Reviewerly/exHarmony huggingface dataset:
The Model of Strategies Employed by English Teachers in Teaching Writing Skill in National Plus Schools

In teaching writing, there are number of strategies that can be used. This study was intended to explore (1) the teaching writing strategies used by the teacher, (2) the differences in teaching, and (3) the problems encountered by the English teacher in teaching writing skill. This study was a descriptive qualitative study. Teachers from Bintang Mandiri and Widyatmika Junior High Schools were involved as the informant of the study. They were observed during the teaching and learning process and also interviewed. The data taken from observation and interview were analyzed in order to find out the answer the research questions. It was identified that In Bintang Mandiri the teacher used 1 more teaching writing strategy namely creative writing while in Widyatmika, creative writing was not identified. There were three main problems identified from the teachers. The first was students’ difficulties in expressing ideas in English. The second problem was students’ grammar. The third was students’ passiveness in classroom. Suggestions and implications are furtherly discussed.
EMPIRICAL STUDY ON STUDENT SATISFACTION AS MEDIATOR BETWEEN SERVICE QUALITY AND STUDENT LOYALTY IN TVET HLIs

Recent studies discovered that there is a direct relationship between service quality and loyalty. However, very limited studies in the context of higher TVET and furthermore the investigation of mediation effect between the two constructs. The purpose of this research is to investigate the mediating effect of student satisfaction on the relationship between service quality and student loyalty in the context of higher TVET. This is quantitative method of research applying survey questionnaire for data collection. Based on purposive sampling, 431 questionnaires have been distributed to ten TVET HLIs. 398 of completed questionnaires have been analyzed using Smart PLS 3.0. The PLS-SEM analysis shows that there is a direct and significant relationship between the service quality and student loyalty. Mediating test has been conducted and it is discovered that, there is a partial mediation of student satisfaction between service quality and loyalty. Further research work is suggested to explore more coverage of TVET HLIs in Malaysia for better generalization of the result.
Probabilistic computing based on spintronics technology

There have been increasing demands on realizing computing hardware capable of addressing complex tasks that classical von-Neumann computers cannot readily execute. Here we show an unconventional computing scheme – probabilistic computing – based on a spintronics technology, which is promising to address various computationally hard problems. We present a proof-of-concept of the probabilistic computer with stochastic magnetic tunnel junctions, that can perform optimization problems at room temperature.
Adversarial Learning in the Cyber Security Domain

In recent years, machine learning algorithms, and more specially, deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learning systems are vulnerable to adversarial attacks, and this limits the application of machine learning, especially in non-stationary, adversarial environments, such as the cyber security domain, where actual adversaries (e.g., malware developers) exist. This paper comprehensively summarizes the latest research on adversarial attacks against security solutions that are based on machine learning techniques and presents the risks they pose to cyber security solutions. First, we discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain. Following that, we define a unified taxonomy, where the adversarial attack methods are characterized based on their stage of occurrence, and the attacker's goals and capabilities. Then, we categorize the applications of adversarial attack techniques in the cyber security domain. Finally, we use our taxonomy to shed light on gaps in the cyber security domain that have already been addressed in other adversarial learning domains and discuss their impact on future adversarial learning trends in the cyber security domain.
An elephant herd grey wolf optimization (EHGWO) algorithm for load balancing in cloud

