Reviewerly/exHarmony huggingface dataset:
Semi-Automatic Adaptation of Diagnostic Rules in the Case-Based Reasoning Process

The paper presents a new approach to effectively support the adaptation phases in the case-based reasoning (CBR) process. The use of the CBR approach in DSS (Decision Support Systems) can help the doctors better understand existing knowledge and make personalized decisions. CBR simulates human thinking by reusing previous solutions applied to past similar cases to solve new ones. The proposed method improves the most challenging part of the CBR process, the adaptation phase. It provides diagnostic suggestions together with explanations in the form of decision rules so that the physician can more easily decide on a new patient’s diagnosis. We experimentally tested and verified our semi-automatic adaptation method through medical data representing patients with cardiovascular disease. At first, the most appropriate diagnostics is presented to the doctor as the most relevant diagnostic paths, i.e., rules—extracted from a decision tree model. The generated rules are based on existing patient records available for the analysis. Next, the doctor can consider these results in two ways. If the selected diagnostic path entirely covers the actual new case, she can apply the proposed diagnostic path to diagnose the new case. Otherwise, our system automatically suggests the minimal rules’ modification alternatives to cover the new case. The doctor decides if the suggested modifications can be accepted or not.
Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension

Machine reading comprehension (MRC) is the task that asks a machine to answer questions based on a given context. For Chinese MRC, due to the non-literal and non-compositional semantic characteristics, Chinese idioms pose unique challenges for machines to understand. Previous studies tend to treat idioms separately without fully exploiting the relationship among them. In this paper, we first define the concept of literal meaning coverage to measure the consistency between semantics and literal meanings for Chinese idioms. With the definition, we prove that the literal meanings of many idioms are far from their semantics, and we also verify that the synonymic relationship can mitigate this inconsistency, which would be beneficial for idiom comprehension. Furthermore, to fully utilize the synonymic relationship, we propose the synonym knowledge enhanced reader. Specifically, for each idiom, we first construct a synonym graph according to the annotations from the high-quality synonym dictionary or the cosine similarity between the pre-trained idiom embeddings and then incorporate the graph attention network and gate mechanism to encode the graph. Experimental results on ChID, a large-scale Chinese idiom reading comprehension dataset, show that our model achieves state-of-the-art performance.
Feature Selection for Text and Image Data Using Differential Evolution with SVM and Naïve Bayes Classifiers

Classification problems are increasing in various important applications such as text categorization, images, medical imaging diagnosis and bimolecular analysis etc. due to large amount of attribute set. Feature extraction methods in case of large dataset play an important role to reduce the irrelevant feature and thereby increases the performance of classifier algorithm. There exist various methods based on machine learning for text and image classification. These approaches are utilized for dimensionality reduction which aims to filter less informative and outlier data. Therefore, these approaches provide compact representation and computationally better tractable accuracy. At the same time, these methods can be challenging if the search space is doubled multiple time. To optimize such challenges, a hybrid approach is suggested in this paper. The proposed approach uses differential evolution (DE) for feature selection with naïve bayes (NB) and support vector machine (SVM) classifiers to enhance the performance of selected classifier. The results are verified using text and image data which reflects improved accuracy compared with other conventional techniques. A 25 benchmark datasets (UCI) from different domains are considered to test the proposed algorithms. A comparative study between proposed hybrid classification algorithms are presented in this work. Finally, the experimental result shows that the differential evolution with NB classifier outperforms and produces better estimation of probability terms. The proposed technique in terms of computational time is also feasible.
SalamNET at SemEval-2020 Task 12: Deep Learning Approach for Arabic Offensive Language Detection

This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media.Our approach focuses on applying multiple deep learning models and conducting in depth error analysis of results to provide system implications for future development considerations.To pursue our goal, a Recurrent Neural Network (RNN), a Gated Recurrent Unit (GRU), and Long-Short Term Memory (LSTM) models with different design architectures have been developed and evaluated.The SalamNET, a Bi-directional Gated Recurrent Unit (Bi-GRU) based model, reports a macro-F1 score of 0.83.
BERT-based Cohesion Analysis of Japanese Texts

