Reviewerly/exHarmony huggingface dataset:
ICT use by pre-service foreign languages teachers according to gender, age and motivation

In the area of foreign languages, it is necessary to develop the digital competence of future teachers in order to improve the teaching-learning process that they will carry out with their students. However, different intrinsic variables of teachers can influence their use of information and communication technologies (ICT). The main objective of this study is to analyse the use of ICT by future primary education teachers. The secondary objective is to find out whether age, gender and motivation affect their use of ICT. Non-experimental research has been carried out with a sample of 134 future teachers, specifically those teaching foreign languages. The results show that the future teachers of foreign languages have a pedagogical digital competence in the use of medium-low ICT, their most used technological devices being laptops and projectors. Moreover, they do not use Web 2.0 tools when teaching languages. Regarding gender, it can be seen that there are no significant differences, while the age variable does influence the level of pedagogical digital competence. In addition, it has been confirmed that motivation constitutes an essential variable at the pedagogical digital competence level, both in terms of the use of technological devices, as well as the use of Web 2.0 tools and of Learning Management Systems.
Leveraging Uncertainty to Rethink Loss Functions and Evaluation Measures for Egocentric Action Anticipation

Current action anticipation approaches often neglect the intrinsic uncertainty of future predictions when loss functions or evaluation measures are designed. The uncertainty of future observations is especially relevant in the context of egocentric visual data, which is naturally exposed to a great deal of variability. Considering the problem of egocentric action anticipation, we investigate how loss functions and evaluation measures can be designed to explicitly take into account the natural multi-modality of future events. In particular, we discuss suitable measures to evaluate egocentric action anticipation and study how loss functions can be defined to incorporate the uncertainty arising from the prediction of future events. Experiments performed on the EPIC-KITCHENS dataset show that the proposed loss function allows improving the results of both egocentric action anticipation and recognition methods.
A Hierarchical Attention Retrieval Model for Healthcare Question Answering

The growth of the Web in recent years has resulted in the development of various online platforms that provide healthcare information services. These platforms contain an enormous amount of information, which could be beneficial for a large number of people. However, navigating through such knowledgebases to answer specific queries of healthcare consumers is a challenging task. A majority of such queries might be non-factoid in nature, and hence, traditional keyword-based retrieval models do not work well for such cases. Furthermore, in many scenarios, it might be desirable to get a short answer that sufficiently answers the query, instead of a long document with only a small amount of useful information. In this paper, we propose a neural network model for ranking documents for question answering in the healthcare domain. The proposed model uses a deep attention mechanism at word, sentence, and document levels, for efficient retrieval for both factoid and non-factoid queries, on documents of varied lengths. Specifically, the word-level cross-attention allows the model to identify words that might be most relevant for a query, and the hierarchical attention at sentence and document levels allows it to do effective retrieval on both long and short documents. We also construct a new large-scale healthcare question-answering dataset, which we use to evaluate our model. Experimental evaluation results against several state-of-the-art baselines show that our model outperforms the existing retrieval techniques.
Present and future of semantic web technologies: a research statement

Semantic web and its technologies have been eyed in many fields. They have the capacity to organize and link data over the web in a consistent and coherent way. Semantic web technologies consist of RDF schema, OWL, and rule and query languages like SPARQL and these technologies will help the various domains to resolve their problems. This review paper starts with analyzing the nature of semantic web and its requirements. We have considered all 10 domains which are closely related to semantic web and its technologies. For a better understanding of the paper, we have separated it into three major contributions. First, we analyze semantic web and those domains that increase the growth of the semantic web. Second, we discuss all domains where semantic web technologies play a vital role. Third, we emphasize those domains that go hand in hand with semantic web technologies. This review paper will be utilized as an unbiased direction for the researchers.
No-Reference Quality Assessment for Screen Content Images Based on Hybrid Region Features Fusion

