Reviewerly/exHarmony huggingface dataset:
The Development of E-Learning Teaching Material Based on Edmodo on Basic Competencies of National Integration at Class X of Senior High School

This research aims to determine (1) the design of E-learning teaching materials, (2) steps for using Edmodo-based learning materials (3) student learning behaviour in learning using Edmodo-based E-learning. The development model used is a procedural model adapted from the instructional design development model according to Dick &amp;amp; Carey while the development research model used is in accordance with Borg &amp;amp; Gall. The instrument used was in the form of a questionnaire instrument, interview guidelines and observation guidelines (observations). The data analysis used in this study is qualitative descriptive statistics. Noting the validation of experts and the results of trials in the field, it can be concluded that: (1) Edmodo-based E-learning teaching materials on Basic Competence (KD) of National Integration are appropriate to use for Class X (Grade 10&lt;sup&gt;th&lt;/sup&gt;) SMA PPKn subjects; (2) Clarity of instructions for using E-learning teaching materials is considered good; (3) Changes in student learning behaviour are students who look very enthusiastic about learning by using E-learning teaching materials; (4) Edmodo-based E-learning materials are considered effective in achieving the learning objectives of the PPKn; (5) Edmodo-based E-learning teaching materials have a high time and place efficiency with regard to the limitations of available teaching materials; (6) Edmodo-based E-learning teaching material is a strategy for delivering messages that have high competitiveness. For research on the development of E-learning teaching materials based on Edmodo that will come to be more refined and better in order to support the smooth learning process.
Multiscale Fusion Method for the Enhancement of Low-Light Underwater Images

To solve the color cast and low contrast of underwater images caused by the effects of light absorption and scattering, we propose a novel underwater image enhancement method via bi-interval histogram equalization. The proposed method consists of three main parts: color correction, contrast enhancement, and multiscale fusion. First, the color cast is eliminated by automatic white balancing. Then, homomorphic filtering is adopted to decompose the image into high-frequency information and low-frequency information, the high-frequency information is enhanced by the gradient field bi-interval equalization which enhances the contrast and details of the image, and the low-frequency information is disposed via gamma correction for adjusting the exposure. Finally, we adopt a multiscale fusion strategy to fuse the high-frequency information, high-frequency after bi-interval equalization, and low-frequency information based on contrast, saturation, and exposure. Qualitative and quantitative performance evaluations demonstrate that the proposed method can effectively enhance the details and global contrast of the image and achieve better exposedness of the dark areas, which outperforms several state-of-the-art methods.
Foams of togetherness in the digital age: Sloterdijk, software sorting and Foursquare

Abstract. The article brings together Peter Sloterdijk's theory of spheres and literatures on the socio-spatial implications of the functioning of software. By examining the growing personalization of search results for recreational places on spatial media like Foursquare, we make the case for Sloterdijk's conceptualization of “foam” offering an interesting contribution to the analysis and critique of contemporary algorithmic life, in particular with regard to the liquidity and fragility of the forms of togetherness in “co-isolation” created by such applications. In emphasizing the impermanence and ambiguities of these fleeting mediations, the article also points to the politics of these algorithmic foams, whose logics of categorization and socio-spatial sorting become increasingly difficult to understand, politically address or challenge.
POLA HUJAN DI BAGIAN HULU DAERAH ALIRAN SUNGAI BENGAWAN SOLO DALAM PERENCANAAN PEMANFAATAN SUMBER DAYA AIR (Rainfall pattern for water resources utilization planning in the upperstream of Bengawan Solo Watershed)

Information about rainfall fluctuations is essential especially for local people who are still depend on natural resources.This study aims to analyze rainfall pattern in the upper stream of Bengawan Solo Watershed.This information can be used as a basis for water resources utilization planning.We analyze the rainfall data from 14 rain gauge stations descriptively to determine rainfall fluctuations and seasonal shifts.Annual rainfall in the upper stream of Bengawan Solo Watershed varies between 1,433.5 mm to 3,231.2 mm with an average of 2,224.6 mm.The beginning of the rainy or dry seasons did not change, however, the duration of the rainy season increased from 7 months (October-April) in 1990-1998 and 1999-2007 to 8 months (October-May) in 2008-2016.About 90% of rainfall was concentrated in the rainy season.Rainwater harvesting should be done to reduce runoff in the rainy season as well as efforts to provide water resources in the dry season.
English communication needs in Indonesian university

