Reviewerly/exHarmony huggingface dataset:
All aboard or still at check-in? Teacher educators’ use of digital technologies: Lessons from a small island

Teachers are increasingly integrating ICT into formal education (McKnight et al. Journal of Research on Technology in Education, 48(3), 194–211, 2016), helping to incorporate technologies in learning and teaching. However, an important expectation relating to ICT integration is that Initial Teacher Educators model their own practice as well as developing digital skills and competencies in pre-service teachers (Haydn 2010; Gudmundsdottir and Hatlevik European Journal of Teacher Education, 41(2), 214–231, 2018). To test these expectations, this paper reports on a convergent mixed methods study that consisted of twelve interviews with Initial Teacher Educators as well as a series of questionnaires that were distributed to four teacher education providers in Ireland and four in Northern Ireland. Findings suggest that, while there are certainly pockets of excellence and some individual models of good practice, it is evident that there is a limited coherent integration of ICT within and across Initial Teacher Education providers, despite efforts to achieve this.
EXACT

Attributed entity is an entity defined by its structural attributes. Extracting attributed entities from textual documents is an important problem for a variety of big-data applications. We propose a system called EXACT for extracting attributed entities from textual documents by performing explorative annotation tasks, which create attributes and bind them to tag values. To support efficient annotation, we propose a novel tag recommendation technique based on a few-shot learning scheme which can suggest tags for new annotation tasks given very few human-annotated samples. We also propose a document recommendation scheme to provide run-time context for the user. Using a novel attribute index, the system can generate the task-relevant attributed entities on-the-fly. We demonstrate how these techniques can be integrated behind a novel user interface to enable productive and efficient extraction of attributed entities at limited cost in human annotation.
Distributed two‐stage state estimation with event‐triggered strategy for multirate sensor networks

Summary This paper investigates the state estimation issue for a class of wireless sensor networks (WSNs) with the consideration of limited energy resources. First, a multirate estimation model is established, and then, a new event‐triggered two‐stage information fusion algorithm is developed based on the optimal fusion criterion weighted by matrices. Compared with the existing methods, the presented fusion algorithm can significantly reduce the communication cost in WSNs and save energy resources of sensors efficiently. Furthermore, by presetting a desired containment probability over the interval [0,1] with the developed event‐triggered mechanism, one can obtain a suitable compromise between the communication cost and the estimation accuracy. Finally, a numerical simulation for the WSN tracking system is given to demonstrate the effectiveness of the proposed method.
Multi-Level Matching Networks for Text Matching

Text matching aims to establish the matching relationship between two texts. It is an important operation in some information retrieval related tasks such as question duplicate detection, question answering, and dialog systems. Bidirectional long short term memory (BiLSTM) coupled with attention mechanism has achieved state-of-the-art performance in text matching. A major limitation of existing works is that only high level contextualized word representations are utilized to obtain word level matching results without considering other levels of word representations, thus resulting in incorrect matching decisions for cases where two words with different meanings are very close in high level contextualized word representation space. Therefore, instead of making decisions utilizing single level word representations, a multi-level matching network (MMN) is proposed in this paper for text matching, which utilizes multiple levels of word representations to obtain multiple word level matching results for final text level matching decision. Experimental results on two widely used benchmarks, SNLI and Scaitail, show that the proposed MMN achieves the state-of-the-art performance.
Addressing Text-Dependent Speaker Verification Using Singing Speech

The automatic speaker verification (ASV) has achieved significant progress in recent years. However, it is still very challenging to generalize the ASV technologies to new, unknown and spoofing conditions. Most previous studies focused on extracting the speaker information from natural speech. This paper attempts to address the speaker verification from another perspective. The speaker identity information was exploited from singing speech. We first designed and released a new corpus for speaker verification based on singing and normal reading speech. Then, the speaker discrimination was compared and analyzed between natural and singing speech in different feature spaces. Furthermore, the conventional Gaussian mixture model, the dynamic time warping and the state-of-the-art deep neural network were investigated. They were used to build text-dependent ASV systems with different training-test conditions. Experimental results show that the voiceprint information in the singing speech was more distinguishable than the one in the normal speech. More than relative 20% reduction of equal error rate was obtained on both the gender-dependent and independent 1 s-1 s evaluation tasks.
Pedestrian Detection in Severe Lighting Conditions: Comparative Study of Human Performance vs Thermal-Imaging-Based Automatic System

