Reviewerly/exHarmony huggingface dataset:
Investigating the Relationship Between Students’ Preferred Learning Style on Their Learning Experience in Virtual Reality (VR) Learning Environment

This study aims to investigate the effects of a virtual reality (VR)-based learning environment on learners with different preferred learning styles by measuring various dimensions of technology mediated approach. The learning outcomes were measured through academic performance, and affectively through “learning experience” and “perceived learning”. “Learning experience” in VR was measured through perceived realism, immersion, interaction, presence, engagement, enjoyment, ease of use, usefulness, technology functionality, task-technology fit and attitude towards using the technology. A pretest-posttest design was employed for this study. Students were categorized based on their preferred learning style and their responses were compared. Results presented here are based on data collected from 130 students (grade 5–11) in NSW, Australia. As the result of our analysis indicates, regardless of students’ preferred learning style, students similarly benefited from VR and their learning experience and learning outcome did not differ significantly.
How to Deal with Scarce Annotations in Answer Selection

Addressing Question Answering (QA) tasks with complex neural networks typically requires a large amount of annotated data to achieve a satisfactory accuracy of the models. In this work, we are interested in simple models that can potentially give good performance on datasets with no or few annotations. First, we propose new unsupervised baselines that leverage distributed word and sentence representations. Second, we compare the ability of our neural network architectures to learn from few annotated samples and we demonstrate how these methods can benefit from a pre-training on an external dataset. With a particular emphasis on the reproducibility of our results, we show that our simple models can approach or reach state-of-the-art performance on four common QA datasets.
Source detection in the bitcoin network

Motivated by analyzing anonymity properties of Bitcoin network and identification of the origin of illegal transactions, we study the problem of detecting the source node of a transaction message in the Bitcoin network, based on the present spreading model-diffusion. We start by adopting a listening model to get the information of which part of nodes have received the message, say an observation, which is an important premise of solving source detection problem. We propose an estimator for regular trees based on independent multi-reporting observations, and theoretically give a lower bound of the correct detection probability when the observation moment tends to infinity. We show that the more independent reporting observations we have, the higher the probability of detection is, and it further approaches one. The effectiveness of our source estimator is also established in several simulations.
Security for a regional network platform in IoT

A regional network is a platform to provide a variety of Internet of Things services with local connections for sensor data sharing. To secure such a regional network, we identify design requirements and then propose a new framework called the Proactive Distributed Authentication Framework (PDAF), which utilizes identity-based signatures and encryption as the basic security primitives. In PDAF, the devices utilize their identity-based signatures to enable mutual authentication among them without relying on a regional network key server (RNKS). As a result, after automatic trustworthy registration, the devices can perform distributed access authentication, mutual authentication, temporary registration and authentication, and handover authentication independent of the RNKS. We implemented PDAF and conducted experiments on a regional network. The results showed that identity-based cryptographic algorithms are suitable for use in securing regional networks, since the computation times are kept at the hundreds of milliseconds level.
From Aggregation to Navigation: Large Image Collections Seeking and Exploration

In this paper, we evaluate managing huge sets of visual content as we know handling large image collections has always been a challenge for technologists. With the rise of various social media platforms that aggregate a vast amount visual data it causes new implications not only for the computer scientists but also for curators, designers, social scientists, statisticians and end users: how to navigate the data efficiently, access to new levels of exploration and visualize the content without too many details being lost. With more and more such platforms, the bigger this challenge is going to get. In 22 semi structured surveys in Oslo (Norway), we find that a set of factors intermix to inform perceptions about large image collections - including how often they look-over large image collections.
Social Choice Methods for Database Aggregation

Knowledge can be represented compactly in multiple ways, from a set of propositional formulas, to a Kripke model, to a database. In this paper we study the aggregation of information coming from multiple sources, each source submitting a database modelled as a first-order relational structure. In the presence of integrity constraints, we identify classes of aggregators that respect them in the aggregated database, provided these are satisfied in all individual databases. We also characterise languages for first-order queries on which the answer to a query on the aggregated database coincides with the aggregation of the answers to the query obtained on each individual database. This contribution is meant to be a first step on the application of techniques from social choice theory to knowledge representation in databases.
Keyword Based System to Enhance the Efficiency of Student’s Performance Report in Computer Science Education

