Reviewerly/exHarmony huggingface dataset:
Empirical Evaluation of the BCOC Method on Multi-Domain Sentiment Analysis Data Sets

Sentiment analysis and opinion mining is an area that has experienced considerable growth over the last decade. This area of research attempts to determine the feelings, opinions, emotions, among other things, of people on something or someone. In particular, this article discusses the task of determining the polarity of reviews using an ordinal classification technique called Barycentric Coordinates for Ordinal Classification (BCOC). The aim of this analysis is to explore the viability of the application of BCOC on the field of sentiment analysis. This method is based on the hypothesis that the ordinal classes can be represented geometrically inside a convex polygon on the real plane by using barycentric coordinates. A set of experiments were conducted to evaluate the capability and performance of the proposed approach relative to a baseline, using accuracy as the general measure of performance. In general, the method is competitive with the baseline. For sentiment analysis with four classes, the results show improvements in the overall accuracy.
Unsupervised Learning of Multi-Sense Embedding with Matrix Factorization and Sparse Soft Clustering

In the natural language environment, accurately inferring the meaning of a token according to its context is crucial to understanding a sophisticated expression. However, this is not easy for a machine. The traditional language models used to train distributed word vectors are often restricted by single-sense embedding. In this paper, we develop a model called MSCvec (Multi-sense Soft Clustering Vector) for word sense disambiguation of polysemy in context. We extract the features of individual words by the co-occurrence PPMI (Positive Pointwise Mutual Information) matrix, and decompose the matrix by NMF (Nonnegative Matrix Factorization) into low-rank representations of target words, which are used as the input of an unsupervised sparse soft clustering method called Sparse Fuzzy C-means (SFCM). We use SFCM to determine the global semantic space of words, and partition the subspaces of multiple senses of a polysemous word. We relabel candidate words by the negative average log likelihood, and train multi-sense embedding with extensional vocabulary by the fastText model. Compared with the traditional static embeddings, the result shows that NMF and SFCM design can improve the performance in word similarity and relatedness tasks as well as in text classification tasks of different types of text. Accurate semantic representation of MSCvec would be necessary to produce outstanding results.
Arabic Paraphrasing Recognition Based Kernel Function for Measuring the Similarity of Pairs

Paraphrasing techniques aim to recognize, generate, or extract linguistic expressions that express the same meaning. These techniques affects positively or negatively the performance of many natural language-processing systems such as Question Answering, Summarization, Text Generation, and Machine Translation.... In this paper, we propose an efficient Arabic paraphrase recognizer based on kernel function and the specificity of terms, which is computed by term co-occurrence and term frequency - inverse document frequency. The experimental results show that our method outperforms the exiting methods based on similarity measures using a standard Arabic paraphrase database PPDB.
π ≈ 3.141, not only now, but forever

In 2016, Peter Trueb computed 22.4 trillion digits of π. Ahead of π Day on 14 March, he reflects on the nature of π and its role in mathematics, science and philosophy.
Dirichlet Series in Complex Network Modeling of Texture Images

This work investigates the use of Dirichlet series in the modeling of texture images, with application in image classification. The proposed model is based on a strategy that associates each pixel with its corresponding color (gray level in our case) to a vertex of a complex network and the gray level dissimilarity within neighbor pixels with edge weights. The degree distribution of such network is known to be very effective in providing image descriptors. Here, we propose an improvement over this technique, by working on this distribution as a Dirichlet (exponential) series and varying the exponential parameter. A family of statistical measures are extracted from the series and compose a feature vector employed here for texture image classification. In our tests, the achieved accuracy is promising when compared with other state-of-the-art approaches in different databases classically used for benchmark purposes.
Structured light smart camera for spatial augmented reality applications

Structured Light Imaging (SLI) is a means of digital reconstruction, or Three-Dimensional (3D) scanning, and has uses that span many disciplines. A projector, camera and Personal Computer (PC) are required to perform such 3D scans. Slight variances in synchronization between these three devices can cause malfunctions in the process due to the limitations of PC graphics processors as real-time systems. Previous work used a Field Programmable Gate Array (FPGA) to both drive the projector and trigger the camera, eliminating these timing issues, but still needing an external camera. This work proposes the incorporation of the camera with the FPGA SLI controller by means of a custom printed circuit board (PCB) design. Featuring a high speed image sensor as well as High Definition Multimedia Interface (HDMI) input and output, this PCB enables the FPGA to perform SLI scans as well as pass through HDMI video to the projector for Spatial Augmented Reality (SAR) purposes. Minimizing ripple noise on the power supply by means of effective circuit design and PCB layout, creates a compact and cost effective machine vision sensing solution.
Framework and Ontology for Modeling and Querying Algorithms

