Reviewerly/exHarmony huggingface dataset:
Sunshine and Temperature Dependent Models for Estimating Global Solar Radiation Across the Guinea Savannah Climatic Zone of Nigeria

This study investigates the most accurate sunshine and temperature dependent models for estimating global solar radiation over Makurdi and Ibadan situated in the Guinea savannah of Nigeria by comparing nine (9) different existing sunshine dependent models.The study also proposed two temperature dependent models that took the form of quadratic logarithmic and quadratic exponential and were compared to three existing temperature dependent models (Chen, Hargreaves and Samani (HS) and Garcia).The measured monthly average daily global solar radiation, sunshine hours, maximum and minimum temperature meteorological parameters during the period of thirty one years was utilized and the accuracy of the sunshine and temperature dependent models to ascertain the most suitable models in each location were tested using seven various statistical validation indicators of coefficient of determination (R 2 ), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-test, Nash-Sutcliffe Equation (NSE) and Index of Agreement (IA).The results revealed that the exponent sunshine dependent model proposed by Bakirci and the linear exponential sunshine dependent model proposed by Bakirci were found more accurate for estimating global solar radiation in Makurdi and Ibadan respectively.The proposed quadratic logarithmic and quadratic exponential temperature dependent models were found more suitable for estimating global solar radiation in Makurdi and Ibadan respectively.These recommended models can be found appropriate, if properly calibrated in regions with similar climatic information.The HS temperature dependent model evaluated in this study for Ibadan was compared with those available in literatures and was found more suitable. Frthermore, the most suitable sunshine dependent model was found more suitable for global solar radiation estimation when compared to the most suitable temperature dependent model in each of the studied locations and this was testified from the figures of the comparison between the measured and estimated sunshine and temperature dependent models as the sunshine dependent models depicts the best fitting with the measured global solar radiation data.
An Incremental Broad Learning Approach for Semi-Supervised Classification

Broad Learning System (BLS) is a fast and accurate supervised learning method without deep structure. However, the classifier trained by BLS cannot achieve expected accuracy if the labeled data are insufficient. In this paper, we develop an Incremental Semi-supervised Broad Learning method (ISBL) based on BLS to classify a partially labeled dataset, which applies manifold regularization to explore the underlying data distribution and improve accuracy. ISBL applies to scenarios where data is generated over time. While new patterns are added to the learning algorithm, the proposed method updates the classification model sustainability without retraining. By comparing with original BLS and other semi-supervised classification techniques on various datasets with different dimensions, we verified that ISBL outperforms these methods on accuracy. Experimental results demonstrate that ISBL utilizes unlabeled data effectively and achieves high accuracy. Meanwhile, the incremental learning method reduces the learning time and storage of historical data.
Control of a Movable Robot Head Using Vision-Based Object Tracking

&lt;span lang="EN-US"&gt;This paper presents a visual tracking system to support the movement of the robot head for detecting the existence of objects. Object identification and object position estimation were conducted using image-based processing. The movement of the robot head was in four directions namely to the right, left, top, and bottom of the robot head. Based on the distance of the object, it shifted the object to many points to &lt;/span&gt;&lt;span lang="IN"&gt;assess&lt;/span&gt;&lt;span lang="EN-US"&gt; the accuracy of the process of tracking the object. The targeted objects are detected through several processes, namely normalization of RGB images, thresholding, and object marking. The process of tracking the object conducted by the robot head varied in 40 various object points with high accuracy. The further the object’s distance to the robot, the smaller the corner of the movement of the robot produced compared to the movement of the robot head to track an object that was closer even though with the same distance stimulant shift object. However, for the distance and the shift of the same object, the level of accuracy showed almost the same results. The results showed the movement of the robot head to track the object under the head of the robot produced the movement with a larger angular error compared to the movement of the robot head in another direction even though with the stimulant distance of the same object position and the distance shift of the same object.&lt;/span&gt;
Levels from Sketches with Example-Driven Binary Space Partition

Procedural content generation via machine learning (PCGML) has been demonstrating its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. In this paper we present an example-driven adaptation of a classic PCG approach, binary space partition (BSP), that takes a structural template or sketch of a level and fills in the details from examples. We show that this example-driven adaptation can generate a diverse set of levels from a single structural template. We evaluate the levels generated in terms of difference between paths through the levels, amount of the level copied from the examples, and other common PCG level evaluation metrics. Furthermore, we compare this method to a Markov chain approach and show that our BSP approach matches the training level distribution better while generating a greater range of interesting features.
Cluster-Gated Convolutional Neural Network for Short Text Classification