The meaning of natural language text is supported by cohesion among various kinds of entities, including coreference relations, predicate-argument structures, and bridging anaphora relations. However, predicate-argument structures for nominal predicates and bridging anaphora relations have not been studied well, and their analyses have been still very difficult. Recent advances in neural networks, in particular, self training-based language models including BERT (Devlin et al., 2019), have significantly improved many natural language processing tasks, making it possible to dive into the study on analysis of cohesion in the whole text. In this study, we tackle an integrated analysis of cohesion in Japanese texts. Our results significantly outperformed existing studies in each task, especially about 10 to 20 point improvement both for zero anaphora and coreference resolution. Furthermore, we also showed that coreference resolution is different in nature from the other tasks and should be treated specially.
Image Texture Analysis for Medical Image Mining: A Comparative Study Direct to Osteoarthritis Classification using Knee X-ray Image

<p class='IJASEITAbtract'>Knee Osteoarthritis (OA) is one of the most prominent diseases in an ageing society and has affected over 10 million people in Thailand. When people suffer from OA, it is very difficult to recover. Therefore, early detection and prevention are important. The typical way to detect OA is by using X-ray imaging. This research study is focused on early detection of OA by applying image processing and classification techniques to knee X-ray imagery. The fundamental concept is to find a region of interest, use feature extraction techniques and build a classifier that can classify between OA or non-OA imageries. There are four regions of interest obtained from each image: (i) Medial Femur (MF), (ii) Lateral Femur (LF), (iii) Medial Tibia (MT), and (iv) Lateral Tibia (LT). The ten texture analysis techniques are then adopted to generate the embedded properties of the bone surface. Once the feature vector has been generated the variety of techniques of machine learning mechanisms are applied to generate the desired classifiers, which can be used to distinguish between OA and non-OA images. From the conducted experiments, a total of 131 images (68 OA cases and 63 non-OA cases) was used, the results obtained show that LF region with Local Binary Pattern descriptor produced the most appropriate classifier with an AUC value of 0.912.
A compact and recursive Riemannian motion descriptor for untrimmed activity recognition

A very low dimension frame-level motion descriptor is herein proposed with the capability to represent incomplete dynamics, thus allowing online action prediction. At each frame, a set of local trajectory kinematic cues are spatially pooled using a covariance matrix. The set of frame-level covariance matrices forms a Riemannian manifold that describes motion patterns. A set of statistic measures are computed over this manifold to characterize the sequence dynamics, either globally, or instantaneously from a motion history. Regarding the Riemannian metrics, two different versions are proposed: (1) by considering tangent projections with respect to updated recursive statistics, and (2) by mapping the covariance onto a linear matrix using as reference the identity matrix. The proposed approach was evaluated for two different tasks: (1) for action classification on complete video sequences and (2) for online action recognition, in which the activity is predicted at each frame. The method was evaluated using two public datasets: KTH and UT-interaction. For action classification, the method achieved an average accuracy of 92.27 and 81.67%, for KTH and UT-interaction, respectively. In partial recognition task, the proposed method achieved similar classification rate as for the whole sequence using only the 40 and 70% on KTH and UT sequences, respectively. The code of this work is available at [code].
Exploring multiobjective training in multiclass classification

Multinomial logistic loss and L2 regularization are often conflicting objectives as more robust regularization leads to restrained multinomial parameters. For many practical problems, leveraging the best of both worlds would be invaluable for better decision-making processes. This research proposes a novel framework to obtain representative and diverse L2-regularized multinomial models, based on valuable trade-offs between prediction error and model complexity. The framework relies upon the Non-Inferior Set Estimation (NISE) method – a deterministic multiobjective solver. NISE automatically implements hyperparameter tuning in a multiobjective context. Given the diverse set of efficient learning models, model selection and aggregation of the multiple models in an ensemble framework promote high performance in multiclass classification. Additionally, NISE uses the weighted sum method as scalarization, thus being able to deal with the learning formulation directly. Its deterministic nature and the convexity of the learning problem confer scalability to the proposal. The experiments show competitive performance in various setups, taking a broad set of multiclass classification methods as contenders.
Media multitasking: from cognitive functions to digital

The article presents the phenomenon of multitasking, representing simultaneous execution of two or more mental operations. Its particular type, media multitasking (MMT), is also considered as a relatively new format for combining various information flows that meets the requirements of the digital environment. The historical perspective of studying multitasking is presented: from individual experiments on the selectivity of attention to the phenomenon of digital everyday life. Modern empirical studies of MMT correlates among the main cognitive functions, including those of “light” and “heavy” multitaskers: attention, memory, thinking, and cognitive control, as well as productivity, academic performance, and metacognition are analyzed. The positive and negative effects of the MMT format are described. The resulting data set suggests that using the MMT format, which for most children and adolescents is gradually becoming a universal strategy of activity, the child adapts to an information-rich environment as a multiple and mixed reality. The importance of developing mechanisms for the formation of managed and controlled MMT for the education system is emphasized.
Gamification for Science Learning Media Challenges of Teacher and Expectations of Students

