Reviewerly/exHarmony huggingface dataset:
VHS to HDTV Video Translation Using Multi-task Adversarial Learning

There are large amount of valuable video archives in Video Home System (VHS) format. However, due to the analog nature, their quality is often poor. Compared to High-definition television (HDTV), VHS video not only has a dull color appearance but also has a lower resolution and often appears blurry. In this paper, we focus on the problem of translating VHS video to HDTV video and have developed a solution based on a novel unsupervised multi-task adversarial learning model. Inspired by the success of generative adversarial network (GAN) and CycleGAN, we employ cycle consistency loss, adversarial loss and perceptual loss together to learn a translation model. An important innovation of our work is the incorporation of super-resolution model and color transfer model that can solve unsupervised multi-task problem. To our knowledge, this is the first work that dedicated to the study of the relation between VHS and HDTV and the first computational solution to translate VHS to HDTV. We present experimental results to demonstrate the effectiveness of our solution qualitatively and quantitatively.
The δ-six-stream spherical harmonic expansion adding method for solar radiative transfer

A δ-six-stream spherical harmonic expansion adding method (δ-6SDA) for solar radiative transfer is proposed. δ-6SDA has similar accuracy as δ-six-stream discrete ordinates method (δ-6DOM) but it is almost three orders of magnitude faster than δ-6DOM in flux calculation. In addition, δ-6SDA is extended to calculate the azimuthally dependent intensity in this study. The truncated-plus-single-scattering (TMS) correction is introduced to improve accuracy. Two single-layer cases with the Cloud C1 phase function and Henyey-Greenstein phase function are considered to investigate the accuracy of δ-6SDA for intensity calculation. The relative errors of these two cases for δ-6SDA are generally less than 2% and 5%, respectively. The root-mean-square errors for δ-6SDA are generally smaller than δ-6DOM. The computing time of δ-6SDA is 3–4 orders of magnitude less than δ-6DOM. Due to its high accuracy and efficiency, δ-6SDA can be widely used in remote sensing for simulating satellite observations.
Categorizing and Visualizing Issue Tickets to Better Understand the Features Implemented in Existing Software Systems

Acquiring knowledge and context of the ongoing project is one of the most challenging issues for new project members. New project members need to comprehend the contents of features already implemented in software systems. However, most software documents (e.g., flow chart and data-model) were initially created and not updated. Herein we focus on tickets issued during the project period because they are created based on stakeholders' requirements as a project evolves. We propose a novel approach to categorize and visualize tickets to better understand the features implemented in the existing software systems. Specifically, tickets are grouped by a clustering method and the number of clusters (i.e., ticket categories) is automatically estimated. Then the ticket categories are visualized by (i) creating a heatmap of the tickets lifetimes, (ii) extracting keywords of the ticket categories, and (iii) analyzing the relationships between ticket categories. We applied our approach to an NTT software development project. Additionally, we interviewed the project members and external experts to evaluate the effectiveness of our approach. Our approach helps comprehend the features of the software system.
Speaker-Aware Speech Emotion Recognition by Fusing Amplitude and Phase Information

The use of a convolutional neural network (CNN) for extracting deep acoustic features from spectrograms has become one of the most commonly used methods for speech emotion recognition. In those studies, however, common amplitude information is chosen as input with no special attention to phase-related or speaker-related information. In this paper, we propose a multi-channel method employing amplitude and phase channels for speech emotion recognition. Two separate CNN channels are adopted to extract deep features from amplitude spectrograms and modified group delay (MGD) spectrograms. Then a concatenate layer is used to fuse the features. Furthermore, to gain more robust features, speaker information is considered in the stage of emotional feature extraction. Finally, the fusion features that considering speaker-related information are fed into the extreme learning machine (ELM) to distinguish emotions. Experiments are conducted on the Emo-DB database to evaluate the proposed model. Results demonstrate the recognition performance of average F1 in 94.82%, which significantly outperforms the baseline CNN-ELM model based on amplitude only spectrograms by 39.27% relative error reduction.
Model-Based and Class-Based Fusion of Multisensor Data

In the recent years, the advancement of technology, the constantly aging population and the developments in medicine have resulted in the creation of numerous ambient assisted living systems. Most of these systems consist of a variety of sensors that provide information about the health condition of patients, their activities and also create alerts in case of harmful events. Successfully combining and utilizing all the multimodal information is an important research topic. The current paper compares model-based and class-based fusion, in order to recognize activities by combining data from multiple sensors or sensors of different body placements. More specifically, we tested the performance of three fusion methods; weighted accuracy, averaging and a recently introduced detection rate based fusion method. Weighted accuracy and the detection rate based fusion achieved the best performance in most of the experiments.
Preliminary Insights in Security Warning Studies: An Exploration in University Context

