Reviewerly/exHarmony huggingface dataset:
Training and Guidance in Use Software Wingeom 2D and 3D in Learning Geometry to Improve the Professionalism of Junior High School Teachers In Tanggetada District, Kolaka District, Southeast Sulawesi Province

This service aims to increase the professionalism of junior high school teachers in learning geometry by using Mathematics Learning Media, namely the use Sofware Wingeom 2D and 3D. This was done because looking at the problems that are generally faced by teachers in research, namely the lack of utilization and knowledge of teachers in the use of Wingeom-Based Mathematics Learning Media, in schools teachers are known that one of their weaknesses is the lack of work in the field of scientific work and research caused by erroneous one of them is the inadequacy of teachers in using mathematics learning media which can make it easier for them to be able to work. Program Wingeom 2D and 3D is a computer application program designed to support geometry learning, both two-dimensional and three-dimensional. This program can be used asmind tools in learning geometry, where teachers can use it to develop a dimensional geometric framework. With programs Wingeom It is hoped that later the teacher will be able to explore, observe, do shape animation and display dimensional geometry material, in addition to the program Wingeom 2D and 3D are expected to help visualize a geometric concept clearly so that teachers will more easily understand geometric concepts so that it will have an impact on improving the quality of teachers in the learning process, especially learning geometry. The mandatory outputs to be achieved are (1). One scientific article published in a journal with ISSN or proceedings from a national seminar. (2) Publication in print/online/repocitory mass media PT. (3). Increased competitiveness (increase quality, quantity, and added value of goods, services, product diversification, or other resources depending on the type of activity proposed. (4). Increasing the application of science and technology in society (mechanization, IT, and management). (5) Improvement of community values (art, culture, social, politics, security, peace, education, and health. Meanwhile, Additional Outcomes are in the form of (1). method or system; product (goods or services). (2). HKI, (3). Books with ISSN, (4). TTG Innovation, and, (5). International Publications. Method used in this training and mentoring include: (1) talk and question and answer about steps usage stepssoftware Wingeom 2D and 3D in learning geometry, (2) discussion groups directed on the steps of use software Wingeom 2D and 3D in learning geometry, (3) guidance and each simulation usage stepssoftware Wingeom 2d and 3D in learning geometry, (4) practice direct use of each step usage software Wingeom 2D and 3D dalam learning geometry, (5) giving related assignments usage software Wingeom 2D and 3D dalam geometry learning.
Reflexive Freedom of Speech in Practice

The overall aim of this book is to provide a framework of freedom of speech that can ensure the most just outcomes for people within our contemporary society. The argument I make is that this aim can be achieved by the application of a reflexive freedom of speech, grounded within a conception of reflexive toleration, as initially described by Rainer Forst. The purpose of this chapter is to take this understanding and apply it to a set of contemporary events and contexts that have called the limits and purpose of free speech into question. By engaging with these examples, I hope to test reflexive freedom of speech in its ability to provide a workable consensus among conflicting parties regarding the boundaries of acceptable speech when the lines become blurred. It is important to remember that the purpose of reflexive freedom of speech, like reflexive toleration, is not to resolve the overarching conflicts that are playing out between parties—the objection component remains—but to provide a framework through which the limits of acceptable speech can be understood by all parties as being reciprocally justifiable.
Strongly solved Ostle: calculating a strong solution helps compose high-quality puzzles for recent games

Pure strategy board games such as chess are popular intellectual activities, and solving them is a challenging task in computer science. In addition to traditional games, many new board games have gained popularity in recent years. Ostle is one such unsolved game published in 2017. It is based on simple rules but is highly competitive. It is a two-player zero-sum game with perfect information in which the game-theoretical values of all game states can be obtained. In this study, we strongly solved Ostle by retrograde analysis. Utilizing various known techniques, including bitboards and succinct indexable dictionaries, significantly reduced the memory consumption in the analyses. We confirmed that the initial position is a draw and found some fundamental properties of Ostle. Additionally, we manually composed a tactical Ostle puzzle with the help of extracted outputs of the analyses. The result demonstrates that solving recent games provided helpful information to compose high-quality problems.
A path planning method for compliant jointing of weakly rigid helicopter fuselage