The paper discusses the problem of human body detection in severe lighting condition from the driver perspective. Results of a study of threat situation recognition, defined as the sudden appearance of a pedestrian in the field of view, are presented. A human reaction efficiency and delay time are contrasted with the automatic detection based on thermal imagery.
FCA for software product line representation: Mixing configuration and feature relationships in a unique canonical representation

Software Product Line Engineering (SPLE) is a set of methods to help build a collection of software systems which are similar enough to enable appropriate artefact reuse. An important task consists in documenting in variability models the common and variable features which may compose the similar software systems along with compatibility constraints between these features. Several models and formalisms have been proposed to model variability: each one of them has specific properties making it pertinent to support certain management operations, which are of primary importance in SPLE. Switching from one kind of variability model to another is thus important to benefit from a wide range of operations and efficiently manage a software product line. In this paper, we review the various approaches proposed to manage and organise features and product configurations (a product configuration being a chosen subset of features). We discuss the originality of concept lattices, canonical structures presenting a dual view on features and configurations, and the advantages to use these structures as variability representations. Switching from existing variability models to an equivalent concept lattice raises scaling issues related to the size of the needed input dataset and thus hinders their exploitation. We propose an alternative relying on implicative systems and define a transformation method which does not suffer from scaling issues.
Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding

Open Information Extraction (Open IE) aims at generating entity-relation-entity triples from a large amount of text, aiming at capturing key semantics of the text. Given a triple, the relation expresses the type of semantic relation between the entities. Although relations from an Open IE system are more extensible than those used in a traditional Information Extraction system and a Knowledge Base (KB) such as Knowledge Graphs, the former lacks in semantics; an Open IE relation is simply a sequence of words, whereas a KB relation has a predefined meaning. As a way to provide a meaning to an Open IE relation, we attempt to align it with one of the predefined set of relations used in a KB. Our approach is to use a Siamese network that compares two sequences of word embeddings representing an Open IE relation and a predefined KB relation. In order to make the approach practical, we automatically generate a training dataset using a distant supervision approach instead of relying on a hand-labeled dataset. Our experiment shows that the proposed method can capture the relational semantics better than the recent approaches.
Investigating the Influence of Virtual Human Entourage Elements on Distance Judgments in Virtual Architectural Interiors

Architectural design drawings commonly include entourage elements: accessory objects, such as people, plants, furniture, etc., that can help to provide a sense of the scale of the depicted structure and "bring the drawings to life" by illustrating typical usage scenarios. In this paper, we describe two experiments that explore the extent to which adding a photo-realistic, three-dimensional model of a familiar person as an entourage element in a virtual architectural model might help to address the classical problem of distance underestimation in these environments. In our first experiment, we found no significant differences in participants' distance perception accuracy in a semi-realistic virtual hallway model in the presence of a static or animated figure of a familiar virtual human, compared to their perception of distances in a hallway model in which no virtual human appeared. In our second experiment, we found no significant differences in distance estimation accuracy in a virtual environment in the presence of a moderately larger-than-life or smaller-than-life virtual human entourage model than when a right-sized virtual human model was used. The results of these two experiments suggest that virtual human entourage has limited potential to influence peoples' sense of the scale of an indoor space, and that simply adding entourage, even including an exact-scale model of a familiar person, will not, on its own, directly evoke more accurate egocentric distance judgments in VR.
Cloud broker proposal based on multicriteria decision‐making and virtual infrastructure migration

