Reviewerly/exHarmony huggingface dataset:
SPATIAL DISTRIBUTION OF HOTSPOTS USING S-NPP VIIRS FOR EARLY DETECTION OF POTENTIAL FIRE

&lt;p&gt;&lt;span lang="EN-US"&gt;Fire is a disaster and its frequency is increasing every year. Seeing this, it is very important to know the spatial distribution of hotspots to determine the potential for fires in each area. Based on this, it is necessary to conduct research with the title "Spatial Distribution of Hotspots Using S-NPP VIIRS for Early Detection of Potential Fires". This research includes the type of descriptive research. The population in the study were all hotspots in Banjar Regency, South Kalimantan Province. Hotspots were taken from the results of the S-NPP VIIRS satellite imagery recording from 2012-2021. The number of samples is equal to the number of populations. The data analysis technique uses nearest neighbor analysis and descriptive analysis which is processed using Arc GIS software. The research results show that fires occur during the dry season, namely in July, August, September and October. Spatial distribution of hotspots from the results of S-NPP VIIRS satellite imagery based on the accuracy of the most confidence level in July, August, September and October. If the spatial distribution of hotspots is known, it can be used as an early detection effort. Early detection is carried out as an effort to prevent and control fires with a greater negative impact. In addition, with the existence of an early warning system, the community is better prepared to deal with fires so that the negative impacts that may arise due to fires can be minimized, including loss of life and property. &lt;/span&gt;&lt;/p&gt;
TTProfiler: Types and Terms Profile Building for Online Cultural Heritage Knowledge Graphs

As more and more knowledge graphs (KGs) are published on the Web, there is a need for tools that show their content. This implies showing the schema-level patterns instantiated in the graph, but also the terms used to qualify its entities. In this article, we present a new profiling tool that we call TTprofiler. It shows the predicates that relate types in the KG, and also the terms present in this KG, because of their paramount importance in most KGs, especially in the Cultural Heritage (CH) domain. We recall the role of terminologies and how they are implemented and used on the Web, we give the algorithm for building a TT profile from an online KG’s endpoint, and we report on experiments performed over a set of Cultural Heritage Web KGs. A tool for visualizing TT profiles is also provided.
Fractional Gegenbauer Kernel Functions: Theory and Application

Because of the usage of many functions as a kernel, the support vector machine method has demonstrated remarkable versatility in tackling numerous machine learning issues. Gegenbauer polynomials, like the Chebyshev and Legender polynomials which are introduced in previous chapters, are among the most commonly utilized orthogonal polynomials that have produced outstanding results in the support vector machine method. In this chapter, some essential properties of Gegenbauer and fractional Gegenbauer functions are presented and reviewed, followed by the kernels of these functions, which are introduced and validated. Finally, the performance of these functions in addressing two issues (two example datasets) is evaluated.
Feature Presentation of Image Saliency Existence Based on Boundary Compactness Hypothesis

The study of salient image is related to the method for detecting the most prominent area within the image. There were numbers of methods applied in describing the image saliency, and the models’ integration with the learning techniques has been able to provide tremendous achievement on the results of salient object detection. However, the approach that is able to describe the saliency existence has never been seriously discussed, and therefore, many models are still unable to produce correct detection for the non-salient image. The non-salient image is a type of image that does not contain any important information. This paper presents a method that can describe the saliency existence of images based on the boundary compactness hypothesis with fact that the compactness of a non-salient image is spatially distributed as being referred to its boundary compared to the salient image. The saliency features were extracted from the image background measurement that consists of boundary contrast compactness and boundary spatial distribution compactness. These compactness components were computed for all image’s superpixel patches and compared with its boundary patches. As these features were computed in the spatial domain, the fast Fourier transform is applied to obtain the saliency features in the frequency domain. Experimental results show that the proposed approach achieve the highest mean difference ratio of 21.65 compared to the state-of-the-art approaches in putting distinct value to identify the saliency existence.
Cooperative of Team Games Tournament to Enhance Learning Outcomes in English Learning

