Reviewerly/exHarmony huggingface dataset:
Adaptive E-Learning

The personalization of feedback by an Intelligent Tutoring System has the potential to greatly improve learner motivation. This PhD investigates how an Intelligent Tutoring System can adapt to the cultural background of learners when giving feedback. The research uses the user-as-wizard method for investigation. To convey the cultural background of the learner in user studies, validated cultural stories (using Hofstede cultural dimensions) are required. These stories are then used to conduct qualitative and empirical studies to investigate how participants from a range of different cultures believe the culture of a learner should affect the kind of feedback given. The insights gathered from these studies will be unified to inspire an algorithm to allow an intelligent tutoring system to utilise these adaptations, and the effects tested on real learners.
User Acceptance of Real-Life Personalized Coaching in Social Fitness Apps

This exploratory study contributes with in-depth insights into the user acceptance of real-life personalized coaching in social fitness smartphone apps. We have defined a set of novel coaching concepts, where a dyad with strong social ties perform real-life exercises, set and track the progress of shared fitness goals. To gain a user-centred context, we have adopted a mixed-methods design and conducted a twofold experiment. First, we have participated in survey where 1530 participants shared their views on the proposed coaching concepts. Second, we have developed a prototype social fitness app and have conducted a small pilot study to learn from the participants' first-hand experience. Our findings highlight a number of approaches for enhancing coaching-based social fitness apps. We identify target groups that clearly show interest towards the presented coaching concepts. We report on user preferences towards taking a particular user role. We discuss the user experiences with the pilot app, and we pinpoint set of user barriers for using apps based on personalized coaching.
Quality assessment of stereoscopic video in free viewpoint video system

It is challenging to develop an effective quality assessment method for the stereoscopic video (SV) in the free viewpoint video (FVV) system because of asymmetric distortion and the interactively changing combinations. This paper proposes a quality assessment method that takes the characteristics of the SV in FVV systems into consideration. Specifically, considering the distortion introduced by the rendering process, the proposed method extracts a critical distortion area that can be perceivable by the human eye. The distortion degree of each critical distortion area is quantified and the overall results are pooled to obtain the initial quality score. The video pairs are classified into different combination types and the initial quality score is refined to obtain the final quality score. The experimental results show that the proposed method outperforms conventional objective methods.
Virtual Machine Sizing in Virtualized Public Cloud Data Centres

Virtual machine (VM) consolidation in data centres is a technique that is used to ensure minimum use of physical servers (hosts) leading to better utilization of computing resources and energy savings. To achieve these goals, this technique requires that the estimated VM size is on the basis of application workload resource demands so as to maximize resources utilization, not only at host-level but also at VM-level. This is challenging especially in Infrastructure as a Service (IaaS) public clouds where customers select VM sizes set beforehand by the Cloud Service Providers (CSPs) without the knowledge of the amount of resources their applications need. More often, the resources are overprovisioned and thus go to waste, yet these resources consume power and are paid for by the customers. In this paper, we propose a technique for determining fixed VM sizes, which satisfy application workload resource demands. Because of the dynamic nature of cloud workloads, we show that any resource demands that exceed fixed VM resources can be addressed via statistical multiplexing. The proposed technique is evaluated using VM usage data obtained from a production data centre consisting of 49 hosts and 520 VMs. The evaluations show that the proposed technique reduces energy consumption, memory wastage and CPU wastage by at least 40%, 61% and 41% respectively.
A Potential Field Simulation Study for Mobile Robot Path Planning in Dynamic Environments

A simulation study for path planning of mobile robots in a 2D space using the potential field method is presented. The mathematical analysis represents the attractive force between the robot and the target, as well as the repulsive forces between robot and obstacles. The forces are decomposed to their resultant x-y components in 2D for simulation implementation purposes. These two forces control the motion of the mobile robot to safely avoid any obstacle within the robot environment and track the dynamic target. The simulations for many cases were carried out using the resultant forces. The integration between the robot's equations of motion for constant acceleration, Newton's second law, and the potential field forces are illustrated for simulation implementation purposes.
Comparing the Quality of Neural Machine Translation and Professional Post-Editing

