Reviewerly/exHarmony huggingface dataset:
2D Face Recognition on CNN with Half-Average-Face

As the size of training dataset of face recognition models becomes larger and larger, we are interested in a method called Average-Half-Face (AHF), which could halve the size of training samples. The AHF method divides a full face into two halves and then averages them together (reversing the columns of one of the halves). We preprocess the dataset with the method of AHF, and train them on two different models, Eigenfaces and Convolutional Neural Network (CNN). We compare the prediction results with those models trained on the original dataset. Previous researches showed that AHF is superior to Full-Face (FF), while our experiment results further showed that in some cases AHF also boosts CNN. The application of AHF can bring both saving in storage and reduction on training cost time.
Toward Interaction-Based Evaluation of Visualization Approaches to Comprehending Program Behavior

Reverse-engineered sequence diagrams are promising tools to comprehend the runtime behavior of object-oriented programs. To improve the readability and understandability of massive-scale sequence diagrams, various techniques for effectively exploring or compressing sequence diagrams have been proposed in the literature.When researchers analyze the effectiveness of these approaches through user studies, it is important to reveal not only what techniques can improve developer productivity but also how developers explore reverse-engineered sequence diagrams and how exploration and compression features are utilized.We developed a feature to record interactions between a developer and recovered sequence diagrams in our tool, SDExplorer. We show how the recorded interaction data can be used for in-depth analysis of developer activities, toward the evaluation of visualization approaches to helping behavioral comprehension.
Computationally-efficient wavelet-based characterization of breast tumors using conventional B-mode ultrasound images

Breast cancer is among the leading causes of mortality in women worldwide. Early detection can increase the survival rate and limit cancer metastasis to other organs. Recently, the role of ultrasound (US) imaging in the diagnosis and monitoring of breast tumors besides X-ray mammography has been increasing. Several computer-aided diagnosis (CAD) systems were proposed to improve the classification of breast tumors. This work presents a fast and computationally-efficient technique to distinguish between malignant and benign breast tumors. The technique applies wavelet packet transform (WPT) on conventional brightness mode (B-mode) US images, and then extracts several textural and morphological features from the approximation decomposition part. Features include first-order statistics (FOS), fractal dimension texture analysis (FDTA), spatial gray-level dependence matrices (SGLDM), area, perimeter, and compactness of the lesion. When the support vector machine was applied for classification on original US images, the classifier exhibited 97.4% accuracy, 98.3% sensitivity, and 92.1% specificity. These performance parameters were slightly changed to 96.9% accuracy, 96.7% sensitivity, and 97% specificity, when the same features were extracted from WPT. However, the frame classification time was reduced drastically from 1.1284s using original US images to 0.0604s after incorporating WPT. Hence, the proposed CAD system using WPT was able to decrease the computational complexity and processing time by, at least, eight times. This shall improve the early detection of breast cancer via developing real-time and noninvasive computer-aided diagnostic software.
Práctica de innovación docente: lee y despatologízate

The project seeks to develop new learning strategies on psychopathology, through literary texts (novels). The participants were 58 students of the subjet “Psychopathology II”, corresponding to fifth cycle of Psychological Degree from Universidad Técnica Particular de Loja (UTPL). Through project-based learning (PBL) and collaborative learning (CA), several topics were addressed: autism, psychosis, obsessive-compulsive disorder (OCD), pedophilia and dissociative identity, Subsequently an audiovisual material was developed to psychoeducate the population on the psychopathological behavior, a well as preventing the problems arising from unknowledge. The results show that through this educational practice the understanding of the different mental disorders is strengthened as well as student's satisfaction with learning.
A fast figure determination method based on sequential Bayesian analysis in radiography

In the fields of customs inspection, airport security inspection, medical diagnosis, industrial inspection and arms control verification, it is often necessary to use radiography to determine whether the targeted object is contained in a closed container. By analyzing whether the specific figure of the targeted object is contained in the irradiation image of the closed container, we can determine if the container should be opened for a further manual check. In many cases, considering the time cost of radiography, the determination should be made as soon as possible, which requires fast analysis of the figure information of the irradiation image. In this paper, a fast figure determination method based on the sequential Bayesian analysis is proposed. This method applies the sequential Bayesian analysis to the irradiation image analysis, and can make a fast determination of the existence of the specific figure according to the location information of the ray particles in the irradiation image. First, the theoretical framework of this method was established in this paper. Second, the mathematical model of this method was established, and its feasibility was also studied. The research results show that, this method can make an accurate and fast determination of the existence of the targeted figure in the irradiation image. Third, the factors influencing the figure determination speed of this method was analyzed in depth. At last, a Monte Carlo simulation study of this method was carried out: the physical model of the inspection scheme of industrial materials based on the X-ray transmission imaging was established, and the figure determination method proposed by this paper can make an accurate and fast determination of the existence of hole defects in the industrial materials according to the X-ray transmission images of the industrial materials, which proves the feasibility of the application of this method in radiography.
Feature-Based Tracking via SURF Detector and BRISK Descriptor