To improve algorithmic problem solving skills and thus coding skills, we need to practice algorithms. In that regard, databases like algoBank and websites provide algorithmic exercises with solutions but with contents that may or may not be semantically structured to be queried efficiently. To better exploit the potential of exercise databases, research suggests to represent their content using ontologies. Nevertheless, such research focuses mainly on representing and querying exercises as well as algorithmic skills or specific programming languages. In this work, we choose to represent, through the ontology CodOnto, the solutions of exercises in pseudocode to enhance understanding and querying of algorithms for learners and facilitate translation into the programming languages.
Context-Awareness-Based Adaptive Modal for Multimedia Documents

Pervasive computing is the result of the convergence of the field of mobile computing and distributed systems. Pervasive computing is based on knowing the user’s context in order to provide a service adapted to its context that is further exploited by multimedia document adaptation processes. In this paper, we introduce an approach for modeling context in pervasive computing environments for multimedia documents adaptation. The main benefit of this model is the ability to reason about various contexts and generate pertinent adaptation services.
Understanding Open Access Data Using Visuals: Integrating Prospective Studies of Children’s Responses to Natural Disasters

As access to open data is increasing, researchers gain the opportunity to build integrated datasets and to conduct more powerful statistical analyses. However, using open access data presents challenges for researchers in understanding the data. Visuals allow researchers to address these challenges by facilitating a greater understanding of the information available.This paper illustrates how visuals can address the challenges that researchers face when using open access data, such as: (1) becoming familiar with the data, (2) identifying patterns and trends within the data, and (3) determining how to integrate data from multiple studies.This paper uses data from an integrative data analysis study that combined data from prospective studies of children's responses to four natural disasters: Hurricane Andrew, Hurricane Charley, Hurricane Katrina, and Hurricane Ike. The integrated dataset assessed hurricane exposure, posttraumatic stress symptoms, anxiety, social support, and life events among 1707 participants (53.61% female). The children's ages ranged from 7 to 16 years (M = 9.61, SD = 1.60).Visuals serve as an effective method for understanding new and unfamiliar datasets.In response to the growth of open access data, researchers must develop the skills necessary to create informative visuals. Most research-based graduate programs do not require programming-based courses for graduation. More opportunities for training in programming languages need to be offered so that future researchers are better prepared to understand new data. This paper discusses implications of current graduate course requirements and standard journal practices on how researchers visualize data.
Some Problems of Advancement of Modern Information Technology in the Russian Engineering Education

New technologies in engineering education are becoming more and more relevant every year. Under the term “new technologies” in the educational process we understand primarily and mainly various kinds of on-line courses, electronic educational resources and other types of information resources. The application of all of the above mentioned tools works to improve the process of students learning and helps to increase their professional relevance on the labor market. In the paper the features of information technology application for the purposes of engineering education in Russia are considered. The review has been conducted on the basis of analysis of the main factors affecting the use of information technology in engineering education. These factors run a whole gamut of the student personalities of the Z-generation, to computer literacy of the teaching staff and the structure of the educational process in engineering education. The factors considered are comprehensively analyzed and the ways of interaction between teachers and students are proposed and on their basis. Google Classroom usage is considered, as a variant of the system of interaction between teachers and students.
Statistical criterion for comparison of binary classifier accuracy

An ABC-method (Accuracy Binary Classifier) for a more accurate assessment of the binary classifier's quality compared to the ROC-method (Receiver Operating Characteristic) is proposed. The ABC-method is suitable for quantitative and qualitative measures in independent and dependent small samples and it is not limited by the laws of distribution of objects in a sample. The ABC-method is effective for classification issues in different scientific and applied fields: IT, physical, technical, medical.
Improved reconciliation for continuous-variable quantum key distribution

The performance of a continuous-variable quantum key distribution (CVQKD) protocol depends on the efficiency of the post-processing of measurement results. The post-processing methods extract statistical information from the raw data, establish the mutual knowledge between the parties, and produce a final key that provides absolute security. Here we define an optimization method for post-processing in continuous-variable quantum key distribution. The optimization framework allows significant performance improvements in the post-processing phase compared to other existing reconciliation approaches.
Exploring the Conditions for Conversion of Competency Curriculum