Purpose The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. However, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing (LB) method for allocating tasks to virtual machines (VMs) without influencing system performance. This paper aims to develop a technique for LB in cloud using optimization algorithms. Design/methodology/approach This paper proposes a hybrid optimization technique, named elephant herding-based grey wolf optimizer (EHGWO), in the cloud computing model for LB by determining the optimal VMs for executing the reallocated tasks. The proposed EHGWO is derived by incorporating elephant herding optimization (EHO) in grey wolf optimizer (GWO) such that the tasks are allocated to the VM by eliminating the tasks from overloaded VM by maintaining the system performance. Here, the load of physical machine (PM), capacity and load of VM is computed for deciding whether the LB has to be done or not. Moreover, two pick factors, namely, task pick factor (TPF) and VM pick factor (VPF), are considered for choosing the tasks for reallocating them from overloaded VM to underloaded VM. The proposed EHGWO decides the task to be allocated in the VM based on the newly derived fitness functions. Findings The minimum load and makespan obtained in the existing methods, constraint measure based LB (CMLB), fractional dragonfly based LB algorithm (FDLA), EHO, GWO and proposed EHGWO for the maximum number of VMs is illustrated. The proposed EHGWO attained minimum makespan with value 814,264 ns and minimum load with value 0.0221, respectively. Meanwhile, the makespan values attained by existing CMLB, FDLA, EHO, GWO, are 318,6896 ns, 230,9140 ns, 1,804,851 ns and 1,073,863 ns, respectively. The minimum load values computed by existing methods, CMLB, FDLA, EHO, GWO, are 0.0587, 0.026, 0.0248 and 0.0234. On the other hand, the proposed EHGWO with minimum load value is 0.0221. Hence, the proposed EHGWO attains maximum performance as compared to the existing technique. Originality/value This paper illustrates the proposed LB algorithm using EHGWO in a cloud computing model using two pitch factors, named TPF and VPF. For initiating LB, the tasks assigned to the overloaded VM are reallocated to under loaded VMs. Here, the proposed LB algorithm adapts capacity and loads for the reallocation. Based on TPF and VPF, the tasks are reallocated from VMs using the proposed EHGWO. The proposed EHGWO is developed by integrating EHO and GWO algorithm using a new fitness function formulated by load of VM, migration cost, load of VM, capacity of VM and makespan. The proposed EHGWO is analyzed based on load and makespan.
Investigating the Role of Politeness in Human-Human Online Tutoring

This study aims to investigate the role of politeness in online-tutoring practices by analyzing a large-scale human-human tutorial dialogue dataset. To this end, we employed linguistic theories of politeness to identify the politeness strategies contained in utterances made by tutors and students, and these strategies were further combined to quantify the politeness levels of tutors and students in a tutorial session. The results revealed that tutors had a similar level of politeness at the beginning of all dialogues, while students were more polite at the end if they successfully solved problems.
Defenders Disrupting Adversaries: Framework, Dataset, and Case Studies of Disruptive Counter-Cyber Operations

Over the past two decades, there have been numerous defensive operations to disrupt malicious cyber activity by hacktivists, criminals, and nation-state actors. Disruption operations seek to affect the adversary's decision-making processes and impose additional costs. Such operations include a wide range of actions, from releasing indicators of compromise and naming-and-shaming, to botnet and infrastructure takedowns, to indictments and sanctions, and may be conducted outside of the defender's own network with the intent to interrupt adversary cyber offense and espionage. The United States Department of Defense recently released a new strategy that calls for "persistent engagement" with malicious cyber actors, suggesting many more disruption operations to come.In this paper, we describe a framework for categorizing disruption operations and their effects - along with detailed descriptions for several of these case studies coded to the framework - so that researchers and practitioners can measure their impact using a common terminology. We also provide a unique dataset of over 100 cases of defensive operational disruption over the last 30 years, from 1987 through 2019.We believe that providing a more complete vocabulary for disruptive operations will give analysts and researchers a better opportunity to compare the different types and effects of various disruption operations. Ideally, this will then provide defenders with the information they need to conduct disruption operations at greatest scale, least cost, and with the lowest chance of escalation.
Augmented Reality Virtual House Model Using ARCore Technology Based on Android

Abstract Scale model is a necessary building design in architecture as a way for architects to represent the results of the design as a physical model. The making of scale model requires components that costs some money. With the development of Augmented Reality (AR) technology, designer could bring the model design without having to make a scale model through marker tracking on a brochure. However, with the development of the technology itself, the researcher applies an AR method that can put an object in real environment by detecting flat surfaces. The method is Markerless AR. With this method, the use of marker tracking which requires a marker to remain on the frame of the camera is no longer needed. The technique in the new method will use plane detection that can detect flat surfaces by specifying points on the textures of the frame which is captured by the camera. The necessary stage is to define point cloud that will be used to mark the textures and the plane as a place to put the model in the form of virtual object. Object placed above the plane will remain stable despite having the camera moves around it, the plane will not move around because it is a result of virtual projections. The result of using plane detection will provide a space for the user to observe the model more clearly, such as on a floor or on a table because the model can be rescaled and rotated. Users also can move freely without being hindered by the marker tracking.
Continuous shrinkage prior revisited: a collapsing behavior and remedy

