Reviewerly/exHarmony huggingface dataset:
The “British” voice of educational technology research: 50th birthday reflection

An introduction to the journal is presented in which the author discuses articles on educational technology research published within the issue.
$n$ D Variational Restoration of Curvilinear Structures With Prior-Based Directional Regularization

Curvilinear structure restoration in image processing procedures is a difficult task, which can be compounded when these structures are thin, i.e., when their smallest dimension is close to the resolution of the sensor. Many recent restoration methods involve considering a local gradient-based regularization term as prior, assuming gradient sparsity. An isotropic gradient operator is typically not suitable for thin curvilinear structures, since gradients are not sparse for these. In this paper, we propose a mixed gradient operator that combines a standard gradient in the isotropic image regions, and a directional gradient in the regions where specific orientations are likely. In particular, such information can be provided by curvilinear structure detectors (e.g., RORPO or Frangi filters). Our proposed mixed gradient operator, that can be viewed as a companion tool of such detectors, is proposed in a discrete framework and its formulation/computation holds in any dimension; in other words, it is valid in [Formula: see text], n ≥ 1 . We show how this mixed gradient can be used to construct image priors that take edge orientation, as well as intensity, into account, and then involved in various image processing tasks while preserving curvilinear structures. The experiments carried out on 2D, 3D, real, and synthetic images illustrate the relevance of the proposed gradient, and its use in variational frameworks for both denoising and segmentation tasks.
CoSummary

This paper presents CoSummary, an adaptive video fast-forwarding technique for browsing surgical videos recorded by wearable cameras. Current wearable technologies allow us to record complex surgical skills, however, an efficient browsing technique for these videos is not well established. In order to assist browsing surgical videos, our study focuses on adaptively changing playback speeds through the learning and detecting collaborative scenes based on surgeon hand placement and gaze information. Our evaluation shows that the proposed method is able to highlight important collaborative scenes and skip less important scenes during surgical procedures. We have also performed a subjective study with surgeons in order to have professional feedback. The results confirmed the effectiveness of the proposed method in comparison to uniform video fast-forwarding.
The support of constructs in thesaurus tools from a Semantic Web perspective: Framework to assess standard conformance

Thesauri are conceptual tools useful to achieve semantic interoperability and reusability, which are relevant goals in the Semantic Web. Thesaurus standards establish, among other issues, the constructs that can appear in a thesaurus. The ISO 25964 standard for thesauri, which supersedes ISO 2788, is the evolution of the ISO thesauri standard to a conceptual approach closer to the Semantic Web. However, it appeared when SKOS -the W3C Recommendation- was already consolidated as the standard for KOS (Knowledge Organization System) representation in the Semantic Web, including thesauri. The evolution from ISO 2788 to ISO 25964, and the relationships between constructs in ISO 2788/ISO 25964 and SKOS, are studied in this paper. From the analysis of this comparison, a methodological framework, that focuses on the construct support, is proposed to evaluate the conformance quality of thesaurus management tools. Target readers are professionals in charge of thesauri edition. A Semantic Web perspective is taken to characterize the effect that using SKOS to represent thesauri can have on the results of the assessment. A proof of concept for the model's feasibility was performed on two tools and the analysis of the results of this experimental validation is presented. The conclusions highlight the model's suitability for assessing conformance to the standards concerning support for thesaurus constructs.
Influence of Deep-Sea Ambient Conditions in the Performance of Pressure-Compensated Induction Motors

Abstract Understanding the influence of low temperature and high hydrostatic pressure in the starting performance of deep-sea pressure-compensated (PC) induction motors is essential for sizing of the motor and upstream power systems. This paper presents the observations from experiments carried out on industry-standard 3000-V rated deep-sea PC induction motors of 30- and 60-kW capacities in a hyperbaric chamber. It is identified that the motors consume about 30% of their rated power when started at a pressure of 600 bar and a temperature of 2°C. The results could be used for sizing of deep sea electric motors taking into consideration the increased starting power requirements due to the viscous drag of the PC fluid and to design PC oil viscosity management systems.
Evaluating narrative-driven movie recommendations on Reddit

