Reviewerly/exHarmony huggingface dataset:
A Semantic Approach for Extracting Medical Association Rules

Healthcare sector has large amounts of data that require careful analysis in order to improve the medical service offered to the patients.Semantic data mining can play an effective rule in analyzing such amounts of data.In this paper, we propose a framework for association rule extraction based on ontology semantics.In the proposed framework, traditional medical datasets are represented using web ontology language.The medical dataset is transformed into an ontology of the form of triples (subject, object, predicate), and SPARQL is used to query the generated ontology.The Apriori algorithm is used to generate the association rules.Intensive experiments have been conducted to measure the quality and significance of the resulting association rules under different scenarios using different support and confidence values.The obtained results have shown that ontology-based Apriori algorithm is much better than the traditional Apriori algorithm.The rules generated using both algorithms have been compared in terms of several performance metrics including the number of frequent items, the number of generated rules, the computation time, the memory consumption, and the average confidence of the generated rules.The different performance metrics revealed the superiority of the proposed semantic Apriori algorithm (the ontology-based Apriori) compared to traditional Apriori algorithm.
A Neuro Fuzzy Classifier with Linguistic Hedges for Speech Recognition

Fuzzy classification is the task of partitioning a feature space into fuzzy classes.A Neuro fuzzy classifier with linguistic hedges is proposed for noisy and clean speech classification.The linguistic Hedges are used to improve the meaning of fuzzy rules up to secondary level.Fuzzy entropy is applied to select optimal features of MFCC for framing the rules for designing the fuzzy inference system.Results obtained from the proposed classifier is compared over conventional and Neuro Fuzzy Classifier.The classification rates of the proposed model is better than other traditional and conventional fuzzy classifiers.0.22 to 5% improved classification accuracy is observed for the FSDD dataset.And 5% to 11% of improved classification accuracy is observed for Kannada dataset.From this study it is identified that LH plays a major role in classifying the overlapped classes of data.
Deploying Digital Media as Innovations in Marketing Government Policies and Enhancing Civic Engagement among Vulnerable Youths in Calabar

Governments have often sought improved ways to market their policies, which are variedly viewed as adequate or inadequate, in a bid to get the citizens actively engaged in the process of development.Digital media usage has inundated the social media space with both individual and governments presences.However, despite strong digital media presence driven by massive deployment of human and material resources, the question still arises as to how well such efforts have been used to deepen civic engagement among the citizens generally, and the vulnerable youth population that is digital media savvy.Data gathered from a sample of 900 respondents, revealed a positive correlation between digital media deployment in marketing government policies and enhancement of civic engagement among the vulnerable community provided such policies are people-oriented, and adequate marketing strategies are used.It recommends creation and deployment of digital media content as marketing tools to harness accrued gains.
The Fast and the FRDR: Improving Metadata for Data Discovery in Canada

The Federated Research Data Repository (FRDR), developed through a partnership between the Canadian Association of Research Libraries’ Portage initiative and the Compute Canada Federation, improves research data discovery in Canada by providing a single search portal for research data stored across Canadian governmental, institutional, and discipline-specific data repositories. While this national discovery layer helps to de-silo Canadian research data, challenges in data discovery remain due to a lack of standardized metadata practices across repositories. In recognition of this challenge, a Portage task group, drawn from a national network of experts, has engaged in a project to map subject keywords to the Online Computer Library Center’s (OCLC) Faceted Application of Subject Terminology (FAST) using the open source OpenRefine software. This paper will describe the task group’s project, discuss the various approaches undertaken by the group, and explore how this work improves data discovery and may be adopted by other repositories and metadata aggregators to support metadata standardization.
A Web service recommendation system based on QoS attribute based collaborative filtering

Service recommendation systems are an effective method for predicting user preference for services.Of late collaborative filtering based methods are popularly used for service recommendation.A similarity computation based on QoS is used, where attribute values between users or items is compared.The service recommendation system is also improvised by utilizing the QoS attributes and response time to predict the unknown value.
Libra: A Concentrate of "Blockchain Antitrust"

Blockchains promise to decentralize the economy, bypassing trusts in favor of decentralized communities. The World Economic Forum predicts that 10 percent of the global gross domestic product will be stored on blockchain by 2027. Gartner further prophesizes that blockchain will create $3.1 trillion worth of business value by 2030. Even if that prediction turns out to be too optimistic, blockchain’s legal implications cannot be neglected.
An Ontological Model of Hadith Texts

