Reviewerly/exHarmony huggingface dataset:
Authorship Identification with Multi Sequence Word Selection Method

Authorship analysis process in which finding the author of unknown text when the history of the writing style of the author known. It can be viewed as a multi-class, single-mark content classification assignment. A lot of author identification problems had been created and solved with the different methods. Character and word n-gram are most commonly used methods for feature construction and participated in authorship identification task. In this paper, we point out the methodology of formation of word n-gram. The approach described in the paper does not depend on constant value n, the value of n changes according to the occurrence of word sequences. The methodology applied to the collection of text which is from the varied time domain. We used a dataset of 13 authors, whose text generation time is big. Dynamic value of n chosen to generate word sequence. In terms of accuracy, the result shows improvement as compared to the fixed value of n in word n-gram. We also explore the significance of the dynamic value of n on the occurrence of a specified set of consecutive words.
How Do We Prepare the Next Generation for a Career in Our Digital Era?

hether it is at the World Economic Forum or in the suites of CEOs, there is strong agreement that artificial intelligence, machine learning, mobile technologies, robotics, biological technologies, quantum computing, and other technological innovations require the "digital transformation" of organizations.Digital transformation is not fragmented digitization; it is a complete rethinking of the overall business model with a customer-driven emphasis supported by the use of digital technologies throughout the business process.Are businesses prepared for this transition?In a MIT Sloan Management Review/Deloitte University Press study of more than , executives, managers, and analysts around the globe, of respondents recognized that digital trends would moderately or greatly disrupt their industries.Only , however, believed that their organizations were prepared for the disruption.Another insight from the MIT/Deloitte report is that the companies that appear more successful in their digital transformation efforts put their focus on soft skills.In response to a question about the most important skill for leaders to succeed in a digital environment, only of respondents listed technological skills as mostHow Do We Prepare the Next Generation for a Career in Our Digital Era?
Developing Social Media-Based Textbook for Speaking Class in English Study Program

Lack of reference, guidelines, facilities, time and place of practice English become the problems of this research. This study aims to develop textbook based on social media which is used in Speaking Class at English Study Program. The research adapted some steps of the development by Ploomp which included Need Analysis, Self-Evaluation, Expert Review, Small Group Evaluation, Field Test, and assessment phase. The product of this research is a textbook which consisted of fourteen chapters. Each chapter applied speaking activities through social media. The materials written based on social media and taught by using social media included; Facebook, Whatsapp, Instagram, and Youtube. The textbook is developed and implemented based on social media in accordance with the competency standards set in the curriculum of department. The training materials and activities by using social media strongly guide students to practice English and provide a different atmosphere in the learning process.
Robot use cases for real needs: A large-scale ethnographic case study

Abstract This article discusses the process of developing robot use cases using large-scale ethnographic observation as a starting point. In particular, during 296 hours of ethnographic observation of the workflows at seventeen departments at Odense University Hospital, 607 processes were described and subsequently annotated. The ethnographic method provided rich, contextually situated data that can be searched and categorized for use case development, which is illustrated on an example use case, describing the process and illustrating the type of data elicited, discussing the problems encountered and providing downloadable tools for other researchers interested in similar approaches to use case development.
An experimental comparison of touch and pen gestures on a vertical display

We present results of a user study that compared touch and pen gestures on a vertical large display in terms of precision, duration, and difficulty. Results of the study revealed that touch gestures were significantly faster, while pen gestures were more precise. However, participants preferred using pen on the vertical display than touch. Results also revealed that performing gestures in the upper-middle area of the display was faster, more accurate, and easier compared to the other areas of the display.
Phase-space quantum profile of Pöschl-Teller two-level systems

The quantum phase-space dynamics driven by hyperbolic P\"oschl-Teller (PT) potentials is investigated in the context of the Weyl-Wigner quantum mechanics. The obtained Wigner functions for quantum superpositions of ground and first-excited states exhibit some non-classical and non-linear patterns which are theoretically tested and quantified according to a non-gaussian continuous variable framework. It comprises the computation of quantifiers of non-classicality for an anharmonic two-level system where non-Liouvillian features are identified through the phase-space portrait of quantum fluctuations. In particular, the associated non-gaussian profiles are quantified by measures of {\em kurtosis} and {\em negative entropy}. As expected from the PT {\em quasi}-harmonic profile, our results suggest that quantum wells can work as an experimental platform that approaches the gaussian behavior in the investigation of the interplay between classical and quantum scenarios. Furthermore, it is also verified that the Wigner representation admits the construction of a two-particle bipartite quantum system of continuous variables, $A$ and $B$, identified by $\sum_{i,j=0,1}^{i\neq j}\vert i_{_A}\rangle\otimes\vert j_{_B}\rangle$, which are shown to be separable under gaussian and non-gaussian continuous variable criteria.
A Study on the Re-structulization of Competency-based Specialized Dance Curriculum in Korea