Purpose The purpose of this paper is to propose a strategy for the final assembly of helicopter fuselage with weak rigidity parts and mismatched jointing butt ends. Design/methodology/approach The strategy is based on path planning methods. Compared with traditional path planning methods, the configuration-space and collision detection in the method are different. The obstacles in the configuration-space are weakly rigid and allow continuous contact with the robot. The collision detection is based on interference magnitudes, and the result is divided into no collision, weak collision and strong collision. Only strong collision is unacceptable. Then a compliant jointing path planning algorithm based on RRT is designed, combined with some improvements in search efficiency. Findings A series of planning results show that the efficiency of this method is higher than original RRT under the same conditions. The effectiveness of the method is verified by a series of simulations and experiments on two sets of systems. Originality/value There are few reports on the automation technology of helicopter fuselage assembly. This paper analyzes the problem and provides a solution from the perspective of path planning. This method contains a new configuration-space and collision detection method adapted to this problem and could be intuitive for the jointing of other weakly rigid parts.
The geochemistry, origin, and hydrothermal alteration mapping associated with the gold-bearing quartz veins at Hamash district, South Eastern Desert, Egypt

Integrating diverse techniques and datasets, significantly enhances the accurate identification of various mineral deposits. This work aims to determine different types of mineral deposits in the Hamash district (Southern Eastern Desert, Egypt) by combining structural features (derived from ALOS PALSAR DEM), alteration zones (detected using ASTER and Sentinel-2), and ore mineralogy. Multispectral imaging, such as ASTER and Sentinel-2 satellite data, provides a cost-effective and efficient tool for lithological and hydrothermal alteration mapping utilizing selective band ratios (SBR), directed principal component analysis (DPCA), feature-oriented false-color composites (FFCC), and constrained energy minimization (CEM). The deductions drawn from the analysis of ASTER and Sentinel 2 satellite data are solidly corroborated through meticulous investigations of pre-existing lithological maps in the study area, on-site validation via fieldwork, and robust laboratory analysis, attesting to reliable results. Validation of remote sensing results was performed through field observations, petrographic investigations, X-ray diffraction technique (XRD), and SEM-EDX analyses. Based on ore mineralogy derived from XRD and SEM results the quartz-vein-associated ore minerals in the Hamash district include chalcopyrite, pyrite, hematite, goethite, bornite, covellite, and gold. According to the present paragenesis, the mineralization in the study area is classified into three types: sulfide mineralized zone, transitional zone, and supergene zone. Using an ore microscope, our studies identified that the alteration zones include gold-bearing sulfide minerals as well as the minerals goethite and malachite. In gold-bearing quartz samples, the concentrations of Cu, As, Ag, and Sb are positively correlated with Au at the degree of shear deformation. According to data gathered from the fire assay results, Au content varied from 0.027 to 57.20 ppm, along with Cu (10-6484 ppm), Ag (0.5-20.5 ppm), As (5-2046 ppm), Zn (3-1095 ppm), Pb (2-1383 ppm), and Sb (5-23). Our results confirmed that the Hamash region is one of the most important gold-bearing sites, with gold concentrations ranging from 0.027 up to 57.20 ppm. Furthermore, the current contribution highlighted four stages in the paragenetic sequence of the recorded ores, including magmatic, metamorphic, hydrothermal, and supergene by origin, indicating a considered similarity with the known Egyptian gold sites regarding host rocks, mineralization style, alteration assemblage, and several ore mineral conditions.
Rancang Bangun Alat Pencetak Rempeyek Bulat Diameter 90mm

