Reviewerly/exHarmony huggingface dataset:
Cross-lingual intent classification in a low resource industrial setting

This paper explores different approaches to multilingual intent classification in a low resource setting.Recent advances in multilingual text representations promise crosslingual transfer for classifiers.We investigate the potential for this transfer in an applied industrial setting and compare to multilingual classification using machine translated text.Our results show that while the recently developed methods show promise, practical application calls for a combination of techniques for useful results.
Augmented Reality and MS-Kinect in the Learning of Basic Mathematics: KARMLS Case

By its nature, the learning of certain complex contents has always been a focus of attention and a challenge in the study of mathematics. This fact acquires greater importance if it is about the learning of children, because the psycho-cognitive skills of this type of user, especially when they attend the first levels of Basic General Education are not yet mature. As a result, children are unable to assimilate correctly and easily certain content of an abstract nature during the early stages of mathematics learning. This study presents the results of the application of a computer system called “Kinect based Augmented Reality Math Learning System - KARMLS”, whose design and development uses the Augmented Reality technology and the motion sensor implemented in MS-Kinect camera. The developed application covers elementary math topics corresponding to the Basic General Education curriculum of the Republic of Ecuador. The study used an experimental quantitative approach, involving 29 third-grade children (13 girls and 16 boys), who attend to 2 Basic General Education schools in Riobamba city, Ecuador. The results that allowed to evaluate the prototype proposed in the study were obtained by means of a pretest and a posttest, which were contrasted with the students’ t-test for paired samples. Through the analysis of data obtained and the discussion, it is concluded that the applied computer system had a positive effect for the learning when used as a supplementary tool in the classroom and that it was more effective in children who previously had low performance than with those of high performance. Also, the children were motivated and with positive attitudes regarding the use of the analyzed software.
An Effective Recursive Technique for Multi-Class Classification and Regression for Imbalanced Data

In machine learning, classification and regression are two of the most noteworthy key topics since they occur extensively in numerous real-world applications.However, real life data is hardly ever found balanced, rather skewed data is the common occurrence.This poses some serious challenges to the standard techniques of classification and regression.The performance and effectiveness of these techniques are substantially affected by overfitting, creating a bias towards the majority class.In recent years, quite a few number of methods have been introduced for classification of imbalanced data.But most of them are designed for binary classes and it is difficult or inefficient to extend them for multiple classes.Moreover, data imbalance problem occurs frequently in regression analysis too, with only a handful of algorithms robust enough to tackle to this problem.In this paper, we propose an effective recursive method for multi-class classification with imbalanced data.Our proposed algorithm partitions and balances the data, and is applied recursively coupled with ensemble techniques.Furthermore, we also extend our proposed method to solve the data imbalance problem in regression analysis.Experimental results demonstrate that the proposed recursive technique is effective and improves the performance when compared to existing methods for classification and regression with imbalanced distribution.
Reviewer Recommendation using Software Artifact Traceability Graphs

Various types of artifacts (requirements, source code, test cases, documents, etc.) are produced throughout the lifecycle of a software. These artifacts are often related with each other via traceability links that are stored in modern application lifecycle management repositories. Throughout the lifecycle of a software, various types of changes can arise in any one of these artifacts. It is important to review such changes to minimize their potential negative impacts. To maximize benefits of the review process, the reviewer(s) should be chosen appropriately.
Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements

We present a novel semantic framework for modeling linguistic expressions of generalization— generic, habitual, and episodic statements—as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of type-level and token-level information—including hand-engineered features and static (GloVe) and contextual (ELMo) word embeddings—for predicting expressions of generalization.
Investigating the Key Factors Influencing the Use of Online Social Networks in Public Sector Context in the UAE

During the past decade, usage of online social network sites has grown dramatically rivaling search engines as the most visited Internet site. The intensive literature review reveals the existing of several studies that have been done on the field of Online Social Network (OSN), but there is a lack of research that deals with usage in the context of public sector organizations. This study aims to employ structural equation modeling via AMOS to analyze 401 valid questionnaires for assessing a model which is proposed based on Unified Theory of Acceptance and Use of Technology (UTAUT) to identify the factors affecting the use of OSN among employees of a public sector organization in the UAE. The proposed model examines the influence of four factors; performance, effort, social influence, and facilitating conditions on the actual use of OSN. Results indicated that all these factors (as independent variables) significantly predicted the actual usage of OSN with various percentages. Our work improved the insights on the online social networking usage in the context of public sector organizations.
VAE-Based Regularization for Deep Speaker Embedding

