Reviewerly/exHarmony huggingface dataset:
Generation of Batik Patterns Using Generative Adversarial Network with Content Loss Weighting

Every craftsman who draws Batik can also not necessarily draw various types of Batik.On the other hand, it takes a long time ranging from weeks to months, to make Batik.Image generation is regarded as an essential part of the field of computer vision.One of the popular methods includes the Generative Adversarial Network, commonly implemented to generate a new data set from an existing one.One model of the Generative Adversarial Network is BatikGAN SL generating batik images by inserting the two Batik patterns to produce a new Batik image.Currently, the generated Batik image does not maintain the input of the Batik pattern.Therefore, this study proposes a GAN model of BatikGAN SL, with the addition of a content loss function using hyperparameters to weight the content loss function.The content loss function is added from the Neural Transfer Style method.Previously, the style loss function in this method has been implemented in BatikGAN SL, and the dataset consists of Batik patches (326 images) and real Batik (163 images).This paper compares the BatikGAN SL model from previous studies with the BatikGAN SL model by implementing hyperparameters on the content loss function.The evaluation is conducted with FID, containing FID Local and FID Global.The results obtained in this study include a collection of Batik images, test evaluation value of 42 on FID Global and 16 on FID Local.These results are obtained by implementing the content loss function with a weight value of 1.
Physics-guided machine learning for laboratory earthquake prediction

Recent laboratory studies of fault friction have shown that deep learning can accurately predict the magnitude and timing of stick-slip sliding events, the laboratory equivalent of earthquakes, from the preceding acoustic emissions (AE) events or time-lapse active-source ultrasonic signals. While there are observations that provide insight into the physics of these predictions, the underlying precursory mechanisms are not fully understood. Furthermore, these purely data-driven models require a large amount of training data and may not generalize well outside their training domain. Here, we present a physics-guided machine learning approach - by incorporating the relevant physics directly in the prediction model architecture - with the objectives of enhancing model predictions and generalizability as well as reducing the amount of required training data. We use data from well-controlled double-direct shear laboratory friction experiments on Westerly granite blocks exhibiting numerous regular and irregular stick-slip cycles. Simultaneously, AEs are recorded while the faults are also regularly probed by ultrasonic waves transmitted through the fault zone to monitor the evolution of the contact stiffness during shearing. Our physics-guided ML models take features extracted from AE time series or time-lapse active source ultrasonic signals and predict the shear stress history, which gives both the timing and size of the laboratory earthquakes. The models are constrained by friction laws as well as simplified physical laws governing ultrasonic transmission and AE generation. Our findings indicate that physics-guided ML models outperform purely data-driven models in important ways; they provide accurate predictions even with little training data and transfer learning is greatly enhanced when physics constraints are incorporated. These findings have important implications for earthquake predictions in the field, where training data are scarce. &amp;#160;
Clinical Outcome Future Prediction with Decision Tree and Naive Bayes Models

Clinical decision-making in health care is even now inspired by data-driven computer forecasts or suggestions. A range of machine learning functions has recently been shown in clinical works, particularly for result prediction patterns spanning from humanity to stroke. We investigate the state of the art in relevant subjects such as data point treatment, interpretation, and simulation assessment in the framework of outcome prediction models improved utilizing data as automated health data. We also look at the flaws in widely used modeling assumptions and offer suggestions for further research
South Africa Education Data and Visualisations

&lt;p&gt; &lt;/p&gt; &lt;p&gt;The tabular and visual dataset focuses on South African basic education and provides insights into the distribution of schools and basic population statistics across the country. This tabular and visual data are stratified across different quintiles for each provincial and district boundary. The quintile system is used by the South African government to classify schools based on their level of socio-economic disadvantage, with quintile 1 being the most disadvantaged and quintile 5 being the least disadvantaged. The data was joined by extracting information from the debarment of basic education with StatsSA population census data. Thereafter, all tabular data and geo located data were transformed to maps using GIS software and the Python integrated development environment. The dataset includes information on the number of schools and students in each quintile, as well as the population density in each area. The data is displayed through a combination of charts, maps and tables, allowing for easy analysis and interpretation of the information.&lt;/p&gt;
A Two-stream Convolutional Neural Network-based Pornography Recognition Method

