Reviewerly/exHarmony huggingface dataset:
Freedom of Education and the Role of the State - Focusing on the Freedom of Private Schools -

Article 31 of the Constitution of the Republic of Korea stipulates the right of all citizens to receive an education. This generally encompasses the idea of this right being a fundamental social right. Considering that the principle of the social state is understood as a basic principle of the Constitution, that fundamental social rights are stipulated in the Constitution, and that education has significant realistic and normative implications, it is natural that the role of the state in education is deemed highly important. However, this does not mean that education should solely be under state responsibility. In the most fundamental sense, parental education of their children is an exercise of natural freedom, and the significance of the freedom of private schools can be recognized in this context. Today’s free and democratic state envisages the realization of a legal and political order that enables free individuals to coexist and prosper as the fundamental starting point of its existence. If education is understood to be only an obligation of a nation as a social state, it would be difficult for the state to act upon the original significance of its status as a guardian of the core values and order of the community that promotes the freedom of different individuals. Following the era when religion occupied the dominant position in education and since the adoption of modern constitutionalism, the cultivation of knowledge and qualifications by members of a political community formed at the national level has become the public goal of education. As the state carried out the task of establishing practical conditions for freedom and equality to thrive, the state’s role in education has been further expanded and justified. However, education is not merely a transfer of a certain consensus shared among members of a political community or a preparatory process that enables economic activity. It goes beyond such functions —it belongs to even a higher dimension in that it has profound significance extending to a very personal and intimate realm that includes exploring, challenging, reflecting on, and envisioning the reality and meaning of existence and life. As a result, it is outside the capacity of the state to provide diverse educational programs on a sufficient scale that meets the personality and judgment of different individuals; even if this were possible, it would not be appropriate. Through the diverse and creative education of private schools, a wide range of possibilities for the realization of individual character and development of personality can be guaranteed.
Speech Emotion Recognition: Recurrent Neural Networks compared to SVM and Linear Regression

Emotion recognition in spoken dialogues has been gaining increasing interest all through current years. A speech emotion recognition (SER) is a challenging research area in the field of Human Computer Interaction (HCI). It refers to the ability of detection the current emotional state of a human being from his or her voice. SER has potentially wide applications, such as the interface with robots, banking, call centers, car board systems, computer games etc. In our research we are interested to how, emotion recognition, can top enhance the quality of teaching for both of classroom orchestration and E-learnning. Integration of SER into aided teaching system, can guide teacher to decide what subjects can be taught and must be able to develop strategies for managing emotions within the learning environment. In linguistic activity, from student's interaction and articulation, we can extract information about their emotional state. That is why learner's emotional state should be considered in the language classroom. In general, the SER is a computational task consisting of two major parts: feature extraction and emotion machine classification. The questions that arise here: What are the acoustic features needed for a most robust automatic recognition of a speaker's emotion? Which methods is most appropriate for classification? How the database used influence the recognition of emotion in speech?
A Method for Service Orchestration in hSFC

Hierarchical SFC is a network architecture for implementing SFC thechain with an ordered set of service functions which could be deployedin multiple geographically dispersed networks. How to forward trafficbetween networks in Hierarchical SFC is what the draft wants topresent. This document proposes a mapping-based forwarding methodwith coordinated orchestration by the translation of H-SFC and I-SFCto forward traffic between networks in Hierarchical SFC.
Order-Preserving Pattern Matching Indeterminate Strings

Given an indeterminate string pattern $p$ and an indeterminate string text $t$, the problem of order-preserving pattern matching with character uncertainties ($\mu$OPPM) is to find all substrings of $t$ that satisfy one of the possible orderings defined by $p$. When the text and pattern are determinate strings, we are in the presence of the well-studied exact order-preserving pattern matching (OPPM) problem with diverse applications on time series analysis. Despite its relevance, the exact OPPM problem suffers from two major drawbacks: 1) the inability to deal with indetermination in the text, thus preventing the analysis of noisy time series; and 2) the inability to deal with indetermination in the pattern, thus imposing the strict satisfaction of the orders among all pattern positions. This paper provides the first polynomial algorithm to answer the $\mu$OPPM problem when indetermination is observed on the pattern or text. Given two strings with length $m$ and $O(r)$ uncertain characters per string position, we show that the $\mu$OPPM problem can be solved in $O(mr\lg r)$ time when one string is indeterminate and $r\in\mathbb{N}^+$. Mappings into satisfiability problems are provided when indetermination is observed on both the pattern and the text, and results concerning the general problem complexity are presented as well, with $\mu$OPPM problem proved to be NP-hard in general.
KAJIAN KONSEP EKOLOGIS PADA GEDUNG PERPUSTAKAAN PUSAT UGM