Deep speaker embedding has achieved state-of-the-art performance in speaker recognition. A potential problem of these embedded vectors (called `x-vectors') are not Gaussian, causing performance degradation with the famous PLDA back-end scoring. In this paper, we propose a regularization approach based on Variational Auto-Encoder (VAE). This model transforms x-vectors to a latent space where mapped latent codes are more Gaussian, hence more suitable for PLDA scoring.
Computational Analysis of the Historical Changes in Poetry and Prose

The esoteric definitions of poetry are insufficient in enveloping the changes in poetry that the age of mechanical reproduction has witnessed with the widespread proliferation of the use of digital media and artificial intelligence. They are also insufficient in distinguishing between prose and poetry, as the content of both prose and poetry can be poetic. Using quotes as prose considering their poetic, context-free and celebrated nature, stylistic differences between poetry and prose are delved into. Novel features in grammar and meter are justified as distinguishing features. Datasets of popular prose and poetry spanning across 1870-1920 and 1970-2019 have been created, and multiple experiments have been conducted to prove that prose and poetry in the latter period are more alike than they were in the former. The accuracy of classification of poetry and prose of 1970-2019 is significantly lesser than that of 1870-1920, thereby proving the convergence of poetry and prose in 1970-2019.
Assessing Parkinson’s Disease from Speech Using Fisher Vectors

Parkinson's Disease (PD) is a neuro-degenerative disorder that affects primarily the motor system of the body.Besides other functions, the subject's speech also deteriorates during the disease, which allows for a non-invasive way of automatic screening.In this study, we represent the utterances of subjects having PD and those of healthy controls by means of the Fisher Vector approach.This technique is very common in the area of image recognition, where it provides a representation of the local image descriptors via frequency and high order statistics.In the present work, we used four frame-level feature sets as the input of the FV method, and applied (linear) Support Vector Machines (SVM) for classifying the speech of subjects.We found that our approach offers superior performance compared to classification based on the i-vector and cosine distance approach, and it also provides an efficient combination of machine learning models trained on different feature sets or on different speaker tasks.
Distance based k-means clustering algorithm for determining number of clusters for high dimensional data

Clustering is one of the most common unsupervised data mining classification techniques for splitting objects into a set of meaningful groups.However, the traditional k-means algorithm is not applicable to retrieve useful information / clusters, particularly when there is an overwhelming growth of multidimensional data.Therefore, it is necessary to introduce a new strategy to determine the optimal number of clusters.To improve the clustering task on high dimensional data sets, the distance based k-means algorithm is proposed.The proposed algorithm is tested using eighteen sets of normal and non-normal multivariate simulation data under various combinations.Evidence gathered from the simulation reveal that the proposed algorithm is capable of identifying the exact number of clusters..
Toward Mitigating Adversarial Texts

Neural networks are frequently used for text classification, but can be vulnerable to misclassification caused by adversarial examples: input produced by introducing small perturbations that cause the neural network to output an incorrect classification.Previous attempts to generate black-box adversarial texts have included variations of generating nonword misspellings, natural noise, synthetic noise, along with lexical substitutions.This paper proposes a defense against black-box adversarial attacks using a spell-checking system that utilizes frequency and contextual information for correction of nonword misspellings.The proposed defense is evaluated on the Yelp Reviews Polarity and the Yelp Reviews Full datasets using adversarial texts generated by a variety of recent attacks.After detecting and recovering the adversarial texts, the proposed defense increases the classification accuracy by an average of 26.56% on the Yelp Reviews Polarity dataset and 16.27% on the Yelp Reviews Full dataset.This approach further outperforms six of the publicly available, state-of-the-art spelling correction tools by at least 25.56% in terms of average correction accuracy.
The Process of the Critical Infrastructure Cyber Security Management using the Integrated System of the National Cyber Security Sector Management in Ukraine

The process model of the Critical Infrastructure Cyber Security Management using the Integrated System of the National Cyber Security Sector Management in Ukraine has been developed and presented. The functional components of the Cyber Security System of Critical Infrastructure Objects have been described. The applied value of the Integrated Management System for managing the cyber security of critical infrastructure objects has been substantiated.
Attribute-based Access Control for Secure and Resilient Smart Grids