The Hadith being the second source of legislation after the Holy Qur'an in the religion of Islam, it represents a large body of knowledge in unstructured textual form.The specification of Hadiths makes its automatic exploitation a rather robust and an almost impossible task.To enable different types of computer systems to exploit this knowledge, various researchers used a formal representation of the semantics of Hadith.The widely used semantic representation is ontology defined as concepts and relations extracted from the Hadith in the form of a structure interpretable both by the machine and the human.In this article, we propose an ontology of the Hadith using an approach inspired by the "METHONTOLOGY" methodology.In this project, we are dealing with religious texts in traditional Arabic, and we face many difficulties in achieving complete precision and correctness.Hence, we decided to follow an entirely manual process to ensure the correctness of the results.Since manual ontology development is both time and effort consuming, we decided to focus only on "Wudhu2" related Hadiths.
SUCCESS ANALYSIS OF KITABISA MOBILE APPLICATION INFORMATION SYSTEM BY USING DELONE AND MCLEAN MODELS

KitaBisa, the public can raise funds for a variety of things they do or donate for things they want to help. Starting from the desire to create a social movement, in 2013 Alfatih Timur (Timmy) made KitaBisa a forum for anyone who wanted to realize his social project. Measuring the success of the KitaC mobile application can use the DeLone and McLean model, it is known that variables that have a significant relationship include Service Quality against User Satisfaction with a t-statistic value of 2.893, System Quality for Users (Use) with a t-statistic value of 10,204, and User Satisfaction of the Net Benefit with a t-statistic value of 3,680. In accordance with the hypothesis testing that user satisfaction with the KitaBisa mobile application as a forum to donate online has been proven by influencing User Satisfaction with the Net Benefit as a whole or it means the user has felt the benefits of the KitaBisa mobile application.
Path Planning for Ellipsoidal Robots and General Obstacles via Closed-Form Characterization of Minkowski Operations

Path planning has long been one of the major research areas in robotics, with PRM and RRT being two of the most effective path planners. Though they are generally very efficient, these two sample-based planners can become computationally expensive in the important special case of narrow passage problems. This paper develops a path planning paradigm which uses ellipsoids and superquadrics to respectively encapsulate the rigid parts of the robot and obstacles. The main benefit in doing this is that configuration-space obstacles can be parameterized in closed form, thereby allowing prior knowledge to be used to avoid sampling infeasible configurations, in order to solve the narrow passage problem efficiently. Benchmark results for single-body robots show that, remarkably, the proposed method outperforms the sample-based planners in terms of the computational time in searching for a path through narrow corridors. Feasible extensions that integrate with sample-based planners to further solve the high dimensional multi-body problems are discussed, which will require substantial additional theoretical development in the future.
OP78 A causal inference perspective on compositional data and collider ‘bias’

<h3>Background</h3> Compositional data (CD) comprise the parts of some whole, for which all parts sum to that whole; the whole may vary across individual units of analysis or remain fixed. Such data are common in many contexts, where interest often lies in understanding the effect of a particular part in relation to a subsequent outcome. Many of the inherent challenges associated with analysing CD have been discussed previously, though not within a formal causal framework by utilising directed acyclic graphs (DAGs). We use DAGs to consider the specific issue of collider bias as it pertains to CD. <h3>Methods</h3> We demonstrate how to depict CD using DAGs, and identify two distinct effect estimands in the generic case: (1) the ‘unbiased’ (total) effect, and (2) the ‘collider biased’ effect. We consider each effect in the context of three specific example scenarios involving CD with variable or fixed totals: (1) the relationship between the economically active population and area-level gross domestic product (GDP) (variable total); (2) the relationship between fat consumption and body weight (variable total); and (3) the relationship between time spent sedentary and body weight (fixed total). For each scenario, we consider the distinct interpretation of each effect, and the resulting implications for related analyses. <h3>Results</h3> In scenario (1), the ‘unbiased’ effect represents the average change in GDP that results from adding economically active individuals to the area whilst doing nothing to the population of economically inactive individuals, whereas the ‘collider biased’ effect represents the average change that results from swapping economically inactive individuals for economically active ones. In scenario (2), the ‘unbiased’ effect represents the average change in weight that results from adding fat to an individual’s diet irrespective of other macronutrient consumption, whilst the ‘collider biased’ effect represents the average change that results in swapping ‘other’ macronutrient consumption for fat consumption. In scenario (3), only the ‘collider biased’ effect is estimable and causally meaningful; it represents the average change in weight that results from swapping time spent physically active for time spent sedentary. <h3>Conclusion</h3> For CD with variable totals, both effects may be estimable and causally meaningful, depending upon the specific question of interest. Researchers should be clear about which effect is being sought and estimated, since they may be radically different quantities. For CD with fixed totals, only the ‘collider biased’ effect has any meaning. Careful attention must be paid to sensibly interpreting the relative effects that characterise this type of data.
Processing SPARQL Aggregate Queries with Web Preemption