Research on screen content images (SCIs) attracts more attention as they are highly applied to image- and video-centric applications on mobile and other devices. It is important to develop an efficient image-quality assessment (IQA) method for SCIs because IQA can guide and optimize various image-processing methods for SCIs and improve user experience. In this paper, we propose a no-reference objective assessment model for SCIs including SCIs segmentation and the analysis of local and global perceptual feature representations. Since the human visual system is highly sensitive to sharp edges that are commonly encountered in SCIs, we utilize the variance of local standard deviation, which is a noise robust index to distinguish the sharp edge patches (SEPes) and non-SEPes of SCIs. For SEPes, we perform two kinds of feature extractions. First, the entropy and contrast features are extracted with a gray-level co-occurrence matrix, which are highly perceptive of microstructural change. Second, the local phase coherence is utilized to capture the loss in sharpness. Then, average pooling is adopted to fuse features obtained from all of the SEPes to represent the local features. We further combine local features with global features that are derived using the BRISQUE method as the hybrid region (HR)-based features. Finally, a regression module is learned using support vector regression to train the mapping function that maps HR-based features to subjective quality scores. Experimental results on the screen image-quality assessment database show that the proposed method can achieve better performance in visual-quality prediction for SCIs than the performance achieved by state-of-the-art methods.
Adaptive Output-Feedback Image-Based Visual Servoing for Quadrotor Unmanned Aerial Vehicles

This brief presents an adaptive output feedback image-based visual servoing (IBVS) law for a quadrotor unmanned aerial vehicle. The control objective is to regulate the relative 3-D position and yaw of the vehicle to a planar horizontal visual target consisting of multiple points. The control is implemented using a minimal number of commonly available low-cost on-board sensors including a strapdown inertial measurement unit and a monocular camera. The IBVS method relies on moment image features which are defined using a virtual camera. Output feedback introduces a filter to the control which removes the common requirement for linear velocity measurement. The method is adaptive and compensates for a constant force disturbance appearing the translational dynamics and parameter uncertainty in thrust constant, desired feature depth, and mass. Exponential stability of the outer loop and combined inner-outer closed-loop error dynamics is proven. Flight tests demonstrate the proposed method's motion control performance and its ability to compensate parametric uncertainty and reject constant force disturbances.
Heterogeneity-Aware Local Binary Patterns for Retrieval of Histopathology Images

Histopathology images exhibit considerable variability, which can make diagnosis prone to uncertainty and errors. Using retrieval systems to locate similar images when a query image is given can assist pathologists in making more reliable decisions when diagnosing, based on accurately diagnosed past cases. Local binary patterns (LBP) have been successfully used as image descriptors for different applications. However, using LBP on histopathology images is still under investigation from different perspectives. The immense texture variability of these images and the lack of labeled training data are among the challenges that must be addressed. In this paper, we propose a new extension of LBP that is explicitly aware of the heterogeneity of local texture patterns through heterogeneity-based weighting. We use both homogeneity and the second moment (variance) of local neighborhoods to extend LBP histograms with heterogeneity information so that they better capture the polymorphism in histopathology images. We applied all concepts at multiple scales and investigated the extensions through both separated and concatenated extended histograms. We applied the proposed method on three publicly available datasets, namely, KimiaPath24, invasive ductal carcinoma (IDC), and BreakHis. The experimental results revealed that our features could retrieve and classify images with the highest accuracy. For KimiaPath24, we achieved 96.4% surpassing both LBP (91.0%) and deep features from VGG network (79.5%). For IDC, we reached the highest F1 measure (0.7665). Only for BreakHis, the proposed method was slightly less accurate than a customized convolutional neural network with rule fusion.
Enhancing Fashion Recommendation with Visual Compatibility Relationship

With the increasing of online shopping services, fashion recommendation plays an important role in daily online shopping scenes. A lot of recommender systems have been developed with visual information. However, few works take into account compatibility relationship when they are generating recommendations. The challenge is that fashion concept is often subtle and subjective for different customers. In this paper, we propose a fashion compatibility knowledge learning method that incorporates visual compatibility relationships as well as style information. We also propose a fashion recommendation method with domain adaptation strategy to alleviate the distribution gap between the items in target domain and the items of external compatible outfits. Our results indicate that the proposed method is capable of learning visual compatibility knowledge and outperforms all the baselines.
Virtual Reality Angiogram vs 3-Dimensional Printed Angiogram as an Educational tool—A Comparative Study