The purpose of this study is to explore the educational significance of competency-based specialized dance curriculum by restructuring artistic high school specialized dance curriculum based on specialized dance competence(SDC). To accomplish this goal, three researches were set up as follows: firstly, confirmation of conformity between SDC and 2015 revised specialized dance curriculum, secondly, exploring the needs to improve the specialized dance education reflecting the meaning of SDC, thirdly, restructuring specialized dance curriculum based on SDC. This study conducted literature review, written survey, interview, F.G.D, and expert meeting based on Walker’s curriculum development model. This study restructured the subjects and contents area of the specialized dance curriculum to enhance SDC. Through this study, the subject and content area of specialized dance curriculum to enhance SDC has been restructured. As a result of the study, the restructured specialized dance curriculum led to the dynamics of the curriculum through the totally linked structure, and this structure reflects the characteristics of competency. In addition, the essential meaning and direction of the specialized dance curriculum were emphasized, and the content and application method to be supplemented from the viewpoint of curriculum improvement were derived. In order to realize competency-based curriculum at the arts high school
A Posture Recognition Method Applied to Smart Product Service

Product service system (PSS), an undeveloped business model, provides consumers with full-life cycle services from the perspective of products, functions and services. This paper proposes a posture recognition method based on smart product service system from the aspects of technology and user’ need. A Posture-CNN model based on the convolutional neural network (CNN) is established in this paper. According to the theory of PSS, this paper analyses the difficulties that the posture recognition technology is facing in the application of products, and resolves the existing problems such as low accuracy of recognition and unsupported complex environment. A self-made posture data set is tested with the results that the method can greatly reduce network parameters and improve network speed compared with other existing classification methods. To some extent, as a core technology of the PSS control terminal, this technology can perfect users’ experiences, better serve consumers and promote the development of the PSS.
Research on geological hazard risk assessment based on the cloud fuzzy clustering algorithm

As there are many uncertain factors in the geological hazard risk, great challenges are brought to the comprehensive evaluation of it. In order to improve the comprehensiveness and accuracy of geological hazard risk assessment, a cloud fuzzy clustering algorithm is constructed in this paper, which can effectively estimate and evaluate uncertain variables. The weight value of the risk cloud droplets is calculated as the input. By setting up clustering conditions and function output conditions, the cluster weights of the inputs which meet requirements can be obtained by multiple clustering iterations. Through the introduction of time parameters, the influence of time factors on data importance and the risk severity of geological disaster emergencies are fully considered. The experimental results show that the calculated risk degree cluster weights are less than 1, which verifies the feasibility and practicability of the algorithm. The research in this paper shows that the clustering dynamic assessment of geological hazards can help to improve the accuracy of risk assessment and provide reference and help for the prevention and control of regional geological hazards.
Reducing Efforts in Web Services Refactoring

In Service-Oriented Computing, systems provide a service interface described by a computer-readable language called Web Services Description Language (WSDL), but document descriptions often exhibit design problems as systems expand. Moreover, a major problem in this type of applications is its growth; as size and complexity of applications increase, the probability of duplicity of code also increases. This issue could have a negative impact on quality attributes, such as performance, maintainability and evolution, among others, providing developers with some clues to detect refactoring opportunities. Conducting a detection process of these opportunities could be a daunting task; however, this work proposes a methodology to conduct manual refactoring of service descriptions of legacy systems. The methodology allows software developers to collect metrics of time effort and space reduction. The metrics indicate a 75% reduction in services and 63% of the lines of code on average. Additionally, a statistical analysis carried out with the obtained metrics yielded as result that the number of services refactored according to the size of its refactoring group is adjusted to a logarithmic function. We also analyzed how refactoring time increases with the size of services. The results indicate that our methodology could be applied in similar case-studies based on legacy systems.
New Cryptographic Algorithm Using ASCII Values and Gray Code (AGC)

In today's era with the fast adaption of internet communication, the exchange of data among individuals, government sectors and corporate levels have increased. Unauthorized users are always on the outlook to steal the conversations that take place on the internet Therefore; protecting data during transmission from unauthorized users is the most important need of time. Hence, the requirement to secure this information exchange has become more hectic task for IT professionals. Cryptography is the science of encrypting data which keeps the message safe from unauthorized users. In this paper, we have proposed a new cryptographic algorithm using ASCII values and Gray Code (AGC) to protect data during communication. Moreover, drawbacks of previous ASCII algorithms and future work is described.
Augmented Reality Enhanced Ubiquitous-Learning in Museum