The main approach taken to identify pornographic video content is achieved by performing pornography detection on the video content. By extracting features from video key frames and using some common neural network models to recognize the extracted key frame images, a certain accuracy rate can be obtained. However, another key information of video recognition, action information, is ignored, which leads to misclassification of some indistinguishable videos such as sumo wrestling and boxing. A dual-stream convolutional neural network-based pornographic video recognition method is proposed to address this problem. The experimental results show that the dual-stream convolutional neural network effectively improves the recognition rate of indistinguishable pornographic videos.
Metalearning using structure-rich pipeline representations for improved AutoML

Automatic machine learning (AutoML) systems have been shown to perform better when they learn from past experience. Examples include Auto-sklearn, which warm-starts the ML pipeline search using existing programs known to perform well on 'similar' tasks, and AlphaD3M, which uses online reinforcement learning to search the ML pipeline space. These metalearning approaches, as well as many others, depend on simplifying assumptions about the pipeline search space and/or the pipeline representation. Here, we attempt to extend the applicability of AutoML by relaxing such simplifications. Using a sizable metadataset of 194 classification tasks and 4,592 pipelines, we show that using pipeline metadata, including the underlying DAG structure, leads to better estimates of pipeline performance and to more robust rankings of pipelines.
Predicting Solar Power Generated by Grid-Connected Two-Axis PV Systems Using Various Empirical Models

The main goal of this paper is to predict the mean monthly electricity generation (EG) produced by Two-axis solar systems using the Adaptive-Neuro Fuzzy Inference System (ANFIS) and Response Surface Methodology. The Two-axis solar systems are located in 25 coastal Mediterranean cities. Accordingly, in this study, the geographical coordinates (latitude, longitude, and altitude) and climate conditions (global solar radiation, air temperature, and clearance index) were collected and used as input variables for the models. The results showed that all proposed models were suitable for EG prediction. The results demonstrated that the RSM model has produced the highest value of R-squared (0.956) and lowest value of RMSE (42.672 kWh) compared to the ANFIS model.
An Analising EFLTeacher’s Politeness Strategies in Classroom Interaction

ABSTRACT&#x0D; The purpose of this study is to investigate at how politeness strategies are used in EFL classroom interactions in a senior high school. This study employed a descriptive qualitative research approach to investigate the teacher and student politeness strategies in their interactions. An English teacher and 35 students took part in this research. This data in this research were taken from the form of utterances containing politeness strategies. The data was provided via a video-recorded 90-minute English class. There were six extracts with three politeness strategies: positive politeness strategy, negative politeness strategy, and bald-on-record method, according to the findings. The teacher was in charge of the exchanges. Furthermore, various characteristics such as age difference, institutional position, authority, and social distance affected politeness strategies used in classroom interactions.&#x0D; Key Words: classroom interaction, politeness, politeness strategy
Shaping Interoperability for the Internet of Things: The Case for Ecosystem-Tailored Standardisation

Abstract No matter how good a smart device may be, it remains useless outside the context of a digital ecosystem. Internet of Things (IoT) environments are possible as long as services and products can interconnect smoothly and exchange data in real time. Therefore, interoperability ranks high in global policy agendas, with the promise of bringing an end to network effects slanted in favour of ecosystem orchestrators. However, recent regulatory initiatives introducing interoperability obligations risk falling short of their intent or even risk generating unintended consequences in the absence of a coherent approach to standardisation. Against this backdrop, focusing on the UK Open Banking experience, this article makes a proposal for workable interoperability in IoT ecosystems aimed at ensuring market contestability without undermining incentives to innovate.
tRANSAC: Dynamic feature accumulation across time for stable online RANSAC model estimation in automotive applications

RANdom SAmple Consensus (RANSAC) is widely used in computer vision and automotive related applications.It is an iterative method to estimate parameters of mathematical model from a set of observed data that contains outliers.In computer vision, such observed data is usually a set of features (such as feature points, line segments) extracted from images.In automotive related applications, RANSAC can be used to estimate lane vanishing point, camera view angles, ground plane etc.In such applications, changing content of road scene makes stable online model estimation difficult.In this paper, we propose a framework called tRANSAC to dynamically accumulate features across time so that online RANSAC model estimation can be stably performed.Feature accumulation across time is done in such a dynamic way that when RANSAC tends to perform robustly and stably, accumulated features are discarded fast so that fewer redundant features are used for RANSAC estimation; when RANSAC tends to perform poorly, accumulated features are discarded slowly so that more features can be used for better RANSAC estimation.Experimental results on road scene dataset for vanishing point and camera angle estimation show that the proposed tRANSAC method gives more stable and accurate estimates compared to baseline RANSAC method.
Conceptual Mapping of the Cybersecurity Culture to Human Factor Domain Framework