The challenge of teaching English to students with low academic achievement is what inspired this line of inquiry. The purpose of this research was to provide an explanation of how the Team Games Tournament was implemented Elementary class in order to improve the English learning outcomes of the pupils. The type of action research that was conducted in the classroom for the purpose of this study was approached from a qualitative point of view. A total of 24 students took part in this research project, both as instructors and as research subjects. As means of collecting data for the purpose of this investigation, observation reports, learning progress examinations, and documentation were utilized. The findings of the research are that the implementation of the Team Games Tournament was generally carried out in an effective manner. Less teacher activity was recorded during Cycle I Meeting I, which sufficient category, however, more teacher activity was recorded during Meeting II, which rated as "good." Because of this, the student's results in learning English have iincreased. The implementation of the Team Games Tournament learning model in an educational of elementary school to improve the learning outcomes for students has been shown to be beneficial.
Game design documents for mobile elementary school mathematic educative games

&#x0D; &#x0D; &#x0D; &#x0D; Mobile games or games in this era are very much in demand by young people and small children as a medium of entertainment. Even the elderly are still often encountered playing this mobile game. This has prompted many game developer programmers to want to make mobile-based games. This aims to add insight, especially at the age of children, so that they are more enthusiastic about learning through this educational game. The academic side of this game comes from simple and fun math puzzles. From within this game, players can enjoy games that have 2D animations and are based on Android, as well as enriching children's knowledge and learning basic mathematical calculations by answering using games or this educational game. The assessment of this educational game is assessed when based on the number of correct answers. The method used in this research is in the form of collecting information and data, which includes recording and studying the literature and will conduct searches using the internet, as well as data sources relating to the problems in this research game Development. Lifecycle (GDLC) is used as a system development method. GDLC is a guide or guidelines that can regulate the rules in making this educational game. The results of this research will be the realization of mobile or android-based games with construct 2 for elementary school children from grades 3, 4, and 5. This android-based educational game is expected to provide experience to children in the world of learning and can increase elementary school children's interest in arithmetic, especially in counting.&#x0D; &#x0D; &#x0D; &#x0D;
Understanding Views Around the Creation of a Consented, Donated Databank of Clinical Free Text to Develop and Train Natural Language Processing Models for Research: Focus Group Interviews With Stakeholders

Information stored within electronic health records is often recorded as unstructured text. Special computerized natural language processing (NLP) tools are needed to process this text; however, complex governance arrangements make such data in the National Health Service hard to access, and therefore, it is difficult to use for research in improving NLP methods. The creation of a donated databank of clinical free text could provide an important opportunity for researchers to develop NLP methods and tools and may circumvent delays in accessing the data needed to train the models. However, to date, there has been little or no engagement with stakeholders on the acceptability and design considerations of establishing a free-text databank for this purpose.This study aimed to ascertain stakeholder views around the creation of a consented, donated databank of clinical free text to help create, train, and evaluate NLP for clinical research and to inform the potential next steps for adopting a partner-led approach to establish a national, funded databank of free text for use by the research community.Web-based in-depth focus group interviews were conducted with 4 stakeholder groups (patients and members of the public, clinicians, information governance leads and research ethics members, and NLP researchers).All stakeholder groups were strongly in favor of the databank and saw great value in creating an environment where NLP tools can be tested and trained to improve their accuracy. Participants highlighted a range of complex issues for consideration as the databank is developed, including communicating the intended purpose, the approach to access and safeguarding the data, who should have access, and how to fund the databank. Participants recommended that a small-scale, gradual approach be adopted to start to gather donations and encouraged further engagement with stakeholders to develop a road map and set of standards for the databank.These findings provide a clear mandate to begin developing the databank and a framework for stakeholder expectations, which we would aim to meet with the databank delivery.
New Tools for Old Services: Implementing WebCheckout for UW-Madison InfoLabs Technology Circulation

UW-Madison's InfoLabs program, which runs and manages computer labs and associated services across campus, has circulated technology in various forms for over two decades. Once the program's Technology Circulation service was established, an application was locally commissioned to support it: the Equipment Checkout System (ECS), which began support in Spring of 2005. In 2020, ECS was flagged as in need of replacement due to its aging code base and lack of campus support. A small team was formed from stakeholder groups to find and decide on a third-party, hosted solution to bring the service into modern norms and provide room to grow into the future.
Knowledge Map Automatic Update System Using Graph Convolutional Network

Instead of face-to-face learning, online learning plays a significant role now. Some teachers, schools, and colleges have started using online learning systems, such as Massive Open Online Courses (MOOCs). The online learning system contains the learning part and the exercising part. Our laboratory has released a brand new way of exercising part. Students could do some exercises through our system, and each exercise would map to a knowledge concept in the knowledge map. In this paper, we propose a knowledge map update system. It comprises two main parts: a Graph Convolutional Network (GCN) and a knowledge map renewing algorithm. This system would give the students a better and more reasonable knowledge map. The proposed system could predict students’ learning condition indicators in GCN, use GCN as a evaluation metric and update a new knowledge map. We collected data for five classes and 486 students. As the results show, our GCN model has an average of 76% accuracy in predicting.
Visualizing Neuroscience Through AI

