Reviewerly/exHarmony huggingface dataset:
Exploring the Antecedents of Mobile Application Usage in the Context of English Learning

With the popularization of wireless network and mobile technology, integration of technology into teaching has gradually become indispensable in teaching and learning. In order to probe into the factors influencing learners' continued use of English Learning Apps (ELAPPs), this study uses the Expectation-Confirmation Theory (practical viewpoint) as the theoretical basis for development of research model and combines the Flow Theory (hedonic viewpoint) to propose an integrated research model, thereby exploring the factors that influence learners' continued use of ELAPPs and explaining why people continued useELAPPs.In this study, an online questionnaire was conducted.A total of 289 valid samples were collected and analyzed using a structural equation modeling (SEM) approach.The results of the study show that user satisfaction, perceived usefulness, and flow experience play a significant role in the learners' continued use of ELAPP services.Among others, the flow experience is an important factor that influences the learner's continued use of ELAPPs.In addition, the study also found that perceived skills and perceived challenges have a positive impact on learners' flow experience, showing that in the context of mobile learning-oriented information systems, learners generate flow perception only when they are able to challenge the use of the content of teaching materials in the learning process.The implications of these findings are discussed.
Developing Deep Learning Models to Simulate Human Declarative Episodic Memory Storage

Human like visual and auditory sensory devices became very popular in recent years through the work of deep learning models that incorporate aspects of brain processing such as edge and line detectors found in the visual cortex.However, very little work has been done on the human memory, and thus our aim is to model human long-term declarative episodic memory storage using deep learning methods.An innovative way of deep neural network was created on supervised feature learning dataset such as MNIST to achieve high accuracy as well as storing the models hidden layers for future extraction.Convolutional Neural Network (CNN) learning models with transfer learning models were trained to imitate the long-term declarative episodic memory storage of human.A Recurrent Neural Network (RNN) in the form of Long Short Term Memory (LSTM) model was assembled in layers and then trained and evaluated.A Variational Autoencoder was also used for training and evaluation to mimic the human memory model.Frameworks were constructed using TensorFlow for training and testing the deep learning models.
Implementation of Multi-Agent based Digital Rights Management System for Distance Education (DRMSDE) using JADE

The main objective of Distance Education (DE) is to spread quality education regardless of time and space.This objective is easily achieved with the help of technology.With the development of World Wide Web and high-speed internet the quality of DE is improved because now Digital Content (DC) can be easily and in no time distributed to many learners of different locations in text, audio and video formats.But, the main obstacle in digital publishing is the protection of Intellectual Property Rights (IPR) of DC.Digital Rights Management (DRM) that manages rights over any digital creation is the only solution to this problem.In this paper, we have made an attempt to implement a Digital Rights Management System for Distance Education known as DRMSDE.We have identified that Multi-Agent System (MAS) based technology is very popular for such type of implementations.Keeping that in mind, we have chosen one of the most popular Multi-Agent based tools, namely JAVA Agent Development Framework (JADE), for our system.This paper presents an overview and the system architecture for the proposed implementation.
Living with volcano hazards

First posted April 5, 2019 For additional information, contact: Contact InformationVolcano Science Center - Menlo ParkU.S. Geological Survey345 Middlefield Road, MS 910Menlo Park, CA 94025 Volcanic eruptions are among Earth’s most dramatic and powerful agents of change. Ash, mudflows, and lava flows can devastate communities near volcanoes and cause havoc in areas far downwind, downstream, and downslope. Even when a volcano is quiet, steep volcanic slopes can collapse to become landslides, and large rocks can be hurled by powerful steam blasts. Hazardous volcanic conditions might last for a day or decades, all the while threatening people’s health and safety. Scientists with the U.S. Geological Survey and partner agencies assess hazards and closely monitor activity at the Nation’s volcanoes. They provide volcano updates and warnings of hazardous situations, as well as guidance on actions to take. You can prepare your family and community by familiarizing yourself with the types of hazards at volcanoes near where you live and visit.
Synchronous automatic training for wearable sensors via knowledge distillation

