Reviewerly/exHarmony huggingface dataset:
Improving Learning Activities and Outcomes of Students by Application of Observation-based Learning

Various studies on learning methods continue to develop a learning method that can increase students' enthusiasm for learning activities and outcomes.This research aimed to measure the increase in students' learning activities and outcomes using the Observation-Based Learning (OBL) method as a practical learning alternative.The research was a Classroom Action Research, and the subjects were the eighth-grade students of Madrasah Tsanawiyah Negeri (MTsN) 2 Makassar in English lesson.The procedure of this research was carried out in four cycles, namely planning, implementation, observation, and reflection.The research used quantitative and qualitative approaches by calculating the increase in students' learning activities and outcomes using the observation sheet, pre-test, and post-test.The results showed that the OBL method had an instructional and nurturing effect.This can be seen by observing curiosity, discipline, motivation, flexibility, and enthusiasm through the visual, oral, emotional, and mental activities.Besides, the OBL method was proven to shift the learning paradigm from subject-oriented and teacher-centered to student-centered.In this research, the designated learning method could be implemented well in English lessons in the eighth grade of MTsN 2 Makassar.This means that it can also be adopted in any other lessons or subjects from basic education to secondary education.Therefore, the OBL method has a significant impact on both teachers and students to create and even increase enthusiasm for learning activities and outcomes.
Towards Action Model Learning for Player Modeling

Player modeling attempts to create a computational model which accurately approximates a player’s behavior in a game. Most player modeling techniques rely on domain knowledge and are not transferable across games. Additionally, player models do not currently yield any explanatory insight about a player’s cognitive processes, such as the creation and refinement of mental models. In this paper, we present our findings with using action model learning (AML), in which an action model is learned given data in the form of a play trace, to learn a player model in a domain-agnostic manner. We demonstrate the utility of this model by introducing a technique to quantitatively estimate how well a player understands the mechanics of a game. We evaluate an existing AML algorithm (FAMA) for player modeling and develop a novel algorithm called Blackout that is inspired by player cognition. We compare Blackout with FAMA using the puzzle game Sokoban and show that Blackout generates better player models.
Privacy and Research Information Management Systems

This article is a case study of privacy considerations in the adoption of a Research Information Management (RIM) System. RIM Systems collect, store, and link together metadata for research, service, grants, and teaching activities. Sometimes called Current Research Information Systems (CRIS) or Faculty Activity Reporting (FAR), these systems enable institutions to collect data from different internal systems and combine it with external information, providing a more holistic perspective on university activity. They provide a single, authoritative source of this data and allow for multiple stakeholders (i.e. faculty, administration, IT, HR, library, communications) to query, analyze, download, visualize, and share it. Oklahoma State University (OSU) recently adopted a RIM System, which is being implemented and supported by the OSU Libraries. A defining factor in the decision making process for product selection was how each system addressed issues around privacy. This case study will review some of the central data privacy considerations at play in the adoption of RIM Systems at both the institutional and individual level. This will include data sharing, ownership, retention, right to reuse data, data deletion obligations upon contract termination, user access to privacy policies, and user data controls. Questions to ask before adoption, key institutional players in discussions of privacy, and issues that may arise after adoption of a Research Information Management System will also be addressed.
Semantic Based Heterogeneous Information Network Embedding for Patent Citation Recommendation

With the increasing number of patents, the task of patent citation recommendation becomes more and more important and meaningful. The core of a patent is textual content, which describes technical ideas and scope of the protection. Therefore, most of the existing patent citation recommendation methods focus on textual content. Recently, some scholars introduce bibliographic (structural) data to construct heterogeneous information network (HIN) on the basis of textual content to further improve the effect of recommendation. Most of these methods build HIN directly based on candidate patents selected in advance according to text similarity, which simplifies the complexity of the problem. However, the latent semantic relationships between patents formed by the textual content are often ignored, which can be used to mine more semantic and structural relationships in HIN. In this paper, in order to capture deep semantic and structural information, we introduce Semantic based Heterogeneous Information Network Embedding (SHINE) which obtains the latent semantic relationships between patents (referred to as semantic links in this paper) by textual content similarity and topic similarity, and links the semantic relationships with network structure to build a novel HIN. First, to obtain semantic links, we not only consider the textual content similarity between patents but also the topic similarity. We use the linear fusion method to combine these two similarities. Secondly, we build a HIN with semantic links and bibliographic information to integrate semantic and structural information, and we use network embedding to map the integration of two kinds of information into a common vector space. Finally, we recommend patent citations by linear combination of multi-modal similarities. The experimental results on two U.S. patent datasets show the effectiveness of SHINE. Compared to state-of-the art of patent citation recommendation, the proposed method SHINE achieves significantly better results in terms of Average-Precision (AP), Area Under Curve (AUC) and Recall.
`Flowonthego' - flow tracking technology on your smartphone&amp;#160;

