine-tuning improves performance over feature extraction and is able to recover the lack of specificity of ImageNet features, as both pre-training sources yield comparable performance.
Developing Effective Software Productively

It is not uncommon to hear laments about how long it takes to build software systems and how often, once built, those systems fail to meet the needs and desires of users. Given that attention has been paid to how we build large software systems for over fifty years, you might wonder why we have not figured out how to build the systems people want in a reasonable amount of time. To put the problems into perspective, fifty years is half the life-span of a Galapagos turtle and many software systems may be amongst the most complex systems ever built by humans. In that light, perhaps it is not surprising that we have not figured it all out. In this talk, I will explore what productivity means to software developers, how we might track the value that is delivered in software produced by developers and how we might begin to think about measuring the productive delivery of effective software.
Passive acoustic switch allows for sound wave control without complex electronics

Using a moving diaphragm connected to shunt circuits, acoustic transmission can be manipulated in a passive design.
Bag of Negatives for Siamese Architectures.

Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy.
A task-based evaluation methodology for visual representation of dynamic networks

Current evaluation approaches for visualization strategies of dynamic networks are focused on maintaining the mental map ofthe network over the time or keeping a certain shape to make iteasy to navigate, however the available tools for analyzing temporal network have not been evaluated in terms of how easy to usethey are to perform exploratory data analysis tasks with dynamicnetworks. In this work we present an evaluation methodologythat guides the usability assessment of software tools used to analyze dynamic networks by using the standard ISO 9241-11. Thismethodology has been applied successfully with two popularopen source tools used to analyze temporal networks.
Implementation of Image SuperDistribution System

Content Providers (e.g. Disney, Sony), who make the content, distribute their own content through Content Platformers (e.g. Netflix, Amazon) on the mobile for the security and platform dependency reasons. The one of reasons why Providers use Platformers is that it is required to not only build and deploy a native application of iOS and Android, but also make users download it to consume digital content on the mobile. However, it is not easy to all the Providers, so that they still have been using Platformers. As a result, users must use Platformers' applications and they take a revenue share, which affects adversely to Providers. In this paper, the implementation and evaluation of Image SuperDistribution System (ISDS) [1] is proposed, which uses a digital image as a content distribution media with Robust Index Code (RIC) [2, 3, 7]. In ISDS, Providers upload their content to our implemented content pool console. Subsequently, a RIC image and a url, linked with the uploaded or linked content, will be generated. RIC is a robust codemark targeting digital images so that it is possible to link content with a digital image and is indispensable in ISDS. Users can easily access to the content clicking the url or decoding the RIC image with the RIC SDK. Hence, saving content as a digital image into the device gallery makes a user manage all the content and open them easily. From the evaluation of a user experience, 96% of testers said to want to use the ISDS and 100% of them also said that saving content as an image is not weird. Therefore, the new system achieves improving user's convenience and independence from Platformers.
Extending Bidirectional Language Model for Enhancing the Performance of Sentiment Analysis

Sentiment analysis provides an analysis about the writer’s emotion conveyed in the text. It uses natural language analysis to predict the sentiment of the writer. The proposed work describes an effective and efficient natural language approach to realize multi-classification for textual data according to their respective sentiments. Proposed system employs a RNN language model based on Long-Short term memory (LSTM) over pre-trained word vectors that was generated from different language model for sentiment classification. In order to improve the performance of sentiment analyser, we have used a bidirectional language model—embeddings from language model layer (ELMo). We have merged the Natural language processing and Deep learning techniques to analyse and achieve an important emotion from the long sentences fed to the system. The proposed system is compared with state-of-art approaches such as—Simple Multilayer Perceptron, recurrent neural network (RNN) and LSTM. These techniques are thus being applied to classify sentiments on the dataset of imdb-movie review (Mass et al. in Learning word vectors for sentiment analysis. Association for Computational Linguistics, pp 142–150, 2011 [1]). These techniques have improved the system to capture syntactic and semantic relationship that can help in identifying the sentiments.
Distributed quantum radar (Conference Presentation)