Since wearable sensors often have high substitutability, it is imperative that newly added wearable sensors have automatic training capabilities. In this paper, we propose a novel automatic model training method called Synchronous Automatic Training (SAT) for wearable sensors, which adopts a knowledge distillation technique to fully exploit the knowledge of the existing trained sensors and a synchronous mechanism to ensure the knowledge is transferred to the untrained sensors to assist the automatic model construction. The automatically trained model by using our proposed SAT method has higher accuracy than existing methods.
ARDL Method: Forecasting Data Kemiskinan di NTB

Abstrak: Penelitian ini bertujuan untuk memprediksi data Jumlah penduduk miskin di Provinsi Nusa Tenggara Barat (NTB) tahun 2019 dengan menggunakan metode Autoregressive Distributed Lag (ARDL). Jenis penelitian ini merupakan penelitian kuantitatif. Data yang digunakan dari tahun 2002-2018, dengan parameter error MAD, MSE, MRSE dan MAPE. Berdasarkan hasil simulasi data diperoleh hasil prediksi tahun 2019 jumlah penduduk miskin di NTB sebesar 718.059 jiwa, dengan nilai MAD sebesar 4.040,26667, MSE sebesar 1.943.057.717, MRSE sebesar 44.080,1284 dan MAPE sebesar 3%.&#x0D; &#x0D; Abstract: This study aims to predict data on the number of poor people in the Province of West Nusa Tenggara (NTB) in 2019 by using the Autoregressive Distributed Lag (ARDL) method. This type of research is quantitative research. Data used from 2002-2018. With MAD, MSE, MRSE and MAPE error parameters. Based on the data simulation results obtained in 2019 prediction results the number of poor people at NTB is 718,059 people, with MAD value of 4,040,26667, MSE of 1,943,057,717, MRSE of 44,080,1284 and MAPE of 3%.
ND-GiST: A Novel Method for Disk-Resident k-mer Indexing

Several challenges are related to metagenomics, one of which is the data management. A related central concept is k-mer which means a possible subsequence of length k from a DNA (sub)sequence. In this work, the focus is on indexing k-mers and supporting box queries where a query string of length k might have multiple allowed nucleobases per position. A novel index structure: ND-GiST is introduced which has capability to handle box queries. Comparing it with full table scan and the traditional B-tree, the performance results of ND-GiST are encouraging.
Introduction to the Minitrack on Data-driven Government: Creating value from Big and Open Linked Data

The collection of more and more data results in the rise of a data-driven government.Although traditional administrative and policy-making process are data intensive, only recently the ability to collect and process big and open linked data (BOLD) at a large scale has resulted in the possibility to create a datadriven government.Data-driven government refers to sensing and the subsequent collecting and processing of data for operational decision-making or policymaking.There is a need for multidisciplinary research to expand our knowledge of data-driven approaches.
Blockchain technologies for leveraging security and privacy

The contemporary internet has developed into a complex ecosystem involving humans, services, applications, machines and applications that interact exchanging information, ranging from e-mail messages and social media content to crowdsourcing data and videoconferencing. In this context, a number of security threats such as viruses and malware exist, while additionally the users’ privacy is jeopardized by threats such as personal data leakage, usage pattern monitoring, and so forth. The IoT trend renders the Internet ecosystem even more complex, by adding a rich set of services, applications and machines, many of them backed by new user roles; these elements are weaved into everyday life and industry alike. This increases both the number of opportunities available to threat agents for exploitation and the volume and value of the underlying infrastructure and data, increasing thus the user risk level. In this paper, we explore how the Blockchain technology can be used to leverage security and privacy in the modern Internet, both by providing underpinnings for preventive measures and by facilitating digital forensic evidence collection storage, safeguarding and controlled access.
Personal Health Technology: CPN based Modeling of Coordinated Neighborhood Care Environments (Hubs) and Personal Care Device Ecosystems