&#x0D; &#x0D; &#x0D; Title: The Study of Ecological Concept in the Central Library of Gadjah Mada University&#x0D; Ecological architecture becomes one of the concept as well as solutions in the building design which minimizes environmental damages and maintains the quality of human life. The central library of Gadjah Mada University in Yogyakarta is used as the research object to rate its ecological level. This study uses a quantitative method to rate its ecological level based on the ecological design principles of Sim Van der Ryn such as ecological accounting, design with nature, and making nature visible. The assessment parameters are based on some references such as the greenship of GBCI, the principles of tropical architecture, and the principle of building accessibility. The results showed that generally, the library has applied ecological concept well through some strategies such as extensive greening, energy efficiency, adequate natural lighting, slim building masses, shading elements, and some facilities for the difable. Some measurements are done to find out the ecological performance of the building such as energy efficiency by 39.17%; daylighting 300 lux at 60.67% of indoor area, visualization of solar radiation reception, and OTTV value by 19,85 watt/m2. However, there are some necessary evaluations such as leakage on the ceiling or the application of greenroof that can be further optimized.&#x0D; &#x0D; &#x0D;
Combining Particle Swarm Optimizer with SQP Local Search for Constrained Optimization Problems

The combining of a General-Purpose Particle Swarm Optimizer (GP-PSO) with Sequential Quadratic Programming (SQP) algorithm for constrained optimization problems has been shown to be highly beneficial to the refinement, and in some cases, the success of finding a global optimum solution. It is shown that the likely difference between leading algorithms are in their local search ability. A comparison with other leading optimizers on the tested benchmark suite, indicate the hybrid GP-PSO with implemented local search to compete along side other leading PSO algorithms.
Does blended learning* techniques using WhatsApp result in better performance by V th semester MBBS students in pathology as compared with lecture classes** alone?

Mobile learning (M learning) is gaining much importance in the field of medical education nowadays. Whats App messenger has become an interesting tool to be incorporated into teaching learning programs. We conducted this study to find out whether blended learning techniques combining lecture class and WhatsApp result in better performance by Vth semester MBBS students in Pathology as compared lecture classes alone and to understand the differences in attitude of students towards M learning. Study design was that of Randomized Controlled Trial. Entire batch divided into control & experimental group and a Whats App group created for those in experimental group. Single topic selected and lecture class given to all followed by 1 hr discussion in Whats app for those in study group and face to face discussion for other. Post test conducted to assess cognitive performance and questionnaire given to assess the attitude of students. Results analyzed using SPSS software. Findings of the study show that student find learning through WhatsApp very interesting eventhough there is no significant difference in cognitive performance. The attitude of the students toward WhatsApp- M learning was favorable. Keywords: E-Learning, M-Learning, Whats App.
ScrAnViz: A Tool for Analytics and Visualization of Unstructured Data

Existing big data visualisation tools are meant for visualising structured data. But survey shows that about 80-90% of potentially usable business information is in unstructured format. Analysing unstructured data is challenging due to lack of structure and relational form. In this paper, we have proposed a tool called ScrAnViz that can structure data, perform analysis and provide visualisation thereby helps in decision making for business people and end users. An attribute based opinion mining algorithm has been developed and implemented. Performance analysis shows that the algorithm has reduced the search time by three times than the traditional document level sentiment analysis systems.
THE ANALYSIS OF ED SHEERAN SONGS TO IMPROVE STUDENT’S LISTENING SKILL

This Study entitled “The Analysis of Ed Sheeran Songs to Improve Student’s Listening Skill”. The research was aimed to find out improved student’s listening skill ability. Wheather Ed Sheeran songs was founding increase student’s listening skill, because Ed Sheeran is a singer from England (UK) and his dialect of English is British English, so we were learning British English from the Ed Sheeran songs. In addition, Ed Sheeran was preferred many people in the world. People in the world had been understanding a lesson if a lesson had found for them, and we can’t deny if people in the world like songs and music. A song is the sound of art tone in the order, combination and the temporal relationship usually with accompanied by a musical instrument, the produce the music that has unity and continuity. The approach of this research is qualitative.
Photon-pair production at the nanoscale with hybrid nonlinear/plasmonic antennas