Human related vulnerability challenges continue to increase as organisations intensify their use of interconnected technologies for operations particularly due to the emergence of COVID-19 pandemic. Notwithstanding the challenge of a human problem on cybersecurity, existing cybersecurity measures predominately focused on technological solutions which on their own have proven to be insufficient. To ensure all-inclusive cybersecurity solution, efforts are shifting to accommodate human angle which complements technological efforts towards eradicating cybersecurity challenges hence the move to cybersecurity culture (CSC). The importance of the human-related factor on the security of information and IT system has been emphasised by various research leading to the development of Human Factor Diamond (HFD) framework. This paper at the conceptual level mapped the articulated list of identified CSC factors to the HFD framework to determine the CSC factors that are associated with the different domains of human factor framework. The mapping depicts that each domain of human factor framework has CSC factors associated to it. Management appeared as the domain with the predominate number of factors, followed by responsibility, environment and preparedness respectively.
Quantum Key Distribution Using a Single-Photon Added–Subtracted Squeezed Coherent State

We investigate the security of continuous-variable BB84 quantum key distribution protocol using a single-photon added and then subtracted squeezed coherent state (SPASSCS). We find that the SPASSCS is a non-Gaussian and nonclassical state; its non-Gaussianity and nonclassicality are exhibited through the Wigner function. We show that the proposed state is generally robust against the eavesdropping strategies, such as intercept-resend attack and superior channel attack. Further, a comparative study proves the strong efficiency of the proposed state over the coherent state, squeezed coherent state and photon-added then subtracted coherent state. In our analysis, we employ bit error rate, mutual information, and secure key gain.
Management of Educational Facilities and Infrastructure in Improving Learning Productivity in Elementary Schools

Management of facilities and infrastructure in educational units is very important, if the planning is well regulated, it will certainly be in accordance with the expectations and desired goals in a quality learning process.The purpose of the study was to describe the management of facilities and infrastructure at the State Elementary School 1 Laloeha.The research method used is the descriptive qualitative method.The research was conducted at the State Elementary School 1 Laloeha with the research subjects including the principal; vice principal for student affairs and curriculum; treasurer; and administrative staff.Data collection techniques are in the form of observation, interviews, and documentation.The data analysis technique used descriptive analysis.The results of the study indicate that the policy for planning for facilities and infrastructure is prepared at the beginning of the year by analyzing needs; The policy for the procurement of facilities and infrastructure is not in accordance with existing procedures; policies for organizing facilities and infrastructure are not in accordance with the school principal's decision; supervision of facilities and infrastructure is less responsible.
Possibilities of the Interval: Heidegger and the Reimagining of the Interval in Luigi Nono’s <i>A Carlo Scarpa</i>

Abstract From 1976, the works of the Venetian composer Luigi Nono (1924–90) are marked by a noticeable change in both his philosophical and his political outlook. What results is a decade (1980–9) of compositions that feature poetry in librettos, live electronics, the spatialization of sound and a prominent use of microtonal pitches. Together these create completely novel soundscapes that are noticeably different from his previous output. This article will examine a particular influence – the philosophy of Martin Heidegger – in the creation of the 1984 piece for large ensemble A Carlo Scarpa. The purpose of this is not only to allow for an insight into the music and structure of A Carlo Scarpa , but also to illuminate how philosophical and political ideas can be represented within the craft of composition, and the new paths of thinking that guided Nono’s artistic output during the 1980s.
Learning signatures of decision making from many individuals playing the same game

Human behavior is incredibly complex and the factors that drive decision making-from instinct, to strategy, to biases between individuals-often vary over multiple timescales. In this paper, we design a predictive framework that learns representations to encode an individual's 'behavioral style', i.e. long-term behavioral trends, while simultaneously predicting future actions and choices. The model explicitly separates representations into three latent spaces: the recent past space, the short-term space, and the long-term space where we hope to capture individual differences. To simultaneously extract both global and local variables from complex human behavior, our method combines a multi-scale temporal convolutional network with latent prediction tasks, where we encourage embeddings across the entire sequence, as well as subsets of the sequence, to be mapped to similar points in the latent space. We develop and apply our method to a large-scale behavioral dataset from 1,000 humans playing a 3-armed bandit task, and analyze what our model's resulting embeddings reveal about the human decision making process. In addition to predicting future choices, we show that our model can learn rich representations of human behavior over multiple timescales and provide signatures of differences in individuals.
Fine grained irony classification through transfer learning approach