Healthcare supported by mobile devices, or "mHealth," has rapidly emerged as a very broad ecosystem that can empower safer, more affordable, and more comfortable independent living environments and assist residents to age in place with a variety of well-understood chronic diseases.mHealth ecosystems leverage every available type of regulated medical and consumer-grade Patient Care Devices (or PCDs).mHealth technologies can also support innovative care and reimbursement models like the Patient-Centered Medical Home (PCMH) and Accountable Care Organizations (ACOs).Although consumer-grade PCDs are becoming ubiquitous, they typically do not provide a large variety of integrated system options for care coordination beyond single individuals.Understanding how to safely implement and use those devices to support heterogeneous mixes of patients, illnesses, devices, medications, and situations in neighborhood contexts is still a case-bycase challenge.By utilizing a well-formalized Colored Petri Nets (CPNs) based approach, this paper provides a proof-of-concept simulation framework for modeling and designing coordinated community care hubs.
Location Privacy Protection Systems in Presence of Service Quality and Energy Constraints

The wide-ranging application of location-based services (LBSs) through the use of mobile devices and wireless networks has brought about many critical privacy challenges. To preserve the location privacy of users, most existing location privacy-preserving mechanisms (LPPMs) modify their real locations associated with different pseudonyms, which come at a cost either in terms of resource consumption or quality of service, or both. However, we observed that the effect of resource consumption has not been discussed in existing studies. In this paper, we present the user-centric LPPMs against location inference attacks under the consideration of both service quality and energy constraints. Moreover, we modeled the precision-based and dummy-based mechanisms in the context of an existing LPPM framework, and also extended the linear program solutions applicable to them. This study allowed us to specify the LPPMs that decreased the precision of exposed locations or generated dummy locations of the users. Based on this, we evaluated the privacy protection effects of optimal location obfuscation function against an adversary's inference attack function using real mobility datasets. The results indicate that dummy-based mechanisms provide better achievable location privacy under a given combination of service quality and energy constraints, and once a certain level of privacy is reached, both the precision-based and dummy-based mechanisms only perturb the exposed locations. The evaluation results also contribute to a better understanding for the LPPM design strategies and evaluation mechanism as far as the system resource utilization and service quality requirements are concerned.
A Design of English Immersion Camp for Supplementing English Teaching and Learning in Indonesia

The primary purpose of this study is to design a supplementary English learning program for English students in Muhammadiyah University of North Maluku called UMMU English Immersion Camp (UEIC). Three kinds of instruments were used to gather the data, which encompassed open-ended questionnaire, UEIC observations, and interviews. To design the program, data from questionnaire were obtained from the participants which was part of Needs Analysis before conducting the UEIC. Observations and Interviews were done during the UEIC. The Results conveyed that The UEIC participants needed English learning programs that can develop and improve their ability to communicate in English confidently and in the right way. The UEIC design tried to cover the participants’ needs by fomulating an English learning program where the goal was “to develop participants’ communicative competence”. The objectives were (1) to provide English environment where participants can practice their English ability in context, (2) to trigger participants to be more active and be confident in expressing their thought using English, and (3) to create participants’ togetherness and stimulate participants ability to work in group.
Chemical Speciation and Fractionation Study of Heavy Metals in Top Sediment Deposit of Owena River, Nigeria

This study investigates the chemical speciation and fractionation of heavy metals (Cd, Cr, Cu, Ni and Pb) in relation to their levels of pollution from six different locations along the course of Owena River during the dry and wet season of 2015. The top sediment deposit was subjected to sequential extraction and the heavy metals were identified and quantified using atomic absorption spectrophotometer. The concentration of the analyzed heavy metals in the top sediment deposit were found to occur below the maximum permissible limit of the Dutch reference standard for soil/sediment, thereby the sediment collected from Owena river is not contaminated with the analyzed heavy metals. The overall results of this research suggest that the mobility and bioavailability of heavy metals in sediment collected from Owena River decreases in the following order: Cu &gt; Pb &gt; Cr &gt; Cd &gt; Ni.
Short Text Classification Improved by Feature Space Extension

