Reviewerly/exHarmony huggingface dataset:
The Digital Divide? Analyzing Regional Differences of Tablet PC Use in Korean Middle Schools for Sustainable Development

The purpose of this study was to investigate the effects of digital learning environments using tablet Personal Computers (PCs) in Korean metropolitan and rural middle schools. After 12 weeks of professional development for six teachers to enhance ICT and instructional design competency, 48 metropolitan and 63 rural students participated in learning with tablet PCs in English, science, and social studies subjects for 12 weeks. As a result, teachers’ various experiences of changes and challenges in digital learning environments were qualitatively analyzed and described. Also, quantitative measurements of students’ self-regulated learning abilities, collaborative learning disposition, and learning satisfaction were conducted and findings indicated that rural students showed significant differences compared to urban students in all three variables. Based on the results, educational implications and suggestions are discussed.
Improving Long Distance Slot Carryover in Spoken Dialogue Systems

Tracking the state of the conversation is a central component in task-oriented spoken dialogue systems.One such approach for tracking the dialogue state is slot carryover, where a model makes a binary decision if a slot from the context is relevant to the current turn.Previous work on the slot carryover task used models that made independent decisions for each slot.A close analysis of the results show that this approach results in poor performance over longer context dialogues.In this paper, we propose to jointly model the slots.We propose two neural network architectures, one based on pointer networks that incorporate slot ordering information, and the other based on transformer networks that uses self attention mechanism to model the slot interdependencies.Our experiments on an internal dialogue benchmark dataset and on the public DSTC2 dataset demonstrate that our proposed models are able to resolve longer distance slot references and are able to achieve competitive performance.
Explaining Computer Predictions with Augmented Appraisal Degrees

An augmented appraisal degree (AAD) has been conceived as a mathematical representation of the connotative meaning in an experience-based evaluation, which depends on a particular experience or knowledge.Aiming to improve the interpretability of computer predictions, we explore the use of AADs to represent evaluations that are performed by a machine to predict the class of a particular object.Hence, we propose a novel method whereby predictions made using a support vector machine classification process are augmented through AADs.An illustrative example, in which the classes of handwritten digits are predicted, shows how the augmentation of such predictions can favor their interpretability.
Direct F0 Estimation with Neural-Network-Based Regression

Pitch tracking, or the continuous extraction of fundamental frequency from speech waveforms, is of vital importance to many applications in speech analysis and synthesis.Many existing trackers, including conventional ones such as Praat, RAPT and YIN, and newly proposed neural-network-based ones such as DNN-CLS, CREPE and RNN-REG, have conducted an extensive investigation into speech pitch tracking.This work developed a different end-to-end regression model based on neural networks, where a voice detector and a newly proposed value estimator work jointly to highlight the trajectory of fundamental frequency.Experiments on the PTDB-TUG corpus showed that the system surpasses canonical neural networks in terms of gross error rate.It further outperformed conventional trackers under clean condition and neural-network classifiers under noisy condition by the NOISEX-92 corpus.
Using Alexa for Flashcard-Based Learning

Despite increasing awareness of Alexa’s potential as an educational tool, there remains a limited scope for Alexa skills to accommodate the features required for effective language learning. This paper describes an investigation into implementing ‘spaced-repetition’, a non-trivial feature of flashcard-based learning, through the development of an Alexa skill called ‘Japanese Flashcards’. Here we show that existing Alexa development features such as skill persistence allow for the effective implementation of spaced-repetition and suggest a heuristic adaptation of the spaced-repetition model that is appropriate for use with voice assistants (VAs). We also highlight areas of the Alexa development process that limit the facilitation of language learning, namely the lack of multilingual speech recognition, and offer solutions to these current limitations. Overall, the investigation shows that Alexa can successfully facilitate simple L2-L1 flashcard-based language learning and highlights the potential for Alexa to be used as a sophisticated and effective language learning tool.
Comparing Human Computation, Machine, and Hybrid Methods for Detecting Hotel Review Spam

Most adults in industrialized countries now routinely check online reviews before selecting a product or service such as lodging. This reliance on online reviews can entice some hotel managers to pay for fraudulent reviews – either to boost their own property or to disparage their competitors. The detection of fraudulent reviews has been addressed by humans and by machine learning approaches yet remains a challenge. We conduct an empirical study in which we create fake reviews, merge them with verified reviews and then employ four methods (Naïve Bayes, SVMs, human computation and hybrid human-machine approaches) to discriminate the genuine reviews from the false ones. We find that overall a hybrid human-machine method works better than either human or machine-based methods for detecting fraud – provided the most salient features are chosen. Our process has implications for fraud detection across numerous domains, such as financial statements, insurance claims, and reporting clinical trials.
Feedback preparation of Bell states for two-qubit systems with time delay