Recommender systems have become omni-present tools that are used by a wide variety of users in everyday life tasks, such as finding products in Web stores or online movie streaming portals. However, in situations where users already have an idea of what they are looking for (e.g., 'The Lord of the Rings', but in space with a dark vibe), most traditional recommender algorithms struggle to adequately address such a priori defined requirements. Therefore, users have built dedicated discussion boards to ask peers for suggestions, which ideally fulfill the stated requirements. In this paper, we set out to determine the utility of well-established recommender algorithms for calculating recommendations when provided with such a narrative. To that end, we first crowdsource a reference evaluation dataset from human movie suggestions. We use this dataset to evaluate the potential of five recommendation algorithms for incorporating such a narrative into their recommendations. Further, we make the dataset available for other researchers to advance the state of research in the field of narrative-driven recommendations. Finally, we use our evaluation dataset to improve not only our algorithmic recommendations, but also existing empirical recommendations of IMDb. Our findings suggest that the implemented recommender algorithms yield vastly different suggestions than humans when presented with the same a priori requirements. However, with carefully configured post-filtering techniques, we can outperform the baseline by up to 100%. This represents an important first step towards more refined algorithmic narrative-driven recommendations.
Salient object detection by local and global manifold regularized SVM model

In this paper, a bottom-up saliency detection algorithm is proposed by introducing the manifold regularized Support Vector Machine (SVM) model. The local manifold regularization can well capture local saliency cues. However, its local-to-global mechanism often ignores the completeness of salient objects especially when the contrast between the foreground and background is low, while the global regularization can discover the long-range semantic structure but easily neglect the details. To comprehensively utilize their advantages, we respectively construct a local regularizer (LR) and a global regularizer (GR), and incorporate them into the SVM models, which are then self-trained across superpixels by pseudo labeled ones. The LR-SVM and GR-SVM models are further refined by updating training sample with higher-level object proposals. Extensive experiments on five benchmark datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art saliency detection methods. In addition, we show that the manifold regularized SVM model can be easily applied to some existing saliency models and achieve significant performance improvement.
Improving requirements-test alignment by prescribing practices that mitigate communication gaps

The communication of requirements within software development is vital for project success. Requirements engineering and testing are two processes that when aligned can enable the discovery of issues and misunderstandings earlier, rather than later, and avoid costly and time-consuming rework and delays. There are a number of practices that support requirements-test alignment. However, each organisation and project is different and there is no one-fits-all set of practices. The software process improvement method called Gap Finder is designed to increase requirements-test alignment. The method contains two parts: an assessment part and a prescriptive part. It detects potential communication gaps between people and between artefacts (the assessment part), and identifies practices for mitigating these gaps (the prescriptive part). This paper presents the design and formative evaluation of the prescriptive part; an evaluation of the assessment part was published previously. The Gap Finder method was constructed using a design science research approach and is built on the Theory of Distances for Software Engineering, which in turn is grounded in empirical evidence from five case companies. The formative evaluation was performed through a case study in which Gap Finder was applied to an on-going development project. A qualitative and mixed-method approach was taken in the evaluation, including ethnographically-informed observations. The results show that Gap Finder can detect relevant communication gaps and seven of the nine prescribed practices were deemed practically relevant for mitigating these gaps. The project team found the method to be useful and supported joint reflection and improvement of their requirements communication. Our findings demonstrate that an empirically-based theory can be used to improve software development practices and provide a foundation for further research on factors that affect requirements communication.
A linear fourth-order PDE-based gray-scale image inpainting model

In this paper, we will present a variational PDE-based image inpainting model in which we have used the square of the $$L^2$$ norm of Hessian of the image u as regularization term. The Euler–Lagrange equation will lead us to a fourth-order linear PDE. For time discretization, we have used convexity splitting and the resulting semi-discrete scheme is solved in Fourier domain. Stability analysis for the semi-discrete scheme is carried out. We will demonstrate some numerical results and compare with $$\text {TV}-L^2$$ and $$\text {TV}-H^{-1}$$ model.
Rethinking the role of the academy: cognitive authority in the age of post-truth

The concept of 'post-truth' is here explored within the context of education and educational technology. Contemporary political discourse is often characterised by a polarisation of political belief and scepticism about scientific and expert authority has become commonplace. We explore tensions between democratic and technocratic impulses in describing changes that are taking place in the way that authority typically operates in higher education. We analyse changing notions of academic authority to understand some of the implications for the practice of teaching, learning and administration. We argue that technocratic, administrative authority increasingly supplants cognitive authority and subject expertise. One result of increased emphasis on performative/administrative authority is the nature of authority both within the academy and the wider public sphere is changed. We examine the implications for pedagogy, curriculum and academic practice, suggesting that performative approaches to criticality, openness, truth and transparency offer potential routes to new constellations of cognitive authority.
An Efficient Segmentation Technique for Urdu Optical Character Recognizer (OCR)

