Reviewerly/exHarmony huggingface dataset:
Using Mobile-based Social Media to Facilitate Interaction and Build Communities through the lens of Ubuntu in Distance Education

This paper examines how WhatsApp can be used to build learning communities and facilitate interaction, through the lens of Ubuntu – an African concept of humanness, which means that an individual person owes his or her existence to the existence of others. The Afrocentric concept of Ubuntu and interaction work concomitantly towards the realisation of vocabulary teaching and learning within distance education. Through this study, students were encouraged to work together through a mobile-based social network to learn English vocabulary. Ubuntu was used as a framework to analyse the WhatsApp chats to find salient themes relating to building communities and enhancing interaction. The key elements of Ubuntu grounded the findings and demonstrated how technology can be used to bridge the interaction distance and how Ubuntu principles are articulated in the communities of learning. This paper illustrates how Ubuntu is manifested in a local context, with principles that can be applied globally to enhance collaboration.
Human Centric AR&amp;VR Display and Interface Technologies for Automobile

The articles in this special section examine interface technologies for automobiles. The recognition of head-up displays (HUDs) n the world is increasing as being adopted by the car manufacturers. With the development of advanced driving support systems and automatic driving technology, there are voices that “if these systems become widespread, HUD may not be necessary.” In response to these opinions, we consider that “the importance of HUD, which is the basis of human-machine interface (HMI).” In this special section, we focus on the HUD and automatic driving technologies as human centric technologies in addition to the mechanism and features of HUD that is the basis of HMI.
An Exploration of the Evolving Reporting Organizational Structure for the Chief Information Security Officer (CISO) Function

An Exploration of the Evolving Reporting Organizational Structure for the Chief Information Security Officer (CISO) Function Conrad Shayo, Frank Lin Abstract The ideal reporting structure for the Chief Information Security Officer (CISO) function is not yet settled. Should the CISO report to the Chief Information Officer, Chief Operations Officer, Chief Financial Officer, Chief Internal Auditor, General Counsel, or Chief Executive Officer? Although existing literature provides recommended reporting structures of the CISO position, most practitioners and researchers discourage the adoption of a ―one size fits all‖. This study borrows from Complexity Theory and Interaction Theory to shed light on ―Why‖ we may have so many different reporting CISO structures even for companies of the same size in the same industry faced with the same information security risks. Using Complexity Theory, we posit that although the initial CISO reporting structure is unpredictable; organizations as open systems have an inbuilt capacity to self-organize, self-motivate, and learn to adapt the CISO reporting structure to their own work environment. Using Interaction Theory, we posit that the emerging reporting structure is created by the interaction between factors inherent in decision makers of the organization and factors inherent in the CISO function. This implies that ideal reporting structures of the information security organization will inevitably vary according to the organization‘s industry, mission, maturity, culture, risk exposure, resources, capabilities, and prevailing decision making and governance infrastructure. Using a case study research method, we relied on numerous CISO interviews available on open source and our own interviews of two seasoned CISOs. The study recommends best practices for evolving an effective reporting structure for the CISO function. Full Text: PDF DOI: 10.15640/jcsit.v7n1a1
An Efficient Normalized Restricted Boltzmann Machine for Solving Multiclass Classification Problems

Multiclass classification based on unlabeled images using computer vision and image processing is currently an important issue.In this research, we focused on the phenomena of constructing high-level features detector for class-driven unlabeled data.We proposed a normalized restricted Boltzmann machine (NRBM) to form a robust network model.The proposed NRBM is developed to achieve the goal of dimensionality reduction and provide better feature extraction with enhancement in learning more appropriate features of the data.For increment in learning convergence rate and reduction in complexity of the NRBM, we add Polyak Averaging method when training update parameters.We train the proposed NRBM network model on five variants of Modified National Institute of Standards and Technology database (MNIST) benchmark dataset.The conducted experiments showed that the proposed NRBM is more robust to noisy data as compared to state-of-art approaches.
Conic sections fitting in disperse data using Differential Evolution