Marker-less tracking has become vital for a variety of vision-based tasks because it tracks some natural regions of the object rather than using fiducial markers. In marker-less feature-based tracking salient regions in the images are identified by the detector and information about these regions are extracted and stored by the descriptor for matching. Speeded-Up Robust Feature (SURF) is considered as the most robust detector and descriptor so far. SURF detects the feature points that are unique and repeatable. It uses integral images which provide a base for the low computational expense. However, descriptors generation and matching for SURF is a time-consuming task. Binary Robust Invariant Scalable Key-points (BRISK) is a scale and rotation invariant binary descriptor. It reduces the computational cost due to its binary nature. This paper presents a marker-less tracking system that tracks natural features of the object in real-time and is very economical in terms of computation. The proposed system is based on SURF detector, as it identifies highly repeatable interest points in the object and BRISK descriptor, due to its low computational cost and invariance to scale and rotation which is vital for every visual tracking system.
QoE Modelling of High Dynamic Range Video

The level of user satisfaction has no standard way of measuring for HDR video content due to the proven difficulty of building HDR quality assessment metrics. To overcome this limitation, Quality of Experience (QoE) modelling of HDR video has been proposed to find a robust and accurate HDR video QoE metric. The proposed model is the first attempt towards assessing and devising a non-reference quality metric for HDR video. It is based on finding the correlation between the HDR video features and the subjective test results. The proposed model achieves a significant correlation score of 0.724 with the subjective results.
HWE: Word Embedding with Heterogeneous Features

Distributed word representation is widely used in Natural Language Processing. However, traditional approaches, which learn word representations from the co-occurrence information in large corpora, might not capture fine-grained syntactic and semantic information. In this paper, we propose a general and flexible framework to incorporate heterogeneous features (e.g., word-sense, part-of-speech, topic) for learning feature-specific word embeddings in an explicit fashion, namely Heterogeneous Word Embedding (HWE). Experimental results on both intrinsic and extrinsic tasks show that HWE outperforms the baseline and various state-of-the-art models. Moreover, through the concatenation over HWE and the corresponding feature embeddings, each word would have different contextual representation under different contexts, which achieves even more significant improvement. Finally, we illustrate the insight of our model via visualization of the learned word embeddings.
Dynamic Ensemble of Diversified Encodings for Event Nugget Detection

We propose Dynamic Ensemble method that selects appropriate encoding models for each input to handle a wide variety of event expressions of different types. The main contribution is (1) we focused on compatibility problem of encoding models and a huge variety of linguistic patterns that is difficult to cope with conventional ensemble method, (2) proposed a novel ensemble approach that dynamically selects appropriate encoding models for every input token, and (3) proved the effectiveness of the ensemble method by comparing with official evaluation results of NIST TAC KBP2016 event nugget track. Our proposed method achieved 37.26% in F1 score without syntactic nor semantic parser, and outperformed the score 35.24% of the best system by 2.02% point.
Improved Variable Neighbourhood Search Heuristic for Quartet Clustering

Given a set of n data objects and their pairwise dissimilarities, the goal of quartet clustering is to construct an optimal tree from the total number of possible combinations of quartet topologies on n, where optimality means that the sum of the dissimilarities of the embedded (or consistent) quartet topologies is minimal. This corresponds to an NP-hard combinatorial optimization problem, also referred to as minimum quartet tree cost (MQTC) problem. We provide details and formulation of this challenging problem, and propose a basic greedy heuristic that is characterized by a very high speed and some interesting implementation details. The solution approach, though simple, substantially improves the performance of a Reduced Variable Neighborhood Search for the MQTC problem. The latter is one of the most popular heuristic algorithms for tackling the MQTC problem.
Increasing Precision of Automatically Generated Trace Links

