Reviewerly/exHarmony huggingface dataset:
CoRE

In this paper, we propose the Cold-start Resistant and Extensible Recommender (CoRE), a novel recommender system that was developed as part of collaborative research with Ryanair, the world's most visited airline website. CoRE is an algorithmic approach to the recommendation of hotel rooms that can function in extreme cold-start situations. It is a hybrid recommender that blends elements of naïve collaborative filtering, content-based recommendation and contextual suggestion to address the various shortcomings which exist in the underlying user and product data. We evaluated the performance of CoRE in a number of scenarios in order to assess different aspects of the algorithm: personalization, multi-model and the resistance to the extreme cold-start situations. Experimental results on an authentic, real-world dataset show that CoRE effectively overcomes the different problems associated with the underlying data in these scenarios.
Paired Conversational Agents for Easy-to-Understand Instruction

Conversational agents such as those hosted by the 'smart speakers' have become popular over the last few years. Although users can accomplish tasks as if they were asking a person, users still have problems in utilizing conversational agents effectively. To address this problem, some proposals explain how to input agent requests by using visual information such as instruction manuals and displays. However, such instructions create problems such as occupying the hands and eyes. The purpose of this study is to effectively enhance request entry by issuing instructions for use in an easy-to-understand manner without using visual information. Our proposal uses a pair of conversational agents, one is called the main agent, and the other is called the sub-agent, that have different voice types. Experiments show that agent pairing yields easier to understand instructions than using just the main agent. Furthermore, experiments also show that use instructions are easier to understand if the sub-agent reads aloud specific examples of use.
Impact of Ambiguity: Wh-Questions Versus Other Questions in Question Paper Translation

Word Sense Disambiguation (WSD) is a prominent area of research in the field of linguistics. A number of researches have been made to resolve the ambiguity issue in natural sentences. If a sentence has ambiguity or ambiguous word in it, then the meaning of this sentence may differ from context. If the meaning of the sentence is not appropriately inferred from the context, then the WSD algorithms are used to remove the ambiguity. This paper discusses the issue of ambiguity in translation of question paper through various MT tools. In our experiment, we have collected different types of questions for analyzing the impact of ambiguity for wh-questions with respect to other questions (objective, match, fill in the blank and keyword specific). Some machine translators often fail to understand different types of questions and treat them as normal question/sentence. We used five different types of questions in English to translate them using five standard online/offline translators into respective Hindi translation. Our aim is to analyze the impact of translations that arise due to ambiguities. The experiment carried using 150 questions of different types, and the result suggests that most of the translations have performed better in objective questions while the keyword specific questions (such as discuss, explain, etc.) performed poorly.
Observations on Design Thinking in Novice Designers While Empathizing With Persona

Personas are a rich source of user information that assists designers at various stages of the design process. HCI designers use personas to help them explicate the understanding of their target users. Design literature advocates that personas are not only a source of information about users but also a tool to empathize with them. This chapter reports observations from an experimental study done on novice designers on how empathizing with persona affected their design solutions. A total of 50 novice designers participated in this study. Participants were asked to solve a graphical design problem for the persona. The data collected was analyzed through expert rating on design solutions on the basis of directness of illustration of persona information and verbal protocols. On the basis of direct illustration of persona information on design solutions, it was observed that empathizing with persona helped designers to iterate more on design thinking.
Learning English as a Foreign Language through Social Media: Perspectives from Hong Kong Adolescents

With a view to better understanding how social media is perceived by young English learners in Hong Kong, this paper examines the EFL students’ perspectives on the use of social media for English language learning purposes. In a small-scale study conducted in May 2017, the author invited 30 Hong Kong students at the age of 10–15 to take part in a survey and respond to the questions of (1) how they perceive social media, (2) what social media platforms they most commonly use, and (3) how they use the social media as a learning tool for improving English, in particular English writing. Data collected from the study reveal that in most Hong Kong schools, the EFL teachers do regularly use social media as a tool to teach their English lessons. Most teenage EFL learners in our study were not certain whether online English might lead to a decline in English standards. They embraced social media as an easy and convenient way for learning and improving English. Based on the findings, this paper explores the possible approaches to learn English via digital literacy practices.
$$F_{0}$$ F 0 Modeling Using DNN for Arabic Parametric Speech Synthesis

