Reviewerly/exHarmony huggingface dataset:
Usability of the Virtual Agent Interaction Framework

The Virtual Agent Interaction Framework (VAIF) is an authoring tool for creating virtual-reality applications with embodied conversational agents. VAIF is intended for use by both expert and non-expert users, in contrast with more sophisticated and complex development tools such as the Virtual Human Toolkit. To determine if VAIF is actually usable by a range of users, we conducted a two-phase summative usability test, with a total of 43 participants. We also tried porting to VAIF a scene from an earlier VR application. The results of the usability study suggest that people with little or even no experience in creating embodied conversational agents can install VAIF and build interaction scenes from scratch, with relatively low rates of encountering problem episodes. However, the usability testing disclosed aspects of VAIF and its user’s guide that could be improved to reduce the number of problem episodes that users encounter.
A FUZZY RULE-BASED EXPERT SYSTEM FOR ASTHMA SEVERITY IDENTIFICATION IN EMERGENCY DEPARTMENT

The emergency department (ED) of a hospital is an important unit that deals with time-sensitive and life-threatening medical cases. Rapid treatment and accuracy in diagnosis are considered the main characteristics of excellent operational processes in ED. However, in reality, long waiting time and uncertainty in the diagnosis has affected the quality of ED services. Nonetheless, these problems can be improved by utilising computing technologies that assist medical professionals to make fast and accurate decisions. This paper investigates the issues of under-treatment and uncertainty condition of acute asthma cases in ED. A novel approach, known as the fuzzy logic principle is employed to determine the severity of acute asthma. The fuzzy set theory, known as Fuzzy Rule-based Expert System for Asthma Severity (FRESAS) determination is embedded into the expert system (ES) to assess the severity of asthma among patients in ED. The proposed fuzzy methodology effectively manages the fuzziness of the patientâ€™s information data, and determines the subjective judgment of medical practitionersâ€™ level on eight criteria assessed in severity determination. Knowledge acquisition and representation, fuzzification, fuzzy inference engine, and defuzzification are the processes tested by the FRESAS development that incorporates expert advice. The system evaluation is performed by using datasets that were extracted from the ED clerking notes from one of the hospitals in Northern Peninsular Malaysia. System evaluation demonstrates that the proposed system performs efficiently in determining the severity of acute asthma. Furthermore, the proposed system offers opportunities for further research on other types of diseases in ED, and improves other hybridisation approaches.
OxKBC: Outcome Explanation for Factorization Based Knowledge Base Completion

State-of-the-art models for Knowledge Base Completion (KBC) are based on tensor factorization (TF), e.g, DistMult, ComplEx. While they produce good results, they cannot expose any rationale behind their predictions, potentially reducing the trust of a user in the model. Previous works have explored creating an inherently explainable model, e.g. Neural Theorem Proving (NTP), DeepPath, MINERVA, but explainability comes at the cost of performance. Others have tried to create an auxiliary explainable model having high fidelity with the underlying TF model, but unfortunately, they do not scale on large KBs such as FB15k and YAGO. In this work, we propose OxKBC -- an Outcome eXplanation engine for KBC, which provides a post-hoc explanation for every triple inferred by an (uninterpretable) factorization based model. It first augments the underlying Knowledge Graph by introducing weighted edges between entities based on their similarity given by the underlying model. In the augmented graph, it defines a notion of human-understandable explanation paths along with a language to generate them. Depending on the edges, the paths are aggregated into second-order templates for further selection. The best template with its grounding is then selected by a neural selection module that is trained with minimal supervision by a novel loss function. Experiments over Mechanical Turk demonstrate that users find our explanations more trustworthy compared to rule mining.
Linking Knowledge Oriented Leadership and Innovation towards Organizational Performance

The need for organizational performance to be aligned is well established in the literature in different context. Consequently, the need for improved leadership has directed massive attention towards employee performance planning, performance management and performance evaluation. Knowledge oriented leadership (KOL) is one of the most significant and basic leadership methodology in the organization and information is required for every single qualities, attributes and styles of administration. The study investigated the direct and indirect relationship among Knowledge oriented leadership and organizational performance through employee innovative behavior within the public sector. A valid 441 questionnaires were gathered from employees and analyzed by using SPSS and Smart-PLS3 software.  The finding revealed that, all study hypotheses are supported. Additionally, theoretical and managerial implications were discussed.
A Study on the Prevention Mechanisms for Kernel Attacks