Executing aggregate queries on the web of data allows to compute useful statistics ranging from the number of properties per class in a dataset to the average life of famous scientists per country. However, processing aggregate queries on public SPARQL endpoints is challenging, mainly due to quotas enforcement that prevents queries to deliver complete results. Existing distributed query engines allow to go beyond quota limitations, but their data transfer and execution times are clearly prohibitive when processing aggregate queries. Following the web preemption model, we define a new preemptable aggregation operator that allows to suspend and resume aggregate queries. Web preemption allows to continue query execution beyond quota limits and server-side aggregation drastically reduces data transfer and execution time of aggregate queries. Experimental results demonstrate that our approach outperforms existing approaches by orders of magnitude in terms of execution time and the amount of transferred data.
The Impact of Social Media on Students’ Academic Performance: A Case Study at the University of Jordan

There is no doubt that social media continues to influence the communication setting of our modern world, and it continues to contribute to and influence the academic lives of the students across the globe.The aim of this study is to survey the impact of using social media on the academic performance of Business College's Students at the University of Jordan.Four independent variables were selected including: The duration of use, the nature of use, the number of social media outlets used and the appropriateness of use.A sample of 235 students was randomly selected and analyzed using SPSS.The data were quantified using descriptive means and then analyzed using regression analysis.The findings of this research have provided a strong statistical evidence that social media indeed tends to positively impact student GPAs, with duration of use and appropriateness of use as the most influencing factors.
Texture Smoothing Quality Assessment via Information Entropy

Image texture smoothing (ITS) aims at completely removing textures while preserving as much as possible different-scale structures of an image. However, few quality assessment metrics have been formulated to objectively evaluate the ITS results, due to the lack of ground-truth texture-free images. Considering both the smoothness of textures and the preservation of structures, we make the debut of objective evaluation of ITS results, and design an intuitive Texture Smoothness and Structural Similarity Index (T3SI) based on human perception. Specifically, we first intuitively select some patches which contain relatively more texture/structure information. We then employ the Edge Preservation Index and the Structural Similarity Index Measure to evaluate the texture smoothness and the structure similarity on the selected patches, respectively. We finally formulate the novel exponential entropy function to balance the texture smoothness and the structure similarity for the quality assessment of ITS. T3SI is objective, easy to use, simple to implement, and stable. An independent User Study is performed to verify our proposed T3SI, and large experiments show that T3SI competes successfully to the state-of-the-art metrics. Our code is publicly available.
Cyber Security, Forensics and Its Impact on Future Challenges in Saudi Arabia Smart Cities Case Study on the Modern, Urban Planning and Design

This study discloses major challenges of maintaining smart cities in Saudi Arabia.The smart city provides several amazing facilities for the population of the city that ensures the effective e-governance system with data-driven decision-making.The major challenges of smart cities include maintenance of modern technology and infrastructure, public awareness regarding usage of digital equity, cyber security, forensics etc.The research initiatives reveal the impacts of cyber security and forensics on modern and urban planning & design in Saudi Arabia that ensure the protection from unethical practices like cybercrime by using holistic and comprehensive strategy.Based on the review of significant literatures and analysis of the selected articles, the findings provide the gap between challenges for smart cities in Saudi Arabia and actual initiatives to protect through cyber security and forensics.The results of the present study describe the implementation of cyber security and forensics to protect from illegal access of valuable information and occurrences happening physical disruptions in public services for smart cities in Saudi Arabia.The outcomes of the research work revealed the positive impacts and prospects of revolutionary changes of developing and maintaining smart cities by implementing modern and urban strategic planning and design.
A CNN channel pruning low-bit framework using weight quantization with sparse group lasso regularization

The deployment of large-scale Convolutional Neural Networks (CNNs) in limited-power devices is hindered by their high computation cost and storage. In this paper, we propose a novel framework for CNNs to simultaneously achieve channel pruning and low-bit quantization by combining weight quantizati on with Sparse Group Lasso (SGL) regularization. We model this framework as a discretely constrained problem and solve it by Alternating Direction Method of Multipliers (ADMM). Different from previous approaches, the proposed method reduces not only model size but also computational operations. In experimental section, we evaluate the proposed framework on CIFAR datasets with several popular models such as VGG-7/16/19 and ResNet-18/34/50, which demonstrate that the proposed method can obtain low-bit networks and dramatically reduce redundant channels of the network with slight inference accuracy loss. Furthermore, we also visualize and analyze weight tensors, which showing the compact group-sparsity structure of them.
A Deep Hyper Siamese Network for Real-Time Object Tracking