&lt;p&gt;The purpose of this research is to determine the response of teachers and students towards the use of computer-based games as a medium for learning Sciences. Data were obtained from students of two ninth-grade classes of Junior high school in Surabaya, East Java, Indonesia, through observation, questionnaire, and interviews. Science learning on atomic subjects was carried out by each science teacher, while students used computerized games and worksheets. The results showed that 87.50% to 100% of students supported the use of computer-based games as a science learning media to achieve the specified target. Meanwhile, teachers believe that the use of this technique is very good because it enables students to play and learn. Therefore it has the ability to prevent them from participating in uneducational games.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
Automatic Early Detection and Classification of Leukemia from Microscopic Blood Image

Leukemia is a form of blood cancer that affects white blood cells, and is one of the leading causes of death among humans. Currently, diagnosis of leukemia is done through visual inspection of microscopic images of blood cell, which is time consuming, tedious, and requires trained human experts. Therefore, the lack of an automatic, early, and effective leukemia detection system is a great challenge in Ethiopian hospitals. The main objective of this research is to develop an automatic early detection, and classification system to diagnose leukemia from blood image using machine learning and image processing algorithm. To do the research, 400 leukemic blood images and 50 normal blood images had acquired from Jimma University Specialized Hospital using digital microscope, and preprocessed with contrast enhancement. K-means image segmentation and feature extraction were applied by the system. Multi Class Support Vector Machine has used to provide detection and classification of leukemia disease based on the extracted features parameter. The leukemia disease detection and classification accuracy achieved by developed system is 94.62%. Moreover, 94.17% sensitivity and 100% specificity level has been gained by the system. It takes an average of one minute to provide the diagnosis result. The potential of digital image analysis for leukemia disease diagnosis using artificial intelligence; which is not tedious and time consuming is very beneficial when compared to the manual method. In the future, direct diagnosing system of leukemia without staining process is recommended.
Computationally efficient progressive approach for single-image super-resolution using generative adversarial network

Single-image super-resolution (SISR) refers to reconstructing a high-resolution image from given low-resolution observation. Recently, convolutional neural network (CNN)-based SISR methods have achieved remarkable results in terms of peak-signal-to-noise ratio and structural similarity measures. These models use pixel-wise loss functions to optimize their models, which results in blurry images. However, the generative adversarial network (GAN) has the ability to generate visually plausible solutions. The different GAN-based SISR methods obtain perceptually better SR results when compared to that with the existing CNN-based methods. However, the existing GAN-based SISR methods need a large number of training parameters in the architecture to obtain better SR performance, which makes them unsuitable for many real-world applications. We propose a computationally efficient enhanced progressive approach for SISR task using GAN, which we referred as E-ProSRGAN. In the proposed method, we introduce a novel design of residual block called enhanced parallel densely connected residual network, which helps to obtain better SR performance with less number of training parameters. The quantitative performance of the proposed E-ProSRNet (i.e., generator network of E-ProSRGAN) model is better for higher upscaling factors ×3 and ×4 for most of datasets when compared to the same obtained using different CNN-based methods whose trainable parameters are less than 7 M. In the case of upscaling factor ×2, E-ProSRNet obtains second highest structural similarity index measure values for Set5 and BSD100 datasets. The proposed E-ProSRGAN model generates SR samples with better high-frequency details and perception measures than that of the other existing GAN-based SISR methods with significant reduction in the number of training parameters for larger upscaling factor.
CONVERGENCE OF HUMAN DEVELOPMENT INDEX: CASE STUDY OF FOREIGN DIRECT INVESTMENT IN ASEAN

This article examines the role of bilateral FDI and income convergence in affecting HDI increase in countries with middle HDI in ASEAN. The ASEAN Economic Community has been developing and it is expected that the gaps between nations can be covered. This article explores how economic intervention through HDI and income convergence can boost HDI improvement. The writers examine the presence of a statistically significant causal relationship between source country’s GDP, home country’s GDP, source country’s FDI towards the home country, source country total FDI, home country total FDI, and the percentage of source country FDI towards source country total FDI with both countries HDI convergence. Measurement is carried out using the generalized method of moments. Based on yearly samples of high HDI countries (Malaysia, Thailand, Singapore) couple with medium HDI countries (Laos, Vietnam, Cambodia, Myanmar, Indonesia, and Philippine) during 2013–2017 period, the writers find statistically significant impact of home country GDP, source country FDI towards all countries, and FDI percentage of the home country compared to all countries.
Implementation of an e‑Learning Project in Tshwane South District: Towards a Paperless Classroom in South African Secondary Schools