Summary The adoption of infrastructure as a Service (IaaS) is a reality for academic, industrial, and governmental institutions. Cloud tenants request dynamically provisioned virtual infrastructures (VIs) tailored to their application requirements, detailing not only the virtual compute/storage resources but also the network components, topology, and services. The creation of a large number of cloud providers came along with the widespread use of VIs. The selection of an appropriate provider is a challenging task due to the diversity of the IaaS market and formally is a multicriteria analysis (NP‐hard). Notwithstanding the provider selection complexity, the mobility of VI‐hosted applications is limited due to the optimization anchors introduced by providers. Although the existing IaaS cloud brokers can indicate a hosting provider, they lack on conceptual and technical skills to migrate a VI and all its internal components between providers. This work enhances the state‐of‐the‐art on IaaS cloud brokerage by proposing virtual infrastructure multicriteria allocation and migration–based broker (VIMAM), which performs a multicriteria analysis of providers and VI migration. VIMAM is driven by an analytic hierarchy process (AHP) to select an IaaS provider, offering a set of predefined weighting schemas to represent distinct tenant perspectives. Moreover, to migrate a VI, VIMAM takes into account the virtual machines, containers, switches, and other topology elements. In addition to discussing the AHP ranking weights and frequency of providers selection, the experimental analysis details the implementation of an OpenStack and Docker–based prototype for VI migration.
Planning for Flexible Surgical Robots via Bézier Spline Translation

In a minimally invasive surgery, new flexible instruments enable safer and easier access to difficult-to-reach anatomical regions. However, their introduction into the clinical workflow requires robust replanning because navigation errors during surgery render initially planned trajectories infeasible. Such replanning requires to regularly solve an expensive two-point boundary value problem (BVP) that connects the target pose of the instrument with the currently measured one. We propose a hybrid planning scheme that features both robust and safe replanning. This two-step approach first solves the BVP and then transforms the result to circular arcs that fit the motion of our instruments' models. We exploit implicitly defined Bézier splines as a robust method for interpolation in the first step. A novel geometric translation of these splines, then, provides a convenient solution for movement along circular arcs. We consider two example applications: 1) planning for a drilling unit in temporal bone surgery; and 2) guidewires in catheter insertion. Evaluation on real patient data of both temporal bone and aorta show that our proposed hybrid two-step approach achieves, on average, 55% higher replanning
Fractal Assessment of the Disturbances of Phosphate Series Using Lacunarity and Succolarity Analysis on Geoelectrical Images (Sidi Chennane, Morocco)

In Sidi Chennane phosphate deposit, the disturbances cause serious challenges for the OCP mining engineers during both exploration and the exploitation stages. They are qualified as worst rocky hard to be quantified since they interfere with phosphates series. In this paper, we propose the fractal analysis as a simple efficient tool to quantify the rate of the disturbances by two different methods, namely, the Lacunarity and the Succolarity. The analysis was carried out on eight geoelectrical images of a disturbed area of 50 hectares located in the northern part of Sidi Chennane. The results proved that there is a strong correlation between the disturbances rate and the corresponding fractal value indexes. It appears that the distinction between two disturbed areas is the difference between their corresponding fractal values. This has an important implication to discriminate between the phosphate deposit at high risk of disturbances and the deposit at low risk. The fractal analysis can be thus used as a crucial concept in ranking the prospective zones of phosphate as well as improving the phosphate reserve estimation.
Interactive image segmentation using geodesic appearance overlap graph cut

Image segmentation is a fundamental step in many applications such as image editing, medical image analysis and processing. It is quite common to use graph cuts for image segmentation in recent years. Tang, Gorelick, Veksler, and Boykov proposed a new image segmentation model which uses the appearance overlap on unnormalized histograms and graph cut framework. Their model is highly effective for interactive segmentation but is prone to isolated points. To avoid that problem, we propose an effective interactive image segmentation method, that is appropriately incorporating geodesic distance information, appearance overlap information, and edge information together into the well-known graph-cut framework. Rather than a simple union of these information, the respective strengths of each information term can be tuned adaptively in our method. We utilize the user's scribbles to obtain the estimated foreground/background color models via fast kernel density estimation, and then get the appearance overlap intricateness according to the inferred color models. By taking comprehensive advantage of the geodesic distance and the global appearance overlap color clues, our method requires less user effort and achieves higher accuracy of segmentation than the latest interactive segmentation techniques, such as Geodesic Graph Cut, GrabCut in One Cut, Semi-Supervised Normalized Cuts, and Convexity Shape Prior for Binary Segmentation, as shown in our experiments.
P-Gram: Positional N-Gram for the Clustering of Machine-Generated Messages