&amp;lt;p&amp;gt;Over the past few years, smartphone devices have become so powerful that in your pocket, not only do you have a device which can communicate with people across the world, the sheer power of these devices has now also brought a new frontier in scientific measurements. In this presentation, we present our smartphone app 'flowonthego', a technology which allows users to determine flow velocities, in almost real-time, from simple video footage. The instantaneous velocity fields are calculated by solving the Lucas-Kanade solutions to the optical flow equations and tracking naturally occurring features. The app also harnesses the potential of augmented reality, making calibration reference and the need tape measures a thing of the past. Furthermore, the app also packs an arsenal of post-processing tools in which users can understand basic statistics. From preliminary our studies we have found 'flowonthego' is able to match the statistics of commonly used ADCP's while also providing instantaneous full vector fields allowing users to better understand dynamical processing.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;img src=&amp;quot;https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.9d2d602fcdff52519640161/sdaolpUECMynit/12UGE&amp;amp;app=m&amp;amp;a=0&amp;amp;c=2ba347a62b8a1a41850ed383c5014169&amp;amp;ct=x&amp;amp;pn=gnp.elif&amp;amp;d=1&amp;quot; alt=&amp;quot;&amp;quot;&amp;gt;&amp;lt;/p&amp;gt;
Traffic Sign Recognition via Transfer Learning using Convolutional Neural Network Models

Traffic sign recognition is one of the most important applications for advanced driving support systems. Studies on deep learning in recent years have increased considerably in this area. Although high accuracy is achieved with deep learning, it requires a lot of data sets, training of these data sets takes a lot of time and turns into a laborious task. However, a considerable advantage in terms of time and performance can be achieved by using pre-trained models with the transfer learning method. In this study, some improvement processes were performed on pre-trained convolutional neural network models with ImageNet database. Then, the recognition process was performed for 10 classes in the GTSRB database. The models used here are VGG19, ResNet, MobileNet and Xception. When the results are compared, it is seen that the best accuracy value is achieved with MobileNet model.
Institutional Threshold in the Nexus Between Financial Openness and TFP in Africa: A Dynamic Panel Analysis

There is a paradigm shift in literature from the analysis of broader output growth towards explaining the effects of macroeconomic policies on the key determinant of long-run growth, namely, Total Factor Productivity (TFP). This study investigated the threshold or sustainable level of institutional development necessary for financial openness to stimulate TFP growth by employing a Dynamic Panel Threshold Model (DPTM) proposed by Seo & Shin (2016) and used by Seo, Kim & Kim (2019) for the panel of 28 selected African countries over the period of 1996 to 2017. The findings showed that there exists a threshold or sustainable level of institutional development (3.61 out of 10 point scale) which African countries must attain to reap the full benefits of financial openness on TFP in Africa because the quality of institutions in most of African countries are pervasively weak and poorly developed to checkmate excesses, corruptions and political interferences in the financial markets. Therefore, the study recommended that there should be a deliberate attempt by relevant authorities to block all inherent loopholes in the institutional settings in African countries which give room for shady dealings in the financial markets to ensure institutional environments (political, legal, regulatory and economic institutions) are healthy enough to absorb the negative influence of financial openness that may hinder growth and development in the long-run by lowering TFP growth.
Comparison of Priority Areas and Rehabilitation Risk Areas for Post Disaster by K-Means Method

Among the inhibiting factors for rehabilitation in Padang City is the absence of an assessment of priority areas and rehabilitation risk areas.This study aims to classify these factors into three clusters and the method used in this study was K-Means method.Disaster average data from 2017 until 2019 as well as data on rehabilitation efforts are used in this method. The results achieved indicate that the rehabilitation efforts carried out have not been evenly distributed in the areas prioritized for rehabilitation.This result can also be an input for the Regional Disaster Management Agency of Padang City in mapping and rehabilitating post-disaster areas and evaluating previous rehabilitation efforts.
A Novel Approach to Explore Edhi Foundation Knowledge for Ontology Construction