An e‑Learning project has been rolled out in Gauteng schools in an attempt to improve the quality of educationand move towards paperless classrooms. The purpose of this study was to investigate the challenges faced by Tshwane SouthSecondary Schools associated with the implementation of the e‑Learning project with a view to suggest possible solutions.The study followed a qualitative research approach with a case study research design. The study population consisted ofteachers, school management team (SMT), and learners. Purposive sampling was used to select three teachers, six learnersand three school management team members of the schools’ e‑Learning committees. Data was collected through individualsemi‑structure interviews and focus group interview. The Analysis, Design, Development, Implementation, and Evaluationmodel (ADDIE model) was used to provide a theoretical perspective on how instructional designers, training developers, andeducators should design and develop a program such as e‑Learning. The data was analysed using a thematic approach. Thestudy found that teachers were not adequately trained prior to the implementation of e‑Learning, which impacted negativelyon the project. Teachers had difficulty in operating the smartboards and accessing the learning resources that has beenloaded in the smartboards which affected the implementation of the project. The study concluded that poor planning on thesite of Department of Basic Education has resulted into challenges that hampered the implementation of e‑Learning. Thestudy recommends extensive training of both learners and teachers in the use of e‑Learning.
An Examination Into the Causal Links Among Inward FDI Determinants: Empirical Evidence From Jordan

This paper examined the causal links between inward foreign direct investments (FDI) and its determinants (i.e., gross domestic product, education, trade openness, infrastructure, and technological abilities) for Jordan over (the period 1980 – 2018). The paper used vector error correction model. The results of the study considered that gross domestic product, trade openness, education, infrastructure, and technological abilities are primary engine of inward FDI in (long term and short term). Thus, the results have vital role for the policy makers in Jordan to formulate domestic and foreign policies. This study relied on three essential parts. Firstly, FDI is a significant source of capital that promotes economic growth. Secondly, the question of what are the leading drivers of FDI remains inadequate in the literature. Finally, this research adds to the literature by using different econometrics techniques and long span of yearly time series data.
Application of improved k-means k-nearest neighbor algorithm in the movie recommendation system

In this paper, we propose a clustering and reclassification method for movie recommendation. We use the improved K-means algorithm to cluster according to the scores of similar users, Firstly, the elbow function is used to estimate the number of clusters, and the elbow method is used to determine the K value. Then, the K-means algorithm of the maximum and minimum distance method is used to select the initial cluster center, and finally the cluster and cluster center are obtained. According to the similarity between the test data of the user's rating and user's personal information and the clustering center, they are divided into the cluster to which they belong, and the sample set in the cluster is used as the training set for K-nearest neighbor classification.
SketchZooms: Deep Multi‐view Descriptors for Matching Line Drawings

Abstract Finding point‐wise correspondences between images is a long‐standing problem in image analysis. This becomes particularly challenging for sketch images, due to the varying nature of human drawing style, projection distortions and viewport changes. In this paper, we present the first attempt to obtain a learned descriptor for dense registration in line drawings. Based on recent deep learning techniques for corresponding photographs, we designed descriptors to locally match image pairs where the object of interest belongs to the same semantic category, yet still differ drastically in shape, form, and projection angle. To this end, we have specifically crafted a data set of synthetic sketches using non‐photorealistic rendering over a large collection of part‐based registered 3D models. After training, a neural network generates descriptors for every pixel in an input image, which are shown togeneralize correctly in unseen sketches hand‐drawn by humans. We evaluate our method against a baseline of correspondences data collected from expert designers, in addition to comparisons with other descriptors that have been proven effective in sketches. Code, data and further resources will be publicly released by the time of publication.
The Challenges of Software Cybersecurity Certification [Building Security In]

In 2019, the new European Union (EU) cybersecurity regulation "Cybersecurity Act" ("CSA")<sup>1</sup> entered into force to create a common framework for the certification of any information and communication technology (ICT) system, including products, services, and processes. The main purpose of this framework is to reduce the current fragmentation of cybersecurity certification schemes2 as well as to increase end users? trust in a hyperconnected society3 by fostering a mutual recognition of certified ICT components in any EU country.