Abstract With the explosive development of mobile Internet, short text has been applied extensively. The difference between classifying short text and long documents is that short text is of shortness and sparsity. Thus, it is challenging to deal with short text classification owing to its less semantic information. In this paper, we propose a novel topic-based convolutional neural network (TB-CNN) based on Latent Dirichlet Allocation (LDA) model and convolutional neural network. Comparing to traditional CNN methods, TB-CNN generates topic words with LDA model to reduce the sparseness and combines the embedding vectors of topic words and input words to extend feature space of short text. The validation results on IMDB movie review dataset show the improvement and effectiveness of TB-CNN.
Promoting mobile collaborative language learning in Islamic higher education

&lt;span lang="EN-US"&gt;This study sought to explore students’ experience in using Instagram to participate in mobile collaborative language learning (MCLL)&lt;/span&gt;&lt;span lang="EN-US"&gt;. T&lt;/span&gt;&lt;span lang="EN-US"&gt;he&lt;/span&gt;&lt;span lang="EN-US"&gt; contents they &lt;/span&gt;&lt;span lang="EN-US"&gt;produced and shared, a&lt;/span&gt;&lt;span lang="EN-US"&gt;s well as&lt;/span&gt;&lt;span lang="EN-US"&gt; their interaction and collaboratio&lt;/span&gt;&lt;span lang="EN-US"&gt;n, were analyzed&lt;/span&gt;&lt;span lang="EN-US"&gt;. The participants of this study were &lt;/span&gt;&lt;span lang="EN-US"&gt;110 students in an &lt;/span&gt;&lt;span lang="EN-US"&gt;English education department&lt;/span&gt;&lt;span lang="EN-US"&gt;, in three different classes,&lt;/span&gt;&lt;span lang="EN-US"&gt;who all had taken a &lt;/span&gt;&lt;span lang="EN-US"&gt;translation course&lt;/span&gt;&lt;span lang="EN-US"&gt;their third semester. Online tasks were assigned to students and they were required to produce, share, interact and collaborate to accomplish tasks. This study applied &lt;/span&gt;&lt;span lang="EN-US"&gt;a &lt;/span&gt;&lt;span lang="EN-US"&gt;qualitative method &lt;/span&gt;&lt;span&gt;with case study research design &lt;/span&gt;&lt;span lang="EN-US"&gt;using observation, focus group discussion and content analysis. Data were analyzed through stages namely categorization, reduction and interpretation. &lt;/span&gt;&lt;span&gt;Results showed &lt;/span&gt;&lt;span lang="EN-US"&gt;that &lt;/span&gt;&lt;span&gt;the &lt;/span&gt;&lt;span lang="EN-US"&gt;students were able to participate in MCLL using Instagram. They produced and shared contents that met the requirement of MCLL. The contents include&lt;/span&gt;&lt;span lang="EN-US"&gt;d&lt;/span&gt;&lt;span lang="EN-US"&gt; English sentences for structural collaboration in terms of subject-verb agreement and English sentences for word meaning collaborations. They interacted with their peer&lt;/span&gt;&lt;span lang="EN-US"&gt;s&lt;/span&gt;&lt;span lang="EN-US"&gt; by writing feedback and comment&lt;/span&gt;&lt;span lang="EN-US"&gt;s on the uploaded content&lt;/span&gt;&lt;span lang="EN-US"&gt;. They collaborated with their peer&lt;/span&gt;&lt;span lang="EN-US"&gt;s&lt;/span&gt;&lt;span lang="EN-US"&gt; by providing multiple interactions to accomplish tasks in MCLL like writing structural analysis, word meaning, paraphrasing sentences, and sentence translation.&lt;/span&gt;
La ineptitud del digital academic: precariedad y salud en el mundo universitario