Distributed Sensor Networks are often implemented to overcome some of the challenges presented by a single monostatic sensor system. In this paper we consider the possibility of geographically-distributed quantum radar nodes for improved target detection. Our theoretical design assumes N quantum sensor nodes with transmit-detect capabilities. One of these nodes is chosen to an “entangler node”, which generates entangled photon pairs and uses quantum channels to transmit, swap or teleport the photons to nodes A and B. One of the nodes retains the photon as an idler while the other transmits its photon as the signal. When this process is repeated across several nodes, there is a clear advantage of having different bearing angles to the target among the N nodes. Furthermore, the position and orientation of these sensor nodes could be actively optimized to maximize information about the state of the target. In addition, because the photon frequencies can be chosen to be independent of N, the system generates virtual modes that increase the performance of a single quantum sensor. The proposed design could be generalized to maintain the equivalent of a distributed quantum register among the N nodes so that the sharing of classical information from the detection among the nodes can permit state estimation to be performed in a completely uniform and consistent way. More specifically, each node receives a signal photon which is compared to its idler photon to produce something that relates to the state of the target but is completely non-informative to the individual nodes. It is only when the quantum information from the nodes is received at a secure central node that a full estimate comprising all information about the target from all nodes can be constructed. As we will discuss, the system not only is more information-efficient but also provides a certain level of security because any classical information leakage at a node (i.e., compromised by an adversary) will not actually reveal anything about the state of the target. Therefore, a set of geographically distributed quantum sensors can be treated as a single logical quantum radar device.
Development of NN classifier for recognition of human moods

Numerous works have been carried out in the field of recognition using artificial neural network, based on audio, video and image processing. Face recognition has been found to be one of the major topics of interest in this field. Recognition of moods opens its ways, when it comes to face recognition. Face recognition can pave its ways to new ideology of recognising human moods. This paper focuses on development of a NN classifier for recognition of human moods from facial expressions. Apart from a normal mood and the six basic moods (anger, disgust, fear, sad, surprise and happy), the three added moods viz. contempt, courage and desire forms the new and unique feature of this work. Gabor filter and principal component analysis have been utilised for feature extraction and neural network as classifier for classifying various moods on the basis of various face features related to the moods.
Introduction to Open Source - Introduzione all'Open Source

Speaker for the AIB Toscana Training. Course funded by the Regional Government of Tuscany9 January - 16 February 2012AIB - Associazione Italiana Biblioteche (Italian Library Association)First draft of presentation.https://orcid.org/0000-0002-8183-8483SlideShare
THE IMPLEMENTATION OF PROJECT-BASED LEARNING (PBL): VIDEO PRODUCTION AS A PROJECT IN TEACHING SPEAKING SKILL

The research is aimed to answer the question how project-based learning using video production is implemented in speaking class. The research also tries to find out whether or not the implementation of project-based learning using video production in speaking class significantly improves students’ speaking skill. Furthermore, the students’ responses to the implementation of project-based learning using video production are also investigated. This research involved two classes. The data were analyzed by using mixed method. The results showed that all of the activities in the classroom met the standard of PBL classroom activities. However, in the learning process, the students found the problems in how to construct the sentence, pronounce the word, and record and edit the video. The teacher become the facilitator to solve their problems. Overall, the students learn how to speak while creating the project. The result of the statistical computation showed that the implementation of project-based learning using video production improved students’ speaking skill by the value of tobt(9.811) which greater than tcrit (2.002, ?=0.05, df= 58). In conclusion, the null hypothesis was rejected. In addition, the data from questionnaire demonstrated that the students show positive response toward the implementation of project-based learning using video production.
The Online Social Networks Analysis: State of the Art

Nowadays, the Online Social Networks OSN emergence has expanded the scope of these needs.In fact, besides the direct links between users, the OSN have various important information about user profiles, tastes and interactions or activities.This information richness has been exploited in the literature in particular social mining techniques to answer new analytical purposes such as influence analysis, trust analysis, opinion mining, recommendation, e-reputation, protection of privacy and detection of experts.
The Impact of Technology on Education in Iraqi Kurdistan Region, Sulaimani Province Schools