An IT system generates messages for other systems or users to consume, through direct interaction or as system logs. Automatically identifying the types of these machine-generated messages has many applications, such as intrusion detection and system behavior discovery. Among various heuristic methods for automatically identifying message types, the clustering methods based on keyword extraction have been quite effective. However, these methods still suffer from keyword misidentification problems, i.e., some keyword occurrences are wrongly identified as payload and some strings in the payload are wrongly identified as keyword occurrences, leading to the misidentification of the message types. In this paper, we propose a new machine language processing (MLP) approach, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${P}$ </tex-math></inline-formula> -gram, specifically designed for identifying keywords in, and subsequently clustering, machine-generated messages. First, we introduce a novel concept and technique, positional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -gram, for message keywords extraction. By associating the position as meta-data with each <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -gram, we can more accurately discern which <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -grams are keywords of a message and which <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -grams are parts of the payload information. Then, the positional keywords are used as features to cluster the messages, and an entropy-based positional weighting method is devised to measure the importance or weight of the positional keywords to each message. Finally, a general centroid clustering method, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -Medoids, is used to leverage the importance of the keywords and cluster messages into groups reflecting their types. We evaluate our method on a range of machine-generated (text and binary) messages from the real-world systems and show that our method achieves higher accuracy than the current state-of-the-art tools.
Large-scale noise-resilient evolution-strategies

Ranking-based Evolution Strategies (ES) are efficient algorithms for problems where gradient-information is not available or when the gradient is not informative. This makes ES interesting for Reinforcement-Learning (RL). However, in RL the high dimensionality of the search-space, as well as the noise of the simulations make direct adaptation of ES challenging. Noise makes ranking points difficult and a large budget of re-evaluations is needed to maintain a bounded error rate. In this work, the ranked weighting is replaced by a linear weighting function, which results in nearly unbiased stochastic gradient descent (SGD) on the manifold of probability distributions. The approach is theoretically analysed and the algorithm is adapted based on the results of the analysis. It is shown that in the limit of infinite dimensions, the algorithm becomes invariant to smooth monotonous transformations of the objective function. Further, drawing on the theory of SGD, an adaptation of the learning-rates based on the noise-level is proposed at the cost of a second evaluation for every sampled point. It is shown empirically that the proposed method improves on simple ES using Cumulative Step-size Adaptation and ranking. Further, it is shown that the proposed algorithm is more noise-resilient than a ranking-based approach.
Cross‐cumulative residual entropy‐based medical image registration via hybrid differential search algorithm

Abstract Image registration is the process of overlaying images of the same scene taken at different times by different sensors from different viewpoints. The cross‐cumulative residual entropy (CCRE)‐based medical image registration could achieve a high precision and a strong robustness performance. However, the optimization problem formulated by CCRE consists of some local extrema, especially for noise images. In order to address these difficulties, this article proposes a new optimization algorithm named hybrid differential search algorithm (HDSA) to optimize CCRE. As HDSA consists of simple control parameters, it is independent of the initial searching point. In addition, HDSA ameliorated the search method and the iterative conditions. As a result, the optimization process is more stable and efficient. Image registration experiments of HDSA are performed and compared with the conventional differential search algorithm (DSA) and adaptive differential evolution with optional external archive (JADE). The results show that HDSA does not only overcome the difficulties of sticking in the local extrema but also enhances the precision of registration. It is effective, robust, and fast for both the single‐mode rigid medical image registration and the multispectral‐mode rigid medical image registration.
The Influence of Different AOI Models in Source Code Comprehension Analysis

In source code comprehension, researchers frequently analyze eye movement data in order to find patterns to test hypotheses. For such an analysis, fixations are often assigned to objects of a stimulus, like tokens, code lines or code regions, through areas of interest (AOI). There are multiple ways to define an AOI which can have a broad impact on the analysis of the eye movement data. In this paper, we analyzed the impact of choosing two different AOI models, both as extreme points from a methodical point of view. We propose one AOI model and compare it to the AOI model from the EMIP dataset. Within the data, we found indications, that one AOI model captures less AOI transitions compared to the other model. A qualitative investigation showed, that some of these AOI transitions can be important for understanding the viewing and comprehension strategy of the participant. In conclusion, we argue, that every researcher should report the chosen AOI model and the particular AOI definitions used in a study, to understand their research. Additionally, we suggest a simple algorithm to test, which AOI model configuration will capture a majority of data points.