Robust conics detection and extraction have received an increased interest due to the potential applications in many critical tasks. In medical imaging, the conics can help to detect optic disk abnormalities in retinographic images, or cranial sections and bone structures in radiography, magnetic resonance, and computed tomography images. Moreover, the physicians can be guided in the prostate cancer diagnosis by assessing the size and shape of the prostate in ultrasound or magnetic resonance images. Some of these structures are composed of discontinuous points, imprecise directions, multiple bifurcations, and variable line–trace width, or may present undesired noise, which makes the curve fitting hard to compute. Image acquisition also plays a fundamental role in adding artifacts, as unexpected outliers, which jeopardizes the applicability and efficiency of the current methods. This article proposes a strategy for determining the general equation of oblique conics in noisy and sparse data sets. As a novelty, this methodology employs four uncorrelated points from a randomly sampled population to estimate the initial space search. Then, Differential Evolution (DE) method iterates until the solution that best fits the oblique conic section is found. Advantages of the method include a DE implementation using a weighted fitness function based on the Mean Squared Error (MSE) and reinforced with the Random Sample Consensus (RANSAC) inliers measurement idea. This objective function allows associating the fitting error to the number of points into the inliers-region. Additionally, the number of parameters to tune is reduced and almost generalized. A distinctive feature is related to the eccentricity, which could conduct to a variety of rich solutions. However, some disadvantages have arisen trying to identify a specific conic. For instance, when some parabolas are considered, the method could evolve optimally to an ellipse. The proposed methodology has been evaluated using synthetic and real images. These images were contaminated with additive white Gaussian noise to prove the method stability. Numerical results showed that the proposed method is a reliable and fast technique for computing any conic from scattered data. Results obtained from two dissimilar applications prove that the proposed method can be used in a variety of sensitive applications. Those applications include the optic disk segmentation and traffic sign classification. The assessment showed an improvement of 2% across the metrics for detecting the optic disk and reached a classification accuracy of 0.9730 for the speed limit traffic signs.
Deep Learning for Identification of Adverse Effect Mentions In Twitter Data

Social Media Mining for Health Applications (SMM4H) Adverse Effect Mentions Shared Task challenges participants to accurately identify spans of text within a tweet that correspond to Adverse Effects (AEs) resulting from medication usage (Weissenbacher et al., 2019).This task features a training data set of 2,367 tweets, in addition to a 1,000 tweet evaluation data set.The solution presented here features a bidirectional Long Shortterm Memory Network (bi-LSTM) for the generation of character-level embeddings.It uses a second bi-LSTM trained on both character and token level embeddings to feed a Conditional Random Field (CRF) which provides the final classification.This paper further discusses the deep learning algorithms used in our solution.
Unsupervised Anomaly Detection Using Optimal Transport for Predictive Maintenance

Anomaly detection is of crucial importance in industrial environment, especially in the context of predictive maintenance. As it is very costly to add an extra monitoring layer on production machines, non-invasive solutions are favored to watch for precursory clue indicating the possible need for a maintenance operation. Those clues are to be detected in evolving and highly variable working environment, calling for online and unsupervised methods. This contribution proposes a framework grounded in optimal transport, for the specific characterization of a system and the automatic detection of abnormal events. This method is evaluated on acoustic dataset and demonstrate the superiority of metrics derived from optimal transport on the Euclidean ones. The proposed method is shown to outperform one-class SVM on real datasets, which is the state-of-the-art method for anomaly detection.
Convolutional and Recurrent Neural Networks for Face Image Analysis

Abstract In the presented research two Deep Neural Network (DNN) models for face image analysis were developed. The first one detects eyes, nose and mouth and it is based on a moderate size Convolutional Neural Network (CNN) while the second one identifies 68 landmarks resulting in a novel Face Alignment Network composed of a CNN and a recurrent neural network. The Face Parts Detector inputs face image and outputs the pixel coordinates of bounding boxes for detected facial parts. The Face Alignment Network extracts deep features in CNN module while in the recurrent module it generates 68 facial landmarks using not only this deep features, but also the geometry of facial parts. Both methods are robust to varying head poses and changing light conditions.
Fuzzy Multiset Clustering for Metagame Analysis

Developing agents for automated game playing is a demanding task in the general game production cycle.Especially the involvement of frequent balance changes after the release, which for example often occur in collectible card games, require constant updates of the developed agent.The game's developers need to continuously analyze and understand the current meta-game for adjusting the agent's parameters, making balance changes to the game, and, thereby, sustaining the satisfaction of its player base.The underlying analysis largely depends on evaluating players' play traces.Necessary adjustments to the agent's and the game's parameters are taken care of by the game's developers.This paper proposes a first step in automatically observing the current state of a collectible card game, which will assist the developers in their understanding of established deck archetypes and, therefore, speed up the update cycle.Fuzzy multisets are used for modeling decks and frequently occurring subsets of cards.We propose the definition of a (fuzzy) multiset centroid to uniquely represent the cluster and its contained decks and show that it is better able to match the deck archetype than the often reported deck core.The proposed clustering procedure identifies deck archetypes and keeps track of its common variants in the current meta-game.We evaluate the approach by comparing the result of our clustering procedure with a hand-labeled data set and show that it is able to reproduce clusters of similar quality to a labeling provided by experts.
Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing

While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications.One performance bottleneck is predicting the most likely next token over a large vocabulary; methods to circumvent this bottleneck are a current research topic.We focus specifically on using seq2seq models for semantic parsing, where we observe that grammars often exist which specify valid formal representations of utterance semantics.By developing a generic approach for restricting the predictions of a seq2seq model to grammatically permissible continuations, we arrive at a widely applicable technique for speeding up semantic parsing.The technique leads to a 74% speed-up on an in-house dataset with a large vocabulary, compared to the same neural model without grammatical restrictions.
Role of Health Literacy on Menstrual Hygiene Practice among the Girl Students

The main aim of this study was to find out the role of health literacy on menstruation hygiene practice of campus girls. The population of this study were girls studying health and physical education in Surkhet Campus and HA/Staff Nurse in SEDA Campus. Surkhet was selected by proportional stratified sampling. This study followed descriptive and cross-sectional design. The study used two tools Short Test of Functional Health Literacy in Adults (S-TOFHLA) and Self Administrated Questionnaire for collecting data. It was found that health literacy among girls of study area is not satisfactory. The findings indicate that 53.93 percent respondents have adequate health literacy. Adequate health literacy level of girls of HA/Staff nurse, aged 16-18 years and 19-21 years and Chhetri and Janajati had adequate health literary. The findings support that higher proportions of the adequate health literate respondents have good menstruation hygiene practice than inadequate health literate respondents. Finally, the results indicated that health literacy among the girls is inadequate, that calls more concern to the matter of health literacy in educational programs.
The conceptual sphere of the analysis of abstraction in the fine art as an element of artistic culture

The purpose of the article is to find out the essence and description of the features of abstraction as a form of fine art. The methodology is the general principles of art criticism and cultural science of scientific discourse, which is based on analytical, system-structural, semiotic methods and generalization. Scientific novelty represents a conceptually new view of abstract art as a transhistorical style of fine art, which is based on universal, immutable cultural codes that are constantly reproduced from the past to the present. It is proposed in the scientific use of the notion of “abstract sign” as a concept of semiotic analysis of non-objective art. Conclusions. The basis of the figurative abstraction language is a sign and a symbol-code that has no similarity to a particular subject or concept, but expresses the general nature of the representation and is a universal form in the fine arts from ancient times to the present.
Generating a Fuzzy Rule Based Classification System by genetic learning of granularity level using TOPSIS

Fuzzy Rule Based Classification Systems (FRBCSs) are widely used tools in classification problems.An important aspect in the design of a FRBCS is the number of fuzzy labels per variable (granularity level), which significantly influences the performance of the fuzzy system.Another relevant issue to be considered when generating a FRBCS is the accuracy-interpretability tradeoff, which can be addressed in the context of multiobjective optimization.Thus, in this work, we propose a new approach to design a FR-BCS in which the accuracy and the interpretability (number of rules) of the FRBCS are considered objectives to be treated with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).We applied our method to several well-known standard classification datasets and the results show the feasibility of the proposed approach.
Modeling a Historical Variety of a Low-Resource Language: Language Contact Effects in the Verbal Cluster of Early-Modern Frisian

Certain phenomena of interest to linguists mainly occur in low-resource languages, such as contact-induced language change.We show that it is possible to study contact-induced language change computationally in a historical variety of a low-resource language, Early-Modern Frisian, by creating a model using features that were established to be relevant in a closely related language, modern Dutch.This allows us to test two hypotheses on two types of language contact that may have taken place between Frisian and Dutch during this time.Our model shows that Frisian verb cluster word orders are associated with different context features than Dutch verb orders, supporting the 'learned borrowing' hypothesis.
Deep Learning Classification of Biomedical Text using Convolutional Neural Network