The most common way to show student's education result is by using a single value to evaluate his/her study. However, this method does not have the capability to track the content delivered during the class session. In computer science, this may become a major problem since student's capability to use programming language, software, and hardware are important to measure their skill. In this paper, we propose a system to enhance the efficiency of students' performance record. The system is aimed to give a more detailed skill regarding student's education history by supplementing the final score with the related technologies such as programming language, software, and hardware, used during the class. In this paper, we will go through the design of the proposed system and present a thorough review on previous related researches and available technologies that may be used to support the system.
An Assertion Language for Slicing Constraint Logic Languages

Constraint Logic Programming (CLP) is a language scheme for combining two declarative paradigms: constraint solving and logic programming. Concurrent Constraint Programming (CCP) is a declarative model for concurrency where agents interact by telling and asking constraints in a shared store. In a previous paper, we developed a framework for dynamic slicing of CCP where the user first identifies that a (partial) computation is wrong. Then, she marks (selects) some parts of the final state corresponding to the data (constraints) and processes that she wants to study more deeply. An automatic process of slicing begins, and the partial computation is “depurated” by removing irrelevant information. In this paper we give two major contributions. First, we extend the framework to CLP, thus generalizing the previous work. Second, we provide an assertion language suitable for both, CCP and CLP, which allows the user to specify some properties of the computations in her program. If a state in a computation does not satisfy an assertion then some “wrong” information is identified and an automatic slicing process can start. We thus make one step further towards automatizing the slicing process. We show that our framework can be integrated with the previous semi-automatic one, giving the user more choices and flexibility. We show by means of examples and experiments the usefulness of our approach.
AI Based Voice Translator to Sign Language

The paper presents a solution for an application that makes communication easier for people with hearing deficiencies, making a conversion from users’ voice messages to the sign language.
A localized higher-order regularization model for simultaneous image reconstruction and bias field correction

This paper addresses a preprocessing method of treating intensity inhomogeneity and mixed Laplace-Gaussian (MLG) noise for image processing and data analysis. The background of our problem is introduced firstly. Then some assumptions on the data are listed briefly. Moreover, the main idea of mathematical modeling and numerical algorithm for our proposed model is given. At last, some numerical results are present to validate our method.
Knowledge representation analysis of graph mining

This paper analyses the graph mining problem, and the frequent pattern mining task associated with it. In general, frequent pattern mining looks for a graph which occurs frequently within a network or, in the transactional setting, within a dataset of graphs. We discuss this task in the transactional setting, which is a problem of interest in many fields such as bioinformatics, chemoinformatics and social networks. We look at the graph mining problem from a Knowledge Representation point of view, hoping to learn something about support for higher-order logics in declarative languages and solvers. Graph mining is studied as a prototypical problem; it is easily expressible mathematically and exists in many variations. As such, it appears to be a prime candidate for a declarative approach; one would expect this allows for a clear, structured, statement of the problem combined with easy adaptation to changing requirements and variations. Current state-of-the-art KR languages such as IDP and ASP aspire to be practical solvers for such problems (Bruynooghe, Theory Practice Logic Program. (TPLP) 15(6), 783–817 2015). Nevertheless, expressing the graph mining problem in these languages requires unexpectedly complicated and unintuitive encoding techniques. These techniques are in contrast to the ease with which one can transform the mathematical definition of graph mining to a higher-order logic specification, and distract from the problem essentials, complicating possible future adaptation. In this paper, we argue that efforts should be made towards supporting higher-order logic specifications in modern specification languages, without unintuitive and complicated encoding techniques. We argue that this not only makes representation clearer and more susceptible to future adaptation, but might also allow for faster, more competitive solver techniques to be implemented.
The Fiction of the Beautiful: Digital Eros

Love has always liked, as we can observe since the same lyrical beginnings, to show itself, proclaim itself, as if something vital was played in that revelation that, in a certain sense, does not stop being strange because we are talking about deep experiences of each one’s soul. Now, that showing, which has found a place of privilege, must be thought under the digital cloak that dresses Eros, and think about it, then, as digital Eros. From Plato, Eros is a desire for the beautiful, Eros loves the beautiful. Therefore, the showing itself beautiful of love, requires a reflection in relation with how we show ourselves beautiful, that is, how the possibilities of networks allow us to make, sculpt, elaborate for that purpose. Finally, this implies a revision of the fictitious and the authentic of us, what the networks allow of us.
On undocumented features of software implementation of discrete-time signal reconstruction method based on interpolation filter