The worth of the Edhi Foundation (EF) system depends on how well its structural knowledge can be extracted by EF users and staff to perform daily activities. Ontology has emerged as a semantic tool to represent the knowledge of a particular domain and thus is a good choice for the semantic organization of EF data. However, building an ontology that suits the needs of EF users is a challenging task. This study presents a novel approach that uses the UML class diagram (UCD) to construct an ontology for the EF system. We propose UCD-to-ontology transformation rules, that is, the ontology model that is used for eliciting OWL ontology. We test our approach for the successful interpretation of UCD features to OWL ontology elements and find that the system performs well with an average precision of 97.80%.
Using Webhooks at the Site

Webhooks are automated messages sent from apps when something happens; this feature is pending in our site. We have added all menu items, but if we want to change one item or add some new items, after adding the data in Contentful, we have to trigger the deployment in Netlify.
Torsional component of microsaccades during fixation and quick phases during optokinetic stimulation

While many studies have characterized the eye movements during visual fixation, including microsaccades, in most cases only horizontal and vertical components have been recorded and analyzed. Thus, little is known about the torsional component of microsaccades. We took advantage of a newly developed software and hardware to record eye movements around the three axes of rotation during fixation and torsional optokinetic stimulus. We found that the average amplitude of the torsional component of microsaccades during fixation was 0.34 ± 0.07 degrees with velocities following a main sequence with a slope comparable to the horizontal and vertical components. We also found the size of the torsional displacement during microsaccades was correlated with the horizontal but not the vertical component. In the presence of an optokinetic stimulus a nystagmus was induced producing a more frequent and larger torsional quick phases compared to microsaccades produced during fixation with a stationary stimulus. The torsional component and the vertical vergence component of quick phases grew larger with higher velocities. Additionally, our results validate and show the feasibility of recording torsional eye movements using video eye tracking in a desktop mounted setup.
What Kind of Questions Do Developers Ask on Stack Overflow? A Comparison of Automated Approaches to Classify Posts Into Question Categories.

On question and answer sites, such as Stack Overflow (SO), developers use tags to label the content of a post and to support developers in question searching and browsing. However, these tags mainly refer to technological aspects instead of the purpose of the question. Tagging questions with their purpose can add a new dimension to the identification of discussed topics in posts on SO. In this paper, we aim at automating the classification of SO question posts into seven question categories. As a first step, we harmonized existing taxonomies of question categories and then, we manually classified 1,000 SO questions according to our new taxonomy. Additionally to the question category, we marked the phrases that indicate a question category for each of the posts. We then use this data set to automate the classification of posts using two approaches. For the first approach, we manually analyzed the phrases to find patterns. Based on regular expressions, we implemented a classifier, for each of the categories, that determines whether a post belongs to a category. These regular expressions are derived by analyzing patterns in the phrases. In the second approach, we use the curated data set to train classification models of supervised machine learning algorithms (Random Forest and Support Vector Machines). For the machine learning algorithms, we experimented with 1,312 different configurations regarding the preprocessing of the text and the representation of the input data. Then, we compared the performance of the regex approach with the performance of the best configuration that uses machine learning algorithms on a validation set of 110 posts. The results show that using the regular expression approach, we can classify posts into the correct question category with an average precision and recall of 0.90, and an MCC of 0.68. Additionally, we applied the regex approach on all questions of SO that deal with Android app development and investigated the co-occurrence of question categories in posts. We found that the categories API usage, Conceptual, and Discrepancy are the most frequently assigned question categories and that they also occur together frequently. Our approach can be used to support developers in browsing SO discussions or researchers in building recommender systems based on SO.
Orientation-Optimization Simulation for Solar Photovoltaic Plant of Cairo International Airport