The field of neuroscience explains how the neural networks in the brain work together to perform a variety of tasks, including pattern recognition, relative memory, object recognition, and more. The mental activity that makes different jobs possible is difficult to understand. Understanding the various patterns present in natural neural networks requires a combination of artificial intelligence and neuroscience, which requires less computation. As a result, it is possible to understand a large number of brain reactions in relation to the activity that each person is engaged in. Human brain neurons need to be trained by experience in order to perform activities like moving the hands, arms, and legs while also considering how to respond to each activity. In the past 10 years, artificial intelligence (AI), with its potential to uncover patterns in vast, complex data sets, has made amazing strides, in part by emulating how the brain does particular computations. This chapter reviews the replication of neuroscience via AI in a real-time scenario.
What Information Variables Predict Bitcoin Returns? A Dimension-Reduction Approach

This article investigates the determinants of Bitcoin returns. The authors consider a comprehensive set of information variables under five categories: macroeconomics, blockchain technology, other assets, stress level, and investor sentiment. Their approach toward this large dataset is built upon dimension-reduction models such as Backward Elimination, least absolute shrinkage and selection operator (LASSO), principal component regression (PCR), and three-pass regression filter (3PRF). The empirical results show that blockchain technology, stress level, and investor sentiment have positive, negative, and positive predicting power on Bitcoin returns, respectively. Macroeconomic variables exhibit insignificant impacts on Bitcoin returns. Other asset variables show little predicting power until 2019, but some become a significant predictor during the COVID-19 pandemic. Overall, the authors caution against using Bitcoin as a risk-hedging device in financial portfolios. They also find that, consistent with other financial assets such as equities, Bitcoin shows increased predictability with a longer return horizon. Due to their empirical results, they also advocate the use of 3PRF; relative to other dimension-reduction methods under consideration, they observe superior performance of 3PRF in predicting both the level and the direction of future Bitcoin returns across all return horizons.
The communicative dimension of graphic design elements - Such as infographics

The purpose of the current study is to address a research question about the role that visual design elements have in communication. The data were collected based on their technical relevance, which highlights the general description of the field of research that includes graphics, infographics in the field of communication, and elements of visual design Review of the relevant literature and concepts: To begin with, we must consider the communication aspects of the design in order to produce truly successful visual design elements. The communicative dimension of graphic design elements such as infographics are invaluable in conveying information in a clear and engaging way. Two opened the way for the expansion of the use of visual elements in the manufacture of the media message. This study discusses the communicative dimensions of visual design elements, in general, and infographics in particular, as infographic is part of information visualization. This study discusses the communicative dimensions of visual design elements, in general, and infographics in particular, as Infographic is part of information visualization, in addition to that it is a research field related to communication as it focuses on patterns and trends in abstract data sets, and the field of infographic design, and information visualization combines the concepts of computer science, data mining, cognitive sciences and graphic design, this is important in controlling and measuring the quality of communication because the ability to collect, store and manage data, despite the increasing pace of its updating, remains our ability On their understanding relatively constant. The size, shape, and color of visual design elements can also play a role in communication. According to the results obtained, it was determined that the use of graphs as a visual communication tool was effective in evaluation, understanding and continuity.
New Permutation Reversed Dickson Polynomials over Finite Fields

Let [Formula: see text] be an odd prime, and [Formula: see text], [Formula: see text] be nonnegative integers. Let [Formula: see text] be the reversed Dickson polynomial of the [Formula: see text]-th kind. In this paper, by using Hermite's criterion, we study the permutational properties of the reversed Dickson polynomials [Formula: see text] over finite fields in the case of [Formula: see text] with [Formula: see text]. In particular, we provide some precise characterizations for [Formula: see text] being permutation polynomials over finite fields with characteristic [Formula: see text] when [Formula: see text], or [Formula: see text], or [Formula: see text].
CAT: Using Cross Attention Transformation to Build Correlation of RGB-D for Road Detection