Museums are ideal informal learning environments.This study develops an augmented reality (AR) system (APP and management platform) for visitors to actively explore learning resources in museum.There are three main parts of the system, public mode (for adults), child mode (for elementary or high school students), and backend management platform (for museum manager).To arouse visitors' motivation, this study adopts problem-based learning (PBL) as main theory base of the system.Along with iBeacon indoor positioning technology, the AR (augmented reality) is adopted to display questions (inspire visitors thinking and interaction) nearby the exhibition items.In this sense, the learning may actually happen in museums.
A Data-Driven Methodology to Assess Text Complexity Based on Syntactic and Semantic Measurements

In this paper we propose a data driven methodology to assess text complexity of Spanish school texts. We model the problem as a classification task, that can be solved in a data-driven fashion using machine learning techniques. We show empirically that the discriminative power of the classifier depends on school grade level. Our proposal includes multiple predictors that capture different dimensions of text complexity such as coherence and cohesion. We provide an importance analysis of predictors across several complexity levels. Finally, we assess the model performance using accuracy and correlation measurements. The proposed model achieves accuracies of 0.7.
Comment: Minimalist $g$-Modeling

Efron's elegant approach to g-modeling for empirical Bayes problems is contrasted with an implementation of the Kiefer-Wolfowitz nonparametric maximum likelihood estimator for mixture models for several examples.The latter approach has the advantage that it is free of tuning parameters and consequently provides a relatively simple complementary method.
Efficient Synthesis with Probabilistic Constraints

We consider the problem of synthesizing a program given a probabilistic specification of its desired behavior. Specifically, we study the recent paradigm of distribution-guided inductive synthesis (digits), which iteratively calls a synthesizer on finite sample sets from a given distribution. We make theoretical and algorithmic contributions: (i) We prove the surprising result that digits only requires a polynomial number of synthesizer calls in the size of the sample set, despite its ostensibly exponential behavior. (ii) We present a property-directed version of digits that further reduces the number of synthesizer calls, drastically improving synthesis performance on a range of benchmarks.
Learned Collaborative Stereo Refinement

Abstract In this work, we propose a learning-based method to denoise and refine disparity maps. The proposed variational network arises naturally from unrolling the iterates of a proximal gradient method applied to a variational energy defined in a joint disparity, color, and confidence image space. Our method allows to learn a robust collaborative regularizer leveraging the joint statistics of the color image, the confidence map and the disparity map. Due to the variational structure of our method, the individual steps can be easily visualized, thus enabling interpretability of the method. We can therefore provide interesting insights into how our method refines and denoises disparity maps. To this end, we can visualize and interpret the learned filters and activation functions and prove the increased reliability of the predicted pixel-wise confidence maps. Furthermore, the optimization based structure of our refinement module allows us to compute eigen disparity maps , which reveal structural properties of our refinement module. The efficiency of our method is demonstrated on the publicly available stereo benchmarks Middlebury 2014 and Kitti 2015.
A Collision-Free Path Planning Method Using Direct Behavior Cloning

An effective path planning approach based on deep learning for robotic arms is presented in this paper. Direct behavior cloning is applied to extract the obstacle avoidance policy in collision-free paths generated by reliable motion planners, such as RRT* algorithm in our case. Behavior cloning is the simplest form of imitation learning, also known as Learning from Demonstration (LfD), where an agent tries to learn a policy to recover the expert’s action with respect to the state of the environment. The designed policy in this paper gives the obstacle avoidance action in a scene knowing the pose of the obstacle, the initial and the goal configurations. The action is taken each time the state changes and thus this method is able to achieve online motion planning regardless of whether the environment is static or dynamic. We build a simulation environment with V-REP and Python client program to collect the state-action dataset and validate the trained policies. Policy models with and without visual input are constructed and tested in the same experiment setting to determine the best solution. Results show that the policy model with accurate obstacle pose input handles the path planning issue well.
Generalized Zero-Shot Vehicle Detection in Remote Sensing Imagery via Coarse-to-Fine Framework