Inspirado por el trabajo de Deborah Lupton, Inger Mewburn y Pat Thomson, The Digital Academic: Critical Perspectives on Digital Technologies in Higher Education (2017), este artículo examina el yo digital académico contemporáneo. Más allá de la utilidad que pueden tener en cuanto a la interacción, plataformas digitales como Academia.edu, Linkedin, Google Scholar, etc. transforman al académico o académica en un sujeto digital cuyo rendimiento se controla constantemente hasta convertirlo en prisionero de una creciente dataveillance, una vigilancia a través de los datos. En este contexto, analizamos el impacto de los mencionados procesos de monitorización continua del rendimiento y la productividad sobre las condiciones de vida y trabajo de los profesionales universitarios contemporáneos, en un momento en el que las tensiones, los conflictos y el malestar que habitan la universidad neoliberal parecen volverse cada vez más incapacitantes.Drawing inspiration from the work of Deborah Lupton, Inger Mewburn and Pat Thomson, The Digital Academic: Critical Perspectives on Digital Technologies in Higher Education (2017), this paper looks at the digital self of contemporary academics. Using digital platforms such as Academia.edu, Linkedin, Googlescholar, digital academics can increase their daily interactions and the impact of their research. More than being interaction tools, these tools transform the academics into digital individuals whose performance is constantly monitored until they find themselves prisoner of a growing «dataveillance» (Lupton, Mewburn and Thomson, 2017). In this context, the question concerns not only the impact of digital technologies on the quality of contemporary research, but also the impact of digital surveillance on the living and working conditions of contemporary academics, at a time where the tensions, conflicts and malaise that inhabit the neoliberal university have become ever more manifest.
Development of Talent Model based on Publication Performance using Apriori Technique

The main problem or challenge faced by Human Resource Management (HRM) is to recognize, develop and manage talent efficiently and effectively.This is because HRM is responsible for selecting the correct talent for suitable positions at the right time, aligned with their existing qualifications, talents and achievements.Furthermore, the decision in identifying talent for a position must be fair, truthful and appropriate.In the academic field, publication is a core component in the evaluation of academic talent that is affected by research, supervision and conference.Therefore, this study proposed an academic talent model based on publication factor using the Apriori technique for the purpose of promotion.This study applies the Apriori based Association Rules algorithm to identify a set of meaningful rules for the assessment of significant relevant talents for the promotion of academic staff in local universities.The findings have successfully developed a model based on talent acquisition of knowledge related to the issuance and have been evaluated by comparing the guidelines for the promotion of academic experts.This knowledge helps to improve the quality of the evaluation process of academic talent management and future planning in HRM.
Maze Terrain Authoring System in Immersive Virtual Reality for New Visual Realism

This paper proposes a maze terrain authoring system with which a user can automatically calculate various and complex maze patterns to compose maze terrains in an easier and more intuitive structure. Using the maze terrain information calculated by using the proposed authoring system, a 3D maze terrain is generated quickly and effectively, and through this, the user’s visual realism in an immersive virtual reality is increased to provide a new presence. The proposed maze terrain authoring system consists of three core functions: a function that automatically generates a grid maze of various sizes and patterns based on a maze generation algorithm; a function that calculates a circular maze in an intuitive structure; and a function that transforms a sketch-based maze by using an image-processing algorithm. Through the authoring system consisting of these functions, various maze terrains from uniform mazes to irregular mazes can be generated effectively. This study produces maze terrains of various concepts directly by using maze information calculated through the authoring system. In addition, through experiments, this study analyzes the efficiency in an immersive virtual reality and the presence through visual realism. In this process, the suitability of the authoring system is also analyzed in combination with a survey.