The kernel of the operating system is the lifeline of a system. It controls all the components of the system and is needed for a problem-free execution of all the processes in the system. In the past decade, attackers are launching attacks that modify the kernel and remain undetected due to less security protection to the kernel. Kernel rootkits were used to manipulate the code to gain root-level access. Now, with anti-viruses and firewalls blocking malicious codes in the system, attackers have started targeting the kernel memory to subvert the running behavior of the system without any code injection. This is known as a kernel data attack. This paper discusses the different types of attacks that have been launched by the attackers to manipulate the kernel in the operating system and classifies the different attacks based on their types. It also discusses the currently existing techniques to prevent kernel attacks and their characteristics.
Laboratory Clustering using K-Means, K-Medoids, and Model-Based Clustering

&lt;p&gt;Institut Teknologi Sepuluh Nopember (ITS) is an institute which has about 100 laboratories to support some academic activity like teaching, research and society service. This study is clustering the laboratory in ITS based on the productivity of laboratory in carrying out its function. The methods used to group laboratory are &lt;em&gt;K&lt;/em&gt;-Means, &lt;em&gt;K&lt;/em&gt;-Medoids, and Model-Based Clustering. &lt;em&gt;K&lt;/em&gt;-Means and &lt;em&gt;K&lt;/em&gt;-Medoids are non-hierarchy clustering method that the number of cluster can be given at first. The difference of them is datapoints that selected by &lt;em&gt;K&lt;/em&gt;-Medoids as centers (medoids or exemplars) and mean for &lt;em&gt;K&lt;/em&gt;-Means. Whereas, Model-Based Clustering is a clustering method that takes into account statistical models. This statistical method is quite developed and considered to have advantages over other classical method. Comparison of each cluster method using Integrated Convergent Divergent Random (ICDR). The best method based on ICDR is Model-Based Clustering.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;&lt;strong&gt; : &lt;/strong&gt;&lt;em&gt;K&lt;/em&gt;-Means, &lt;em&gt;K&lt;/em&gt;-Medoids, Laboratory, Model-Based Clustering&lt;/p&gt;
Simulating Across the Cloud-to-Edge Continuum

As growth and adoption of the Internet of Things continue to accelerate, cloud infrastructure and communication service providers (CSPs) need to assure the efficient performance of their services while meeting the Quality of Service (QoS) requirements of their customers and their end users, while maintaining or ideally reducing costs. To do this, testing and service quality assurance are essential. Notwithstanding this, the size and complexity of modern infrastructures make real-time testing and experimentation difficult, time-consuming, and costly. The RECAP Simulation Framework offers cloud and communication service providers an alternative solution while retaining accuracy and verisimilitude. It comprises two simulation approaches, Discrete Event Simulation (DES) and Discrete Time Simulation (DTS). It provides information about optimal virtual cache placements, resource handling and remediation of the system, optimal request servicing, and finally, optimal distribution of requests and resource adjustment, with the goal to increase performance and concurrently decrease power consumption of the system.
Social Media Disclosure of Intellectual Capital and firm value

This paper aims to examine the relationship between firm value and the level of social media disclosure of intellectual capital by Kuwaiti and Omani listed firms. The level of social media disclosure of intellectual capital was measured by adapting a checklist of 78 items. Also, the financial reports were used to determine Tobin's Q. The results showed that the level of social media disclosure of intellectual capital was 71% by Kuwaiti and Omani firms. Moreover, the findings indicate that there is a significant positive relationship between the firm value and the level of social media disclosure of intellectual capital. The study recommends that the Kuwaiti and Omani bourses must develop a formal guideline for social media disclosure of intellectual capital to create harmony in disclosing information and to enhance the firms' value through taking the advantages of social media in attracting the more talented employees and to maximise the customers and investors benefits.
Mobile Multimedia-based Batakologi Learning Model Development

The purpose of this study is to develop a mobile multimedia-based Batakologi learning model as the tradition of the Batak culture and lead computer based innovative module products for Junior High School students. The application development method consists of seven phases, namely (1) Research and Preliminary Information Collecting; (2) Planning; (3) Developing Preliminary Product; (4) Preliminary Field Testing; (5) Preliminary Product Revision; (6) Main Field Testing; (7) Operational Product Revision. Data were collected in the research and development using qualitative descriptive analysis of the implementation and results of the design development of the model. Product of this research was the computer based Batakologi learning modules that could be accessed using mobile devices. The findings of the research shows improving student learning atmosphere and it was more fun and independent in learning about Batak culture.
An Improved Algorithm for Mining Correlation Item Pairs