Bell states are the maximally entangled states in two-qubit quantum systems, and play a significant role in quantum computation and quantum communication. Feedback control of stochastic quantum systems usually suffers from time-delay problems caused by the computation time for filter states and the control input. This paper addresses the preparation of Bell states in two-qubit systems with a constant delay time. A Lyapunov method is used to design a switching control law and a constant is introduced to compensate for the computation time of the estimated states and the feedback control input, thereby stabilizing the target Bell state globally. Numerical results show the effectiveness of the proposed control law.
Sensor-based Traffic Control Network with Neural Network Based Control System

Vehicle traffic congestion is one of the major problems in today's society.It produces negative effects such as pollution and disorganized management of traffic flow.This paper provides research on a traffic control system using sensors and a neural network.It utilizes vision-based sensors to monitor intersection congestion data and sends this data to the surrounding stoplights to optimize traffic flow.The neural network will be trained to intercept the data collected in each stoplight and control the stoplight signals to direct the cars in the most efficient way possible.The neural net will be trained via simulation and be optimized based on the average travel time of each simulated vehicle tor rate its performance.
Metadata for information management and retrieval: understanding metadata and its use

This updated book (originally published in 2004) is an accessible introduction to metadata for library and information science students as well as professionals. It is divided into three sections: ...
Bézier curve parametrisation and echo state network methods for solving optimal control problems of SIR model

In this work, we introduce an optimal control problem with two control variables of the SIR (susceptible-infected-recovered) epidemic model to minimise the infective and susceptible individuals. To solve the control problem, we use direct Bernstein–Bézier parametrisation of the control variables and metaheuristic optimisation methods of an objective function, and indirect methods, adaptive critic design (ACD) with echo state networks (ESNs) and Pontryagin's maximum principle. We propose Bernstein–Bézier parametrisation and ESNs based algorithms to solve optimal control problems for systems governed by differential equations. We use ACD with ESNs to approximate co-state equations. Our simulations have shown that the proposed two methods are able to solve optimal control problems.
Hiding Confidential File using Audio Steganography

Steganography is the art of hiding information in ways that prevent the detection of hidden messages. It includes a vast array of secret communications methods that conceal the message's very existence. These methods include invisible inks, microdots, character arrangement, digital signatures, covert channels, and spread spectrum communications. In this research we have proposed a simple and effective way to hide data in audio file with 256 bit AES encryption based on Least Significant Bits technique. This approach can hide not only text message but also any type of files currently used e.g. .jpg, .pdf, .mp3, mpeg4 etc in a wav audio file.
Auto-Construction of Course Knowledge Graph based on Course Knowledge

Course knowledge informationization increases the difficulties to present intrinsic connections among knowledge points in a visual way.This paper presents research on the auto-construction of course knowledge graphs based on knowledge graphs.Detailed questions regarding the auto-construction of course knowledge graphs are illustrated through a series of mature and reliable technical means.Additionally, this paper studies the application of course knowledge graph visualized navigation, providing new methods of course knowledge information construction and decreasing the learning costs for new knowledge.
An Empirical Study on Evolutionary Algorithms for Traveling Salesman Problem

Evolutionary computation (EC) has been one of the most important methods to solve NP-hard optimization problems. When using EC algorithms to solve combinatorial optimization problems (COPs), the common dilemmas are how to make the candidate solutions satisfy the problems’ constraints, which values of parameters are more proper for different problems and so on. In order to discover some general rules on solving COPs using EC algorithms, this paper takes the traveling salesman problem (TSP) as an example and performs a comprehensive empirical study on three popular EC algorithms. Genetic algorithm, ant colony optimization and particle swarm optimization are adopted in this study. Further, the configuration parameters of algorithms are fine-tuned and the effect of operators and parameters are also analyzed on TSP instances with different scales. We also discuss the effect of different initialization strategies and apply candidate set and local search strategies to these three algorithms to further accelerate the search speed when solving large-scale problems. Based on the analysis, we hope to give some guidance for designing efficient evolutionary algorithms to solve COPs.
Differences between SMT and NMT Output - a Translators’ Point of View