Nowadays irony appears to be pervasive in all social media discussion forums and chats, offering further obstacles to sentiment analysis efforts. The aim of the present research work is to detect irony and its types in English tweets We employed a new system for irony detection in English tweets, and we propose a distilled bidirectional encoder representations from transformers (DistilBERT)light transformer model based on the bidirectional encoder representations from transformers (BERT) architecture, this is further strengthened by the use and design of bidirectional long-short term memory (Bi-LSTM) network this configuration minimizes data preprocessing tasks proposed model tests on a SemEval-2018 task 3, 3,834 samples were provided. Experiment results show the proposed system has achieved a precision of 81% for not irony class and 66% for irony class, recall of 77% for not irony and 72% for irony, and F1 score of 79% for not irony and 69% for irony class since researchers have come up with a binary classification model, in this study we have extended our work for multiclass classification of irony. It is significant and will serve as a foundation for future research on different types of irony in tweets.
Digital Research Skills in Secondary Science Education: A Guiding Framework and University Teachers’ Perception

This study focuses on the perceived gap between the required and actual level of &lt;i&gt;digital research skills&lt;/i&gt; (DRS) of students entering tertiary science education. By combining existing frameworks for research skills and digital literacy skills, a guiding framework of DRS was constructed. The DRS framework incorporates seven categories and is evaluated using an exploratory qualitative study employing semi-structured interviews with university teachers (&lt;i&gt;N&lt;/i&gt; = 15). The level of DRS at the start of university science education and university teachers’ perceptions of first-year students’ level of DRS have been investigated. The results show that the skills of writing a research paper using digital tools, using proper resources, and analysing, transforming, and visualising data were generally found to be wanting.
Batik Images Retrieval Using Pre-trained model and K-Nearest Neighbor

Batik is an Indonesian cultural heritage that should be preserved. Over time, many batik motifs have sprung up, which can lead to mutual claims between craftsmen. Therefore, it is necessary to create a system to measure the similarity of a batik motif. This research is focused on making Content-Based Image Retrieval (CBIR) on batik images. The dataset used in this research is big data Batik images. The authors used transfer learning on several pre-trained models and used Convolutional Neural Network (CNN) Autoencoder from previous studies to extract features on all images in the database. The extracted features calculate the Euclidean distance between the query and all images in the database to retrieve images. The image closest to the query will be retrieved according to the number of r, namely 3, 5, 10, or 15. Before the image is retrieved, the retrieval system is used to re-ranked with K-Nearest Neighbor (KNN), which classifies the retrieved image. The results of this study prove that MobileNetV2 + KNN is the best model in terms of Image Retrieval Batik, followed by InceptionV3 and VGG19 as the second and third ranks. Moreover, CNN Autoencoder from previous research and InceptionResNetV2 are ranked fourth and fifth. In this study, it was also found that the use of KNN re-ranking can increase the precision value by 0.00272. For further research, deploying these models, especially for MobileNetV2 is an approach for seeing a major impact on batik craftsmanship for decreasing batik motif plagiarism.
Boiler Wall Temperature Prediction Based on Temporal Convolution Networks

In the power plant boiler operation, temperature is one of the most significant parameters, and the boiler wall temperature prediction is the key to the safe and stable production of power plant. Traditional deep learning neural networks are difficult to retain long-term memory of boiler wall temperature prediction when dealing with temperature data stream in DCS system of power plant, and there is “leakage” from future to past. In this paper, Temporal Convolution Network (TCN) is an architecture that incorporate dilated and causal convolutional layers, which can adapt to temperature data with large receptive fields and time attributes. TCN is composed of 4 convolutional layers, each layer achieves causal convolution by padding, and the dilation factor increases exponentially with the depth of the network. We train the TCN on the historical wall temperature data and use it to predict the development trend. Experiments show that the proposed TCN is superior to other comparison models in boiler wall temperature prediction.