Reviewerly/exHarmony huggingface dataset:
AN ANALYSIS OF ENGLISH CLASSROOM INTERACTION USING BROWN’S INTERACTION ANALYSIS SYSTEM (BIAS) AT EIGHT GRADE STUDENTS OF MTS NURUL HIKMAH

The problems formulation of this research were: 1) what kinds of English classroom interaction that occured during the teaching and learning process using BIAS at Eight grade of MTs Nurul Hikmah?, 2) which is more dominant between teacher talk and student talk during classroom interaction in teaching English using BIAS?. The subject of this research were English teacher and students at Eight grade of MTs Nurul Hikmah. The object of this research were classroom interaction that occured in teaching learning process at Eight grade of MTs Nurul Hikmah. To collect the data, the researcher used some techniques such as observation and document analysis. Data analysis in this research divided into four phases: transcribing, coding, grouping, and interpreting. Then, all the data were analyzed descriptively and concluded inductively. The result of this research stated that the kinds of classroom interaction that occured during the teaching and learning process were Teacher Talk (TL), Teacher Question (TQ), Teacher Response (TR), Pupils Response (PR), Pupils Volunteer (PV), Pauses or Silence (S), and Unclassified (X). The teacher talk was more dominant than students talk. It can be seen from the tables that the proportion of teacher talk in class A, B, and C at Eight grade of MTs Nurul Hikmah always higher than the proportion of students talk.
A Taxonomy of Blockchain Applications

A taxonomy of blockchain application uses cases that include application and apps. A major obstacle identified in several disciplines is the categorization of objects in a particular area of interest into a taxonomy. Blockchain is an innovation that is decentralized and characterized by value-based information that is distributed over a vast network with many untrusted users. Developing a taxonomy for blockchain applications is a complex process. This chapter thus attempts to discuss what internal factors contribute to the success of a blockchain application. This is followed by an analysis of which organizations use blockchain and which applications and apps they use. Finally, we classify blockchains according to what they are used for. For example, Blockchains are used in such fields as finance, agriculture, education, development, etc.
Taking A Closer Look at Synthesis: Fine-grained Attribute Analysis for Person Re-Identification

Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data, which benefits from the popularity of synthetic data engine, has achieved remarkable performance. However, in pursuit of high accuracy, researchers in the academic always focus on training with large-scale datasets at a high cost of time and label expenses, while neglect to explore the potential of performing efficient training from millions of synthetic data. To facilitate development in this field, we reviewed the previously developed synthetic dataset GPR and built an improved one (GPR+) with larger number of identities and distinguished attributes. Based on it, we quantitatively analyze the influence of dataset attribute on re-ID system. To our best knowledge, we are among the first attempts to explicitly dissect person re-ID from the aspect of attribute on synthetic dataset. This research helps us have a deeper understanding of the fundamental problems in person re-ID, which also provides useful insights for dataset building and future practical usage.
Deep Adversarial Domain Adaptation Based on Multi-layer Joint Kernelized Distance

Domain adaptation refers to the learning scenario that a model learned from the source data is applied on the target data which have the same categories but different distribution. While it has been widely applied, the distribution discrepancy between source data and target data can substantially affect the adaptation performance. The problem has been recently addressed by employing adversarial learning and distinctive adaptation performance has been reported. In this paper, a deep adversarial domain adaptation model based on a multi-layer joint kernelized distance metric is proposed. By utilizing the abstract features extracted from deep networks, the multi-layer joint kernelized distance (MJKD) between the $j$th target data predicted as the $m$th category and all the source data of the $m'$th category is computed. Base on MJKD, a class-balanced selection strategy is utilized in each category to select target data that are most likely to be classified correctly and treat them as labeled data using their pseudo labels. Then an adversarial architecture is used to draw the newly generated labeled training data and the remaining target data close to each other. In this way, the target data itself provide valuable information to enhance the domain adaptation. An analysis of the proposed method is also given and the experimental results demonstrate that the proposed method can achieve a better performance than a number of state-of-the-art methods.
Flexible Trinocular: Non-rigid Multi-Camera-IMU Dense Reconstruction for UAV Navigation and Mapping

In this paper, we propose a visual-inertial framework able to efficiently estimate the camera poses of a non-rigid trinocular baseline for long-range depth estimation on-board a fast moving aerial platform. The estimation of the time-varying baseline is based on relative inertial measurements, a photometric relative pose optimizer, and a probabilistic wing model fused in an efficient Extended Kalman Filter (EKF) formulation. The estimated depth measurements can be integrated into a geo-referenced global map to render a reconstruction of the environment useful for local replanning algorithms. Based on extensive real-world experiments we describe the challenges and solutions for obtaining the probabilistic wing model, reliable relative inertial measurements, and vision-based relative pose updates and demonstrate the computational efficiency and robustness of the overall system under challenging conditions.
A Reversible Data hiding Scheme in Encrypted Domain for Secret Image Sharing based on Chinese Remainder Theorem