Modern genomic studies are increasingly focused on identifying more and more genes clinically associated with a health response. Commonly used Bayesian shrinkage priors are designed primarily to detect only a handful of signals when the dimension of the predictors is very high. In this article, we investigate the performance of a popular continuous shrinkage prior in the presence of relatively large number of true signals. We draw attention to an undesirable phenomenon; the posterior mean is rendered very close to a null vector, caused by a sharp underestimation of the global-scale parameter. The phenomenon is triggered by the absence of a tail-index controlling mechanism in the Bayesian shrinkage priors. We provide a remedy by developing a global-local-tail shrinkage prior which can automatically learn the tail-index and can provide accurate inference even in the presence of moderately large number of signals. The collapsing behavior of the Horseshoe with its remedy is exemplified in numerical examples and in two gene expression datasets.
Strategic Leadership Practices on Team Effectiveness: The Mediating Effect of Knowledge Sharing in the UAE Municipalities

This study aims to identify the relationships between strategic leadership practices, knowledge sharing, and team effectiveness in the Municipalities in UAE. The present study followed a quantitative research design and used a questionnaire to elicit insight from employees. A total of 500 questionnaires were distributed, and 405 questionnaires were received for analyses following PLS-SEM (Partial Least Squire- Structural Equation Modelling) using Smart PLS (3.2.9). The findings of the study revealed that strategic leadership practices significantly affect knowledge sharing and team effectiveness. Besides, strategic leadership practice effects team effectiveness through knowledge sharing and knowledge sharing significantly affects team effectiveness. The importance of strategic leadership practice and knowledge sharing for team effectiveness showed that teamwork in municipal tend to be pragmatic by focusing the long-term success. Further research recommended finding the factors and conditions that affect team effectiveness within the municipal teamwork.
Riemannian optimization and automatic differentiation for complex quantum architectures

Optimization methods on Riemannian manifolds are a powerful class of optimization methods that allow performing constrained optimization. We apply first-order Riemannian optimization methods to various problems of quantum physics including entanglement renormalization of local many-body Hamiltonians, quantum control, and quantum tomography. We show that the Riemannian optimization forms a new powerful numerical tool for solving different problems of quantum technologies. Besides, we provide a package written on top of TensorFlow for the Riemannian optimization in quantum physics
An Encrypted Speech Retrieval Scheme Based on Long Short-Term Memory Neural Network and Deep Hashing

Due to the explosive growth of multimedia speech data, how to protect the privacy of speech data and how to efficiently retrieve speech data have become a hot spot for researchers in recent years.In this paper, we proposed an encrypted speech retrieval scheme based on long short-term memory (LSTM) neural network and deep hashing.This scheme not only achieves efficient retrieval of massive speech in cloud environment, but also effectively avoids the risk of sensitive information leakage.Firstly, a novel speech encryption algorithm based on 4D quadratic autonomous hyperchaotic system is proposed to realize the privacy and security of speech data in the cloud.Secondly, the integrated LSTM network model and deep hashing algorithm are used to extract high-level features of speech data.It is used to solve the high dimensional and temporality problems of speech data, and increase the retrieval efficiency and retrieval accuracy of the proposed scheme.Finally, the normalized Hamming distance algorithm is used to achieve matching.Compared with the existing algorithms, the proposed scheme has good discrimination and robustness and it has high recall, precision and retrieval efficiency under various content preserving operations.Meanwhile, the proposed speech encryption algorithm has high key space and can effectively resist exhaustive attacks.
Tablet-assisted education incorporating group activities in a university

This paper deals with utilizing tablets for university education incorporating group activities. Ideathons for undergraduates were held in 2017 and 2018 at Ehime University, Japan. When holding an ideathon, we had to meet some requirements such as appropriate and prompt team organization considering interest and characteristics of the students. In order to meet those requirements, tablets were distributed to students and faculty members. The tablets were used for organizing teams, answering a survey, sharing files, and using the Internet. As a result, we received positive feedback from the students in the survey conducted after the ideathon.
HYBRID MACHINE LEARNING BASED FRAMEWORK FOR OUTLIER DETECTION IN HIGH DIMENSIONAL DATA

High dimensional data throws challenges like curse of dimensionality and data imbalance. This often leads to suboptimal performance of algorithms used for outlier detection. The existing methods based on unsupervised and supervised learning have their strengths. For instance, supervised learning can predict outliers based on quality of training while the unsupervised learning methods are good at learning complex patterns. Combining both supervised and unsupervised approaches gain the synergic effect of the both methods. In this paper, we proposed a framework known as Hybrid Machine Learning based Framework for Outlier Detection (HMLF-OD). It has an underlying algorithm named Hybrid Outlier Prediction (HOP). The algorithm combines the strengths of unsupervised outlier and supervised outlier methods. It results in an improved feature space that causes performance improvement. The algorithm is evaluated with seven real-world outlier detection datasets namely Cardio, Letter, Arrhythmia, Mnist, Satellite, Speech and Mammography. TensorFlow is used as backend. The experimental results revealed that the proposed framework outperforms the state of the art.
A Scientific Perspective on Big Data in Earth Observation