Vehicle detection and recognition in remote sensing images are challenging, especially when only limited training data are available to accommodate various target categories. In this paper, we introduce a novel coarse-to-fine framework, which decomposes vehicle detection into segmentation-based vehicle localization and generalized zero-shot vehicle classification. Particularly, the proposed framework can well handle the problem of generalized zero-shot vehicle detection, which is challenging due to the requirement of recognizing vehicles that are even unseen during training. Specifically, a hierarchical DeepLab v3 model is proposed in the framework, which fully exploits fine-grained features to locate the target on a pixel-wise level, then recognizes vehicles in a coarse-grained manner. Additionally, the hierarchical DeepLab v3 model is beneficially compatible to combine the generalized zero-shot recognition. To the best of our knowledge, there is no publically available dataset to test comparative methods, we therefore construct a new dataset to fill this gap of evaluation. The experimental results show that the proposed framework yields promising results on the imperative yet difficult task of zero-shot vehicle detection and recognition.
Commodity-Based 2PC for Arithmetic Circuits

We revisit the framework of Commodity-based Cryptography presented by Beaver (STOC’97) with a focus on updating the framework to fit with modern multiparty computation (MPC) protocols. We study the possibility of replacing the well-known preprocessing model with a commodity-based setting, where a set of independent servers (some of which may be corrupt) provide clients with correlated randomness. From this, the clients then distill correct and secure correlated randomness that they can use during the online phase of the MPC protocol. Beaver showed how to do OT with semi-honest security in the commodity setting. We improve on Beaver’s result as follows: In a model where one of two clients and a constant fraction of the servers may be maliciously corrupted, we obtain unconditionally secure multiplication triples and oblivious linear evaluations (OLEs) such that the amortized communication cost of one triple/OLE is a constant number of field elements (when the field is sufficiently large). We also report on results from an implementation of the OLE protocol. Finally, we suggest an approach to practical realization of a commodity based system where servers need no memory and can be accessed asynchronously by clients, but still a maliciously corrupt client cannot get data he should not have access to.
On the Improvement of Multiple Circles Detection from Images using Hough Transform

The automatic detection of lines and curves from color images is a very important task in many applications, such as object recognition and scene reconstruction. Although there are closed formulation for curve fitting to a set of points, if the point set describes more than one instance of the object, as two circles for example, there is no closed formulation for obtaining the individual set of parameters without a priori information of which points belong to each object. However, it is usual the presence of multiple instances of objects such as lines and circles on an image. The well known Hough Transform is an efficient tool for recovering multiple objects from images using a voting process where the usual presence of false positives is an issue. In our work, we present an improvement on the voting process to detect multiple circles using Hough Transform in order to avoid false positives. Our experiments show that our voting process leads to a more robust detection, reducing the number of false positive and providing a more accurate detection even with large number of circles.
Implementing the Harmonized Model for Digital Evidence Admissibility Assessment

Standardization of digital forensics has become an important focus area for researchers and criminal justice practitioners. Over the past decade, several efforts have been made to encapsulate digital forensic processes and activities in harmonized frameworks for incident investigations. A harmonized model for digital evidence admissibility assessment has been proposed for integrating the technical and legal determinants of digital evidence admissibility, thereby providing a techno-legal foundation for assessing digital evidence admissibility in judicial proceedings. This chapter presents an algorithm underlying the harmonized model for digital evidence admissibility assessment, which enables the determination of the evidential weight of digital evidence using factor analysis. The algorithm is designed to be used by judges to determine evidence admissibility in criminal proceedings. However, it should also be useful to investigators, prosecutors and defense lawyers for evaluating potential digital evidence before it is presented in court.
Comparative Study on the Environmental Education Acts in Japan, Taiwan, and Korea

There are eight countries in the world that have enacted the Environmental Education Acts (hereinafter referred to as “EEAs”) as of 2019. Of these, three countries in East Asia―Japan, Taiwan, and Korea―established the EEAs and they conduct pioneering efforts around the world. In Japan, the EEAs was enacted in 2003 and revised in 2011. In Korea, it was enacted in 2008 and revised in 2015. In Taiwan, it was promulgated in 2010 and put into effect in 2011.
Asymptotic Normality of $Q$ -Ary Linear Codes

Sidel'nikov proved in 1971 that the weight distribution of long binary codes is asymptotically Gaussian. Delsarte sketched in 1975 an extension of this result to Q -ary codes when Q > 2. In this note, we complete Delsarte's proof.
Recommending Personalized Search Terms for Assisting Exploratory Website Search

When visiting a website, the main task is to search for relevant information available on the site. The search is typically conducted using search terms. Choosing the right search terms is crucial to a successful search, but is often difficult, especially for exploratory searches. In this paper, we propose a recommender system called mySearchCLUE that recommends personalized search terms within a website search engine via collaborative filtering of similar visitors. The proposed recommender system helps website visitors explore and find better personalized search terms compared with no recommendations, and thus can be used to aid the personalized lookup and/or exploratory search process.