This empirical corpus study explores the quality of neural machine translations (NMT) and their post-edits (NMTPE) at the German Department of the European Commission's Directorate-General for Translation (DGT) by evaluating NMT outputs, NMTPE, and respective revisions (REV) with the automatic error annotation tool Hjerson (Popović 2011) and the more fine-grained manual MQM framework (Lommel 2014). Results show that quality assurance measures by post-editors and revisors at the DGT are most often necessary for lexical errors. More specifically, if post-editors correct mistranslations, terminology or stylistic errors in an NMT sentence, revisors are likely to correct the same type of error in the same sentence, suggesting a certain transitivity between the NMT system and human post-editors.
Anomaly Detection for Time Series Infrastructure Metric Data

Time series metric data is used by monitoring systems to analyze the performance of hardware and software from a data center. This data can be easily displayed using line graphs and, as such, can be manually checked by operators for anomalies. Manual reviews have some major disadvantages, since they are time consuming and error prone. In this paper, we describe our approach for automatically detecting outliers in metric data, which have some unique properties like high seasonality and extreme seasonal spikes. We use the "Prophet" library to create a model of the time series data, which is then automatically analyzed with our own distance formula for detecting anomalies in the time series data. A part of our metrics data have a propensity for extreme recurrent spikes, for example network usage during daily backups that is millions of times higher than the average. To handle such special cases we have also implemented a seasonal spike detector.
LBCLCT: Location Based Cross Language Cipher Technique

Encryption is a cryptographic technique to secure information that is being transmitted over the network. In the present era, most of the applications hosted on cloud are affected by enumerable number of threats. Any web application hosted on cloud has several challenges for data privacy and security. In this work, a mobile bill payment application has been designed and developed in Java programming language for the purpose of secure bill payment over the cloud. Google Cloud Platform, Google App Engine, is used for the deployment of mobile bill payment application. The proposed system encapsulates two phases: In the first phase, affine cipher encryption technique is used to encrypt the original information. Affine cipher technique is improved using dynamic key, which encapsulates geographic coordinates of the user and its results are compared with rail fence cipher technique. In the second phase, generated cipher text has been further translated into two Indian languages: character by character. The result of two-phase encryption makes the original data stronger and secure.
Learning Fuzzy Measures

This chapter is a key contribution of this work in which various computational approaches to learning fuzzy measures are described. The learning problem is framed from the perspective of data fitting, where we aim to define a model that interpolates or approximates a set of observed or user-specified instances. Fitting is performed with respect to different metrics, and by solving different convex and non-convex optimisation problems. The computational complexity of fuzzy measures is addressed by using simplifying assumptions, in particular the notion of k-order fuzzy measures and the software packages implementing the presented fitting approaches are also described.
Document Images Binarization Using Hybrid Combination of Fuzzy C-Means and Deghost Method

Abstract This paper presents a document binarization approach to the document image. Thousands of historical documents usually hold important information within them. It is usually stored in the national archives and library around the globe waiting to be scanned to retrieve the content it holds. However, many environmental factors, improper handling, and the poor quality of the materials used in the document creation cause it to suffer a high degree of degradation of various types. In order to retrieve the content of the degraded historical document, a binarization approach on document images must be applied. The processes of the binarization separate pixel value of an input image into two values which is white as the background and black as foreground text. The proposed system consists of three parts. In the first part, the image pre-processing operation is done before the binarization process to enhance image quality. In this part, Contrast Stretching and Mean Filter is applied onto the image to remove noise on the image. The second part will be to apply the binarization algorithm on the document image that has undergone an image pre-processing operation. By applying the Fuzzy C-Means algorithm to the document images, the images will be converted to a binary image and divided into two components, which is text and background. The last step of the proposed method will be performing the Deghost operation to remove “ghost” entities that may have appeared on the document image. The method will undergo imaging quality analysis, such as PSNR, Accuracy, and F-measure to determine the effectiveness of the proposed method. The experimental results on H-DIBCO 2013 dataset show the robustness, reliability, and efficiency in the proposed approach.
Speech Recognition Model Based on Deep Learning And Application in Pronunciation Quality Evaluation System

In order to improve the performance of the speech recognition and pronunciation quality evaluation system, the deep learning-based computer aided foreign language pronunciation evaluation and learning system has become a research focus of current artificial intelligence technology. Combining with the current advanced voice information technology theory, based on the previous research results, proposed to sparse since the encoder deep learning neural network is applied to speech recognition, using sparse automatic encoder based on MFCC features in-depth study, the imitation of the auditory nerve sparse touches the depth of feature extracting signal, beneficial to the improvement of the HMM model for speech recognition accuracy, meet the needs of the current computer assisted English teaching. The simulation results show that the recognition rate of the deep learning neural network is obviously superior to that of the traditional speech recognition algorithm, which realizes more accurate human-computer interaction and improves the reliability of the evaluation of the quality of foreign language pronunciation.
A RSA-Based Efficient Dynamic Secure Algorithm for Ensuring Data Security