The peanut brittle product is very popular with the public so that many processors produce it, which are sold in various places, ranging from traditional markets to modern markets. The application of appropriate technology to increase production capacity is very much needed by Micro, Small and Medium Enterprises (MSMEs). In this research, the design of the peanut brittle fryer has been made using a round mold which has 10 peanut brittle molds with a spring lever system that is operated manually. The results showed that the frying pan had a diameter of 90 mm and a mold thickness of 2 mm and totaled 10 pieces in one frying process. The use of stainless material in the mold causes the dough to stick at the peanut frying stage.
Fast bilateral weighted least square for the detail enhancement of COVID-19 chest X-rays

Background X-ray is an effective measure in the diagnosis of coronavirus disease 2019. However, it suffers from low visibility and poor details. A plausible solution is to decompose the captured images and enhance the details. The bilateral weighted least square model can be an effective tool for this task. However, it is highly computationally expensive. Method In this article, we propose an efficient algorithm for the bilateral weighted least square model. We approximate the bilateral weight with the bilateral grid and then incorporate it into the optimization model. This significantly reduces the number of variables in the linear system. Therefore, the model can be efficiently solved. We employ the proposed algorithm to decompose the input X-rays into base and detail layers. The detail layers are then boosted and added back to the input to derive the detail-enhanced results. Results The subjective results indicate that our method achieves higher contrast than the best-performing method ([Formula: see text], [Formula: see text], [Formula: see text]). Furthermore, our method is highly efficient. It takes 0.92 s to process a 720P color image on an Intel i7-6700 CPU. The objective results derive from the chi-square test indicate that subjects hold more positive attitudes toward our detail-enhanced images than the original X-ray images ([Formula: see text], [Formula: see text], [Formula: see text]). Conclusion We have conducted extensive experiments to evaluate the proposed image detail enhancement method. It can be concluded that (1) our method could significantly improve the visibility of the X-ray images. (2) our method is fast and effective, thus facilitating real applications.
On the Added Value of State-of-the-Art Probabilistic Forecasting Methods Applied to the Optimal Scheduling of a PV Power Plant with Batteries

Efforts have been made to develop methods to quantify the uncertainty related to the power production of renewable energy power plants, allowing producers to ensure more reliable engagements related to their future power delivery. Even though diverse probabilistic approaches have been proposed in the literature, giving promising results, the added value of adopting such methods is still unclear. This paper comprehensively assesses the profits obtained when probabilistic forecasts generated with state-of-the-art methods are fed into a stochastic programming decision-making model to optimally schedule an existing PV power plant operating in highly unstable weather. Different representative probabilistic forecasting methods are assessed and compared against deterministic forecasts submitted to varying levels of uncertainty, used to schedule the power plant in standalone operation and hybrid operation with batteries. The main findings reveal that although probabilistic forecasts offer potential benefits in handling uncertainty and utilizing battery assets to mitigate forecast errors, deterministic forecasts consistently yield higher profits than probabilistic forecasts. It is shown that this disparity is primarily attributed to the scenario diversity present in probabilistic forecasts, which leads to over-conservative decisions and the loss of temporal correlation with PV power production variations, resulting in increased imbalances and penalties.
Scale-Hybrid Group Distillation with Knowledge Disentangling for Continual Semantic Segmentation

Continual semantic segmentation (CSS) aims to learn new tasks sequentially and extract object(s) and stuff represented by pixel-level maps of new categories while preserving the original segmentation capabilities even when the old class data is absent. Current CSS methods typically preserve the capacities of segmenting old classes via knowledge distillation, which encounters the limitations of insufficient utilization of the semantic knowledge, i.e., only distilling the last layer of the feature encoder, and the semantic shift of background caused by directly distilling the entire feature map of the decoder. In this paper, we propose a novel CCS method based on scale-hybrid distillation and knowledge disentangling to address these limitations. Firstly, we propose a scale-hybrid group semantic distillation (SGD) method for encoding, which transfers the multi-scale knowledge from the old model's feature encoder with group pooling refinement to improve the stability of new models. Then, the knowledge disentangling distillation (KDD) method for decoding is proposed to distillate feature maps with the guidance of the old class regions and reduce incorrect guides from old models towards better plasticity. Extensive experiments are conducted on the Pascal VOC and ADE20K datasets. Competitive performance compared with other state-of-the-art methods demonstrates the effectiveness of our proposed method.
Energy gains at daily periodic changes of the azimuths of PV modules in north-east Bulgaria