Reducing a nonlinear photon pair source to the nanoscale entails drastic reduction in efficiency and thus requires better tools for its evaluation and optimization. In this context, we develop a formalism for the investigation of spontaneous parametric downconversion in hybrid plasmonic structures that combine plasmonic resonances and intrinsic nonlinearity of a nonlinear medium. Combining quantum and numerical approaches, we quantitatively evaluate photon pair correlation for a realistic experimental configuration. We thereby show that hybrid structures embedding a nanocrystal and plasmonic antennas allow for a 10 5 -fold increase in pair production rate in comparison with a single nanocrystal, thus displaying an efficiency close to the best nanoscale sources to date.
BİLGİ FAALİYETLERİNDE ÖRGÜTSEL ETKİLEŞİM VE TÜRK SANAYİ İŞLETMELERİNDEKİ GELİŞMELER

This study aims to reveal the present State of information activities in the Turkish manufacturing industries and elucidate the modes, trends, and problems encountered in the practice. According to the results of the research, the benefıts of the information activities presented by the department responsible fo r the information activities change from om organization to another organization. But, there has been a revision about information activities in almost ali the corporations to adapt themselves to the changing conditions of the environment and to compete with the competitors.
An Exploration Tool for Predicting Stealthy Behaviour

Stealthy movement is an important part of many games in the First Person Shooter (FPS) and Role Playing Games (RPG) genres. Structuring a game level to match stealth goals, however, is difficult, and can depend on subtle and fragile interactions between the game space, enemy motion, and other factors. Here we apply a probabilistic path-finding approach to efficiently analyze a 2D space and find stealthy paths. This approach naturally accommodates variation in the level design, numbers and movements of enemies, fields of view, and player start and goal placement. Our design is integrated directly into the Unity 3D game development framework, allowing for interactive and highly dynamic exploration of how different virtual spaces and enemy configurations affect the potential for stealthly movement by players, or other NPCs.
Ceptre: A Language for Modeling Generative Interactive Systems

We present a rule specification language called Ceptre,intended to enable rapid prototyping for experimental game mechanics, especially in domains that depend on procedural generation and multi-agent simulation. Ceptre can be viewed as an explication of a new methodology for understanding games based on linear logic, a formal logic concerned with resource usage. We present a correspondence between gameplay and proof search in linear logic, building on prior work on generating narratives. In Ceptre, we introduce the ability to add interactivity selectively into a generative model, enabling inspection of intermediate states for debugging and exploration as well as a means of play. We claim that this methodology can support game designers and researchers in designing, anaylzing, and debugging the core systems of their work in generative, multi-agent gameplay. To support this claim, we provide two case studies implemented in Ceptre, one from interactive narrative and one from a strategy-like domain.
Generative adversarial dehaze mapping nets

Single image haze removal is a challenging task with few effective constraints, which seriously affect performance of machine learning algorithms. In this paper, we propose a Generative Adversarial Dehaze Mapping Nets (GADMN) to estimate a medium transmission for an input hazy image. GADMN adopts Generative Adversarial Nets (GAN) based deep architecture, which maps haze-relevant features to medium transmission and uses the network to carry on the feedback restrain. We also propose a multiple-light scattering model, which adds artificial light source and diffuses reflection light emerged from reflected light in the mist. Since the interference light is estimated in this model, we name it Local Multi-scale Hierarchical Prediction Method (LMHPM), which is beneficial to recover the large luminance range image. Experimental result demonstrates that the proposed algorithm outperforms state-of-the-art methods, and exhibits better robustness and adaptability.
A Nonparametric Deep Generative Model for Multimanifold Clustering

Multimanifold clustering separates data points approximately lying on a union of submanifolds into several clusters. In this paper, we propose a new nonparametric Bayesian model to handle the manifold data structure. In our framework, we first model the manifold mapping function between Euclidean space and topological space by applying a deep neural network, and then construct the corresponding generation process of multiple manifold data. To solve the posterior approximation problem, in the optimization procedure, we apply a variational auto-encoder-based optimization algorithm. Especially, as the manifold algorithm has poor performance on the real dataset where nonmanifold and manifold clusters are appearing simultaneously, we expand our proposed manifold algorithm by integrating it with the original Dirichlet process mixture model. Experimental results have been carried out to demonstrate the state-of-the-art clustering performance.
Superconducting qubits beyond the dispersive regime

We present a theoretical description for circuits consisting of weak anharmonic qubits coupled to cavity multimodes. We obtain a unitary transformation that diagonalizes harmonic sector of the circuit. Weak anharmonicity does not alter the normal mode basis, however it can modify energy levels. We study two examples of a transmon and two transmons coupled to bus resonator, and we determine dressed frequencies and Kerr nonlinearities in closed form formulas. Our results are valid for arbitrary frequency detuning and coupling within and beyond dispersive regime.
Low-resource text classification using domain-adversarial learning