The development of technology has become faster and more people are engaging with technology for various purposes. As a result, the usage of data is increasing with the rapid growth of technology. Nowadays,’s ensuring data security has become a major challenging factor for the service providers. Various techniques are implemented to increase performance and security for data communication. In this paper, a method is proposed using Ron Rivest, Shamir, and Adleman (RSA) algorithm and Diffie–Hellman (DH) algorithm. RSA algorithm is used for encryption and decryption purpose. DH is used for exchanging generated keys. Also, a password protection mechanism is applied in the proposed algorithm. The interesting part in password system is that for every decryption password, there will be a decrypted text. The system won’t tell you that you have entered wrong password. Decrypted correct text will appear only if anyone enters the correct password, which was used during encryption. The proposed algorithm is simulated and the results are presented, which represent the evaluation of algorithm within a secure and efficient way for ensuring secure data communication.
A CNN-Based Classification Model for Recognizing Visual Bengali Font

Automatic font recognition or similar font suggestions from an image or picture are the core design works for many designers. This paper proposes a framework based on Convolutional Neural Network (CNNs) to the widely neglected problem of Bangla font recognition by the vision community. First of all, we build up the available large-scale dataset consisting of both labeled synthetic data by Adobe and partly labeled real-world data. Next, CNN is trained to classify images into predefined font classes. Global average pooling layer is proposed instead of fully connected layers over feature maps in the classification layer to correspondence between feature maps and output. Thus, the feature maps can be easily interpreted as font categories confidence maps. We show that our method achieves state-of-the-art performance on a challenging dataset of 10 selected Bangla computer fonts with 96% line-level accuracy. Large-scale experiments show that our approach is exceptionally viable on our synthetic test images and achieves promising results on real-world test images.
Search and Justification Behavior During Multimedia Web Search for Procedural Knowledge

In an eye-tracking study, N = 38 participants performed two procedural-knowledge search tasks by using a mockup multimedia search engine results page (SERP). By presenting both conventional websites and videos as results on the SERP, we aimed at examining the role of the modality of information resources in individuals' retrieval behavior as well in their final recommendation of one most suitable information resource. Across both tasks, the results of this study indicate that participants who finally recommended a video resource spent a greater proportion of time inspecting video results on the SERP as well as on the video resources themselves. Furthermore, participants' written justifications for the recommended information resource revealed that in both tasks about one third of the participants mentioned the modality of the information resource to justify their recommendation decision. Our findings indicate that the modality of information resources at least to some extent plays a role during web search for procedural learning resources.
AI-Powered Tangible Interfaces to Transform Children's Mental Well-Being

Mental health is placing increasing pressure on global health organisations with modern lifestyles significantly contributing to worsening mental well-being. Mental health challenges do not only affect adults but also children with increasing social and academic pressure. Technologies for mental well-being possess many qualities that offer the potential for people who may otherwise not receive help due to fear of stigma or lack of resources to improve how they feel. In this work, we pursue the design of multiple tangible interfaces for children containing sensors with the aim of using Artificial Intelligence (AI) to automatically infer their mental well-being and custom buttons to enable the labelling of real-world data. Tangible interfaces for mental well-being are ideal as they provide a non-intrusive means to infer and communicate mental well-being through play.
School Violence Detection Based on Multi-sensor Fusion and Improved Relief-F Algorithms

School bullying is a common social problem around the world, and school violence is considered to be the most harmful form of school bullying. This paper proposes a school violence detecting method based on multi-sensor fusion and improved Relief-F algorithms. Data are gathered with two movement sensors by role playing of school violence and daily-life activities. Altogether 9 kinds of activities are recorded. Time domain features and frequency domain features are extracted and filtered by an improved Relief-F algorithm. Then the authors build a two-level classifier. The first level is a Decision Tree classifier which separates the activity of jump from the others, and the second level is a Radial Basis Function neural network which classifies the remainder 8 kinds of activities. Finally a decision layer fusion algorithm combines the recognition results of the two sensors together. The average recognition accuracy of school violence reaches 84.4%, and that of daily-life reaches 97.3%.
User Profiles of Healthcare Professionals in a Chronic Obstructive Pulmonary Disease Care Pathway