In this digital era, the document entries have been increasing days by days, causing a situation where the volume of the document entries in overwhelming.This situation has caused people to encounter with problems such as congestion of data, difficulty in searching the intended information or even difficulty in managing the databases, for example, MEDLINE database which stores the documents related to the biomedical field.This research will specify the solution focusing in text classification of the biomedical abstracts.Text classification is the process of organizing documents into predefined classes.A standard text classification framework consists of feature extraction, feature selection and the classification stages.The dataset used in this research is the Ohsumed dataset which is the subset of the MEDLINE database.In this research, there is a total number of 11,566 abstracts selected from the Ohsumed dataset.First of all, feature extraction is performed on the biomedical abstracts and a list of unique features is produced.All the features in this list will be added to the multiword tokenizer lexicon for tokenizing phrases or compound word.After that, the classification of the biomedical texts is conducted using the deep learning network, Convolutional Neural Network which is an approach widely used in many domains such as pattern recognition, classification and so on.The goal of classification is to accurately organize the data into the correct predefined classes.The Convolutional Neural Network has achieved a result of 54.79% average accuracy, 61.00% average precision, 60.00% average recall and 60.50% average F1-score.In short, it is hoped that this research could be beneficial to the text classification area.
PEMANFAATAN VIDEO ONLINE DALAM PEMAHAMAN MATERI PREPOSITION OF TIME AND PLACE (AT, IN, ON)

In this high technology era, there are various media which can be used in teaching and learning activity including English Course where students feel difficult to use at, in, and on as preposition of time and place. Lecturer can combine the conventional technique through lecturing with online video playing inside and outside the classroom as the usage of e-learning concept. This research was held to know the students’ perception of online video usage in understanding Preposition of Time and Place (At, In, On) material. Descriptive method is used by using qualitative and quantitative data. Qualitative data were taken from literature review, whereas quantitative data were taken from students’ test and questionnaire results. The respondents are 100 students of Bina Sarana Informatika University majoring Information System who were randomly chosen. The result of this research shows that most of the students assume that online video is easily accessible and can help them review and understand the concept of Preposition of Time and Place (At, In, On).
PASNIC

In this paper, we propose PASNIC, a lightweight privacy-aware sensor node for image capturing. PASNIC masks the region of faces and private screens (smartphone and portable computer displays) in captured visible images before sending them to remote sites. Thermal information, which can also be captured with a thermal image sensor, is used to detect the regions of faces and private screens. Our masking algorithms are so simple that even sensor nodes with low processing power can sufficiently process them.
Lexical Normalization of User-Generated Medical Text

In the medical domain, user-generated social media text is increasingly used as a valuable complementary knowledge source to scientific medical literature.The extraction of this knowledge is complicated by colloquial language use and misspellings.Yet, lexical normalization of such data has not been addressed properly.This paper presents an unsupervised, data-driven spelling correction module for medical social media.Our method outperforms state-of-the-art spelling correction and can detect mistakes with an F 0.5 of 0.888.Additionally, we present a novel corpus for spelling mistake detection and correction on a medical patient forum.
Improvement in the Online Handwritten Kannada Numeral Recognition with the Difference Feature

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Content Determination for Natural Language Descriptions of Predictive Bayesian Networks

The dramatic success of Artificial Intelligence and its applications has been accompanied by an increasing complexity, which makes its comprehension for final users more difficult and damages their trustworthiness.Within this context, the emergence of Explainable AI aims to make intelligent systems decisions more transparent and understandable for human users.In this paper, we propose a framework for the explanation of predictive inference in Bayesian Networks (BN) in natural language to non-specialized users.The model represents the embedded information in the BN by means of (fuzzy) quantified statements and reasons using the a fuzzy syllogism.The framework provides how this can be used for the content determination stage in Natural Language Generation explanation systems for BNs.Through a number of realistic scenarios of use examples, we show how the generated explanations allows the user to trace the inference steps in the approximate reasoning process in predictive BNs.
Single Image Defogging Method Based on Adaptive Modified Dark Channel Value

In the conventional image defogging algorithm based on dark channel prior, the calculation deviation of dark channel value of image edge position could lead to halo effect on image.In this paper, a method for correcting dark channel value is proposed.Firstly, according to the gray difference between the minimum image and dark channel image, an adaptive threshold is obtained to determine the edge image pixels with drastic depth gray value changes.Secondly, with the threshold, the gray values are actively modified.Finally, clear fog-free image can be restored according to the atmospheric scattering model.The test results of several groups of foggy images show that after the application of the method, the halo effect effectively removed, the image color deviation corrected, and the contrast improved.The restored image looks clearer and more natural.