Apriori algorithm is often used in traditional association rules mining, searching for the mode of higher frequency. Then the correlation rules are obtained by detected the correlation of the item sets, but this tends to ignore low-support high-correlation of association rules. In view of the above problems, some scholars put forward the positive correlation coefficient based on Phi correlation to avoid the embarrassment caused by Apriori algorithm. It can dig item sets with low-support but high-correlation. Although the algorithm has pruned the search space, it is not obvious that the performance of the running time based on the big data set is reduced, and the correlation pairs can be meaningless. This paper presents an improved mining algorithm with new association rules based on interestingness for correlation pairs, using an upper bound on interestingness of the supersets to prune the search space. It greatly reduces the running time, and filters the meaningless correlation pairs according to the constraints of the redundancy. Compared with the algorithm based on the Phi correlation coefficient, the new algorithm has been significantly improved in reducing the running time, the result has pruned the redundant correlation pairs. So it improves the mining efficiency and accuracy.
PENGGUNAAN MULTIMEDIA DAN ANIMASI INTERAKTIF TERHADAP KETERAMPILAN MEMBACA PERMULAAN SISWA

The purpose of this study was to determine the effect of the use of multimedia and interactive animation on early reading skills in early childhood. This research was conducted at TK Lab School FIP UMJ and Kindergarten Ketilang which involved 60 students in TK B group. The research method was using experimental research design statistics with 2x2 treatment with level design. The results of the multiple regression statistical analysis prove that multimedia and interactive animation have positive and significant effects. This can be proven from the results of the multiple regression statistical analysis it is known that Multimedia has a positive and significant effect on Early Reading Skills, meaning that multimedia and animation together (simultaneously) affect early reading skills.
Developing an Efficient Vector-Friendly Implementation of the Breadth-First Search Algorithm for NEC SX-Aurora TSUBASA

Breadth-First Search (BFS) is an important computational kernel used as a building-block for many other graph algorithms. Different algorithms and implementation approaches aimed to solve the BFS problem have been proposed so far for various computational platforms, with the direction-optimizing algorithm being the fastest and the most computationally efficient for many real-world graph types. However, straightforward implementation of direction-optimizing BFS for vector computers can be extremely challenging and inefficient due to the high irregularity of graph data structure and the algorithm itself. This paper describes the world’s first attempt aimed to create an efficient vector-friendly BFS implementation of the direction-optimizing algorithm for NEC SX-Aurora TSUBASA architecture. SX-Aurora TSUBASA vector processors provide high-performance computational power together with a world-highest bandwidth memory, making it a very interesting platform for solving various graph-processing problems. The implementation proposed in this paper significantly outperforms the existing state-of-the-art implementations both for modern CPUs (Intel Skylake) and NVIDIA V100 GPUs. In addition, the proposed implementation achieves significantly higher energy efficiency compared to other platforms and implementations both in terms of average power consumption and achieved performance per watt.
The Students’ Self-Confidence in Public Speaking

This study aims to describe and analyze the students’ self-confidence in public speaking at Advanced Speaking Class of English Education Department at Walisongo State Islamic University in the Academic year of 2018/2019. This study deals with Lauster theory, that tells about self-confidence is convincing in the ability and self-assessment of the task and it has an effective approach. This study applied a descriptive qualitative method. The data were collected through a questionnaire with 15 questions for 30 students, observation of the students’ performance in public speaking with 10 checklists and interview with 6 students in order to gain the students’ self-confidence.  The technique of data analysis involved data reduction, data display, and conclusion drawing/verification by using Creswell. The result shows that the students’ confidence level at an advanced speaking class was at the adequate level. From the result of a questionnaire, it shows that the mean score of students’ self-confidence was 3,2 while from the result of observation, it shows that students did several lacks of performances, such as no eye contact, speaking with vibration, making repetition, giving many movements, and also giving many pauses. The influencing factors which lead to students’ self-confidence were such as having a negative mindset, lack of motivation, lack of practice, lack of vocabulary, lack of ability, and lack of preparation.
Real Time Pedestrian Detection-based Faster HOG/DPM and Deep Learning Approaches

The work presented aims to show the feasibility of scientific and technological concepts in embedded vision dedicated to the extraction of image characteristics allowing the detection and the recognition/localization of objects.Object and pedestrian detection are carried out by two methods: 1. Classical image processing approach, which are improved with Histogram Oriented Gradient (HOG) and Deformable Part Model (DPM) based detection and pattern recognition.We present how we have improved the HOG/DPM approach to make pedestrian detection as a real time task by reducing calculation time.The developed approach allows us not only a pedestrian detection but also calculates the distance between pedestrians and vehicle.2. Pedestrian detection based Artificial Intelligence (AI) approaches such as Deep Learning (DL).This work has first been validated on a closed circuit and subsequently under real traffic conditions through mobile platforms (mobile robot, drone and vehicles).Several tests have been carried out in the city centre of Rouen in order to validate the platform developed.
Hybrid Method in Identifying the Fraud Detection in the Credit Card