Security warning is a form of message that is designed to imitate authorized security alerting function to notify, warn and advice user about the consequence of an action by allowing random applications to run on the computer system. On the other hand, regular computer users tend to ignore the security warning that conveys excessive technical messages which are difficult for them to understand and lead to lack of motivation for decision making. Consequently, an online survey has been utilized by a group of 250 participants to gain general insights based on the user’s understanding of security warning. This paper describes one main finding utilizing the Chi-Square test. From the finding, it can be suggested that 2 hypotheses were statistically significant in terms of user’s understanding and their assessments towards computer security warning. On the other hand, variable of gender indicates with the highest significant values with three statistically significant 0.004, 0.017 and 0.000 respectively The overall results from the test given indicate that for each variable were connected between computer user’s skills and their understanding in order to improve security warning in the future.
Categorization and Classification of Uber Reviews

This paper presents a technique for the categorization of reviews of the brand, Uber. This paper contains a classification algorithm which takes textual data as input. This algorithm takes many reviews and concepts (say, cost, safety, etc.). This algorithm is performed on online conversations happening on social media, taking into account the considered concepts. Further, the categorized reviews are classified according to the sentiment (that is, positive and negative). This paper also performs a comparison of different algorithms—Naïve Bayes, KNN, decision tree and random forest. The results of comparison of these different models are then represented using certain identified parameters.
File Recovery of High-Order Clearing First Cluster Based on FAT32

The core technical problems of file recovery are the first cluster number clearing of high-order two bytes and the fragmentation in the file. The algorithm proposed in this paper makes full use of file system information such as FAT, creation time, file identification and fragmentation characteristics, recover easy ones and the files difficult to recover which are recoverable at first, and then analyzes the status of remaining fragments after eliminating many interference of fragments. Experimental results show that the algorithm can significantly reduce the complexity of fragments and improve the accuracy of file recovery.
Parameter Optimization of Polynomial Kernel SVM from miniCV

Polynomial kernel support vector machine (SVM) is one of the most computational efficient kernel-based SVM. Implementing an iterative optimization method, sequential minimal optimization (SMO) makes it more hardware independent. However, the test accuracy is sensitive to the values of hyperparameters. Moreover, polynomial kernel SVM has four hyperparameters which complicate cross-validation in parameter optimization. In this research, we transform polynomial kernels to have bounded values and analyze the relations between hyperparameters and the test error rate. Based on our discoveries, we propose mini core validation (miniCV) to fast screen out an optimized hyperparameter combination especially for large datasets. The proposed miniCV is a parameter optimization approach completely built on the distribution of the data generated via the iterative SMO training process. Since miniCV depends on the kernel matrix directly, it saves miniCV from cross-validation to optimize hyperparameters in kernel-based SVM.
Describing the Structures of Socio-economic Systems Using Metagraphs

The graphostructural approach to modeling socioeconomic systems is widely known. It describes the functioning of the system in terms of graph theory. At the same time the rapidly developing approaches of graph theory have considerable potential. The aim of the work is the developing of common methodology of mathematical modeling for a wide class of hierarchical socio-economic systems obtained from complete network graph structures using generalizations of the concept of graph such as metagraphs.
ESSENTIAL CHARACTERISTIC OF PEDAGOGICAL PROJECTING WHILE TRAINING A FUTURE TEACHER OF VOCATIONAL EDUCATION

Analysing the definitions “project”, “projecting”, “pedagogical project”, the author reveals the essence of the category “pedagogical projecting”, describes the basic stages of a project life cycle. The peculiarities of pedagogical projecting while training future teachers of vocational education according to the Federal State Educational Standard of new generation are identified; objects and subjects of pedagogical projecting are ascertained. The author’s definition of the category “pedagogical projecting of a vocational education teacher” is suggested.
Enhancing Personalized Response to Product Queries Using Product Reviews Incorporating Semantic Information