Three-dimensional (3D) visualization of the neurovascular structures has helped preoperative surgical planning. 3D printed models and virtual reality (VR) devices are 2 options to improve 3D stereovision and stereoscopic depth perception of cerebrovascular anatomy for aneurysm surgery.To investigate and compare the practicality and potential of 3D printed and VR models in a neurosurgical education context.The VR angiogram was introduced through the development and testing of a VR smartphone app. Ten neurosurgical trainees from Australia and New Zealand participated in a 2-part interactive exercise using 3 3D printed and VR angiogram models followed by a questionnaire about their experience. In a separate exercise to investigate the learning curve effect on VR angiogram application, a qualified neurosurgeon was subjected to 15 exercises involving manipulating VR angiograms models.VR angiogram outperformed 3D printed model in terms of resolution. It had statistically significant advantage in ability to zoom, resolution, ease of manipulation, model durability, and educational potential. VR angiogram had a higher questionnaire total score than 3D models. The 3D printed models had a statistically significant advantage in depth perception and ease of manipulation. The results were independent of trainee year level, sequence of the tests, or anatomy.In selected cases with challenging cerebrovascular anatomy where stereoscopic depth perception is helpful, VR angiogram should be considered as a viable alternative to the 3D printed models for neurosurgical training and preoperative planning. An immersive virtual environment offers excellent resolution and ability to zoom, potentiating it as an untapped educational tool.
A modified intuitionistic fuzzy c-means algorithm incorporating hesitation degree

Fuzzy c-means (FCM) algorithm is an unsupervised machine learning algorithm and has been used in many applications. But, FCM does not consider hesitation in the case of imprecise data. The intuitionistic fuzzy c-means (IFCM) algorithm, which is based on intuitionistic fuzzy set theory, has been proposed in the literature to handle the hesitation during clustering. However, the IFCM still does not consider the hesitation properly. To overcome this problem of the IFCM, we proposed a modified intuitionistic fuzzy c-means (mIFCM) algorithm incorporating hesitation degree in this paper. We have generated the triangular dataset and tested the proposed mIFCM algorithm on the triangular dataset and also validated the algorithms on publicly available simulated brain data. The experimental results show that mIFCM performs better in comparison to existing intuitionistic fuzzy clustering algorithms. A nonparametric statistical test is also carried out to demonstrate the significant performance of the proposed mIFCM algorithm in comparison to other existing clustering algorithms.
The research of virtual face based on Deep Convolutional Generative Adversarial Networks using TensorFlow

Since Generative Adversarial Nets (GANs) has been proposed in 2014, it has become one of the most popular hot topics. Deep Convolutional Generative Adversarial Networks (DCGAN) is greatly promoted the development and application of GANs. In this paper, we have made an in-depth exploration for the most popular DCGAN at present via utilizing TensorFlow deep learning framework, using the open CelebA face dataset of The Chinese University of Hong Kong as the data source. By comparing DCGAN unconstrained and DCGAN constrained, the experimental results show that the DCGAN model significantly improves the virtual face generation model after adding constraints in the training phase, which enhance the ability of the generator to deceive the discriminator. Finally, we have evaluated the proposed model from the perspective of TensorBoard and achieved the desired experimental results.
What Predicts Software Developers’ Productivity?

Organizations have a variety of options to help their software developers become their most productive selves, from modifying office layouts, to investing in better tools, to cleaning up the source code. But which options will have the biggest impact? Drawing from the literature in software engineering and industrial/organizational psychology to identify factors that correlate with productivity, we designed a survey that asked 622 developers across 3 companies about these productivity factors and about self-rated productivity. Our results suggest that the factors that most strongly correlate with self-rated productivity were non-technical factors, such as job enthusiasm, peer support for new ideas, and receiving useful feedback about job performance. Compared to other knowledge workers, our results also suggest that software developers' self-rated productivity is more strongly related to task variety and ability to work remotely.
Active Transfer Learning

A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains. Thus, there might be negative similarities between different domains so that the negative transfer problem caused by negative similarity may happen. In this paper, we propose a novel method named active transfer learning (ATL) to solve the above problem. Specifically, the orthogonal projection matrix and the weight coefficient vector are introduced to extend maximum mean discrepancy (MMD) so that it can minimize MMD and simultaneously eliminate the negative transfer. To find the informative and discriminative subsets from the source domain, we then propose an information diversity term by using the local geometric structure information of the source samples. Besides, by using the label information of source samples, our method can guarantee the selected subsets as discriminative as possible. Finally, to efficiently implement the proposed method, an alternating optimization approach, which is based on the alternating direction method of multipliers (ADMM), is designed to solve the optimization problem. To demonstrate the effectiveness of the proposed ATL model, experiments are conducted on five real-world data sets. The experimental results show the superiority of our method over the state-of-the-art methods.
Compressed kNN: K-Nearest Neighbors with Data Compression

