Reviewerly/exHarmony huggingface dataset:
세브란스 학생 독립운동-네트워크 형성과 3･1운동의 전국적 확대를 중심으로

This paper examines the independence movement of students, who played an important role in the March 1st Movement, along with religious leaders. In particular, I looked into the Independence Movement developed by the students of Severance Union Medical College, which had the following characteristics. First, they belonged to the religious community of Christianity, and all of them were members of the YMCA and showed solid bonds through meetings. Based on this, they were able to establish solidarity with students from other career (technical) colleges. Second, they had various backgrounds. Since Severance Union Medical College was founded as a union of several Christian denominations, young Koreans, especially young Christians, from throughout the Korean Peninsula who wanted to receive a medical education gathered at this college. Their hometowns were diverse, which served as a great advantage in the independence movement in Seoul and other regions. They lived near the school, where the Seoul station was located, and shared plans of specific steps for the independence movement, exchanging opinions at their boarding house. It was at 126 Hwacheonjeong in Seoul where Bae Dong-seok, Kim Moon-jin and other Severance students lived together and planned the independence movement of students. They were the leaders of the student independence movement who received from several months to one and a half year sentences. In addition, students of Severance Union Medical College distributed the Declaration of Independence, centering on their hometowns, and created the cause of the March 1st Movement occurring in each area. Kim Byung-soo and Bae Dong-seok are representative figures in charge of the movement. As a result, the students of Severance Union Medical College based their independence movement on their educational institutions and their hometowns. These activities were also a big part of the March 1st Movement.
Securing healthcare system using quantum cryptography mechanism

In today’s fastest-growing life, all need to be more secure in terms of advancement in technology. Everything is going to be a part of digitization. In this, almost everyone is using online storage for storing their personal and important and official secret information. To sustain integrity there should be trustworthy services must be carried out in the back end. Online security has been provided for the cloud with a very powerful privacy system. But still, there is a possibility of different interrupts in terms of stacks like DoS, Denial of Service attack can happen any time to break the normal flow of service. Henceforth, this paper will provide a strong security system for data storage with double encryption format and a more complex structure, for we will express the algorithm through the quantum cryptographic environment to use under the homomorphism technique of modern cryptography.
Recent Activities of Frontiers in Education Committee of IEE Japan, from 2018 to 2019

In Frontiers in Education Committee, we develop activity through announcement and discussion in Meetings on Frontiers in Education mainly. Its purpose is to contribute to information sharing about new development of higher education and various solutions to problem such as liberal arts or SDGs.
Human–machine metacommunication towards development of a human-like agent: A short review

This article briefly reviews the research works related to metacommunication. Metacommunication is a term meaning ``communication on communication,'' which is related to marginal communication such as conveying recognition, comprehension, and evaluation of an interlocutor's words. Herein, several research works are reviewed from the metacommunication point of view.
Efficient Training of Deep Classifiers for Wireless Source Identification using Test SNR Estimates

We study efficient deep learning training algorithms that process received wireless signals, if a test Signal to Noise Ratio (SNR) estimate is available. We focus on two tasks that facilitate source identification: 1- Identifying the modulation type, 2- Identifying the wireless technology and channel in the 2.4 GHz ISM band. For benchmarking, we rely on recent literature on testing deep learning algorithms against two well-known datasets. We first demonstrate that using training data corresponding only to the test SNR value leads to dramatic reductions in training time while incurring a small loss in average test accuracy, as it improves the accuracy for low SNR values. Further, we show that an erroneous test SNR estimate with a small positive offset is better for training than another having the same error magnitude with a negative offset. Secondly, we introduce a greedy training SNR Boosting algorithm that leads to uniform improvement in accuracy across all tested SNR values, while using a small subset of training SNR values at each test SNR. Finally, we demonstrate the potential of bootstrap aggregating (Bagging) based on training SNR values to improve generalization at low test SNR values with scarcity of training data.
LibGuides: Library Accessibility: VPATs

Looking for library database accessibility information for SUNY Empire State College? VPATs? Screenreader instructions? Contacts to get a PDF converted or a video captioned? Here you go!
Current Trends for Blockchain and Smart Contracts (In Financial Services)