The Earth is facing unprecedented climatic, geomorphologic, environmental and anthropogenic changes, which require global-scale observation and monitoring. The interest is in a global understanding involving surveillance of large extended areas over long periods of time, using a broad variety of Earth Observation (EO) sensors, complemented by a multitude of ground-based measurements and additional information. The challenge is the exploration of these data and the timely delivery of focused information and knowledge in a simple understandable format. In this context, the world is witnessing the rise of a new field, EO Big Data, which will revolutionize the perception on our surroundings and provide a new insight and understanding on our planet. The emerging opportunities are amplified by algorithmically and technological breakthroughs successfully demonstrated so far in both EO and non EO domain. In addition, the open science initiatives are propelling the scientific evolution, as presented during the last edition of the Conference on Big data from Space. This new paradigm is forcing a record of innovation in terms of exploitation platforms which enable a virtual open and collaborative environment bringing together EO and non-EO data, dedicated software, ICT resources on one hand and researchers, engineers, end-users, infrastructures and service providers on the other hand. With a pool of resources at hand, the collaboration within the EO community is progressing towards a universal framework to provide consistency to learn generalized models from live archives of Big Data.
MINING OPINION TARGETS AND OPINION WORDS FROM REVIEWS USING NATURAL LANGUAGE PROCESSING (NLP) TECHNIQUES

Since electronic business is ending up being well known reliably, the amount of customer analysis that things get increases at a faster rate considering the way that the web has gotten conventional in each home. For standard things, overviews can show up at an enormous number of people. For producers, there are additional issues considering the way that various broker destinations may sell comparable things and generally creators will make various things. We intend to abbreviate all reviews of things that customers get. Condensing objective,fuses including little anyway unequivocal nuances, for instance, features, points and words that portray the thing. Right now, work is one of a kind according to standard substance overviews since we are simply motivated by the specific pieces of things that customers have commented. Besides, we similarly separate nuances, for instance, positive or negative comments. This assessment will allow producers to adjust things and dispatch new things with better buying possibilities. We don't condense suppositions by picking or making a subset out of the principal sentences from reviews to get the focal issues as in the extraordinary substance outline. Our rule place is to inspect the evaluations of the thing incorporates that the expert has commented on. There are various frameworks to present such features. At the present time, plan to tunnel and abbreviate all customer reviews of the thing. We proposed to develop a mutt approach that will allow creators to utilize the studies and appraisal sof the customer and help in analyzing and improving the thing. We will use unmistakable evaluative assessments, for instance, precision, audit,f-measure to perceive the key document parameters and capability of our system.
A secure communication approach in OFDM using optimized interesting region-based video watermarking

Purpose The technique for hiding confidential data in specific digital media by enhancing the graphical contents is known as watermarking. The dissemination of information over a secure channel is essential for multimedia applications. The purpose of this study is to develop a secure communication approach for OFDM system. Design/methodology/approach This paper exploits a secure communication in the orthogonal frequency division multiplexing (OFDM) system using wavelet-based video watermarking technique. In this work, the Chronological-MS algorithm is used for securing the data communication in the OFDM system. Here, the secret message is embedded in video frames using wavelet transform for hiding sensitive information and the hidden information is transmitted over the OFDM system. The Chronological-MS algorithm is used for selecting the optimal regions in the video for embedding secret message. In embedding phase, wavelet coefficients are obtained by applying wavelet transform on the frame for embedding the secret message. Meanwhile, in extraction phase, the inverse wavelet transform is applied to extract the secret message. Findings Considering number of frames, the maximum Peak signal-to-noise ratio (PSNR) value is attained by proposed Wavelet + Chronological MS method for Video 2 with value 73.643 dB, respectively. Meanwhile, the minimum mean squared error (MSE) attained by the proposed Wavelet + Chronological MS method is when considering number of frames with MSE values as 0.001 for both Videos 1 and 2. The minimum bit error rate (BER) value is attained by the proposed method with value 0.00009 considering random noise with Video 1. Thus, the proposed Wavelet + Chronological MS have shown better results than the existing techniques. Originality/value This work proposes a wavelet-based watermarking method using Chronological-MS, for initiating secured communication over an OFDM. One of the main advantages of wavelets is that they offer a simultaneous localization in time and frequency domain. Hence, the proposed method offers the highly secured data transmission over the OFDM.