Reversible data hiding in encrypted domain (RDH-ED) schemes based on symmetric or public key encryption are mainly applied to the security of end-to-end communication. Aimed at providing reliable technical supports for multi-party security scenarios, a separable RDH-ED scheme for secret image sharing based on Chinese remainder theorem (CRT) is presented. In the application of (t, n) secret image sharing, the image is first shared into n different shares of ciphertext. Only when not less than t shares obtained, can the original image be reconstructed. In our scheme, additional data could be embedded into the image shares. To realize data extraction from the image shares and the reconstructed image separably, two data hiding methods are proposed: one is homomorphic difference expansion in encrypted domain (HDE-ED) that supports data extraction from the reconstructed image by utilizing the addition homomorphism of CRT secret sharing; the other is difference expansion in image shares (DE-IS) that supports the data extraction from the marked shares before image reconstruction. Experimental results demonstrate that the proposed scheme could not only maintain the security and the threshold function of secret sharing system, but also obtain a better reversibility and efficiency compared with most existing RDH-ED algorithms. The maximum embedding rate of HDE-ED could reach 0.5000 bits per pixel and the average embedding rate of DE-IS is 0.0545 bits per bit of ciphertext.
의료기관 선택에 영향을 미치는 요인 분석 : 외래환자 중심으로

One of the most important things for hospital managements is understanding the patients' needs. The aim of this study is to analyse the priorities of the factors influencing patients' choice for hospital with analytic hierarchy process (AHP), which can derive the relative importances of factors. Wit...
Implementation Of The C4.5 Algorithm For Recruitment Of E-Sports Team Members

Fenomena E-Sport in an increasingly fast-paced world, creating a global culture ranging from international E-Sport tournaments to the birth of management that houses the competing E-Sport teams to be the best. E-Sport teams need players or gamers who have high skills, but it is not only the skill level of a player that determines the success of an E-Sport team in a tournament, there are other factors that determine the success of an E-Sport team, where factors this can be used in determining the decision to recruit players or gamers to become members of the E-Sport team. Decision support systems (DSS) is one of the systems that can be relied upon as a method to assist an organization or E-sports team management in assisting the decision-making process. One method that can be used in DSS is to use the Decision Tree C4.5 Algorithm. The solution technique is to use entropy and information gain for the expansion of decision trees. C4.5 algorithm is a decision tree-based method. In the C4.5 algorithm, the selection of attributes is done using Gain, Ratio, by finding the Entropy value. C4.5 algorithm can provide effective results in supporting a decision
2015 개정 교육과정 중학교 1학년 영어 교과서 분석

The purpose of this study was first to examine key features within the content of first year middle school English textbooks based on the 2015 Revised National Curriculum, and thereby to draw several implications with respect to the improvement of the middle school English textbooks for future curri...
Metrics for Evolution of Aspect Oriented Software

Aspect Oriented Software Development (AOSD) is a promising methodology which provides powerful techniques to improve the modularity of the software by separating the cross-cutting concerns from the core functionality. Since evolution is a major requirement for the sustainability of any software, it is necessary to quantitatively measure its impact. In order to quantify, it is essential to define metrics that will capture the evolution of Aspect Oriented (AO) software. It is also necessary to compare the metric values of various versions of software to draw inferences on the evolution dynamics of AO software. This needs identification of artifacts that were added, deleted or modified across versions and study the consequence of these types of changes. This paper defines a new set of metrics for measuring the evolution of Aspect Oriented software. As a case study, an aspect refactored software, AJHotDraw has been chosen and its four versions have been analyzed for their capability to evolve over time.
A Framework to Learn with Interpretation

To tackle interpretability in deep learning, we present a novel framework to jointly learn a predictive model and its associated interpretation model. The interpreter provides both local and global interpretability about the predictive model in terms of human-understandable high level attribute functions, with minimal loss of accuracy. This is achieved by a dedicated architecture and well chosen regularization penalties. We seek for a small-size dictionary of high level attribute functions that take as inputs the outputs of selected hidden layers and whose outputs feed a linear classifier. We impose strong conciseness on the activation of attributes with an entropy-based criterion while enforcing fidelity to both inputs and outputs of the predictive model. A detailed pipeline to visualize the learnt features is also developed. Moreover, besides generating interpretable models by design, our approach can be specialized to provide post-hoc interpretations for a pre-trained neural network. We validate our approach against several state-of-the-art methods on multiple datasets and show its efficacy on both kinds of tasks.
Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning Approach