[Context and Motivation] In order to use automatically created trace links during a project directly, the precision of the links is essential. Our interaction-based trace link creation approach (IL) utilizes the interactions of developers recorded in an integrated development environment (IDE) while working on a requirement. For this, developers need to indicate the requirement they are going to work on before coding. This approach worked well in an open-source project with developers who were interested in the interaction logs, but did not work well with students who were not particularly motivated to trigger the interaction recording. [Question/problem] Developers often create trace links themselves by providing issue identifiers (IDs) in commit messages. This causes little effort and does not require the awareness for interaction recording. However, as confirmed by recent research, typically only 60% of the commits are linked. In this paper, we study whether and how IL can be improved by a combination with links created by issue IDs in commit messages. [Principal ideas/results] We changed our approach so that interaction logs are associated with requirements based on the IDs in the commit-messages. Thus, developers do not need to manually associate requirements and interaction logs. We performed a new student study with this approach. [Contribution] In this new study, we show that with this new approach and link improvement techniques precision is above 90% and recall is almost 80%. We also show that for our data this is better than using commit-messages only and better than the often used information retrieval-based approaches.
Data Structure and Simulation of the Centralized Control System for Transport Robots

In this article, the data structure and simulation of the centralized control system for transport robots is implemented. In the simulation transporting a definite number of products using a group of robots is performed. The purpose is to reduce the total waiting time during the transportation process. The crucial issue to be considered is implementing the most sensible and effective way to assign tasks to individual robots. We present a solution for products transportation using multiple robots with the centralized control system. Our approach has been performed and established in simulation using the AnyLogic environment and implemented the transport robot model based on Arduino Microcontroller using centralized control system.
Automatic Gender Identification from Children Facial Images Using Texture Descriptors

Soft biometric such as gender significantly works to enhance the performance of biometric systems and also having applications in human–computer interaction, content-based indexing and retrieval, demographic studies, security, and surveillance. Gender identification among adults is easy as compared to in children, due to similarity of facial skin texture and appearance of faces. In this paper, we have introduced a technique to identify/classify the gender from facial images of children. In our method, we have applied three basic steps namely preprocessing, feature extraction, and classification. In preprocessing stage, face detection and normalization are performed. To extract the powerful features, we have computed different texture descriptors and after feature extraction process and SVM is applied for classification purpose. We have achieved the encouraging results in our experiments.
Factors influencing health managers’ acceptance of eHealth services in Saudi Arabia

Introduction: The World Health Organization (WHO) refers to the term of Electronic Health (eHealth) as “the use of Information and Communication Technology (ICT) for health”. In the Kingdom of Saudi Arabia (KSA) where difficult geographical terrain makes the delivery of healthcare services challenging, eHealth offers innovative solutions for improving the access and management of healthcare delivery. Health managers are professionals with responsibility for services, resources and partnership in healthcare including eHealth. They are drawn from a range of backgrounds, many are clinicians employed in health management roles. For the purpose of gaining a deeper and broader understanding of the questions under investigation, this doctoral study has been planned to be carried out in three sequential phases in which findings from each phase informs the next one. A systematic review (SR) of the status of eHealth in Saudi Arabia from the perspectives of multiple professionals was the first phase. It was conducted in 2017. Findings showed that despite the growth of eHealth services in KSA, research publications related to the end-users’ acceptance/intention to use services remains scarce. Thirty-nine influencing factors to the acceptance and use of eHealth services were identified and similarities and differences between these outcomes and what had been found from other studies such as were highlighted.
Generating image descriptions using capsule network

Using Capsule network to generate natural language descriptions of images with different orientation, shapes, sizes and regions is a novel technique to address image to text problem. In this paper we are going to present a model that generates image descriptions using convolutional neural network and capsule network. First, the model will be trained on MNIST dataset for testing the accuracy of identifying the images with different orientation using capsule network and after that we used Flickr8K [3] and Flickr30K [4, 5] datasets over CNN and bidirectional recurrent neural network to generate text descriptions. We have added an additional steps to change the orientation of these images by rotating them to certain angles which will help in creating a new set of training samples for images with different orientations. Second, we analysed the performance of both the system separately and combined, calculate the aggregated result while taking in account the complexity of image descriptor and pose content of images. We found that this idea outperform significantly on describing images with different orientations and scalable to adapt new images apart from the training samples without predefined set of guidelines.
Skip Residual Pairwise Networks With Learnable Comparative Functions for Few-Shot Learning