Deep neural networks (DNN) are gaining increasing interest in speech processing applications, especially in text-to-speech synthesis. Actually state-of-the-art speech generation tools, like MERLIN and WAVENET are totally DNN-based. However, every language has to be modeled on its own using DNN. One of the key components of speech synthesis modules is the prosodic parameters generation module from contextual input features, and more particularly the fundamental frequency ( $$F_{0}$$ ) generation module. Actually $$F_{0}$$ is responsible for intonation, that is why it should be accurately modeled to provide intelligible and natural speech. However, $$F_{0}$$ modeling is highly dependent on the language. Therefore, language specific characteristics have to be taken into account. In this paper, we aim to model $$F_{0}$$ for Arabic speech synthesis with feedforward and recurrent DNN, and using specific characteristic features for Arabic like vowel quantity and gemination, in order to improve the quality of Arabic parametric speech synthesis.
Left-Handed Control Configuration for Side-Scrolling Games

Nowadays video games are more inclusive: children, disabled people, and seniors are considered. However, there are players that require special configuration so they could enjoy playing equally. One of these types of players is left-handed players. In order to determine whether to create a special configuration designed for the needs of left-handed audience, we carried out a study where left-handed players were targeted and offered to play with two types of control: a standard right-handed and a customized left-handed configuration. A significant main effect on player experience on the left-handed control configuration was demonstrated. The study reveals the importance of a catered control configuration to create a fair, non-stressful and user-friendly environment for players.
Prediction Algorithms and Consecutive Estimation of Software Product Line Feature Model Usability

Evaluating the quality of software product line (SPL) feature models is essential because a low quality design will be eventually reflected in almost all the products of the family. Assessment of usability in particular improves reusability which is the ultimate aim of SPL. Because feature models are used in the early stages of development, their usability assessment will help developers to design highly useable product lines. In this paper, it is proposed to develop a prediction model which can be used to forecast the usability of feature models. In our previous work we had proposed metrics for usability assessment of feature models. In this current work, we have used these metrics to develop and compare prediction accuracy of five machine learning algorithms in terms of usability and predict usability of feature models on the basis of the best performing model.
Linguistic analysis of datasets for semantic textual similarity

Abstract Semantic Textual Similarity (STS), which measures the equivalence of meanings between two textual segments, is an important and useful task in Natural Language Processing. In this article, we have analyzed the datasets provided by the Semantic Evaluation (SemEval) 2012–2014 campaigns for this task in order to find out appropriate linguistic features for each dataset, taking into account the influence that linguistic features at different levels (e.g. syntactic constituents and lexical semantics) might have on the sentence similarity. Results indicate that a linguistic feature may have a different effect on different corpus due to the great difference in sentence structure and vocabulary between datasets. Thus, we conclude that the selection of linguistic features according to the genre of the text might be a good strategy for obtaining better results in the STS task. This analysis could be a useful reference for measuring system building and linguistic feature tuning.
Interpretations and models for assumption-based argumentation

This work investigates an alternative characterization of semantics for Assumption-Based Argumentation (ABA) frameworks. The semantics of ABA frameworks are traditionally retrieved by the corresponding concepts of assumption extensions and assumption labellings, which only evaluate a subset of literals in the framework's language which are called assumptions. We argue about the interplay of non-assumptions in ABA frameworks and take inspiration from similarities between ABA and Logic Programming (LP) to propose new operations and semantic computation concerning ABA frameworks. To do so, we followed Przymusinski's work on Three-Valued Stable Models for LP to define interpretations and models for ABA frameworks (which also evaluate non-assumptions) and investigate whether this approach provides different results from the original. Amongst other results, we show that our complete model semantics is equivalent to the complete assumption labelling semantics for flat ABA frameworks, but the semi-stable model semantics is not equivalent to the semi-stable assumption labelling semantics.
Evaluating Open-source Toolkits for Automatic Speech Recognition of South African Languages

Automatic speech recognition is a critical component of human language technologies. It concerns the translation of speech into textual data which can be processed by computers. Thus, it offers the creation of an intimate link allowing humans to interact with machines on a completely natural level. A variety of open-source toolkits exist for the development of these systems. These toolkits have been successfully implemented and tested for use on well-resourced languages. However, the same level of testing has not been performed for South African languages. This investigation sets out to evaluate popular open-source tools for South African languages and identify optimal toolkit configurations for each language and toolkit. The NCHLT corpora were used to set up automatic speech recognition systems for English and isiXhosa using Kaldi, CMU Sphinx, and HTK. The word error rates achieved during this investigation showed that the best configurations from this investigation achieved better performance than those which were reported by the developers of the NCHLT corpus.
Studying quantum correlations dynamics in the phase space for continuous-variable Werner states and Bell-diagonal states