Text classification plays a crucial role for understanding natural language in a wide range of applications. Most existing approaches mainly focus on long text classification (e.g., blogs, documents, paragraphs). However, they cannot easily be applied to short text because of its sparsity and lack of context. In this paper, we propose a new model called cluster-gated convolutional neural network (CGCNN), which jointly explores word-level clustering and text classification in an end-to-end manner. Specifically, the proposed model firstly uses a bi-directional long short-term memory to learn word representations. Then, it leverages a soft clustering method to explore their semantic relation with the cluster centers, and takes linear transformation on text representations. It develops a cluster-dependent gated convolutional layer to further control the cluster-dependent feature flows. Experimental results on five commonly used datasets show that our model outperforms state-of-the-art models.
ÚFAL MRPipe at MRP 2019: UDPipe Goes Semantic in the Meaning Representation Parsing Shared Task

We present a system description of our contribution to the CoNLL 2019 shared task, Cross-Framework Meaning Representation Parsing (MRP 2019).The proposed architecture is our first attempt towards a semantic parsing extension of the UDPipe 2.0, a lemmatization, POS tagging and dependency parsing pipeline.For the MRP 2019, which features five formally and linguistically different approaches to meaning representation (DM, PSD, EDS, UCCA and AMR), we propose a uniform, language and framework agnostic graph-tograph neural network architecture.Without any knowledge about the graph structure, and specifically without any linguistically or framework motivated features, our system implicitly models the meaning representation graphs.After fixing a human error (we used earlier incorrect version of provided test set analyses), our submission would score third in the competition evaluation.The source code of our system is available at https://github.com/ufal/mrpipe-conll2019.
Developing the Students’ Character through Literacy Activities in A Child-Friendly School Model

This case studyaimed at investigating the implementation of literacy activities in developing the students’ character of SD Negeri Secang 1 as one of child-friendly school models in Magelang Regency developed by Lund University, Sweden. This study described the practical guidance on literacy activities of child-friendly schools in developing the students’ character through the literacy programs. Since it was a case study, the writer only focused on one school and the result could not be used to generalize to child friendly schools in Indonesia. Interview and observation were used in collecting the data of this study. The interview was used to gain information on how literacy activities were normally conducted at SD Negeri Secang 1 as a friendly school model and observation was used to see the real implementation of Literacy activities in developing the students’ character. Based on the data analysis, it revealed that through literacy activitiesof a 15-minute reading activity, the students were able to develop their characters. By reading different topics, they could build their knowledge and values based on the topics they read.
The impact of accounting information systems reliability on enhancing the requirements of planning process at Jordanian commercial banks

The study aimed to identify the impact of Accounting Information Systems (AIS) reliability on enhancing the requirements of planning process at Jordanian Commercial Banks, and in order to achieve the objectives of the study the researcher implemented the descriptive analytical approach by reviewing the literature related to AIS and planning process requirements.Researcher also designed a questionnaire to target the executive managers, financial managers, and departments' heads at the (13) Jordanian commercial banks, where (145) questionnaires were distributed, recovered (122), and (118) of those were found to be valid for analysis.The study used the SPSS and the Multiple Regression test to reach the objectives of the study and test its hypotheses.The results indicate a major impact of AIS reliability on fostering the planning process requirements at the Jordanian commercial banks, and recommended the need of managements to focus on AIS to assist for the development of plans and policies related to the various activities, which may be practiced by management.
Design of parabolic solar dish tracking system using arduino

This paper demonstrates the designing parameters of a solar parabolic dish prototype for rustic areas with great solar irradiance rate availability, where have no access of electricity services or low-income people survives to buy a stove (electric or gas). The solar parabolic dish prototype intends a solution against these types of remedies and pursues solar light to work. The parabolic dish has a polished surface, where the solar radiations fall and collected at a single concentrated focal point. At this point the collected form of energy is used to derive different thermal applications like as; cooking &amp;amp; heating with single and dual axis schemes. This paper discusses the important stages of dual axis prototype; implementation, solar location strategy, the analysis in terms of theory, structural design &amp;amp; material. The dual axis prototype is implemented through the help of Arduino chipboard that is easily in maintenance, along with that this prototype is configured with anti-lock H-bridge (L298) module to overcome the control circuit complexity and AVR modules. Two rotational motors of 12V are installed on 4*4ft designed aluminum frame with a dual-axis tracking system. The jerks among trackers are also reduced with this prototype which maintains the experimental declination angle about .To finish, this paper results that parabolic solar dish tracker obtains 3.43% improved power efficiency in comparison of photovoltaic panel tracker.
An In-depth Analysis of the Effect of Lexical Normalization on the Dependency Parsing of Social Media