As the world is rapidly moving towards digitalization and money transactions are becoming cashless, the utilization of credit cards has rapidly heightened. The fraud activities associated with it have also been increasing which leads to a huge loss to the financial institutions. Therefore, we need to analyze and detect the fraudulent transaction and separate them from the non-fraudulent ones. The paper proffers the frame work to detect credit card frauds. A combination of the three techniques is used. These methodologies include Decision Trees, Neural Network and K-Nearest Neighbor. For each new incoming transaction, the new label is assigned by taking the majority of the labels from the output of these techniques. This model is believed to work fairly good for all sizes and kinds of dataset as it combines the advantages of the individual techniques. We conclude the paper with a comparison of our model with the existing ones.
Task Scheduling Optimization Based on Firefly Algorithm in Storm

As an open source distributed real-time computing framework, Storm has been widely used in social network, e-commerce, stock analysis and other fields. The default scheduler of Storm try to distribute all executors of topology among all worker nodes via an even strategy using a round-robin algorithm, which may result in performance bottleneck due to high topology processing latency and low throughput. Aiming at optimizating it, we design a task scheduling optimization based on firefly algorithm to reallocate tasks to more suitable nodes according to a task scheduling scheme. We use the location of firefly to represent a feasible scheduling scheme, and the fluorescence brightness represents the node’s ability to process tasks, while the process of finding the best task scheduling scheme is simulated as the process of firefly approaching the brightest position. The Experimental results show that compared to the default scheduling algorithm, the scheduling algorithm we proposed has better task scheduling efficiency, less average processing time and higher throughput, which can optimize the performance of the cluster.
Toxic Text Classification

The users of the Internet increase every moment with increasing population and accessibility of the Internet. With the increase in the number of users of the Internet, the number of controversies, arguments and abuses of all kinds increases. It becomes necessary for social media and other sites to identify toxic content amongst a large number of content being posted by the users of the sites every second. The traditional algorithms that depend on users reporting toxic content for it to be deleted and necessary actions to be taken against the users posting the content would take a long time, within which it would have gained media attention and would have lead to huge fights over the content. Thus, it becomes important for the content to be evaluated for toxicity at the time it is posted in order to stop it from being posted. Therefore, we have designed and trained a deep learning model that can be read through the textual content given through it and determine if it is toxic or not.
Level Sets Driven by Adaptive Hybrid Region-Based Energy for Medical Image Segmentation

Medical image segmentation has a great significance for medical diagnosis. In this article, a new level set method (LSM) driven by adaptive hybrid region-based energy is proposed to achieve accurate medical image segmentation. First, new median region intensity descriptions are computed using the filtered input image and combined with traditional mean region intensity descriptions to design a novel global region-based signed pressure force (GRSPF). Then, the global region-based energy is defined using this GRSPF. Second, a similar new local region-based SPF (LRSPF) is also designed and the local region-based energy is defined using this LRSPF, which enhances the model’s versatility. Furthermore, an adaptive weight for controlling the roles of the global and local region-based energies is introduced to construct the hybrid region-based energy, which drives the level set more appropriately. Segmentation results for medical images show that the proposed LSM can segment medical images more accurately than existing LSMs.
Multiswarm Multiobjective Particle Swarm Optimization with Simulated Annealing for Extracting Multiple Tests

Education is mandatory, and much research has been invested in this sector. An important aspect of education is how to evaluate the learners’ progress. Multiple-choice tests are widely used for this purpose. The tests for learners in the same exam should come in equal difficulties for fair judgment. Thus, this requirement leads to the problem of generating tests with equal difficulties, which is also known as the specific case of generating tests with a single objective. However, in practice, multiple requirements (objectives) are enforced while making tests. For example, teachers may require the generated tests to have the same difficulty and the same test duration. In this paper, we propose the use of Multiswarm Multiobjective Particle Swarm Optimization (MMPSO) for generating k tests with multiple objectives in a single run. Additionally, we also incorporate Simulated Annealing (SA) to improve the diversity of tests and the accuracy of solutions. The experimental results with various criteria show that our approaches are effective and efficient for the problem of generating multiple tests.
A Near Metal Platform for Intensive Big Data Processing Using A Novel Approach

In this paper we present a new Golang based framework for distributed intensive data processing and also micro batching. It uses a novel approach, the persistent distributed channels, based on the concept of Share memory by communicating, and inspired from Resilient distributed datasets of Apache Spark. The architecture of our proposed system is considered as near-metal platform for Big Data operations in order to enhance the speed of massive data processing.