Siamese networks have drawn increasing interest in the field of visual object tracking due to their balance of precision and efficiency. However, Siamese trackers use relatively shallow backbone networks, such as AlexNet, and therefore do not take full advantage of the capabilities of modern deep convolutional neural networks (CNNs). Moreover, the feature representations of the target object in a Siamese tracker are extracted through the last layer of CNNs and mainly capture semantic information, which causes the tracker's precision to be relatively low and to drift easily in the presence of similar distractors. In this paper, a new nonpadding residual unit (NPRU) is designed and used to stack a 22-layer deep ResNet, referred as ResNet22. After utilizing ResNet22 as the backbone network, we can build a deep Siamese network, which can greatly enhance the tracking performance. Considering that the different levels of the feature maps of the CNN represent different aspects of the target object, we aggregated different deep convolutional layers to make use of ResNet22's multilevel feature maps, which can form hyperfeature representations of targets. The designed deep hyper Siamese network is named DHSiam. Experimental results show that DHSiam has achieved significant improvement on multiple benchmark datasets.
Prediction Graduate Student Use Naive Bayes Classifier

Student graduation is important in the accreditation assessment process.Because student graduation there are standards that must be Achieved by the Study Program items, namely a four-year study period and a 3.0 GPA.Therefore we need a prediction that can Anticipate from the beginning of the graduation standard level that has been set.This study aims to predict student graduation using Naïve Bayes Classifier with a data mining approach.Naïve Bayes provides accurate prediction results with a minimum error rated compared to all other data mining components.With the prediction of the student, graduation can be used as input, especially the Information System Study Program in making policies for improvement in the future.The software used in data processing is WEKA.The test results showed that from the Information Systems Study Program Faculty of Computer Science Faculty of Sriwijaya University in 2015 as many as 141 students as training data and in 2016 as many as 127 students as testing data, the prediction accuracy was 97,6378%.
TEACHERS’ PROBLEMS IN USING INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) AND ITS IMPLICATIONS IN ELEMENTARY SCHOOLS

This research is a qualitative research with a type of case study research. The purpose of this study is to find out the problems / obstacles faced by teachers in using (ICT). Information and Communication Technology (ICT) is a tool used to convey information related to the processing, management and transfer of information. As for the types of Information and Communication Technology (ICT) that were the focus of research include: computers / laptops and LCD projectors. In this era of globalization, teachers are required to be able to use ICT in the learning process to add insight and can help teachers in adding teaching material. with the use of ICT in the learning process. The findings from the research results concluded that problems / obstacles that occur in the use of ICT in primary schools such as: time-consuming, limited amount of infocus, students lack of focus on the material, the unavailability of.
Feature Terms Prediction

In Software Product Lines (SPL), feature extraction from software requirements specifications has been subject to intense research in order to assist domain analysis in a time-saving way. Although various approaches are proposed to extract features, there still exists a gap to achieve the complete view of features, that is, how to figure out the intention of a feature. Feature terms as the smallest units in a feature can be regarded as vital indicators for describing a feature. Automated feature term extraction can provide key information regarding the intention of a feature, which improves the efficiency of domain analysis. In this paper, we propose an approach to train prediction models by using machine learning techniques to identify feature terms. To this end, we extract candidate terms from requirement specifications in one domain and take six attributes of each term into account to create a labeled dataset. Subsequently, we apply seven commonly used machine algorithms to train prediction models on the labeled dataset. We then use these prediction models to predict feature terms from the requirements belonging to the other two different domains. Our results show that (1) feature terms can be predicted with high accuracy of ≈ 90% within a domain (2) prediction across domains leads to a decreased but still good accuracy (≈ 80%), and (3) machine learning algorithms perform differently.
Mobile Based Sharing Class Presentation Display Management

In this paper, we present an application that helps the teachers in identifying slow learners in a class; similarly, the system will help slow learners cope up with their fast learner peers. Specifically, we evaluated the development of the Mobile-based Sharing Presentation Display Management to assist the teachers in identifying students that don’t understand the topic during lectures. Furthermore, the system automatically collects the students’ attendance, allows the learners to interact with each other, learners can ask questions through a messaging facility, while anonymous to their classmates they can inform the teacher to discuss the topic further presented in the current slide or to give them some time to process the item. Developed using open source technologies, the system can run on multiple platforms, including Android and Windows. Few issues arise during the testing; however, findings revealed that both students and teacher perceive the use of the system positively, and they find it very advantageous to improve their performance in the class.