In this study, we compare the output quality of two MT systems, a statistical (SMT) and a neural (NMT) engine, customised for Swiss Post's Language Service using the same training data.We focus on the point of view of professional translators and investigate how they perceive the differences between the MT output and a human reference (namely deletions, substitutions, insertions and word order).Our findings show that translators more frequently consider these differences to be errors in SMT than NMT, and that deletions are the most serious errors in both architectures.We also observe there to be less agreement on differences to be corrected in NMT than SMT, suggesting that errors are easier to identify in SMT.These findings confirm the ability of NMT to produce correct paraphrases, which could also explain why BLEU is often considered to be an inadequate metric to evaluate the performance of NMT systems.
Mixture Model Clustering Using Variable Data Segmentation and Model Selection: A Case Study of Genetic Algorithm

A genetic algorithm for mixture model clustering using variable data segmentation and model selection is proposed in this study.Principle of the method is demonstrated on mixture model clustering of Ruspini data set.The segment numbers of the variables in the data set were determined and the variables were converted into categorical variables.It is shown that variable data segmentation forms the number and structure of cluster centers in data.Genetic Algorithms were used to determine the number of finite mixture models.The number of total mixture models and possible candidate mixture models among them are calculated using cluster centers formed by variable data segmentation in data set.Mixture of normal distributions is used in mixture model clustering.Maximum likelihood, AIC and BIC values were obtained by using the parameters in the data for each candidate mixture model.Candidate mixture models are established, to determine the number and structure of clusters, using sample means and variance-covariance matrices for data set.The best mixture model for model based clustering of data is selected according to information criteria among possible candidate mixture models.The number of components in the best mixture model corresponds to the number of clusters, and the components of the best mixture model correspond to the structure of clusters in data set.
An Unsupervised Framework for Online Spatiotemporal Detection of Activities of Daily Living by Hierarchical Activity Models

Automatic detection and analysis of human activities captured by various sensors (e.g., sequences of images captured by RGB camera) play an essential role in various research fields in order to understand the semantic content of a captured scene. The main focus of the earlier studies has been widely on supervised classification problem, where a label is assigned to a given short clip. Nevertheless, in real-world scenarios, such as in Activities of Daily Living (ADL), the challenge is to automatically browse long-term (days and weeks) stream of videos to identify segments with semantics corresponding to the model activities and their temporal boundaries. This paper proposes an unsupervised solution to address this problem by generating hierarchical models that combine global trajectory information with local dynamics of the human body. Global information helps in modeling the spatiotemporal evolution of long-term activities, hence, their spatial and temporal localization. Moreover, the local dynamic information incorporates complex local motion patterns of daily activities into the models. Our proposed method is evaluated using realistic datasets captured from observation rooms in hospitals and nursing homes. The experimental data on a variety of monitoring scenarios in hospital settings reveals how this framework can be exploited to provide timely diagnose and medical interventions for cognitive disorders, such as Alzheimer's disease. The obtained results show that our framework is a promising attempt capable of generating activity models without any supervision.
Automatic Identification of Economic Activities in Complaints

In recent years, public institutions have undergone a progressive modernization process, bringing several administrative services to be provided electronically. Some institutions are responsible for analyzing citizen complaints, which come in huge numbers and are mainly provided in free-form text, demanding for some automatic way to process them, at least to some extent. In this work, we focus on the task of automatically identifying economic activities in complaints submitted to the Portuguese Economic and Food Safety Authority (ASAE), employing natural language processing (NLP) and machine learning (ML) techniques for Portuguese, which is a language with few resources. We formulate the task as several multi-class classification problems, taking into account the economic activity taxonomy used by ASAE. We employ features at the lexical, syntactic and semantic level using different ML algorithms. We report the results obtained to address this task and present a detailed analysis of the features that impact the performance of the system. Our best setting obtains an accuracy of 0.8164 using SVM. When looking at the three most probable classes according to the classifier’s prediction, we report an accuracy of 0.9474.
Exponential Elimination for Bicartesian Closed Categorical Combinators

Categorical combinators offer a simpler alternative to typed lambda calculi for static analysis and implementation. Since categorical combinators are accompanied by a rich set of conversion rules which arise from categorical laws, they also offer a plethora of opportunities for program optimization. It is unclear, however, how such rules can be applied in a systematic manner to eliminate intermediate values such as exponentials, the categorical equivalent of higher-order functions, from a program built using combinators. Exponential elimination simplifies static analysis and enables a simple closure-free implementation of categorical combinators--reasons for which it has been sought after.
Sufficient Causes: On Oxygen, Matches, and Fires

Abstract We demonstrate how counterfactuals can be used to compute the probability that one event was/is a sufficient cause of another, and how counterfactuals emerge organically from basic scientific knowledge, rather than manipulative experiments. We contrast this demonstration with the potential outcome framework and address the distinction between causes and enablers.
ROLE OF LIP PRINTS IN PERSONAL IDENTIFICATION AND CRIMINALIZATION.