The kNN (k-nearest neighbors) classification algorithm is one of the most widely used non-parametric classification methods, however it is limited due to memory consumption related to the size of the dataset, which makes them impractical to apply to large volumes of data. Variations of this method have been proposed, such as condensed KNN which divides the training dataset into clusters to be classified, other variations reduce the input dataset in order to apply the algorithm. This paper presents a variation of the kNN algorithm, of the type structure less NN, to work with categorical data. Categorical data, due to their nature, can be compressed in order to decrease the memory requirements at the time of executing the classification. The method proposes a previous phase of compression of the data to then apply the algorithm on the compressed data. This allows us to maintain the whole dataset in memory which leads to a considerable reduction of the amount of memory required. Experiments and tests carried out on known datasets show the reduction in the volume of information stored in memory and maintain the accuracy of the classification. They also show a slight decrease in processing time because the information is decompressed in real time (on-the-fly) while the algorithm is running.
Sector reforms and institutional corruption: Evidence from electricity industry in Sub-Saharan Africa

In order to reduce the influence of corruption on electricity sector performance, most Sub-Saharan African countries have implemented electricity sector reforms. However, after nearly two and half decades of reforms, there is no evidence whether the reforms have mitigated corruption. Neither is there evidence of performance improvement of the reforms in terms of technical, economic or welfare impact. This paper aims to fill this gap. We use a dynamic panel estimator with a novel panel data of 47 Sub-Saharan African countries from 2002 to 2013. We analyse the impact of corruption and two key aspects of electricity reforms – creations of independent regulatory agencies and private sector participation – on three key performance indicators: technical efficiency, access to electricity and income. We find that corruption can significantly reduce technical efficiency of the sector and constrain the efforts to increase access to electricity and national income. The adverse effects are reduced where independent regulatory agencies are established and privatisation is implemented. These findings suggest that well-designed reforms not only boost the performance of the sector directly, but also indirectly reduce the negative effects of macro level institutional deficiencies such as corruption on micro and macro performance indicators.
Sentiment analysis and opinion mining applied to scientific paper reviews

Sentiment analysis and opinion mining is an area that has experienced considerable growth over the last decade. This area of research attempts to determine the feelings, opinions, emotions, among other things, of people on something or someone. To do this, natural language techniques and machine le arning algorithms are used. This article discusses the problem of extracting sentiment and opinions from a collection of reviews on scientific articles conducted under an international conference on computing in northern Chile. The first aim of this analysis is to automatically determine the orientation of a review and contrast this with the assessment made by the reviewer of the article. This would allow scientists to characterize and compare reviews crosswise and more objectively support the overall assessment of a scientific article. A hybrid approach that combines an unsupervised machine learning algorithm with techniques from natural language processing is proposed to analyze reviews. This method uses part-of-speech (POS) tagging to obtain the syntactic structure of a sentence. This syntactic structure, along with the use of dictionaries, allows determining the semantic orientation of the review through a scoring algorithm. A set of experiments were conducted to evaluate the capability and performance of the proposed approaches relative to a baseline, using standard metrics, such as accuracy, precision, recall, and the F1-score. The results show improvements in the case of binary, ternary and a 5-point scale classification in relation to classical machine learning algorithms such as SVM and NB, but they also present a challenge to improve the multiclass classification in this domain.
Effect of despeckle filtering on classification of breast tumors using ultrasound images

Ultrasound is the most widely used imaging modality for screening of breast tumors. However, due to the presence of speckle noise in an ultrasound image, the diagnostic information gets masked and the interpretation of the breast abnormalities becomes difficult for the radiologist. The texture of the tumor region and the shape/margin characteristics are considered to be important parameters for the analysis of the breast tumors. In the present work, exhaustive experimentation has been carried out for the design of CAD systems for classification of breast tumors by considering (a) original images only, (b) despeckled images only and (c) both original and despeckled images together (hybrid approach). Total 100 breast ultrasound images (40 benign and 60 malignant) have been used for the analysis. Initially, these images have been despeckled using six filters namely Lee sigma, BayesShrink, DPAD, FI, FB and HFB filters. Total 162 features (149 texture and 13 morphological features) have been computed from both original and despeckled breast ultrasound images and SVM classifier has been used extensively for the classification. The results of the study indicate that the hybrid approach of CAD system design using texture features computed from original images combined with morphological features computed from images despeckled by DPAD filter yield optimal performance for classification of benign and malignant breast tumors with a classification accuracy of 96.0%. From the promising results of the study it can be concluded that the proposed hybrid CAD system design could be used as a second opinion tool in clinical setting.