E-commerce is very much in trend in the current era for selling/purchasing products online. Hence, consumers tend to visit question answer forums to know about a product before making a purchase. The proposed work is to build a web application which would form an alternative to a traditional question answer system. Rather than focusing on a knowledge-based question answer system, the proposed work attempts to mine reviews related to the product and provides critical reviews on the product which are relevant to the question asked by the consumer. This application would be used by any user looking for supporting critical reviews related to product functionalities. Given a question answer dataset and a review dataset on a product, the similarity between the questions and the reviews is calculated and three top reviews which are most relevant to the question along with their relevance score are the output of the system. The model uses powerful similarity measures based on WordNet and Word embedding in addition to the basic similarity measures based on cosine similarity and TF-IDF. The model is evaluated in terms of how well the sentiment extracted from the output reviews of the proposed model confines with that of the answer in the question answer dataset.
Multi‐directional local adjacency descriptors (MDLAD) for heterogeneous face recognition

This paper presents new image descriptors for heterogeneous face recognition (HFR). The proposed descriptors combine directional and neighborhood information using a rotating spoke and concentric rings concept. We name the descriptors as multi-directional local adjacency descriptors (MDLAD). This family of descriptor captures the directional information through successive rotations of a pair of orthogonal spokes. Likewise, they capture the adjacency information through a comparison against the central pixel of a window with concentric rings around the central pixel. The MDLAD is found to describe the face images well for recognition purposes, which when matched using the chi-squared distance. The face recognition performance with MDLAD improves with its use as a layer in a deep neural network, which yields a robust classification for heterogeneous face recognition with respect to the state-of-the-art methods. The MDLADNET deep network is easily trainable with few hyperparameters and limited data samples as compared to existing similar deep networks. We have experimented on different heterogeneous modalities viz. Extended Yale B, CASIA, CUFSF, IIITD, LFW, Multi-PIE, and CARL, and have found proficient results.
HoloLucination: A Framework for Live Augmented Reality Presentations Across Mobile Devices

We envision that in the future, presentations for business, education, and scientific dissemination can invoke 3D spatial content to immersively display and discuss animated 3-dimensional models and spatial data visualizations to large audiences. At the moment, current frameworks have targeted a highly technical user base, prohibiting the widespread curation of immersive presentations. Furthermore, solutions for real-time multi-user interactions have focused on multiplayer gaming, rather than large format immersive presentation. However, modern mobile devices (smartphones, tablets, headsets) have the capability of rendering virtual models over the physical environment through visual anchors for Augmented Reality (AR). Our ongoing research thrust is to leverage contemporary AR infrastructure to develop an easy-to use tool for users to curate and spatially present augmented presentations to large audiences. In this demo, we have built an Augmented Reality framework that allows users to curate mixed reality presentations. Our framework allows users to prepare a sequential state of animations. At the time of presentation, presenters can invoke the animations to simultaneously occur on HMDs and mobile devices.
Annotations in the Corpus of Texts of Students Learning Slovak as a Foreign Language (ERRKORP)

Abstract The article presents the upcoming acquisition corpus of written texts of students learning Slovak as a Foreign Language and focuses on the annotation of texts, which includes information about the text as well as social and linguistic details about the student. The article also discusses the tags that identify individual errors in the texts and concept of creating the tagset itself.
On the AC^0[oplus] Complexity of Andreev's Problem

Andreev's Problem is the following: Given an integer d and a subset of S subset F_q x F_q, is there a polynomial y = p(x) of degree at most d such that for every a in F_q, (a,p(a)) in S? We show an AC^0[oplus] lower bound for this problem. This problem appears to be similar to the list recovery problem for degree-d Reed-Solomon codes over F_q which states the following: Given subsets A_1,...,A_q of F_q, output all (if any) the Reed-Solomon codewords contained in A_1 x *s x A_q. In particular, we study this problem when the lists A_1, ..., A_q are randomly chosen, and are of a certain size. This may be of independent interest.
Efficient Data Analytics on Augmented Similarity Triplets

Data analysis requires a pairwise proximity measure over objects. Recent work has extended this to situations where the distance information between objects is given as comparison results of distances between three objects (triplets). Humans find comparison tasks much easier than the exact distance computation, and such data can be easily obtained in big quantities via crowdsourcing. In this work, we propose triplets augmentation, an efficient method to extend the triplets data by inferring the hidden implicit information from the existing data. Triplets augmentation improves the quality of kernel-based and kernel-free data analytics. We also propose a novel set of algorithms for common data analysis tasks based on triplets. These methods work directly with triplets and avoid kernel evaluations, thus are scalable to big data. We demonstrate that our methods outperform the current best-known techniques and are robust to noisy data.
Applying Virtual Reality Technology to Biology Education: The Experience of Vietnam