In Cursive languages like Urdu, segmentation of handwritten text lines is such a problem because of context sensitivity, diagonality of text etc. In this work, we presented a simple and robust line segmentation algorithm for Urdu handwritten and printed text. In the proposed line segmentation algorithm, modified header and baseline detection method are used. This technique purely depends on the counting pixels approach. Which efficiently segment Urdu handwritten and printed text lines along with skew detection. Handwritten and printed Urdu text dataset is manually generated for evaluating algorithm. Dataset consists of 80 pages having 687 handwritten Urdu text lines and printed dataset consist of 48 pages having 495 printed text lines. The algorithm performed significantly well on printed documents and handwritten Urdu text documents with well-separated lines and moderately well on a document containing overlapping words.
One-dimensional quantum walks with a position-dependent coin

We investigate the evolution of a discrete-time one-dimensional quantum walk driven by a position-dependent coin. The rotation angle, which depends upon the position of a quantum particle, parameterizes the coin operator. For different values of the rotation angle, we observe that such a coin leads to a variety of probability distributions, e.g. localized, periodic, classical-like, semi-classical-like, and quantum-like. Further, we study the Shannon entropy associated with position and the coin space of a quantum particle, and compare them with the case of the position-independent coin. Our results show that the entropy is smaller for most values of the rotation angle as compared to the case of the position-independent coin. We also study the effect of entanglement on the behavior of probability distribution and Shannon entropy by considering a quantum walk with two identical position-dependent entangled coins. We observe that in general, a wave function becomes more localized as compared to the case of the position-independent coin and hence the corresponding Shannon entropy is lower. Our results show that a position-dependent coin can be used as a controlling tool of quantum walks.
PUMA: Parallel subspace clustering of categorical data using multi-attribute weights

There are two main reasons why traditional clustering schemes are incompetent for high-dimensional categorical data. First, traditional methods usually represent each cluster by all dimensions without difference; and second, traditional clustering methods only rely on an individual dimension of projection as an attribute’s weight ignoring relevance among attributes. We solve these two problems by a MapReduce-based subspace clustering algorithm (called PUMA) using multi-attribute weights. The attribute subspaces are constructed in our PUMA by calculating an attribute-value weight based on the co-occurrence probability of attribute values among different dimensions. PUMA obtains sub-clusters corresponding to respective attribute subspaces from each computing node in parallel. Lastly, PUMA measures various scale clusters by applying the hierarchical clustering method to iteratively merge sub-clusters. We implement PUMA on a 24-node Hadoop cluster. Experimental results reveal that using multi-attribute weights with subspace clustering can achieve better clustering accuracy on both synthetic and real-world high dimensional datasets. Experimental results also show that PUMA achieves high performance in terms of extensibility, scalability and the nearly linear speedup with respect to number of nodes. Additionally, experimental results demonstrate that PUMA is reasonable, effective, and practical to expert systems such as knowledge acquisition, word sense disambiguation, automatic abstracting and recommender systems.
Having Fun in Learning Formal Specifications

There are many benefits in providing formal specifications for our software. However, teaching students to do this is not always easy as courses on formal methods are often experienced as dry by students. This paper presents a game called FormalZ that teachers can use to introduce some variation in their class. Students can have some fun in playing the game and, while doing so, also learn the basics of writing formal specifications in the form of pre-and post-conditions. Unlike existing software engineering themed education games such as Pex and Code Defenders, FormalZ takes the deep gamification approach where playing gets a more central role in order to generate more engagement. This short paper presents our work in progress: the first implementation of FormalZ along with the result of a preliminary users' evaluation. This implementation is functionally complete and tested, but the polishing of its user interface is still future work.
Optimal joint estimation and identification theorem to linear Gaussian system with unknown inputs