The most popular virtual currency (bitcoin) has become so popular because it relies on the concept of Distributed Ledger, realized by means of a technology known as BlockChain. This technology removes the need for third parties (such as authorities) that authorize transactions; in fact, it is a peer-to-peer network where consensus to transactions is given by peers. Furthermore, the distributed nature of BlockChain ensures a high-level of robustness to attacks. However, since its birth (a decade ago) many evolutions have been introduced: now, we distinguish between permissionless and permissioned BlockChains, the concept of smart contract is now supported, and various platforms are available. The contribution of this paper is to provide novices with the current trends, in particular by discussing the adoption of BlockChain technology in financial services.
Awareness and perception of use of web 2.0 technologies for collaborative learning in universities: A study of library and information science undergraduate students in Edo State, Nigeria

The study investigated undergraduates' awareness and perception of the use of web 2.0 technologies for collaborative learning in universities in Edo State. Five (5) research objectives were raised to guide the study and were converted into research questions. The survey type of the descriptive research design was adopted with a population of 810 library and  information science undergraduate students from three selected  universities in Edo State. Using the multistage sampling technique, a sample size of 162 respondents was used for the study and questionnaire was adopted as the tool for data collection. Out of 162 copies of  questionnaire administered, 157(97%) was retrieved and found usable. The data were analysed using descriptive statistics (frequencies and percentages). The findings revealed that the level of undergraduates' awareness of web 2.0 technologies for collaborative learning is high and they have positive perception towards the use of the technology. The study also revealed that the level of undergraduates' use of web 2.0 technologies for collaborative learning in universities is high. The study concluded that web 2.0 present almost unlimited opportunities to facilitate collaboration with undergraduate students in different universities and also to enhance learning. The researchers recommended that Libraries should help improve students' perception of the use of web 2.0 technologies by organizing periodic trainings for students on the use of emerging technologies for knowledge acquisition and collaboration among colleague.Keywords: Web 2.0, Collaborative Learning, Undergraduates Students, Social Media, Library and Information Science.
Light Multi-Segment Activation for Model Compression

Model compression has become necessary when applying neural networks (NN) into many real application tasks that can accept slightly-reduced model accuracy but with strict tolerance to model complexity. Recently, Knowledge Distillation, which distills the knowledge from well-trained and highly complex teacher model into a compact student model, has been widely used for model compression. However, under the strict requirement on the resource cost, it is quite challenging to make student model achieve comparable performance with the teacher one, essentially due to the drastically-reduced expressiveness ability of the compact student model. Inspired by the nature of the expressiveness ability in NN, we propose to use multi-segment activation, which can significantly improve the expressiveness ability with very little cost, in the compact student model. Specifically, we propose a highly efficient multi-segment activation, called Light Multi-segment Activation (LMA), which can rapidly produce multiple linear regions with very few parameters by leveraging the statistical information. With using LMA, the compact student model is capable of achieving much better performance effectively and efficiently, than the ReLU-equipped one with same model complexity. Furthermore, the proposed method is compatible with other model compression techniques, such as quantization, which means they can be used jointly for better compression performance. Experiments on state-of-the-art NN architectures over the real-world tasks demonstrate the effectiveness and extensibility of the LMA.
Learning Reusable Options for Multi-Task Reinforcement Learning

Reinforcement learning (RL) has become an increasingly active area of research in recent years. Although there are many algorithms that allow an agent to solve tasks efficiently, they often ignore the possibility that prior experience related to the task at hand might be available. For many practical applications, it might be unfeasible for an agent to learn how to solve a task from scratch, given that it is generally a computationally expensive process; however, prior experience could be leveraged to make these problems tractable in practice. In this paper, we propose a framework for exploiting existing experience by learning reusable options. We show that after an agent learns policies for solving a small number of problems, we are able to use the trajectories generated from those policies to learn reusable options that allow an agent to quickly learn how to solve novel and related problems.
Doubly Sparse Variational Gaussian Processes

The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their complexity and memory footprint. The two most commonly used methods to overcome this limitation are 1) the variational sparse approximation which relies on inducing points and 2) the state-space equivalent formulation of Gaussian processes which can be seen as exploiting some sparsity in the precision matrix. We propose to take the best of both worlds: we show that the inducing point framework is still valid for state space models and that it can bring further computational and memory savings. Furthermore, we provide the natural gradient formulation for the proposed variational parameterisation. Finally, this work makes it possible to use the state-space formulation inside deep Gaussian process models as illustrated in one of the experiments.
Algorithms for Interactive Machine Learning