Existing natural language processing systems have often been designed with standard texts in mind.However, when these tools are used on the substantially different texts from social media, their performance drops dramatically.One solution is to translate social media data to standard language before processing, this is also called normalization.It is well-known that this improves performance for many natural language processing tasks on social media data.However, little is known about which types of normalization replacements have the most effect.Furthermore, it is unknown what the weaknesses of existing lexical normalization systems are in an extrinsic setting.In this paper, we analyze the effect of manual as well as automatic lexical normalization for dependency parsing.After our analysis, we conclude that for most categories, automatic normalization scores close to manually annotated normalization and that small annotation differences are important to take into consideration when exploiting normalization in a pipeline setup.
A Novel Text Encryption and Decryption Scheme using the Genetic Algorithm and Residual Numbers

Data Security is a major concern for both individuals and organisations that are engaged in one form of communication or the other, especially in the cyberspace as a result of the emergence of digital computing and communication. In this paper, we present a novel three-layered text encryption and decryption scheme that is capable of encrypting and decrypting any character or symbol using Genetic Algorithm (GA) and some inherent properties from the Residue Number System (RNS). Simulated results of the proposed scheme shows that it is chaotic by sense of sight, robust with a very wide key space composed at different stages of the scheme and has a good throughput rate as well as being able to encrypt both smaller and larger messages.
An innovative magnetic state generator using machine learning techniques

Abstract We propose a new efficient algorithm to simulate magnetic structures numerically. It contains a generative model using a complex-valued neural network to generate k -space information. The output information is hermitized and transformed into real -space spin configurations through an inverse fast Fourier transform. The Adam version of stochastic gradient descent is used to minimize the magnetic energy, which is the cost of our algorithm. The algorithm provides the proper ground spin configurations with outstanding performance. In model cases, the algorithm was successfully applied to solve the spin configurations of magnetic chiral structures. The results also showed that a magnetic long-range order could be obtained regardless of the total simulation system size.
Social Media Utilization for Policing and Crime Prevention in Lagos, Nigeria

The benefits embedded in the use of social media for policing and law enforcement are increasingly becoming recognized globally. Despite the growing popularity of social media in Nigeria, empirical information on the disposition of police personnel toward their adoption for policing and crime investigation are generally scarce. Therefore, this study investigated the use of social media for policing and crime prevention among 122 police officers serving at the headquarters of the Lagos State Police Command. The research design was descriptive and cross-sectional. Survey questionnaire and key informant interview methods were employed for data collection. Results indicated that respondents were generally positively disposed toward the adoption of social media for policing and crime prevention (c&lt;sup&gt;2&lt;/sup&gt; =13.68; &lt;em&gt;p &lt;/em&gt;&gt; .05). Although social media platforms were being used for different police duties, 47.0% of the respondents identified intelligence gathering as the major purpose for which they were being used. The majority of the officers (77.2%) claimed that they had not been exposed to any form of training regarding social media usage for policing. It is pertinent for the Nigeria Police Force to professionally train its officers on the use of social media platforms for policing and law enforcement.
Penerapan Usability pada Aplikasi PENTAS Dengan Menggunakan Metode System Usability Scale (SUS)

Assessment Test is one of the routine agenda that is carried out every semester change. This test is conducted to see the development of students in learning activities. Currently SMK 5 Kota Tangerang already uses a mobile application using the PENTAS application in answering the exam questions. But this application needs to be done to understand applications that can be used properly, or vice versa. This study uses the System Usability Scale (SUS) method to measure the use of this application for use. This SUS method has 10 questions that will be answered by respondents who are directly involved in using the PENTAS application. The average score obtained by the System Use Scale method is 46.00. These results fall into the Unacceptable category, so this PENTAS application is not good for use.
Narratives 2.0

Based on a corpus of voice message narratives in German WhatsApp group chats, the present study contributes to research on social media storytelling in that it focusses on stories of personal experience which are embedded in a communication platform which favours a continuous dialogic exchange, narrated to well-defined non-anonymous publics and multimodal (comprised of visual and audible posting types). To capture the characteristics of this type of social media storytelling, the paper argues that Ochs and Capps’ (2001) dimensional model originally developed for conversational narratives (including the dimensions of tellability, tellership, embeddedness, linearity, moral stance) should be expanded by the dimensions of publicness, multimodality and sequencing. The prototype of storytelling in WhatsApp group chats is based on recent personal experiences; it is related by a single teller as an initial, sequentially non-embedded and linearly organised “big package” story (in a single voice message sometimes introduced by a text message containing an abstract); other group members routinely document their evaluative stances in rather conventionalised text message responses in the semi-public group space.
NLPRL at WAT2019: Transformer-based Tamil – English Indic Task Neural Machine Translation System