We investigate the ability of Wigner function to reveal and measure general quantum correlations in two-qubit open system. For this purpose, we analyze comparatively their dynamics for two different states, continuous-variable Werner states (CWS) and Bell-diagonal states (BDS), independently interacting with dephasing reservoirs. Then, we explore the effects of decreasing the degree of non-Markovianity on their behavior. We show that the presence of both quantum entanglement and quantum discord allow to have a negative Wigner function, in contrast to the result obtained for the closed two-qubit system [F. Siyouri, M. El Baz and Y. Hassouni, The negativity of Wigner function as a measure of quantum correlations, Quantum Inf. Process. 15(10) (2016) 4237–4252]. In fact, we conclude that negativity of Wigner function can be used to capture and quantify the amount of general non-classical correlations in open quantum systems.
Securely deploying distributed computation systems on peer-to-peer networks

More and more off-the-shelf processors support the dynamic construction of Trusted Execution Environments. For instance, Intel Software Guard Extensions (Intel SGX) supports the construction of so-called enclaves on modern Intel Core processors. Hence, it is interesting to design and evaluate practical security architectures that leverage this new technology.
Improved collaborative filtering recommendations using quantitative implication rules mining in implication field

Collaborative filtering recommendation based on association rule mining has become a research trend in the field of recommender systems. However, most research results only focus on binary data, whereas in practice sets of transactions are usually quantitative data. Moreover, association rule mining algorithms are designed to focus on optimizing for basket analysis, so that in order to better serve for recommendation, they need to be adjusted. Therefore, a solution for recommender systems to deal with association rules on both binary and quantitative data as well as improve the quality of recommendation based on the rule set is a challenge today. This paper proposes a new approach to improve the accuracy, the performance and the time of recommendation by the model based on quantitative implication rules mining in the implication field.
Solar Irradiance Model for the South African Solar Challenge

The South African Solar challenge is a competition that challenges international teams to design and build a vehicle powered only by solar power. This international endurance challenge is held in South Africa within the month of September. To be successful in this challenge, teams need to acquire the most distance from their solar car in the eight days. This is achieved by completing the main route as well as optional additional loops. An important part in the challenge strategies would include a model of solar radiation for September in South Africa. The purpose of this paper is twofold. Firstly, to create a model of radiation power for the time and location of the race and secondly evaluate the model. This paper shows how to create a model using historical data from GeoSun, a South African based company with the goal of acquisition and analysis of solar radiation data and solar energy. GeoSun offers a variety of services and products for solar energy projects and research. The results of this paper consist of benchmarking the created model against measurements taken with the system deployed on the actual challenge. The results indicates error percentage and actual power loss between the race data and the model, thus proving the model's use in future challenges. However, a recommendation would be to evaluate the on day cloud cover and adjust the model accordingly.
An Adaptive Algorithm for Rule Learning: Case Study and Preliminary Results

Problem partitioning strategies such as sequential covering are commonly used in rule learning algorithms, such that the task of finding a complete rule base is reduced to a sequence of subproblems. In this scenario, each solution to a subproblem consists of adding a single rule to the entire set. In this paper, we propose an alternative for rule learning based on the use of an adaptive formalism whose behavior is determined by a dynamic set of rules. Preliminary results yield a compact yet significantly comprehensible rule set, as well as an efficient model representation, from raw data using adaptive techniques. To this end, we include further discussions regarding features and enhancements to be contemplated in future works.
SIGCHI Outstanding Dissertation Award

Computers are now ubiquitous. However, computers and digital content have remained largely separate from the physical world - users explicitly interact with computers through small screens and input devices, and the "virtual world" of digital content has had very little overlap with the practical, physical world. My thesis work is concerned with helping computing escape the confines of screens and devices, spilling digital content out onto the physical world around us. In this way, I aim to help bridge the gap between the information-rich digital world and the familiar environment of the physical world and allow users to interact with digital content as they would ordinary physical content. I approach this problem from many angles: from the low-level work of providing high-fidelity touch interaction on everyday surfaces, easily transforming these surfaces into enormous touchscreens; to high-level questions surrounding the interaction design between physical and virtual realms. To achieve this end, building on my prior work, I developed two physical embodiments of this new mixed-reality design: a tiny, miniaturized projector and camera system providing the hardware basis for a projected on-world interface, and a head-mounted augmented-reality head-mounted display modified to support touch interaction on arbitrary surfaces.
Are Cryptocurrencies a More Equitable Form of Money?