Road detection plays a vital role in advanced driver assistance systems (ADAS). In recent years, the mainstream methods use the data fusion to combine the advantages of RGB images and depth images. However, the fusion operation utilizes a simple pixel-wise summation and concatenation strategy, leading to lose the correlation between the two types of data. To this end, inspired by the core idea of Transformer, we propose a cross attention transformation module, which effectively fuses the RGB features and depth features using the attention mechanism. Specifically, the correlation attention features of RGB-D are generated by the cross-attention mechanism. Then the attention features are fused to enhance the attention response and capture global context information. Experiments are conducted in the two popular datasets including the KITTI road dataset and Cityscapes dataset. Empirical results show that the accuracy of road detection is improved by our fusion method. In the KITTI road Benchmark, Our method gets the better MaxF in UM_Road(95.02 %) and UMM_Road(97.03%).
Online Distributed Identification for Wireless Sensor Networks

This chapter introduces an online dual-rate distributed identification framework for wireless sensor networks. Distributed identification is a concept that allows an individual unit to obtain local estimation using part of the data, and the obtained local estimation can then be used as a basis for global estimation. In this chapter, typical architectures of wireless sensor networks will first be introduced, including centralized, decentralized and distributed networks. Then, the online dual-rate distributed identification approach is introduced for wireless sensor networks. Filtering method using only raw observations collected at each sensor is introduced. The preliminary local identification results are then compressed before transmitting to the central station for fusion. At the central station, Bayesian fusion is developed to integrate the compressed local identification results transmitted from the sensor nodes in order to obtain reliable global estimation. As a result, the large identification uncertainty in the local identification results can be substantially reduced. In addition to data compression, a dual-rate strategy for sampling and transmission/fusion is used to alleviate the data transmission burden so that online model updating can be realized efficiently for wireless sensor networks. The computational framework in this chapter will be followed in the next chapter, where specific algorithms for handling asynchronous data and multiple outlier-corrupted data will be introduced.
The 13th International Automated Negotiating Agent Competition Challenges and Results

An international competition for negotiating agents has been organized for years to facilitate research in agent-based negotiation and to encourage the design of negotiating agents that can operate in various scenarios. The 13th International Automated Negotiating Agents Competition (ANAC 2022) was held in conjunction with IJCAI2022. In ANAC2022, we had two leagues: Automated Negotiation League (ANL) and Supply Chain Management League (SCML). For the ANL, the participants designed a negotiation agent that can learn from the previous bilateral negotiation sessions it was involved in. In contrast, the research challenge was to make the right decisions to maximize the overall profit in a supply chain environment, such as determining with whom and when to negotiate. This chapter describes the overview of ANL and SCML in ANAC2022, and reports the results of each league, respectively.
Integrating Interactive Web-Based Modules to Enhance Explainability of a Medical AI Dashboard for Predicting Readmission to Cardiovascular ICUs

Background: Artificial intelligence (AI)-driven decision assistance systems have great potential in the medical field, however they are still considered a black box by most medical experts. Hence, the goal of this project was to provide real-time AI-driven decision assistance for monitoring patients in cardiovascular intensive care units (ICUs) by means of explainable interactive graphical user interface (GUI) modules. To this end, we developed and analyzed different web-based modules to enhance the interpretability of the decisions made by the AI model, given the example of readmission prediction.
Efficient Rate Control Algorithm for 3-D Wavelet-Based Scalable Video Coding

Rate control technique is the typical application in rate-distortion optimization, also is an indispensable module in video coding systems, which makes the encoder output a specific bit rate to meet the requirements of channel bandwidth and storage space. 3-D wavelet-based scalable video coding provides a natural way in producing embedded bitstreams with full scalability and fine granularity for in-network adaptation. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda$</tex> domain rate control technique has been integrated into the latest 3-D wavelet-based SVC, due to its accurate bit estimation and high rate-distortion performance. However, it is found that the rate control algorithm is not the optimal choice within the open-loop motion compensated temporal filtering structure. In this paper, an efficient rate control algorithm is proposed in the context of rate distortion optimization for 3-D wavelet-based scalable video coding. First, different temporal subband content perception characteristics, subband weighting factors, and subband coupling phenomenon have been incorporated into our algorithm to select the Lagrange multiplier adaptively; Second, the proposed model is obtained to enhance the rate control performance. Finally, based on the proposed rate control model, the coding parameters are adaptively adjusted to realize the rate control objective. Experimental results show that the proposed rate control algorithm turns out to be much better than the reference methods in terms of the rate control accuracy, rate-distortion performance, and reconstructed video quality with negligible additional computational complexity. These research results of test and comparison above demonstrate that the proposed rate control algorithm is effective and feasible, and the corresponding model has an excellent performance.