Establishing a person's identity is a very important process in civil and criminal cases.In the field of forensic sciences, the lip prints have been proven to be an imperative tool in identifying a person positively.Today, in India, as well as worldwide, crimes of diverse character are on the rise.Both criminals and knowledgeable elite of the public are making use of stylish methodological measures while committing crimes to put forensic scientist, police, and the public off the scene.Hence, the role of crime scene detectives has become more challenging than ever in this civilized modern world.Dental, fingerprint, and DNA comparisons are probably the most common techniques, allowing fast and secure identification processes.However, since they cannot always be used, sometimes it is necessary to apply different and less-known techniques.Nevertheless, in certain circumstances related to the scene of the crime or due to the lack of experienced personnel, these techniques might be unavailable; therefore, there is still an increasing need for reliable alternative methods of establishing identity.Copy Right, IJAR, 2019,.
A Contrario Detection of Faces with a Short Cascade of Classifiers

The a contrario framework has been successfully used for the detection of lines, contours and other meaningful structures in digital images. In this paper we describe the implementation of an algorithm for face detection published in 2017 by Lisani et al. which applies the a contrario approach to the computation of the detection thresholds of a classical cascade of classifiers. The result is a very short cascade which obtains similar detection rates than a classical (and longer) one at a much lower computational cost.
DOES THE RIGHT TO USE DIGITAL CONTENT AFFECT OUR DIGITAL INHERITANCE?

Rights in the digital world affect our property in a special way. This paper aims to explain how users right in a digital world reflect on user’s estate of inheritance. Firstly, it is explained what digital content is and what digital services are. After that, digital content and services are discussed from the user’s point of view having in mind rights that users have in a digital world. Although those rights contain a right to use data or services they are created and regulated through different provisions (copyright, intellectual property, ownership or licences). Under the principle of universal succession, everything that belonged to decedent can belong to his or her heirs unless it is explicitly regulated otherwise or rights are strictly personal. Despite this principle there are some rights in the digital world that cannot belong to the heirs. Also, existence of a digital inheritance might cause some practical problems.
Switch Techniques to Recover Spatial Consistency Between Virtual and Real World for Navigation with Teleportation

In many virtual reality systems, user physical workspace is superposed with a particular area in a virtual environment. The spatial consistency between the real and virtual interactive space allows users to take advantage of physical workspace to walk and to interact intuitively with the real and virtual contents. To maintain such spatial consistency, application designers usually deactivate user virtual navigation capability. This limits user reachable virtual area, and segments the spatial consistency required sub-task from a continuous scenario mixing large scale navigation. In order to provide users with a continuous virtual experience, we introduce two switch techniques to help users to recover the spatial consistency in some predefined virtual areas with teleportation navigation: simple switch and improved switch. We conducted a user study with a box-opening task in a CAVE-like system to evaluate the performance and usability of these techniques under different conditions. The results highlight that assisting the user on switching back to a spatially consistent situation ensures entire workspace accessibility and decreases time and cognitive effort used to complete the sub-task. The simple switch results in less task completion time, less cognitive load, and is globally preferred by users. With additional visual feedback of user switch destination, the improved switch seems to provide the user with a better understanding of the resulting spatial configuration of the switch.
A complex structure representation of the US critical infrastructure protection program based on the Zachman framework

Critical infrastructures are vital assets for public safety, economic welfare or national security of countries. The importance of critical infrastructures necessitates state-level coordination of security efforts based on some rigid policies, strategies, and procedures. This hierarchical set of rules is collectively referred to as the critical infrastructure protection program (CIPP). As the pioneer of CIPP, the USA has a very complex program in which partners and stakeholders have multiple and varied interacting roles and responsibilities. The complexity of roles and interactions creates a need to make a representation of these complex structures by using intuitive tools. The Zachman framework is such a tool that provides a formal and structured way of viewing and defining a complex enterprise. It is represented by a 6×6 matrix with rows defining stakeholders and columns defining underlying interrogatives. In this article, a proof-of-concept study is performed to represent the US CIPP using the Zachman framework. The proof-of-concept study showed that the Zachman framework could be beneficial in overcoming challenges of a CIPP program which can be regarded as a complex enterprise.