Joint estimation and identification to the linear system with unknown input(s) (UI, UIs) is critical in the control community as well as signal processing. In this paper we present the solution to the problem based on the expectation-maximization (EM) method to alternately estimate system states and identify the UIs. The dominant advantage of the proposed method is that we could handle the UI(s) in not only the system dynamics model but also the measurement model. Specifically we make the following contributions: (1) providing the rigorous mathematical definitions of the problem, (2) theoretically proving the existence and uniqueness of the solution to the joint estimation and identification problem, (3) presenting the theoretical proof of convergence and effectiveness of the EM-based algorithm, and (4) supplying with sufficiently insightful explanations for the mathematical derivation.
Graduate students’ perspectives on using e-books for academic learning

The purpose of this qualitative research was to explore (a) how graduate students interpret their experiences with the use of e-books for learning, (b) which reasons influence their preference for e-books or printed books when they learn, (c) how they perceive the impact e-books have on their learning, and (d) how they compare learning experiences between the use of a given printed book and an e-book containing similar content but also offering additional multimedia options. Participants consisted of 20 graduate students from one midsize university in the United States. The major findings of this research were that graduate students generally prefer using e-books to printed texts. When studying and preparing for an exam, however, graduate students will only supersede their familiarity with printed text if a given e-book offers similar content as the printed text and provides additional resources. Research implications and recommendations for future research were also provided.
Enhanced Cryptocurrency Security by Time-Based Token Multi-Factor Authentication Algorithm

A noble multi-factor authentication (MFA) algorithm is developed for the security enhancement of the Cryptocurrency (CR). The main goal of MFA is to set up extra layer of safeguard while seeking access to a targets such as physical location, computing device, network or database. MFA security scheme requires more than one method for the validation from commutative family of credentials to verify the user for a transaction. MFA can reduce the risk of using single level password authentication by introducing additional factors of authentication. MFA can prevent hackers from gaining access to a particular account even if the password is compromised. The superfluous layer of security introduced by MFA offers additional security to a user. MFA is implemented by using time-based onetime password (TOTP) technique. For logging to any entity with MFA enabled, the user first needs username and password, as a second factor, the user then needs the MFA token to virtually generate a TOTP. It is found that MFA can provide a better means of secured transaction of CR.
Calculating the trust of providers through the construction weighted Sec-SLA

Information systems are often exposed to various types of threats, which can cause different types of damage and lead to significant financial losses. Security issues can range from small losses to a destruction of the whole information system. The effects of various threats vary considerably: some affect the confidentiality or integrity of data, while others affect the availability of a system. Organizations are now struggling to understand what the threats to their information assets are, and how to fight them, which remains a challenge. We can consider than one of the key requirements to consolidate cloud computing as a robust and reliable solution is security. Organizations that seek to embrace the cloud as a solution should be aware that this technology inherits all the security vulnerabilities from traditional solutions. These are aligned with the complexity and heterogeneity of configurations related to the architecture, privacy and compliance of cloud computing. Uniform management practices should be applied between providers in relation to the security control policies agreed with their clients - defined by the Security Service Level Agreement (Sec-SLA). Because of this, we present a standardization of responsibilities, taking into account the security perimeter of the cloud, and the main mitigation and resolution measures for the types of attacks enumerated by a vulnerability base, to be included in Sec-SLAs. This enables us to measure and classify the provider's reliability regarding the services offered. Thus, the cloud is expected to improve its control and security and get efficient response to incidents. We propose a calculation model to determine the reliability of a provider based on the solution measures and mitigation of security incidents offered in its service catalog, as well as the development of an abstract method to support consumers in the definition of weights to be attributed to the architecture, privacy and compliance used in the calculation of trust. From the vulnerabilities found, impacts and mitigation measures in the subcategories of Architecture, Privacy and Compliance of the suggested providers, considering the weights given by the consumers, allowed us to validate the abstract model of the proposed confidence calculation. This allows us to classify the providers according to the confidence presented to the consumer in a context of the security vulnerabilities and policies adopted.
Heracles: A framework for developing and evaluating text mining algorithms

Many of today’s businesses are driven by data, and while traditionally only quantitative data is considered, the role of textual data in our digital world is rapidly increasing. Text mining allows to extract and aggregate numerical data from textual documents, which in turn can be used to improve key decision processes. In this paper, we propose Heracles, a framework for developing and evaluating text mining algorithms, with a broad range of applications in industry. In contrast to other frameworks, Heracles supports both the development and evaluation stages of text mining algorithms. A practical use case shows the versatility and ease-of-use of the proposed framework in the domain of aspect-based sentiment analysis.