This paper explores the communication needs of students of English Department, Universitas Muhammadiyah Malang (ED UMM) as well as investigates the obstacles in fulfilling their needs. Collecting and analysing data on needs allows teachers to analyse the gap between what has been done and what needs to be done. Accordingly, a Needs Analysis can support the development and implementation of language policies at both macro and micro levels, with far-reaching consequences. The research participants were ED UMM students who are still attending speaking subjects and those who have already completed the speaking subjects. Two stages of data collections: Focus Group Discussion (FGD) and Questionnaire were employed. In FGD, the participants were selected purposively whereas convenience sampling was employed to get the participants for questionnaires. As many as 475 students or equal to 60 % of ED students enrolled at English Department filled in the questionnaires. The findings indicate that to be able to speak in English was students first priority skill to master due to several reasons. To achieve the goals, improving teaching capacity and evaluating institutional policies are required.
The Development of an Online Platform for Studying Ukrainian Literature

The authors of this article adduce facts according to which the development of an online platform and an electronic book for the study of Ukrainian literature by secondary school students is relevant. We described the structure and basic functionality of the process. The features and advantages of e-learning resources have been identified. The quantitative status of the development of e-books in Ukraine has been analyzed. The lack of ebook regarding Ukrainian literature for secondary school is indicated. It is reported that the developed online platform operates in three modes. The e-book of Ukrainian literature contains fiction texts, analysis of fiction texts, presentations, video and audio files for fiction texts, control unit. The results of the students' surveys regarding the use of the online platform in the educational process have been presented.
Piecewise Linear Representation of Finance Time Series: Quantum Mechanical Tool

In this work, a piecewise linear representation of economic time series is presented.The representation is based on calculating local maxima and minima in time series.Then, we study the localization properties of the 1D tight-binding equation, where the on-site potential is replaced by a piecewise linear function.The piecewise linear representation is derived from economic time series databases.We carry out numerical work involving direct diagonalization to study eigenvalues of the system.This numerical scheme is applied to different segments of the time series.It is shown that this mathematical tool could be used as a moving indicator to study finance charts.
Towards a knowledge graph for open healthcare data

Open Government data present valuable knowledge that supports innovation and value creation.Despite the efforts deployed by different countries to open government data, their usage and exploitation remain limited due to several challenges such as data discoverability, processability, and integration.Used by major search engines such as Google, Knowledge graphs have emerged as a potential supportive technology that can handle the OGD usage challengeS.In this, work we propose to construct the KG4OGD, a knowledge graph that drives the transformation of open government raw data to valuable knowledge.To support the construction of the KG4OGD, we propose a knowledge representation model for both OGD Metadata and content as a schema model for the KG4OGD.The model enhances the descriptive and contextual dataset's background and aims to improve the KG coherence and consistency.We implemented our model for the public healthcare domain as a high interest has arisen toward this domain due to the international pandemic context.
A Study on the Correlation among Social Interaction, Career Self-Efficacy and Well-Being of Employees in Rural Tourism

Rural areas gradually become modern people in cities taking good care mentally and physically due to the pressure in life or the memory of past life in rural areas.Rural tourism and vacation in rural areas are therefore gradually preferred by city people.With the eff ect of fi nancial crises in past years, a lot of people become dissatisfi ed with the life.Well-being would have a person present joy, content, and rich psychological feelings.Countries in the world expect to create well-being cities for the people's well-being.Aiming at rural tourism employees in Fujian Province, total 380 copies of questionnaire are distributed, and 287 valid copies are retrieved, with the retrieval rate 76%.The research results are expected to provide reference for the development of sustainable rural tourism of governmental departments and businesses to achieve the sustainable development of life, production, and ecology in rural areas.
Deep Learning with Recursive Neural Network for Temporal Logic Implementation

Stock prediction in financial market is one of the exciting applications of deep learning (DL).Stock values fluctuate in accordance with time and hence suitability of Recursive Neural Network (RNN) as one of the model for prediction of stocks is a niche choice as a predictor.The temporal pattern of financial market is investigated with plain RNN and also with Long Short Term Memory (LSTM).In contrast to the classical time series forecasting, the widespread use of deep learning network and algorithms using temporal statistical relations built upon RNN have been finding increasing applications in time series domain analysis and provide a better yield in terms of performance.We have implemented temporal logic using Keras framework and show the results on the sequential data sets of stock prediction market.Our main focus is solely on stock prediction using time series forecasting, however, it can be extended to risk assessment, portfolio management etc.The imperative is to use this as a basis model for further investigation and explore additionally with trend forecasting, crypto currency forecasting and many more.Although this area is matured enough, with machine learning techniques, it provides tremendous opportunities for further investigations and improvements thereon.
Exploitation vs. Prevention: The Ongoing Saga of Software Vulnerabilities