Author(s): Poulis, Stefanos | Advisor(s): Dasgupta, Sanjoy | Abstract: In interactive machine learning, the learning machine is engaged in some fashion with an information source (e.g. a human or another machine). In this thesis, we study frameworks for interactive machine learning.In the first part, we consider interaction in supervised learning. The typical model of interaction in supervised learning has been restricted to labels alone. We study a framework in which the learning machine can receive feedback that goes beyond labels of data points, to features that may be indicative of a particular label. We call this framework learning with feature feedback and study it formally in several settings.In the second part, we study interaction in unsupervised learning, in particular, topic modeling. Topic models are popular tools for analyzing large text corpora. However, the topics discovered by a topic model are often not meaningful to practitioners. We study two different interactive protocols for topic modeling that allow users to address deficiencies and build models that yield meaningful topics.In the third part, we study interactive machine teaching. Different from traditional machine teaching, in which teachers do not interact with the learners, we study a framework in which interactive teachers can efficiently teach any concept to any learner.
Entanglement Creations and Quantum Gate Implementations of Spin Qubits With Lyapunov Control

In this paper, we propose a scheme for the entanglement creations and the quantum gate implementations of spin qubits via Lyapunov control. We first show that three types of Bell states can be generated in one step with the traditional Lyapunov control. After that, the traditional Lyapunov control is generalized with the design of two Lyapunov functions that characterize the distance between two unitary operators. Thus, the Lyapunov control can be also used in constructing desired unitary operations. The performance of the entanglement creations and the quantum gate implementations are both estimated via numerical simulations. The results show the scheme holds robustness against the errors of the fields and couplings. As the generalization of Lyapunov control can not only be used in spin systems, the scheme may benefit the quantum information processing in different quantum systems.
Public Broadcasting Footage in the Commons

Around the release of the documentary series The Mind of the Universe (2017), public broadcaster VPRO and The Netherlands Institute for Sound and Vision collaborated to distribute the raw materials shot for the series to be released under open licenses. This article reflects on those experiences and gives recommendations for the various stages of production for open licensing. It aims to lower the threshold for other public broadcasters that wish to publish materials in the commons.
Research on Hybrid Communication System for Nuclear Power Plants Safety-DCS

To guarantee the human and facilities safety during emergency states in Nuclear Power Plant(NPP), numerous detectors and sensors are used in the Safety Distributed Control System(Safety-DCS) to prevent the equipments and operators from the further damages that may happened in the unexpected situations. Together with the detectors and sensors, various cables and switches are involved in the entire system. Those security interfaces and cables increase the complexity and volume of the spatial arrangements of the system. Hundreds of Reactor Protection Cabinets (RPC) are used in the Safety-DCS, and the various of cables are even more in typical Safety-DCS of NPP. Currently most of the efforts on the system optimization are focus on the hardware upgrade, while the system framework stands still. This paper chooses to work on the direction of decreasing the complexity of the entire system by using wireless communication devices to replace part of the wired connection equipments, like the tons of electric cables and interfaces. Based on this guideline, a hybrid communication strategy is presented and discussed in both of the feasibility and benefits. In this structure, the general system can be simplified in the overall arrangement with some extra attached benefits.
Visualized Representation of Data Using Regex

With the advent of various technologies, the need of data is increasing day by day.   Data is a collection of complex datasets and massive data that becomes difficult to make the decisions from the complex datasets. To make the complex data easy to understand various types of analysis techniques are applied which are defined as Visual Analysis (VA) and Big Data Analytics (BDA). The challenging issues are the processing and analyzing of the large amount of data. In any business the data generates in large size which is in terabytes. As a result it becomes difficult to make the decisions. For the decisions making data is transmitted into graphical representation termed as Visual Analytic. Visual analytics is related to the visualization of the data and data sets that make the results more interactive.
Identifying Keywords in the Buddhist Canon