Human demonstrations can provide trustful samples to train reinforcement learning algorithms for robots to learn complex behaviors in real-world environments. However, obtaining sufficient demonstrations may be impractical because many behaviors are difficult for humans to demonstrate. A more practical approach is to replace human demonstrations by human queries, i.e., preference-based reinforcement learning. One key limitation of the existing algorithms is the need for a significant amount of human queries because a large number of labeled data is needed to train neural networks for the approximation of a continuous, high-dimensional reward function. To reduce and minimize the need for human queries, we propose a new GAN-assisted human preference-based reinforcement learning approach that uses a generative adversarial network (GAN) to actively learn human preferences and then replace the role of human in assigning preferences. The adversarial neural network is simple and only has a binary output, hence requiring much less human queries to train. Moreover, a maximum entropy based reinforcement learning algorithm is designed to shape the loss towards the desired regions or away from the undesired regions. To show the effectiveness of the proposed approach, we present some studies on complex robotic tasks without access to the environment reward in a typical MuJoCo robot locomotion environment. The obtained results show our method can achieve a reduction of about 99.8% human time without performance sacrifice.
Learning Global Transparent Models Consistent with Local Contrastive Explanations

There is a rich and growing literature on producing local contrastive/counterfactual explanations for black-box models (e.g. neural networks). In these methods, for an input, an explanation is in the form of a contrast point differing in very few features from the original input and lying in a different class. Other works try to build globally interpretable models like decision trees and rule lists based on the data using actual labels or based on the black-box models predictions. Although these interpretable global models can be useful, they may not be consistent with local explanations from a specific black-box of choice. In this work, we explore the question: Can we produce a transparent global model that is simultaneously accurate and consistent with the local (contrastive) explanations of the black-box model? We introduce a natural local consistency metric that quantifies if the local explanations and predictions of the black-box model are also consistent with the proxy global transparent model. Based on a key insight we propose a novel method where we create custom boolean features from sparse local contrastive explanations of the black-box model and then train a globally transparent model on just these, and showcase empirically that such models have higher local consistency compared with other known strategies, while still being close in performance to models that are trained with access to the original data.
GAZEV: GAN-Based Zero-Shot Voice Conversion over Non-parallel Speech Corpus

Non-parallel many-to-many voice conversion is recently attract-ing huge research efforts in the speech processing community. A voice conversion system transforms an utterance of a source speaker to another utterance of a target speaker by keeping the content in the original utterance and replacing by the vocal features from the target speaker. Existing solutions, e.g., StarGAN-VC2, present promising results, only when speech corpus of the engaged speakers is available during model training. AUTOVCis able to perform voice conversion on unseen speakers, but it needs an external pretrained speaker verification model. In this paper, we present our new GAN-based zero-shot voice conversion solution, called GAZEV, which targets to support unseen speakers on both source and target utterances. Our key technical contribution is the adoption of speaker embedding loss on top of the GAN framework, as well as adaptive instance normalization strategy, in order to address the limitations of speaker identity transfer in existing solutions. Our empirical evaluations demonstrate significant performance improvement on output speech quality and comparable speaker similarity to AUTOVC.
Discriminatively Constrained Semi-supervised Multi-view Nonnegative Matrix Factorization with Graph Regularization

In recent years, semi-supervised multi-view nonnegative matrix factorization (MVNMF) algorithms have achieved promising performances for multi-view clustering. While most of semi-supervised MVNMFs have failed to effectively consider discriminative information among clusters and feature alignment from multiple views simultaneously. In this paper, a novel Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization (DCS^2MVNMF) is proposed. Specifically, a discriminative weighting matrix is introduced for the auxiliary matrix of each view, which enhances the inter-class distinction. Meanwhile, a new graph regularization is constructed with the label and geometrical information. In addition, we design a new feature scale normalization strategy to align the multiple views and complete the corresponding iterative optimization schemes. Extensive experiments conducted on several real world multi-view datasets have demonstrated the effectiveness of the proposed method.
Meme Kanseri Tespiti için Destek Vektör Makinası ile Alexnet Kullanarak Transfer Öğrenimi