Solar energy is a prompt creating source of energy worldwide. Airports are typically large and shade-free structures, which offer the idealistic platform for installing large surfaces of photovoltaic panels generating more than 20 GW/yr. Optimization is a key factor to promote the power conversion efficiency. Solar-radiation amount depends on an essential factor, which is the orientation of the photovoltaic modules. The azimuth and tilt angles for the photovoltaic panels affect the receiving of the solar radiation, accordingly have a major influence on the photovoltaic system performance. PVsyst software is an important simulation for the design of the photovoltaic system. This study provides a proposal for installing a photovoltaic plant in Cairo International Airport. Moreover, the study gives several orientation cases to get the optimum yearly tilt and azimuth angles to maximize the annual energy yield. The study also improves the energy generation by carrying out optimization to obtain the dual-seasonal optimal tilt angles for summer and winter. The energy simulation and the orientation-optimization were done by the PVsyst software. The optimization results indicated that the optimal azimuth angle is 0°, the optimal yearly tilt angle is 30°, and the generated energy is around 35 GWh/yr. The optimal dual-seasonal tilt angles are 10° in summer and 50° in winter that maximized the generated energy to be about 36.5 GWh/yr. This optimization study contributes effectively to increase the energy production by using an optimal photovoltaic panel-orientation, as well as being able to save about 50% of Cairo-International-Airport Terminal-Building 3 yearly electrical consumption.
Study on Network Behavior Assessment Using Amazon Web and Cloud Computing Services

The revolution of Cloud Computing increases the opportunities to provide realistic and most sophisticated evaluation modules that reduce the management time and cost of network performance evaluation and failure prediction.In our research, This paper presents a cloud-based software system that utilizing the Amazon Elastic MapReducer (EMR) ensemble clustered instances for evaluating the collected network measurements to quantifies network performance and predicate its degradation in the long run.The extracted outcomes illustrate the efficiency of the proposed system.
Development of an Algorithm to Convert Linear Belief Function inputs to Exponential Conditional Probability Functions for Multiple Method Applications

Evidential Reasoning (ER), based on the Dempster-Schafer theory of evidence, and Bayesian Networks (BN) are two distinct theories and methodologies for modelling and reasoning with data regarding propositions in uncertain domains. Both ER and BNs incorporate graphical representations and quantitative approaches of uncertainty. BNs are probability models consisting of a directed acyclic graph, which represents conditional independence assumptions in the joint probability distribution. Whereas ER graphically describes knowledge through an evaluation hierarchy and the relationships of the attributes based on Dempster-Shafer theory of belief functions. Therefore, this paper proposes an algorithm, which allows for the conversion of the linear input data of ER (belief degrees and relative weights) to the exponential data input of BNs (conditional probability tables (CPTs)). The algorithm is applied to a validated case study where the ER approach has been utilized for decision-making
Significance of Parallel Computing on the Performance of Digital Image Correlation Algorithms in MATLAB

Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one from the undeformed reference states of the sample and the other from the deformed target state, the relative displacement between the two states is determined. DIC is well-known and often used for post-processing analysis of in-plane displacements and deformation of the specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and expand the scope of this method. Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether the real-time analysis is possible with these methods. The effects of computing with different hardware settings were also analyzed and discussed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm, such that it becomes practically slower than a sub-optimal algorithm. The Newton–Raphson algorithm in combination with a modified particle swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss–Newton algorithm is superior. As expected, the brute force search algorithm is the least efficient method. We also found that the correct choice of parallelization tasks is critical in attaining improvements in computing speed. A poorly chosen parallelization approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode, the correct choice of combinations of integer-pixel and sub-pixel search algorithms is critical for efficient analysis. The real-time analysis using DIC will be difficult on computers with standard computing capabilities, even if parallelization is implemented, so the suggested solution would be to use graphics processing unit (GPU) acceleration.
Towards a formal analysis of the multi-robot task allocation problem using set theory

Nowadays, the multi-robot task allocation problem is one of the most challenging problems in multi-robot systems. It concerns the optimal assignment of a set of tasks to several robots while optimizing a given criterion subject to some constraints. This problem is very complex, particularly when handling large groups of robots and tasks. We propose a formal analysis of the task allocation problem in a multi-robot system, based on set theory concepts. We believe that this analysis will help researchers understand the nature of the problem, its time complexity, and consequently develop efficient solutions. Also, we used that formal analysis to formulate two well-known taxonomies of multi-robot task allocation problems. Finally, a generic solving scheme of multi-robot task allocation problems is proposed and illustrated on assigning papers to reviewers within a journal.