Abstract The higher efficiency of tracking photovoltaic systems is an indisputable fact and computational methods of the respective energy gains are well known. However the investments in the tracking systems are higher in comparison to the fixed ones due to the needs of additional equipment and automation systems. An options of periodical change of the azimuth of PV modules at single axis solar trackers and the correspondent incident solar energy are studied in this paper. An approach for numerical determination of the daily periods of cyclic rotations of the trackers and the subsequent electricity production is developed. It was applied to estimate the energy gains at such cyclic changes of a PV system in ceramic factory in Bulgaria.
Super-Resolution MRH Reconstruction for Mouse Models

Magnetic Resonance Imaging (MRI) has been widely used in pathology research and plays a vital role in clinical diagnoses. However, obtaining high-resolution MRI stays a significant challenge as the process is restrained by the hardware capacity, sampling time, signal-to-noise ratio (SNR), and subject comfort. Compared to MRI, Magnetic Resonance Histology (MRH) is more costly as it has a higher spatial resolution. Deep learning models have recently succeeded in MRI Super-Resolution (SR) reconstruction from Low-Resolution (LR) images. However, it is still hard to overcome artifacts during the restoration process. The models usually have limitations in the high-frequency component representations, which can reflect finer details as in MRH images. In this work, we propose an imaging restoration framework that can conduct SR reconstruction for the whole mouse brain MRH images, and the model is based on a deep dense network. We first synthesized LR images from HR ones to train the proposed model, and then we quantitatively compared the model performance with existing methods proposed for the human brain and natural images. Finally, we demonstrated the superior visual reconstruction and interpretation of the proposed model compared to prior works.
Analysis of the Condition of the Development of Cryptocurrency in China

This article discusses the reasons behind the Chinese government's conservative attitude toward cryptocurrencies, despite the rapid growth of the cryptocurrency market in China. Cryptocurrencies have become popular in finance because of their advantages in privacy, permanence, and scalability. However, they also pose challenges, such as regulatory un-certainties and security risks. China has banned financial institutions from facilitating bitcoin transactions and imposed restrictions on cryptocurrencies. The article suggests that China's attitude towards cryptocurrencies may be due to their defects. Clear regulations and laws are needed to normalize transactions and issuance and mitigate policy risks. The article also provides some possible solutions based on the defects of cryptocurrencies.
Mutations of Nucleic Acids via Matroidal Structures

The matroid concept is an important model in real life applications. Determining the existence of mutations of DNA and RNA plays an essential role in biological studies. The matroidal structures of matrices are used for determining the existence of mutations of DNA; graph theory and matroid theory can be used to identify important mutations in genetic data. We construct an algorithm to determine the existence of a mutation. Finally, we study the similarity and dissimilarity between genes using matroids.
Through the looking glass: evaluating post hoc explanations using transparent models