Deep learning techniques have recently shown to be successful in many natural language processing tasks forming state-of-the-art systems. They require, however, a large amount of annotated data which is often missing. This paper explores the use of domain-adversarial learning as a regularizer to avoid overfitting when training domain invariant features for deep, complex neural networks in low-resource and zero-resource settings in new target domains or languages. In case of new languages, we show that monolingual word vectors can be directly used for training without prealignment. Their projection into a common space can be learnt ad-hoc at training time reaching the final performance of pretrained multilingual word vectors.
SybSub: Privacy-Preserving Expressive Task Subscription With Sybil Detection in Crowdsourcing

The past decade has witnessed the rise of crowdsourcing, and privacy in crowdsourcing has also gained rising concern in the meantime. Task matching or task subscription is one of indispensable services in crowdsourcing, but few mechanisms can achieve the expressive task subscription while protecting the privacy. In this paper, we focus on the privacy leaks and attacks during task subscription in crowdsourcing, and propose a privacy-preserving task subscription scheme with sybil detection, called SybSub. The SybSub scheme achieves the expressiveness of task subscription in the multisubscriber and multipublisher crowdsourcing while protecting the privacy of both subscribers and publishers against the semi-honest crowdsourcing service provider, and meanwhile supports the sybil attack detection against greedy subscribers. We implement the SybSub scheme and evaluate it thoroughly. Performance results validate that the SybSub scheme is efficient and feasible.
Rapid tracking of extrinsic projector parameters in fringe projection using machine learning

In this work, we propose to enable the angular re-orientation of a projector within a fringe projection system in real-time without the need for re-calibrating the system. The estimation of the extrinsic orientation parameters of the projector is performed using a convolutional neural network and images acquired from the camera in the setup. The convolutional neural network was trained to classify the azimuth and elevation angles of the projector approximated by a point source through shadow images of the measured object. The images used to train the neural network were generated through the use of CAD rendering, by simulating the illumination of the object model from different directions and then rendering an image of its shadow. The accuracy to which the azimuth and elevation angles are estimated is within 1 classification bin, where 1 bin is designated as a ±10° patch of the illumination dome. To evaluate use of the proposed system in fringe projection, a pyramidal additively manufactured object was measured. The point clouds generated using the proposed method were compared to those obtained by an established fringe projection calibration method. The maximum dimensional error in the point cloud generated when using the convolutional network as compared to the established calibration method for the object measured was found to be 1.05 mm on average.
Microshift: An Efficient Image Compression Algorithm for Hardware

In this paper, we propose an image compression algorithm called Microshift. We employ an algorithm hardware co-design methodology, yielding a hardware-friendly compression approach with low power consumption. In our method, the image is first micro-shifted, then the sub-quantized values are further compressed. Two methods, the FAST and MRF model, are proposed to recover the bit-depth by exploiting the spatial correlation of natural images. Both methods can decompress images progressively. Our compression algorithm compresses images to 1.25 bits per pixel on average with PSNR of 33.16 dB, outperforming other on-chip compression algorithms. Then, we propose a hardware architecture and implement the algorithm on an FPGA and ASIC. The results on the VLSI design further validate the low hardware complexity and high power efficiency, showing our method is promising, particularly for low-power wireless vision sensor networks.
Interactive dimensionality reduction using similarity projections

Recent advances in machine learning allow us to analyze and describe the content of high-dimensional data like text, audio, images or other signals. In order to visualize that data in 2D or 3D, usually Dimensionality Reduction (DR) techniques are employed. Most of these techniques, e.g., PCA or t-SNE, produce static projections without taking into account corrections from humans or other data exploration scenarios. In this work, we propose the interactive Similarity Projection (iSP), a novel interactive DR framework based on similarity embeddings, where we form a differentiable objective based on the user interactions and perform learning using gradient descent, with an end-to-end trainable architecture. Two interaction scenarios are evaluated. First, a common methodology in multidimensional projection is to project a subset of data, arrange them in classes or clusters, and project the rest unseen dataset based on that manipulation, in a kind of semi-supervised interpolation. We report results that outperform competitive baselines in a wide range of metrics and datasets. Second, we explore the scenario of manipulating some classes, while enriching the optimization with high-dimensional neighbor information. Apart from improving classification precision and clustering on images and text documents, the new emerging structure of the projection unveils semantic manifolds. For example, on the Head Pose dataset, by just dragging the faces looking far left to the left and those looking far right to the right, all faces are re-arranged on a continuum even on the vertical axis (face up and down). This end-to-end framework can be used for fast, visual semi-supervised learning, manifold exploration, interactive domain adaptation of neural embeddings and transfer learning.