In this work we consider the ubiquitous Siamese network architecture and hypothesize that having an end-to-end learnable comparative function instead of an arbitrarily fixed one used commonly in practice (such as dot product) would allow the network to learn a final representation more suited to the task at hand and generalize better with very small quantities of data. Based on this we propose Skip Residual Pairwise Networks (SRPN) for few-shot learning based on residual Siamese networks. We validate our hypothesis by evaluating the proposed model for few-shot learning on Omniglot and mini-Imagenet datasets. Our model outperforms the residual Siamese design of equal depth and parameters. We also show that our model is competitive with state-of-the-art meta-learning based methods for few-shot learning on the challenging mini-Imagenet dataset whilst being a much simpler design, obtaining 54.4% accuracy on the five-way few-shot learning task with only a single example per class and over 70% accuracy with five examples per class. We further observe that the network weights in our model are much smaller compared to an equivalent residual Siamese Network under similar regularization, thus validating our hypothesis that our model design allows for better generalization. We also observe that our asymmetric, non-metric SRPN design automatically learns to approximate natural metric learning priors such as a symmetry and the triangle inequality.
Why Do Experiments? [From the Editor]

Presents the introductory editorial for this issue of the publication.
Opinion and Emotion Mining for Pakistan General Election 2018 on Twitter Data

Online social networks such as Twitter, Facebook, Google+ and LinkedIn are the major sources to get massive data of people, communities and events. In the recent years, opinion mining received a huge amount of attention from researchers to understand the views of people and to extract useful patterns regarding any event or topic. These useful patterns help to predict upcoming events, user behavior, product sale and political elections etc. In this paper, we performed sentimental analysis of people on twitter data for upcoming general election of 2018 of Pakistan. We have chosen three major political parties PPP (Pakistan People Party), PMLN (Pakistan Muslim League Nawaz) and PTI (Pakistan Tehreek-e-Insaf) and their activists to find which party is the most favorable (for win) during upcoming elections. We have generated extensive results to understand the views of users with different aspects shown in the experimental results section. We used R-Studio [1] and its built-in libraries for generating different types of results. According to the results based on positive reviews, PTI and PPP have great competition, but according to the negative reviews PMLN will be the leading party.
How Course Review Horay (CRH) Assisted by The Media Prezi Can Improve Cognitive Abilities of Students

This study aims to know the effect of a cooperative learning of Course Review Horay (CRH) assisted by media Prezi can improve cognitive abilities. The method used is Quasi-Experimental Design with Non-equivalent control group design. The population in this study are class VII which consisted of 200 students, with study sample consisting of 2 classes namely experiment class using cooperative learning of Course Review Horay (CRH) assisted by media Prezi and control class using cooperative learning (CRH). Data collection instrument used were test instrument (pre-test and post-test). The results of the t-test show that there is an influence of cooperative learning of Course Review Horay (CRH) assisted by media Prezi can improve cognitive abilities. The results this study indicate that CRH model assisted by media Prezi gives influence because with this model the students are more active in learning, and motivated when the learning process has media support. So, it can be concluded that the CRH assisted media Prezi can have an influence on increasing cognitive abilities. The cognitive learning outcomes can be improved well especially in the domain of "remembering" and "understanding". This is obtained from two steps CRH that plays a role well enough to improve student cognitive abilities in both of these areas. As well media Prezi that is used make students enthusiastic to learn because of the diverse and interesting visual forms and templates.
Infranetics: The New MAICS-convergent Technology Science

The paper describes the basics of a new convergent science-Infranetics-based on the MAICS-convergent technology (Digital Stochastic Mechanics, Artificial Intelligence, Information Theory, Cognitive and Social Sciences). The name Infranetics comes from Infrastructures + Cybernetics. Infranetics is being constructed for solving the central problem of safe innovative development of a region/territory/municipality by creating a methodology of harmonized regulation of regional risk. This methodology is based on optimal management of systems of interdependent critical infrastructures (ICI) that play crucial role in the sustainable development of a modern society of risk. To this end following four aggregate optimization criteria are proposed: 1) regional average life expectance at birth (RALE); 2) regional (municipal) life quality index (RLQI); 3) resilience of regional systems of interdependent smart infrastructures (RICI) and 4) spatial entropy of creation and destruction of a regional system of ICI. Infranetics is a useful tool of managing and governance for regional decision makers (DMs), as they can track how their decisions influence the RALE, quality of life and the wellbeing of their citizens/constituents. The paper demonstrates how Infranetics is used to solve real life problems related to regional resilience and governance.
A miniature prism-based stereoscopic system for 3D machine vision applications

We have developed a prototype of a miniature prism-based optical system for simultaneous acquisition of two stereoscopic images on a single image sensor. The scheme and optical characteristics of the system are presented. We show that after a proper geometrical calibration and image processing it is possible to calculate three-dimensional (3-D) shape of the inspected objects. The devices based on this system may be effectively used for 3-D machine vision applications and remote visual inspection.