Due to the formal approach to the task of interpolation in the digital signal processing (DSP) statement, existing software implementations of methods for the discrete-time signals (DST) sample rate increase might work incorrectly. In the paper, we have researched features of the method for generation of the DTS with the sample rate increased by integer number of times. We have analyzed results of the DTS reconstruction by means of the MATLAB function interp.m. We have shown that the DTS with an increased sample rate might not satisfy the interpolation task requirements, thus, may vary comparing to results of the DTS reconstruction by means of other classic interpolation methods.
Soft Presuppositions as Scalar Implicatures in Signaling Games

In this paper, I will discuss why soft presuppositions behave differently from hard presuppositions: the former are easily defeasible and project nonuniformly in quantificational sentences. I assume that soft triggers should be associated with alternatives, and thus share many similarities with scalar implicatures. As a generalization of Parikh’s games of partial information, I develop game-theoretic models, which provide a unified account for both scalar implicatures and soft presuppositions. I argue that iterated best response (IBR) reasoning allows us to analyze the behaviors of soft presuppositions in accordance with rational inferences. The models yield the following predictions of soft presuppositions: projection happens unless it is common knowledge that the speaker is ignorant about it, in which case the presupposition is defeasible; projection depends on the type of quantifiers, which may lead to nonuniform behaviors.
The automatic evaluation model of physical education teaching based on two screening algorithms

In a certain network environment, the use of teaching evaluation assistant decision-making system can further promote the rationality and fairness of teaching evaluation. Two screening algorithms are proposed, which combine with the influence factors in the automatic evaluation model of physical ed ucation teaching, delete the relevant factors and leave them behind. After two deep screening, the accuracy of the results is improved. By introducing the artificial neural network technology into the evaluation of physical education teachers’ teaching quality, the evaluation factors of neurons are calculated to establish the evaluation model of BP neural network. Secondly, the factors affecting the evaluation results in the evaluation model of BP neural network are decomposed and screened by using the second screening method, and a certain amount of training and learning is carried out for the teaching quality data. The experimental results show that the second screening algorithm is effective and can improve the accuracy of the results of automatic evaluation of physical education teaching. By establishing the automatic evaluation model of physical education teaching, it can provide reference for the evaluation and assistant decision-making of physical education teaching quality in Vocational colleges.
Transformer and Multi-scale Convolution for Target-Oriented Sentiment Analysis

Target-oriented sentiment analysis aims to extract the sentiment polarity of a specific target in a sentence. In this paper, we propose a model based on transformers and multi-scale convolutions. The transformer which is based solely on attention mechanisms generalizes well in many natural language processing tasks. Convolution layers with multiple filters can efficiently extract n-gram features at many granularities on each receptive field. We conduct extensive experiments on three datasets: SemEval ABSA challenge Restaurant and Laptop dataset, Twitter dataset. Our framework achieves state-of-the-art results, including improving the accuracy of Restaurant dataset to 84.20% (5.81% absolute improvement), improving the accuracy of the Laptop dataset to 78.21% (4.23% absolute improvement), and improving the accuracy of the Twitter dataset to 72.98% (0.87% absolute improvement).
Detection of Small Moving Targets in Videos Using Skew Normal Mixture Model

Background modeling method is one of the most commonly methods for target detection. Gaussian mixture model (GMM) is a widely used background modeling method which can get good performance in video of surveillance scenes. However, when the GMM is directly applied to the detection of small moving targets, it may cause problems such as incomplete and missing detection of the target contour. Therefore, we propose a new background modeling method by using skew normal mixture model (SNMM). A skew normal mixture model is established at each pixel position in frames of video. After updating the frames of video, the parameters of the background model SNMM are updated, and the detection of small moving target is performed. Experimental results show that the SNMM can obtain better contour of the small moving targets in videos than the GMM.
Room to Play: Tinderbox Collective