Recent advancements of Information and CommunicationTechnologies (ICT) have made it a part of almost every domain of everyday life, including the power grid, leading to what is known as the Smart Grid.But the power grid, a critical economic and social infrastructure, is vulnerable to security threats stemming from the use of ICT and to new emerging vulnerabilities and privacy issues.Access control is a fundamental element of the security infrastructure, as, ideally, the principle of less privilege, zero-trust, segregation of duties, and other best practices should be applied without disrupting the functioning of the power grid while also properly maintaining the security of the Smart Grid.The paper presents the work undertaken in the SealedGRID project and the steps taken for implementing Attribute-based Access Control policies specifically tailored to the Smart Grid.
Analysis of the Effectiveness of Learning in the Use of Learning Modules Against Student Learning Outcomes

At present there are still students who have difficulty in learning, due to limited learning resources. One effort that can be done is the use of modules in learning. Modules are printed teaching materials designed to be studied independently by learning participants. This study aims to determine the effect of the use of modules on the learning outcomes of students in MTs Hasanuddin Teluk Betung. Through the analysis of diffrences in the average of mathematics learning outcomes between those who used modules and those who did not use modules. The method used in this study is experiments with quantitative exploratory levels. The basic data collection technique uses a test technique that is to determine students learning outcomes. Testing hypotheses by using the t-test statistical formula. Based on the results of hypothesis testing it was concluded that the average mathematics learning outcomes of students using modules were higher than students who did not use the module.
A Novel Approach for Ontology-Driven Information Retrieving Chatbot for Fashion Brands

Chatbots or conversational agents are the most projecting and widely employed artificial assistants on online social media.These bots converse with the humans in audio, visual, or textual formats.It is quite intelligible that users are keen interested in the swift and relatedly correct information for their hunt in pursuit of desired product, such that their precious time is not wasted through surfing multiple websites and business portals.In this paper, we present a novel incremental approach for building a chatbot for fashion brands based on the semantic web.We organized a dataset of 5,000 question and answers of top-10 brands in the fashion domain, which covers the information about new arrivals, sales, packages, discounts, exchange/return policies, etc.We have also developed a dialogue interface for querying the system.The results generated against the queries are thoroughly evaluated on the criteria of time, context, history, duration, turns, significance, relevance, and fall back questions.
AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation

Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images. To address this problem, previous DL methods proposed to use a global convolution neural network (CNN) or few independent CNNs. In this paper, we present a novel ensemble method based on a large number of CNNs processing different overlapping brain areas. Inspired by parliamentary decision-making systems, we propose a framework called AssemblyNet, made of two “assemblies” of U-Nets. Such a parliamentary system is capable of dealing with complex decisions and reaching a consensus quickly. AssemblyNet introduces sharing of knowledge among neighboring U-Nets, an “amendment” procedure made by the second assembly at higher-resolution to refine the decision taken by the first one, and a final decision obtained by majority voting. When using the same 45 training images, AssemblyNet outperforms global U-Net by 28% in terms of the Dice metric, patch-based joint label fusion by 15% and SLANT-27 by 10%. Finally, AssemblyNet demonstrates high capacity to deal with limited training data to achieve whole brain segmentation in practical training and testing times.
Collective dynamics of rate neurons for supervised learning in a reservoir computing system

In this paper, we study collective dynamics of the network of rate neurons which constitute a central element of a reservoir computing system. The main objective of the paper is to identify the dynamic behaviors inside the reservoir underlying the performance of basic machine learning tasks, such as generating patterns with specified characteristics. We build a reservoir computing system which includes a reservoir—a network of interacting rate neurons—and an output element that generates a target signal. We study individual activities of interacting rate neurons, while implementing the task and analyze the impact of the dynamic parameter—a time constant—on the quality of implementation.
New interactive strategies for virtual reality streaming in degraded context of use

Virtual reality videos are an important element in the range of immersive contents as they open new perspectives for story-telling, journalism or education. Accessing these immersive contents through Internet streaming is however much more difficult owing to the required data rates much higher than for regular videos. While current streaming strategies rely on video compression, in this paper we investigate a radically new stance: we posit that degrading the visual quality is not the only choice to reduce the required data rate, and not necessarily the best. Instead, we propose two new impairments, Virtual Walls (VWs) and Slow Downs (SDs), that change the way the user can interact with the 360∘ video in an environment with insufficient available bandwidth. User experiments with a double-stimulus approach show that, when triggered in proper time periods, these impairments are better perceived than visual quality degradation from video compression. We confirm with network simulations the usefulness of these new types of impairments: incorporated into a FoV-based adaptation, they can enable reduction in stalls and startup delay, and increase quality in FoV, even in the presence of substantial playback buffers.
Effect of Virtual Reality on Learning Motivation and Academic Performance: What Value May VR Have for Library Instruction?