Online IT systems are frequently exposed to cyber-attacks.An Exploit is an advanced attack tool that takes advantage of some software vulnerability to attack and cause harm to IT infrastructures.Developers and manufacturers of operating systems and hardware put huge effort into the prevention of vulnerability exploitation (e.g.Data Execution Prevention, Control Flow Integrity, etc.).However, the number and severity of attacks show that new exploit methods are continuously being invented despite the increasingly sophisticated protection methods.The present article summarizes the current, known and most relevant software vulnerability exploitation methods, as well as, the possible methods used to protect against these exploits.Moreover, the effectiveness of both the exploitation and prevention methods (as seen from both the attacker's and the defender's sides) is analyzed to find a possible future direction, to eliminate exploit attacks against an IT infrastructure.
A dynamic semantics of single-wh and multiple-wh questions

We develop a uniform analysis of single-wh and multiple-wh questions couched in dynamic inquisitive semantics. The analysis captures the effects of number marking on which-phrases, and derives both mention-some and mention-all readings as well as an often neglected partial mention-some reading in multiple-wh questions.
Smart learning environments in a contemporary museum: a case study

Aim. The modern museum becomes an attractive learning place and space where the visitor, depending on age and competence, develops personal experience, and constructs the learning process based on personalized goals. The article aims to reveal how spaces in museums are exploited, in what ways visitors are involved in a narrative that connects the present and the past.&#x0D; Concept. The research uses a case-study method to investigate the POLIN Museum of the History of Polish Jews (Poland), Ruhr Museum (Germany), and Vienna Technical Museum (Austria). Within the smart learning environment context, this study explains how to encourage museum visitors to learn and seek answers.&#x0D; Results and conclusion. Four main directions are emphasized: the construction of a narrative through the creation of spaces and places, the creation of a historical narrative through simulacra, the educational effect of smart solutions, and the edutainment. The findings show that change in the museum by combining design solutions, historical narrative, time experience, and smart technologies leads to cognitive, engaging learning, touching, feeling, and experiencing different emotions, encouraging a return to the museum, inviting to learn, and shaping one's personal experience.&#x0D; Cognitive value. Contemporary museums invite visitors to a new experience combining artistic space design, storytelling, individual time management, and the use of smart learning environments. These challenges are shifting museum narratives and influencing non-formal learning programs. Authors raise a discussion of how, by exploiting museum spaces, the visitors are involved in the stories, and how the smart learning environment is created in a modern museum.
Multi-Layered Multimodal Biometric Authentication for Smartphone Devices

As technological advances in smartphone domain increase, so are the issues that pertain to security and privacy. In current literature, multimodal biometric approach is addressed at length for purpose of improving secured access into personal devices. Moreover, most of the financial institutions such as banks, etc. enforce two or three step access into their corporate data to enforce security. However, personal devices currently do not support similar applications or way of enforcing multilayered access to its different domains/regions of data. In this paper, a multilayered multimodal biometric approach using three biometric methods (such as finger print, face and voice) is proposed for smartphones. It is shown that fusion of biometric methods can be layered to enforce secured access to private data on smartphone. The experimental results are presented.
High Performance Computing using Containers in Cloud

Virtualization is playing as a core component in cloud computing but performance overhead impact in its one of layer is forcing the researchers to think its usage in research areas with cloud for high performance computing.Where containers usage based on operating system virtualization widespread now a days is handling the performance impact via facilitating one of the lightweight layers of virtualization in terms of computing resources such as network bandwidth, throughput, CPU, memory at a large extent.Docker is one of the popular containerized platforms to be used in cloud computing and high-performance computing research areas.In this paper, we are proposing a basic strategy to avoid the performance impact and feasibility using Docker engine along with its evaluation in terms of containers vs virtual machine.Later, we have described the containerized placement now a days and performance specific problems resolution using container images for the development and deployment of HPC applications.The results and related work are focusing on a scalable approach in terms of availability and portability.
Game Level Clustering and Generation Using Gaussian Mixture VAEs