The governance of the fiat money system via democratically elected government and financial institutions aims to ensure that all can transact fairly and safely with confidence in the stability and viability of that system in an equitable manner for all citizens. Rawls (1971) suggested that the principle of “equal liberty” and “fair equality of opportunity” was important to the achievement of the common interest. But the global crisis of 2008 brought into question the fairness of capitalism utilising fiat money. Cryptocurrencies have been proposed as an alternative form of money motivated on the basis of giving users greater fairness and equity than existing fiat money. This paper considers these claims with an analysis that demonstrates that in practice cryptocurrencies are more susceptible to the ‘tragedy of the commons’ due to governance problems and contradictions of claimed benefits. We apply the theoretic framework of Elinor Ostrom (2005) and suggest ways to improve fairness by addressing governance issues to ensure that more equitable objectives are achieved.
Security system development for vehicle using the method of “mail notification” at villa Rizki Ilhami Tangerang residential

Along with the growing number of crimes lately in the housing unit, we need a system that can help the security of housing in minimizing the crime rates of motor vehicle theft. By having the vehicle data system both incoming and outgoing as well as the information received by the residents of the house through a personal email will automatically create a sense of security to the inhabitants. If you just rely on humans will not be able to remember all the vehicles out into the housing unit. By making the application system Securty System Web-based vehicles through Method "Mail Notification" will be very useful. Every vehicle which will enter the housing unit, will be recorded the date and time, also the entry and exit as well, then a notice will be informed to the residents that there will be a guest of the occupant. All data will be stored in the database, if necessary, it can be taken at any time. If you need the data, administrators just enter the date and time which you want to get from the database. This software uses V3.0.12 Xampp and MySQL database used 5.5.25a. The implementing of this software will greatly help in finding the perpetrators who use motor vehicles. Because every vehicle that passes will always be recorded and stored into the database. The data can be viewed at any time if necessary for the investigation to find the perpetrators of crimes.
Improving feedback through computer-based language proficiency assessment

T his paper reports on the proposed transfer of a paper-based English proficiency exam to an online platform.We discuss both the potential predetermined advantages, which were the impetus for the project, and also some emergent benefits, which prompted an in-depth analysis and reconceptualisation of the exam's role, which in turn we hope will promote positive washback as well as washforward.This change will be afforded through more granular feedback on student performance, which will be facilitated by the online platform.
A Semiautomated Question Paper Builder Using Long Short-Term Memory Neural Networks

Through this research, we propose a model to generate different sets of question papers automatically using deep learning methodologies. We first develop LSTM classification models to classify questions into Bloom’s level of taxonomy, chapter name and we have an LSTM prediction model to predict the marks to be allocated. These Deep Learning techniques can help in reducing human effort in deciding the marks, the section, and Bloom’s level to be allocated to a question. Given a pool of hundreds of questions, our deep learning models develop a knowledge base consisting of questions and predicted attributes marks, Bloom’s level, and chapter name. We then we have a randomization algorithm to pick the questions for different units of the question paper, keeping the standards to be maintained and even distribution of questions across all topics.
Visual Analysis Method of Online Learning Path Based on Eye Tracking Data

The perception and visualization of online learning paths are one of the important foundations for online learning process optimization. Eye tracking technology provides technical support for online learning path research by accurately recording online learners’ eye movement data. This study proposes an online learning path visualization method supported by eye tracking technology. The visual model of online learning path mainly includes (1) visual object system, (2) visual element system, (3) visualization method, (4) visualization results presentation, and (5) the specific application of visualization results. The implementation process of the method is divided into three steps: (a) data perception of the online learning process; (b) parameter determination of online learning path; and (c) online learning path visualization. The empirical research results indicate that the method can accurately perceive and visualize the learner’s online learning path and then restore the real online learning process, which provides an important basis for accurately grasping and dynamically optimizing the online learning process.