Room to Play is a multidisciplinary course exploring interactive and digital processes in music, art, performance and game-development. A group of emerging artists, aged 18-25 and from a range of disciplines, worked closely with award-winning musician, game developer and sonic artist, Yann Seznec, and Edinburgh’s Tinderbox Collective to develop a series of interactive, sound-based installations and performance ideas. This paper outlines how the project came about, some outcomes from the first course in 2016, and recent developments leading to its second course currently running in 2019.
Network embedding with class discriminability

Network embedding, which learns low-dimensional representations from networks for network information preservation, has gained considerable attention in recent years. Network embedding has been shown to outperform many traditional node representation learning methods on the tasks such as clustering, classification and visualization. However, focusing on preserving the proximity between nodes, most previous works ignore many prominent features of networks. Some recent methods contrive to preserve cluster (class) structure in network by making representations closer to each other for nodes in the same class and enhancing the discriminability of cluster structure, which can result in better performance on various tasks. In this paper, we propose a simple and general model which can be extended to most network embedding models, especially NMF-based methods, to make the representations more discriminative for preserving cluster structure and therefore improve their results. In the experiments, we employ our model to extend an original and basic algorithm dating back to the early 2000s as a simple example. Comparison results with state-of-the-art algorithms have confirmed the effectiveness of our model.
Introduction to Multi-objective Optimization

In the field of Artificial Intelligence (AI), search algorithms have been popular since their invention. A search algorithm is typically designed to search and find a desired solution from a given set of all possible solutions to maximize/minimize one or multiple objectives. Depending on the mechanism of a search method, this set of solution can be searched entirely or partially. A search algorithm starts with an initial state (solution), and the ultimate goal is to find a target state (solution).
Ghana’s 2012 General Election: Free, Fair, and Flawed?

Ghana has held credible, multi-party elections since 1992, with peaceful alternation of power in 2000, 2008, and 2016. Yet despite this strong democratic track record, Ghanaian elections are still regularly marred by allegations of fraud. This case study details the 2012 general election in Ghana, which was contested in the Supreme Court by the main opposition party, the New Patriotic Party (NPP). The case study highlights the use of mechanisms such as election observation and biometric technology in order to increase the credibility of the election and deter fraud. It also outlines the challenges stemming from the use of these tools, including major malfunction of biometric technology, and relocation of fraud to polling places without the presence of observers. An important conclusion we can draw from the Ghanaian experience of 2012 is that solutions employed to deter electoral fraud, such as the deployment of election observers and the implementation of biometrics, are valuable but insufficient solutions. In some cases, political parties still retain the incentive and ability to manipulate the operation of elections.
Randomness of formal languages via automatic martingales

We define a notion of randomness for individual and collections of formal languages based on automatic martingales acting on sequences of words from some underlying domain. An automatic martingale bets if the incoming word belongs to the target language or not. Then randomness of both single languages and collections of languages is defined as a failure of automatic martingale to gain an unbounded capital by betting on the target language according to an incoming sequence of words, or a text. The randomness of formal languages turned out to be heavily dependent on the text. For very general classes of texts, any nonregular language happens to be random when considered individually. As for collections of languages, very general classes of texts permits nonrandomness of automatic families of languages only. On the other hand, an arbitrary computable language is shown to be nonrandom under certain dynamic texts.
Challenges and Opportunities in Understanding Spoken Queries Directed at Modern Entertainment Platforms

Modern in-home entertainment platforms---representing the evolution of the humble television of yesteryear---are packed with features and content: they offer a dizzying array of programs spanning hundreds of channels as well as a catalog of on-demand programs offering tens of thousands of options. Furthermore, the entertainment platform may serve as an in-home hub, providing capabilities ranging from playing music to controlling the home security system. At a high level, our goal is to provide natural speech-based access to these myriad features as an alternative to physical button entry on a remote control.
Adopting E-Textbooks in Higher Education

E-textbooks make digital learning content portable, transferrable, and searchable, increase students' engagement, and enable highly interactive learning opportunities. However, adopting e-textbooks in higher education is far from its confirmation stage. This chapter examines the relationship between the perceived attributes of using e-textbooks by instructors and their actual use of e-textbooks in higher education settings. Further, the study explores factors that prevent instructors from fully adopting e-textbooks in teaching and explains how to circumvent such challenges. Challenges of adopting e-textbooks for instructors in higher education have been revealed by the survey. The findings suggest that instructors, institutions, and e-textbook publishers should work collaboratively to enhance the adoption of e-textbooks in higher education.