Variational autoencoders (VAEs) have been shown to be able to generate game levels but require manual exploration of the learned latent space to generate outputs with desired attributes. While conditional VAEs address this by allowing generation to be conditioned on labels, such labels have to be provided during training and thus require prior knowledge which may not always be available. In this paper, we apply Gaussian Mixture VAEs (GMVAEs), a variant of the VAE which imposes a mixture of Gaussians (GM) on the latent space, unlike regular VAEs which impose a unimodal Gaussian. This allows GMVAEs to cluster levels in an unsupervised manner using the components of the GM and then generate new levels using the learned components. We demonstrate our approach with levels from Super Mario Bros., Kid Icarus and Mega Man. Our results show that the learned components discover and cluster level structures and patterns and can be used to generate levels with desired characteristics.
Distributed Min–Max Learning Scheme for Neural Networks With Applications to High-Dimensional Classification

In this article, a novel learning methodology is introduced for the problem of classification in the context of high-dimensional data. In particular, the challenges introduced by high-dimensional data sets are addressed by formulating a L1 regularized zero-sum game where optimal sparsity is estimated through a two-player game between the penalty coefficients/sparsity parameters and the deep neural network weights. In order to solve this game, a distributed learning methodology is proposed where additional variables are utilized to derive layerwise cost functions. Finally, an alternating minimization approach developed to solve the problem where the Nash solution provides optimal sparsity and compensation through the classifier. The proposed learning approach is implemented in a parallel and distributed environment through a novel computational algorithm. The efficiency of the approach is demonstrated both theoretically and empirically with nine data sets.
Focus areas for data acquisition for potential domestic resources of 11 critical minerals in the conterminous United States, Hawaii, and Puerto Rico—Aluminum, cobalt, graphite, lithium, niobium, platinum-group elements, rare earth elements, tantalum, tin, titanium, and tungsten

First posted September 18, 2020 For additional information, contact: Coordinator, Mineral Resources ProgramU.S. Geological Survey913 National CenterReston, VA 20192 In response to a need for information on potential domestic sources of critical minerals, the Earth Mapping Resources Initiative (Earth MRI) was established to identify and prioritize areas for acquisition of new geologic mapping, geophysical data, and elevation data to improve our knowledge of the geologic framework of the United States. Phase 1 of Earth MRI concentrated on those geologic terranes favorable for hosting the rare earth elements (REEs). Phase 2 continued to address the REEs and also identified focus areas for potential domestic sources of 10 more of the 35 critical minerals on the U.S. critical minerals list (aluminum, cobalt, graphite, lithium, niobium, platinum-group elements, tantalum, tin, titanium, tungsten). This report describes the methodology, data sources, and summary results for mineral systems that host these 11 critical minerals in the conterminous United States, Hawaii, and Puerto Rico; Alaska is covered in a separate report. The mineral systems framework adopted for this study links critical mineral commodities to families of genetically related mineral deposit types. The mineral systems approach is an efficient approach, providing a simultaneous evaluation of geologic terranes through aggregation of genetically related mineral deposit types that are much larger than individual ore deposits. Geologic, geochemical, topographic, and geophysical mapping provided by Earth MRI will document geologic features that reflect the extent of individual mineral systems and provide information about critical mineral deposits that may not have been recognized previously.Each critical mineral commodity is discussed in terms of importance to the Nation’s economy, modes of occurrence, mineral systems, and deposit types along with maps and tables listing examples of focus areas for each critical mineral. Important mineral systems for these critical minerals include chemical weathering systems for aluminum (bauxite); placer systems for titanium and REEs; metamorphic systems for graphite; mafic magmatic systems for platinum-group elements and cobalt; lacustrine evaporite and porphyry tin systems for lithium; and copper-molybdenum-gold (Cu-Mo-Au) systems for tungsten. REEs occur in many different mineral systems. Focus areas were developed by scientists from the U.S. Geological Survey in collaboration with scientists from State geological surveys and other institutions. This first national-scale compilation of focus areas represents an initial step in addressing the Nation’s critical mineral needs by screening areas for acquisition of new data to provide the geologic framework necessary for identifying domestic sources of critical minerals.
Learning a Cost-Effective Annotation Policy for Question Answering

State-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive.For this reason, customizing QA systems is challenging.As a remedy, we propose a novel framework for annotating QA datasets that entails learning a cost-effective annotation policy and a semi-supervised annotation scheme.The latter reduces the human effort: it leverages the underlying QA system to suggest potential candidate annotations.Human annotators then simply provide binary feedback on these candidates.Our system is designed such that past annotations continuously improve the future performance and thus overall annotation cost.To the best of our knowledge, this is the first paper to address the problem of annotating questions with minimal annotation cost.We compare our framework against traditional manual annotations in an extensive set of experiments.We find that our approach can reduce up to 21.1% of the annotation cost.