The research on whether Virtual Reality (VR) has a positive effect on student learning and engagement is limited. This study aims to examine what impact VR has on student learning motivation and performance. The study conducted by Wang (2017) revealed that VR had a marginally positive impact on student scores and a strong impact on students’ learning engagement. It indicates that VR provides a small improvement in academic performance and a large improvement in student engagement. The application of VR in libraries focuses on providing the VR tools to learn subjects in STEM and history fields. For one-off instructional settings, the boost of learning motivation may increase interest in further investigation and retention. For semester-long courses, increased learning motivation may decrease student attrition or “failure” in the information literacy course.
Interactive Gibson: Benchmark for Interactive Navigation in Cluttered Environments

We present Interactive Gibson, the first comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interaction with objects is allowed and even encouraged to accomplish a task. For example, the robot can move objects if needed in order to clear a path leading to the goal location. Our benchmark comprises two novel elements: 1) a new experimental setup, the Interactive Gibson Environment, which simulates high fidelity visuals of indoor scenes, and high fidelity physical dynamics of the robot and common objects found in these scenes; 2) a set of Interactive Navigation metrics which allows one to study the interplay between navigation and physical interaction. We present and evaluate multiple learning-based baselines in Interactive Gibson, and provide insights into regimes of navigation with different trade-offs between navigation path efficiency and disturbance of surrounding objects. We make our benchmark publicly available(this https URL) and encourage researchers from all disciplines in robotics (e.g. planning, learning, control) to propose, evaluate, and compare their Interactive Navigation solutions in Interactive Gibson.
Mining Task Offloading in Mobile Edge Computing Empowered Blockchain

Blockchain is a decentralized digital ledger to capture and record economic transactions of mobile applications. In blockchain operation process, mining tasks play a key role and pose intensive computation demands on resource constrained mobile devices. Mobile edge computing (MEC) is a promising solution to alleviate the heavy burden on the devices through task offloading. Since only the miner device that first obtains a block can get rewards, there are risks of the devices for consuming edge computing resources without any profit. However, traditional task offloading schemes that purely rely on expected utilities of the devices always ignore the difference of various devices in balancing risks and rewards, thus make the offloading inefficient. To address this problem, we adopt a prospect theoretic approach to design an optimal offloading scheme for MEC-empowered blockchain, where diverse preferences of devices for profits and risks are explicitly taken into account. Specifically, we formulate the offloading process as a Stackelberg game that incorporates notions from prospect theory. We design an efficient algorithm to obtain the optimal offloading strategies, which maximize the utilities of both the miner devices and the MEC service providers. Numerical results are presented to illustrate the performance of the proposed offloading schemes.
A Reference-Free Lossless Compression Algorithm for DNA Sequences Using a Competitive Prediction of Two Classes of Weighted Models

The development of efficient data compressors for DNA sequences is crucial not only for reducing the storage and the bandwidth for transmission, but also for analysis purposes. In particular, the development of improved compression models directly influences the outcome of anthropological and biomedical compression-based methods. In this paper, we describe a new lossless compressor with improved compression capabilities for DNA sequences representing different domains and kingdoms. The reference-free method uses a competitive prediction model to estimate, for each symbol, the best class of models to be used before applying arithmetic encoding. There are two classes of models: weighted context models (including substitutional tolerant context models) and weighted stochastic repeat models. Both classes of models use specific sub-programs to handle inverted repeats efficiently. The results show that the proposed method attains a higher compression ratio than state-of-the-art approaches, on a balanced and diverse benchmark, using a competitive level of computational resources. An efficient implementation of the method is publicly available, under the GPLv3 license.
Let Me Know What to Ask: Interrogative-Word-Aware Question Generation

Question Generation (QG) is a Natural Language Processing (NLP) task that aids advances in Question Answering (QA) and conversational assistants.Existing models focus on generating a question based on a text and possibly the answer to the generated question.They need to determine the type of interrogative word to be generated while having to pay attention to the grammar and vocabulary of the question.In this work, we propose Interrogative-Word-Aware Question Generation (IWAQG), a pipelined system composed of two modules: an interrogative word classifier and a QG model.The first module predicts the interrogative word that is provided to the second module to create the question.Owing to an increased recall of deciding the interrogative words to be used for the generated questions, the proposed model achieves new state-of-the-art results on the task of QG in SQuAD, improving from 46.58 to 47.69 in 21.24 to 22.33 in METEOR,