This paper describes the Machine Translation (MT) system submitted by the NLPRL team for the Tamil -English Indic Task at WAT 2019.We presented the Neural Machine Translation (NMT) system based on the Transformer approach.Training and performance of the model are evaluated on the En-Tam corpus (An English-Tamil Parallel Corpus) collected by researchers at UFAL (Institute of Formal and Applied Linguistics).The evaluation of the model done using Adequacy, BLEU, RIBES, and AM-FM scores, and the model improves translation in terms of Adequacy, RIBES and AM-FM as compared to the baseline.
An Introduction to Immersive Reality

Immersive reality (XR) encompasses the spectrum of enhancing learning through augmented reality to virtual reality. Although there has been much hype around the transformative potential of AR and VR the adoption of these technologies in higher education learning environments has been limited (Cochrane, 2016). With a lack of models of how to integrate XR in higher education AR has fallen into the trough of disillusionment on the Gartner hype cycle for emerging technologies 2018, while VR is on the ‘slope of enlightenment’ (Daniel, 2018). In response, this workshop will provide participants with a hands on experience of creating their own simple immersive reality scenario using the web-based VR platform SeekBeak (https://seekbeak.com). The workshop is a generic version of a workshop run with Journalism students that introduced them to the concepts of immersive journalism practice and the implications for immersive storytelling (Sissons &amp; Cochrane, 2019a, 2019b). The workshop will introduce participants to the state of the art of immersive journalism, and demonstrate a BYOD approach to user-generated virtual reality in higher education as a model of integrating authentic learning within the curriculum.&#x0D; &#x0D; Schedule (100 mins)&#x0D; &#x0D; Introductions (5 min) &#x0D; Participant survey (5 min)&#x0D; &#x0D; &#x0D; Introduction to 360 video and VR (10 min)&#x0D; &#x0D; &#x0D; XR Journalism examples &#x0D; &#x0D; &#x0D; Demo of initial Media Centre VR https://seekbeak.com/v/kvPq47DpjAw (5 min) &#x0D; &#x0D; &#x0D; VR project development (60 min)&#x0D; &#x0D; Google Cardboard Headsets, using participants’ own smartphones&#x0D; Introduction to the Toolkit &#x0D; Participants create SeekBeak accounts&#x0D; Hands on with the 360 cameras &#x0D; Participants choose a topic to work on as a mobile VR production team&#x0D; &#x0D; &#x0D; &#x0D; Sharing and review of participant projects(participants share SeekBeak links) (10 min)&#x0D; Reflections via brief SurveyMonkey survey, and sharing of project URLs and reflections via Twitter and the #SOTELNZ hashtag (5 min)&#x0D; END&#x0D; &#x0D; References&#x0D; &#x0D; Cochrane, T. (2016). Mobile VR in Education: From the Fringe to the Mainstream. International Journal of Mobile and Blended Learning (IJMBL), 8(4), 45-61. doi:10.4018/IJMBL.2016100104&#x0D; Daniel, E. (2018, 21 August 2018). Gartner hype cycle 2018: Mixed reality to overtake VR and AR. Retrieved from https://www.verdict.co.uk/gartner-hype-cycle-2018-mixed-reality/&#x0D; Sissons, H., &amp; Cochrane, T. (2019a, 22 November). Immersive Journalism: Playing with Virtual Reality. Paper presented at the AUT Teaching and Learning Conference: Authentic Assessment - Time to Get Real?, Auckland University of Technology.&#x0D; Sissons, H., &amp; Cochrane, T. (2019b). Newsroom Production: XRJournalism Workshop. Retrieved from https://tinyurl.com/XRJournalism
Entanglement Evolution in a Heisenberg Spin Dimer

The transitory and steady-state entanglement of a two-qubit XY Heisenberg magnetic spin system immersed in an external magnetic field are analyzed in terms of the entanglement measure of concurrence. The dependence of the system entanglement on coupling strength, decay rate, and applied magnetic field is numerically simulated for two distinct models of system evolution involving decoherence to the environment. An applied magnetic field was found to consistently reduce the system entropy. The external magnetic field increases entanglement for small strength, and if too strong, it reduces the entanglement. The maximum entanglement is formed from a balance of external magnetic field and the decay rate. The transitory level of entanglement in the system is found to oscillate with a frequency that is proportional to the magnetic field strength before reaching steady-state entanglement. It was found that collective decay produces more entanglement than individual decay.