Virtual Reality Technology (VRT) is built with hardware and digital formats in order to create a realistic immersive environment. VRT is most suitable for subjects that require labs and real-life experiments such as nature science, biology, chemistry, physics. In Vietnam, VRT has been implemented in education, albeit limited because of many reasons. In this study, the authors suggest a lesson design procedure to integrate VRT into education, specifically designed for teaching and learning Biology subject in high school in Vietnam with 5 steps and several contents that are suitable for applying VRT. This study set up a foundation base for the other deeper studies in using VRT in Biology in Vietnam.
TweetsDaily: Categorised News from Twitter

Today lives of people have become so strenuous and hectic that they are least bothered about getting updated with the latest news. Presently, there are various sources of news ranging from newspaper to various news mobile applications. In case of newspaper, we have to read long articles and regarding news applications, we have to search for the ones which provide us credible and relevant news everyday which is also a very time-consuming task. To address this problem, a web application ‘TweetsDaily’ which fetches news in the form of tweets of two domains—Indian and Global which are posted by various news channels on their respective Twitter user timelines. The tweets of each domain are further classified into five categories, namely, Politics, Sports, Entertainment, Technology, and Miscellaneous (news related to day-to-day crimes, stocks and investment, communal disputes, terror attacks, etc.) In this paper, we represent a method for the classification of tweets in five categories of each domain. The method is a text-based classification technique based on ensemble model of different classification algorithms.
Estimation of Non-radial Geometric Distortions for Dash Cams

Due to the widespread use of dash cams (car DVRs), the estimation of geometric distortions introduced by such equipment is of particular interest. However, such geometric distortions are often complex and cannot be described using classical radial models. In this paper, we propose an original method for the estimation of complex geometric distortions. At the first step of the proposed method, we build distortion models based on the polynomial approximation of the lines of a calibration pattern for each particular processed frame. At the second step, we calculate the correction field and consistently refine it using built polynomial models. In the experimental study, we apply the proposed technique to estimate the distortion of the DVR recording using the calibration pattern. We tune the order of polynomials, select optimal aggregation technique, and show how the reconstruction error changes with the number of processed frames. The experiments show that the proposed method can be successfully applied to estimate complex geometric distortions induced by car DVRs.
A Literature Analysis on Privacy Preservation Techniques

The mainstream technology of recent years is big data which leads to security and privacy concerns. Every individual has a right to preserve his information private and should not be revealed in any way. The wide range of actors like businesses, government, NGOs, etc., are helping in the collection of huge data, storing it, analyzing it, and then sharing it with end users for the purpose of data analytics. Thus the privacy preservation becomes very significant before publishing the data for open use. The data publishing depends on various rules, government policies, and guidelines wherein published data reveal only the required information based on the agreements. Thus, we can consider privacy preservation and publishing of data as a tool that has many methods for preserving privacy of the records before publishing it. Data anonymization technique before publishing of data promotes open use of data for analysis along with ensuring the private information privacy of individuals. This paper analyzes different anonymization methods and algorithms and provides detailed comparison of them on important parameters.
Digital Competence in Initial Teacher Training: Construction and Pilot Test of an Evaluation Tool/Instrument

The objective of the present article is to describe the process of designing and validating an implement which allows to measure the digital competence of the students from the bachelor in Secondary teaching in the area of Telesecundaria. First of all, it is set the context and evolution of the concept of digital competence in order to provide the theory to understand the structure of the tool. The design of the questionnaire was made taking as a reference some questionnaires applied by different researchers at a national and international level. 48 items were defined and these were organized into 3 dimensions based on theoretical elements from different authors. Three different types of validation were evaluated: 1. content, 2. construct and 3. consistency and reliability. The outcomes indicate high levels of reliability, also the feedback received through the opinion of experts provides valuable and trustworthy elements about the implement. The information obtained in this first procedure bring to light the need to consider technical and logistics aspects in the final application of the questionnaire as well as complement the data with another tool which is not of perception.
Generate Images with Obfuscated Attributes for Private Image Classification

Image classification is widely used in various applications and some companies collect a large amount of data from users to train classification models for commercial profitability. To prevent disclosure of private information caused by direct data collecting, Google proposed federated learning to share model parameters rather than data. However, this framework could address the problem of direct data leakage but cannot defend against inference attack, malicious participants can still exploit attribute information from the model parameters. In this paper, we propose a novel method based on StarGAN to generate images with obfuscated attributes. The images generated by our methods can retain the non-private attributes of the original image but protect the specific private attributes of the original image by mixing the original image and the artificial image with obfuscated attributes. Experimental results have shown that the model trained on the artificial image dataset can effectively defend against property inference attack with neglected accuracy loss of classification task in a federated learning environment.