Educational technology is a fast-growing and increasingly developed subject in education during the past 50 years. The focus of the development of its theories and research is oriented into the methods and effectiveness of its implementation. Education is something that was once not available in certain parts of the world. Understanding the ways, technology has helped with education and the actual impact made will help you to see how vital technology has become effective in the education world. This paper uses a mixed method to collect and analyze data from 30 teachers and 30 students from 30 schools in Sulaimani Province gauging the impact of technology on teaching and learning in their schools. It asks whether technology has any impact on the teaching and learning process and also asks to what extent teachers and students use technological means for teaching and learning. As a result of the interview which has been conducted by the researcher with both students and teachers, there is clear and compelling evidence to state that technology has a tremendous impact positively in both learning and teaching processes. Interestingly, as concluded in this research, students more use technological means for education purposes than teachers. http://dx.doi.org/10.14500/icelc2019
PELAKSANAAN DAK FISIK BIDANG PENDIDIKAN DI KABUPATEN AGAM

The aim of this study was to find out the implementation of the Physical Special Allocation Fund (DAK) in the Basic Education level in Agam Regency. This study uses a qualitative approach. Respondents were Head of Elementary and junior SecondarySchool Division, as well as managers of Physical DAK in Elementary and Middle School. Data collection were carried out using clustered group discussions (DKT) and secondary data. Analysis data used qualitative descriptive technique. The results of the study show that the implementation of education infrastructure improvement is carried out in a self-managed manner and the procurement of educational facilities is carried out by auction or by e-catalog. All of the education infrastructure improvements can be carried out (100%) both in elementary and junior high schools. Whereas not all can be implemented to improve education facilities, especially in elementary schools. To procure library collections in the Elementary School, Elementary School Division in the regency must refund as much as IDR 2.6 billion, because it cannot carry out the procurement of library collections due to the absence of providers who pass the selection for auction. Procurement of educational equipment and media can be implemented in Junior Secondary School, because all goods needed in the procurement of equipment and educational media are in the e-catalog, making it easier for DAK managers to procure an increase in educational facilities. In addition, the ecatalog procurement process is faster and safer.
Penerapan Media Komik Digital terhadap Pemahaman Pembelajaran Matematis Siswa SMP

This study aims to determine the increase in understanding of mathematical concepts of students by using the concept of digital comics in learning better than students who use conventional learning. The method used in this study is the quantitative research method, with the design of the Nonequivalent Control Group Design. The results showed that digital math comics were effective enough to improve students' mathematical understanding with an N-Gain value of 69.7%.
Hybrid-Voting: A Hybrid Structured Electronic Voting System

Recent development of electronic voting (e-voting) systems has been focusing on blockchain-based design [4]. Despite blockchain’s advantages in public verifiability, existing blockchain-based voting systems are impractical due to the high block time and transaction costs of the underlying blockchain. To achieve verifiability and efficiency simultaneously, we propose Hybrid-Voting, a hybrid structured e-voting system that combines an untrusted centralized server with smart contracts on Ethereum blockchain. In addition, voter anonymity and privacy are guaranteed by using short linkable ring signature and ElGamal encryption. Our evaluation shows that Hybrid-Voting can support 10k voters by using one commodity computer, and the cost per voter is less than one US dollar, much lower than the cost per voter in today’s elections [1].
RLO: a reinforcement learning-based method for join optimization

Join optimization is one of the most important research problems in database systems. Traditional join optimizers are usually proposed based on heuristics, which are expensive and often fail to generate the optimal execution plan. There are two reasons accounting for this. (1) The optimizers are based on heuristics and only explore a subset of the search space. (2) They do not use the history logs and cannot estimate the goodness of their generated plans on a specific join problem. To tackle these challenges, we propose RLO, a reinforcement learning-based optimizer for join optimization. We model the join optimization problem as a Markov decision process and use deep $Q$-learning to estimate the possible reward of a possible operation. To boost the effectiveness of RLO, we further propose a tree-based embedding method to represent the “state and use a beam search to avoid missing the optimal plans. We implement RLO in Apache Calcite and Postgres. Extensive experiments demonstrate that: (1) Apache Calcite RLO is $10~\\times$–$56~\\times$ faster in finding the execution plan and 80% faster in executing the plan than the state-of-the-art heuristics. (2) Compared with the native Postgres implementation, RLO can be $14~\\times$ faster in finding the execution plan and 12.9% faster in an end-to-end comparison.