The purpose of this research is to define the problems and possibilities of competency curriculum in the base on Curriculum 2015.BR Competencies in education are key-words for future society. Since the Education systems in Korea operate in bureaucratic structure, it is not easy to actualize competency- based curriculum. The structural problems of 2015 National Curriculum are also based on the same bureaucratic structure, which leads to misunderstand the application of competency into classes. As a result, restructuring of the curriculum for the competency education should be changed in the systemic organization, and it should be innovative from the early stage of organizing stages. Learning should be organized based on the context of knowledge in the learner’s routines in the horizontal structure. In addition, an alternative class of competency classes will be able to make a higher cognitive domains through conceptual knowledge, meta knowledge, and procedural knowledge rather than factual knowledge which we focus to teach.
E Return Tool

Every organization has need to pay salary to their employees, this work is done every month. Each and every organization has to maintain the record monthly for future use. Now a day as we know the countries goes become digit so this work is also performed digitally. This project E RETURN TOOL involves everything regarding to employee payment and related to salary tax. This also includes delivering of taxes to the government and also keep track of employee payments. This tool will facilitate to the employers to pre-validate the ECR and also carry out amendments in the ECR file before uploading on Unified Portal. The return tool has member wise details of wages and the contributions including basic details for the new and existing members’ return Tools also maintain the record of EPF member. Employer can check monthly or yearly details of every EPF member. When a company hires its first employee it started its e return tool. In the United States, every new employee must be reported to the state along with a completed W-4 tax form. The W-4 determines how many allowances the employee qualifies for when calculating the federal income tax that should be withheld from each check. Generally, the employee has to pay income tax depending upon their dependents.
Estimating User Interest from Chat Dialogue using Neural Networks

Non-task-oriented dialogue systems are required to chat with users in accordance with their interests. In this study, we propose a neural network-based method for estimating speakers’ levels of interest from dialogues. Our model first converts given utterances into utterance vectors using a word sequence encoder with word attention. Afterward, our novel attention approach, sentence-specific sentence attention extracts useful information for estimating the level of interest. Additionally, we introduce a new pre-training method for our model. Experimental results indicated that it was most effective to use topic-specific sentence attention and proposed pre-training in combination.
Effect of B-doping on the Crystal Structural and Optical Properties of Zinc Oxide Thin Films for Photonic Devices

Abstract. Effect of B-doping B 2 H 6 on the crystal structural and optical properties of zinc oxide (ZnO) thin films has been studied. The crystal orientation of these films were evaluated  by X-ray diffraction. It was found that the (110) reflection peak was dominant for all the film and became less pronounced as B 2 H 6 flow rate was increased. The grain size of thin film decreased as B 2 H 6 flow rate was increased. The transmittance in the ultraviolet  wavelengths region shifted to higher energy as the B 2 H 6 flow rate was increased. I t was also found that refractive index of ZnO thin films increased as the B 2 H 6 flow rate was further increased. These doping effects should be minimized  in order to grow low resistivity ZnO film with excellent optical properties for application to photonic devices. Efek Doping Boron (B) terhadap Struktur Kristal dan Sifat Optik Lapisan Tipis ZnO untuk Divais Optoelektronik Sari. Telah dilakukan studi tentang pengaruh doping B 2 H 6 terhadap struktur Kristal dan sifat optic lapisan tipis ZnO. Orientasi kristal lapisan tipisnya dievaluasi dengan difraksi sinar-x. Diperoleh bahwa puncak refleksi (110) mendominasi seluruh film dan berkurang sejalan dengan penambahan laju aliran B 2 H 6 . Ukuran butir Kristal dari film juga berkurang sejalan dengan penambahan laju aliran B 2 H 6 . Transmitnasi pada daerah panjang gelombang ultra violer bergeser ke arah energy yang lebih tinggi sejalan dengan penambahan laju aliran B 2 H 6 . Ditemukan pula bahwa indeks bias lapisan tipis ZnO bertambah sejalan dengan penambahan laju aliran B 2 H 6 . Efek-efek doping tersebut harus diminimalkan agar dapat diperoleh lapisan ZnO beresistivitas rendah dengan sifat optik yang baik untuk aplikasi pada divais fotonik.
Supporting E-Learning Materials Development Cycle Through Office Application Add-in

The development of e-learning material is not only done once, but is carried out with repeated cycles. The cycle can simply be divided into 3 parts, including making, publishing, and reusing existing material. Various systems have been developed to support this cycle. Learning Management System (LMS) is the most common system used in e-learning. However, LMS alone cannot fully support the cycles mentioned earlier. Therefore, many other systems / applications are used to support the cycle. For example, a document processing application is used at the stage of making the learning material, then published to the LMS, search by using search engine and reused with specific authoring tools. In this article, we will discuss how to integrate several applications that support the development cycle of e-learning materials through an office application add-in. The add-in was used to integrate LMS and Learning Material Reconstruction System (LRMS) so that the entire development cycle of e-learning materials development can be done in one place.
A note on subtowers and supertowers of recursive towers of function fields