Telehealth is a digital service expected to address healthcare demands by supporting self-management and improving access to healthcare facilities for chronic obstructive pulmonary disease. However, results for telehealth efficacy are conflicting partially because of barriers to adoption by end-users. Developing an understanding of users' needs helps to reduce barriers of adoption by allowing service designers to create meaningful experiences. We conduct interviews as part of a case study in a chronic obstructive pulmonary disease care pathway in Greece. Following a user-centered design approach, we describe the goals, tasks, and needs of healthcare professionals as four user profiles. The implications of our findings bring to light a deeper understanding of the attitudes and perceptions of healthcare professionals towards telehealth, as well as a core set of user needs for telehealth service designers.
Byzantine-Robust Stochastic Gradient Descent for Distributed Low-Rank Matrix Completion

To overcome the growing privacy concerns of centralized machine learning, federated learning has been proposed to enable collaboratively training a model with data stored locally in the owners' devices. However, adversarial attacks (e.g., Byzantine attacks in the worst case) still exist in the federated learning systems so that the information shared by the data owners are unreliable. Byzantine-robust aggregation methods, such as median, geometric median and Krum, have been found to perform well in eliminating the negative effects caused by the Byzantine attacks. In this paper, we study the distributed low-rank matrix completion problem in a federated learning setting, where some data owners are malicious. We combine the Byzantine-robust aggregation rules with stochastic gradient descent (SGD) to solve this problem. Numerical experiments on the Netflix dataset demonstrate that the proposed methods are able to achieve comparable performance relative to SGD without attacks.
Batch and Moving Horizon Estimation for Systems subjected to Non-additive Stochastic Disturbances

Nonlinear state estimation techniques for state dynamics involving additive discrete zero mean white noise have received considerable attention in the past. The probabilistic formulation of the conventional moving horizon estimation (MHE) is also developed under this simplifying assumption. In reality, the unmeasured disturbances affect the states and measurements in much more complex way and thus can not be treated in additive manner. In current work, we formally introduce a probabilistic formulation of MHE which can handle such nonlinear uncertainties in the state dynamics. The efficacy of the proposed MHE has been demonstrated by conducting stochastic simulation studies on a benchmark continuous stirred tank reactor (CSTR) system. It is found that the estimation performance of the proposed MHE formulation is comparable to estimation performance of a sampling based Bayesian estimator (unscented Kalman filter or UKF), which deals with non-additive disturbances systematically, and significantly better than the extended Kalman filter (EKF) that deals with non-additive disturbances through successive linearisation.
Handwritten Indic Script Identification – A Multi-level Approach

Script identification is an emerging document analysis problem where we identify scripts type from multilingual documents. It is well known that there are 22 official languages in India and 11 scripts are used to write them. Traditional approaches for script identification consider all the scripts together and perform a classification at single level in brute force manner. In this paper, we propose a novel multi-level approach that separate 11 different scripts (Bangla, Devanagari, Gujarati, Gurumukhi, Kannada, Malayalam, Oriya, Roman, Tamil, Telugu & Urdu) from multi-script documents. A three-level hierarchy is followed during the grouping of different Indic scripts based on their structural similarities. The proposed approach not only performs well in terms of classification accuracy but also it shows more realistic way to separate multiple numbers of Indic scripts. We obtain an average script identification accuracy of 94.43% at individual script-level which is the encouraging observation of the current inherent complex problem.
“Research in the Wild”: Approaches to Understanding the Unremarkable as a Resource for Design

This chapter outlines some key approaches towards understanding the unremarkable. It focuses first on a sociological orientation to the everyday world as key to the enterprise, and then on a variety of complimentary approaches for elaborating or surfacing the unremarkable character of everyday life. It considers the kinds of data resources that are routinely used to elaborate the unremarkable, and the relationship between data resource and analysis as a constituent element of working 'in the wild'. We hope this will be a valuable resource for researchers and students alike.
A Detailed Analysis of the Influence of Saudi Arabia Culture on the Requirement Engineering Process

Requirements Engineering (RE) process involves intensive communication and collaboration among software stakeholder members. Therefore, cultural values might influence both the RE process and its outcomes. The purpose of this study is to explore the influence of culture on the RE process in a context of a conservative culture: Saudi Arabia. Our goal is to understand how the RE process can be adapted, taking into account cultural aspects. We explored and analyzed the influence of Saudi culture though Hofstede’s cultural theory. The empirical data was collected through a pilot case study with Saudi requirements engineering practitioners and software engineering researchers. The results are discussed, with a particular focus on the cultural influences on RE activities and the high-level descriptive models of the RE process.