This paper discusses approaches to keyword identification in Buddhist texts. The paper first defines what is meant by a keyword and how it differs from terminology and other related concepts. Systematic approaches to keyword extraction with natural language processing methods are described as well as visualization techniques to help researchers identify keywords. Examples from the Heart Sūtra are explored followed by extension to the larger body of the Taishō Tripitaka. The use of term frequency, co-occurrence networks, and collocations is also discussed. The results show that term frequency filtered by terms labelled as Buddhist gives a reasonable set of keywords with subjective assessment based on linguistic and stylistic arguments. Ranking of collocations can indicate predominant word senses. Finally, potential future areas of application of the tools and methods are discussed.
Quantum Private Information Retrieval from Coded and Colluding Servers

In the classical private information retrieval (PIR) setup, a user wants to retrieve a file from a database or a distributed storage system (DSS) without revealing the file identity to the servers holding the data. In the quantum PIR (QPIR) setting, a user privately retrieves a classical file by receiving quantum information from the servers. The QPIR problem has been treated by Song \emph{et al.} in the case of replicated servers, both without collusion and with all but one servers colluding. In this paper, the QPIR setting is extended to account for maximum distance separable (MDS) coded servers. The proposed protocol works for any $[n,k]$-MDS code and $t$-collusion with $t=n-k$. Similarly to the previous cases, the rates achieved are better than those known or conjectured in the classical counterparts. Further, it is demonstrated how the protocol can adapted to achieve significantly higher retrieval rates from DSSs encoded with a locally repairable code (LRC) with disjoint repair groups, each of which is an MDS code.
Dream clanger

'Dream Clanger' is a hybrid art/computer science project that re-imagines AFL Player GPS data and match video. Building on Baden Pailthorpe's 2017 major exhibition 'Clanger', this work pushes the envelope further by integrating machine learning.
IMPROVEMENT OF PRACTICAL TRAINING OF EDUCATION MANAGERS AT UNIVERSITY BY MEANS OF MENTORING AND FACILITATION

The problem of practical training of education managers at universities is the one of current importance in modern theory and practice of professional training of specialists at universities.However, progress in solving this problem is impeded by the presence in academic groups of students with different levels of educational background and different management experience.The article proposes a methodology for organization of students' continuous practice which provides an individual educational trajectory and constant educational and methodological support for students without management experience through mentoring and facilitation.The main point of practice is dividing its contents into compulsory and elective modules.The tasks of the compulsory module were fulfilled by the students who had no experience in holding the position of an educational institution manager; the elective module contained a set of tasks for the students who held senior positions in institutions of general education and acted as mentors for their group mates who had no work experience.In addition, the educational program partners -heads of educational institutions who had received a master degree in previous yearswere involved in the process of practical training.It was hypothesized that such arrangement of practice would significantly improve the quality of practical training of students with or without management experience.In order to confirm the hypothesis, during 2013-2018 a pedagogical
Automation of multicriteria ranking of students using ePortfolio

Introduction . The paper considers improving the management quality of an educational organization through automating the personalized data collection, storage and ranking. The authors have developed a module of the university corporate system, an ePortfolio of students, which implements a multicriteria optimization method for calculating the students’ rating on the basis of the collected and stored data. The study object is multicriteria ranking methods. The subject of the study is the automated calculation of student’s rating within the electronic portfolio. The study objective is to develop tools for collecting, storing and processing data on individual achievements of students and the implementation of the mathematical method of multicriteria optimization for ranking students on the basis of the portfolio data. Practical implications include development of a tool for an effective management of the educational process. Materials and Methods . A prototype module of the university corporate system “ePortfolio” on the platform of 1C: Enterprise 8.3 is presented. To rank students, a special case of the alternative ranking method is implemented in the block of the data analysis module – pairwise comparison in order of their relative importance. At that, a unified scale of relations was used. Research Results . The authors described the scheme of the information system (IS) operation “ePortfolio”, presented a process interaction pattern for the portfolio formation, as well as a diagram of business processes under calculating an individual rating. A fragment of the sample is shown on which the performance of the multicriteria optimization block has been tested. The criteria of the calculations are described, as well as the rule of screening alternatives for solving for the optimum. Discussion and Conclusion . The paper presents the results of the IS block operation on ranking a list of students. The calculation results coincide with the practical data.