Breast cancer is one of the leading causes of women death worldwide currently. Developing a computer-aided diagnosis system for breast cancer detection became an interesting problem for many researchers in recent years. Researchers focused on deep learning techniques for classification problems, including Convolutional Neural Networks (CNNs), which achieved great success. CNN is a particular type of deep, feedforward network that has gained attention from the research community and achieved great successes, especially in biomedical image processing. In this paper, transfer learning and deep feature extraction methods are used which adapt a pre-trained CNN model to classify breast cancer histopathological images from the publically available (BreakHis dataset). The data set includes both benign and malignant images with four different magnification factors. A patch strategy method proposed based on the extraction of image patches for training the CNN and the combination of these patches for ﬁnal classiﬁcation. AlexNet model is considered in this work with patch strategy, and pre-trained AlexNet is used for further fine-tuning. The obtained features are then classified by using support vector machines (SVM). The evaluation results show that the pre-trained Alexnet with SVM classification and patch strategy yields the best accuracy. Accuracy between 92% and 96% was achieved using five-fold cross-validation technique for different magnification factors.
Creative Aspects Of The Projective Style Of Thinking

This article reveals the creative and irrational aspects of the projective style of thinking. In this article, the problem of creativity and creativity is analyzed philosophical-epistemological. In addition, the manifestation of irrationality in the creative process has shown the heuristic significance of such situations as dreams, intuition, and spontaneous cognition. And many discoveries and inventions in the history of science are precisely the result of irrationality.
VoteNet++: Registration Refinement for Multi-Atlas Segmentation

Multi-atlas segmentation (MAS) is a popular image segmentation technique for medical images. In this work, we improve the performance of MAS by correcting registration errors before label fusion. Specifically, we use a volumetric displacement field to refine registrations based on image anatomical appearance and predicted labels. We show the influence of the initial spatial alignment as well as the beneficial effect of using label information for MAS performance. Experiments demonstrate that the proposed refinement approach improves MAS performance on a 3D magnetic resonance dataset of the knee.
Text emotion mining on Twitter

Abstract Twitter has become a medium through which a substantial percentage of the global population communicates their feelings and reactions to current events. Emotion mining from text aims to capture these emotions by using a series of algorithms to evaluate the contents of each tweet. In this study, tweets that expressed at least one of seven basic emotions were collected. The resulting dataset was a corpus of 42,000 tweets with a balanced presence of each emotion. From this corpus a lexicon of roughly 40,000 words, each associated with a weighted vector corresponding to one of the emotions, was created. Next, different methods of identifying emotion in these ‘cleaned’ tweets were performed and evaluated. These methods included both lexically-based classification and supervised machine learning-based classification. Finally, an ensemble method involving several multi-class classifiers trained on unigram features of the lexicon was evaluated. This evaluation revealed that the ensemble method outperformed all other tested methods when tested on existing datasets as well as on the dataset created for this study.
LibGuides: GEOG 3410 - Life Cycle Assessment: Using Interlibrary Loan

Library resources and research tips for conducting a life cycle inventory assessment literature review.
Sampling Theory of Bandlimited Continuous-Time Graph Signals

A continuous-time graph signal can be viewed as a time series of graph signals. It generalizes both the classical continuous-time signal and ordinary graph signal. Therefore, such a signal can be considered as a function on two domains: the graph domain and the time domain. In this paper, we consider the sampling theory of bandlimited continuous-time graph signals. To formulate the sampling problem, we need to consider the interaction between the graph and time domains. We describe an explicit procedure to determine a discrete sampling set for perfect signal recovery. Moreover, in analogous to the Nyquist-Shannon sampling theorem, we give an explicit formula for the minimal sample rate.
The Development of Model Advance Organizer Based Mind Mapping for Student at Senior High School

This research aims to develop a valid mind mapping based advance organizer learning model (PAO PaPi Model) for senior high school students.The research question is whether the development of PAO PaPi mode is valid in terms of theoretical basis and learning model components.The research subjects were 33 students in Senior High School 20 Pangkep, Indonesia.The development procedure of PAO Papi Model was carried out by referring to the development model of Plomp [1] which consists of five stages, namely: (1) investigation, (2) design, (3) realization / construction, (4) testing, evaluation and revision, and (5) implementation.The research product is the PAO PaPi Model.The component model developed refers to Joyce, et al. [2].Characteristics of the developed model refer to Arend ( 2012), and product quality criteria refer to Nieeven (2007).Validation data analysis refers to Nurdin (2007) and Asdar (2013).The instrument reliability percentage used the concept of percentage of agreements by Grinnell (1988) in Nurdin (2007).The results showed that the PAO PaPi model was valid in terms of the supporting theories and components of the model.