In this paper we study the problem of constructing non-trivial subtowers and supertowers of recursive towers of function fields over finite fields.
Toward a quantum enhanced holometer: preliminary results of a power-recycled 2D optical cavity at INRIM

In this paper we describe the preliminary results obtained at INRiM laboratories toward realizing a couple of correlated power-recycled Michelson interferometers. This system is the first step toward the realization of a quantum-enhanced holometer.
Feature Extraction and Resident Number Prediction Method using Power Consumption Data

A large amount of power consumption information generated from private houses is being aggregated nowadays, particularly by the spread of smart electric meters. Applications that utilize these data are widely studied, and several services have been proposed. In order to utilize the power demand information effectively, an appropriate feature extraction and selection method is necessary. In this paper, a feature extraction method for power consumption information is proposed. Extracted features are used to predict the “number of household members (number of residents)” using typical machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighborhood (k-NN), and Random Forest (RF). The number of residents represents significant information for the marketing departments of several industries such as real estate and construction industries. The proposed feature extraction method consists of two steps: feature variable generation and feature variable selection. In the feature variable generation step, we have used both fundamental statics and an ARMA model to generate 33 feature variables. In the feature variable selection step, the extracted feature variables are first ranked by applying Analysis of Variance (ANOVA). An appropriate feature variable set is selected by assessing several combinations of the 33 features, based on proposed extended algorithm of Recursive Feature Elimination (RFE). Our overall feature extraction method is evaluated based on the prediction accuracy using extracted feature variables. Compared with the accuracy using feature variables extracted by conventional methods, the accuracy is improved by 6.78%, 4.98%, and 8.11% for k-NN, SVM, and RF, respectively, and we have successfully proven validity of our proposition.
3D SfM as a Measuring Technique for Human Body Transformation

The tracking of fat loss as well as muscle gain has always been one of the most important steps during a person’s fitness journey. It does not only motivate to continue practicing exercises, but also helps to develop specific workout plans to enhance particular body parts of athletes. Structure for Motion (SfM), unlike other reconstruction techniques, produces acceptable results from low-quality inputs. This makes the method applicable for ubiquitous equipment like a smartphone camera, while still being scalable to professional environments with proper equipment. In order to track overall body transformation, we propose a photogrammetry workflow employing SfM, reproducibly generating a model of the human body in different stages of a fitness plan. For visualization, we do a mesh alignment step followed by a comparison between the reconstructed body models of the subject, resulting in color-mapped meshes. Following this workflow the transformation of specific body regions can be analyzed in detail, only using consumer hardware.
EDUCATIONAL AND PEDAGOGICAL POSSIBILITIES OF BOOK TRAILER TECHNOLOGY IN SCHOOLCHILDREN’S LITERARY EDUCATION

The article attempts to reflect on the specifics of the book trailer as a technology of modern literary education of schoolchildren. Its educational and pedagogical possibilities are evaluated. The book trailer is interpreted by the authors as a special kind of media text, which is both a subject of study and a teaching tool. Its methodological mobility, timesaving character and integrational nature of the content represent significant pedagogical possibilities for various levels of pupils’ literary training. The researchers substantiate the educational effectiveness of using this technology at each stage of work on a literary text in Literature class and in extracurricular activity.
Parallel preparation of a set of arbitrary path-polarization hyperentangled states

The simultaneous production of a set of arbitrary hyperentangled states is crucial for quantum machines running variant quantum protocols concurrently, like universal quantum computers and quantum communication hubs. We present an experimental method to prepare a set of arbitrary path-polarization hyperentangled states concurrently using non-collinear spontaneous parametric down-conversion (SPDC). A cascaded pair of type-I crystals are pumped by 405-nm diagonally-polarized beam to produce a noncollinear stream of photon pairs. Compensation crystals are inserted to correct for the angular slope of the relative phase of the produced polarization entangled state. The pathentangled states are created over four pairs of slits positioned at conjugate locations to the pump beam. The local relative-phases of the path and polarization states can be independently tailored by intercepting the SPDC emission by tiltable birefringent and glass plates. The amplitudes of the polarization states is controlled by the manipulation of the polarization state of the pump. Also, the amplitudes of the path states is accessed by translating the slits over the SPDC cone. Here, while a pure state describes the whole SPDC emission, the produced states can be deemed an independent set by avoiding paths balanced to less than the coherence-length.