Abstract Modern machine learning methods allow for complex and in-depth analytics, but the predictive models generated by these methods are often highly complex and lack transparency. Explainable Artificial Intelligence (XAI) methods are used to improve the interpretability of these complex “black box” models, thereby increasing transparency and enabling informed decision-making. However, the inherent fitness of these explainable methods, particularly the faithfulness of explanations to the decision-making processes of the model, can be hard to evaluate. In this work, we examine and evaluate the explanations provided by four XAI methods, using fully transparent “glass box” models trained on tabular data. Our results suggest that the fidelity of explanations is determined by the types of variables used, as well as the linearity of the relationship between variables and model prediction. We find that each XAI method evaluated has its own strengths and weaknesses, determined by the assumptions inherent in the explanation mechanism. Thus, though such methods are model-agnostic, we find significant differences in explanation quality across different technical setups. Given the numerous factors that determine the quality of explanations, including the specific explanation-generation procedures implemented by XAI methods, we suggest that model-agnostic XAI methods may still require expert guidance for implementation.
Penentuan Rute Optimal Untuk Jasa Pengiriman Barang Menggunakan Algoritma Genetika

In the delivery process there is a problem commonly known as the Travelling Salesman Problem (TSP), therefore a program is needed that can minimize shipping costs and find a quick route to maximize the profits obtained. Then, set up alternative routes and optimum routes so that the problem can be solved. The purpose of this research is to determine the route or route and the minimum distance search results in goods delivery services using the method of genetic algorithms. Using 27 randomly selected data points from four sub-districts in the Mojokerto district, Clustering data is divided into three clusters and tested using MATLAB software. Using a crossover probability of 0.6, the probability of mutation is 0.4, and the total population is 27. The results of the test in finding the optimal route and the fast track are in the second cluster with a distance of 20 km and the optimal route of 5-8-7-1-9-4 -10-3-6-2-5, i.e., Dsn. Mojoroto, Banjartanggul, Dsn. Sumber Bendo, Perum Mojo Asri, Wonokoyo, Dsn. Trawas, Wonokusumo, Dsn. Pecuk, Dsn. Resik Bulu, Damarsi, Etc. Mojoroto From the optimal route generated in this study, it is expected that the shipping service can get the optimal route to speed up the delivery of goods.
Research on the Gameplay and Art Style of Survival Games

In modern society, due to the development of human technology and video games, many people have started to know and play video games. One of the main reasons is that video games are a form of entertainment that only requires a computer or a phone, making them a popular activity to do when tired, relaxing, or just passing time. Additionally, there are many types of video games, including sandbox, real-time strategy, shooters, multiplayer online battle arenas, role-playing games, simulations, sports games, puzzlers, party games, action-adventure games, survival games, and platforms. However, survival games are not as well-known as some other types of games. They are a wide category of games that are set in a wide map and force the player to start an adventure with limited equipment. The main parts of a survival game include the background, character, plot, art design, game concept, and design. The way to investigate survival games is through a review of relevant literature and articles. Therefore, the conclusion of this research paper will be a summary of the game design and art style of survival games.
Beyond Dashboards: Operationalising a Measurement Framework for Agile Teams

Context: Abundant literature is available on metrics that can support Agile teams in improving their way of working and performance. However, little knowledge is available on how to operationalise metrics successfully. Objective: Our objective is to understand the challenges and benefits of selecting measures and introducing these via dashboards in supporting Agile teams in their improvement ambitions. Method: We report on Action Research in a large international organisation where metrics dashboards are introduced to Agile teams with varying levels of Agile maturity and diverse purposes. We observed dashboard usage and assessed actionability through surveys and interviews. Results: Our key observations are: (1) Metrics support Agile teams in improving performance with higher gains by teams with lower Agile maturity; (2) Collecting and sharing qualitative survey data interactively during Agile rituals is highly effective; (3) Measurement data quality is a key factor influencing actionability. Conclusions: Metric dashboards support organisations and Agile teams in their improvement ambitions. Optimal results are achieved by selecting just enough metrics based on actionability. Dashboards should be differentiated based on Agile maturity and customised to the needs of teams. Important factors for actionability are the interactive collection of qualitative measures and data quality. Data quality proves to be two-faced: measurement improves adherence to processes and data maintenance while on the other hand, a lack of trust in the numbers impedes the use of measurements.