Reviewerly/exHarmony huggingface dataset:
An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets

Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets taken from the UCI Machine Learning Repository database. The clustering performance of the WOA-LF was compared with the performance of k-means, k-medoids, fuzzy c-means and the original WOA clustering algorithms. Application results showed that WOA-LF has more successful clustering performance in general and can be used as an alternative algorithm in clustering problems.
An Integrative Information Aqueduct to Close the Gaps between Global Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources (iAqueduct)

&amp;lt;p&amp;gt;In the past decades, space-based Earth Observations (EO) have been rapidly advancing in monitoring the global water cycle, in particular for the variables related to precipitation, evapotranspiration and soil moisture, often at (tens of) kilometre scales. Whilst these data are highly effective to characterise water cycle variation at regional to global scale, they are less suitable for sustainable management of water resource, which needs more detailed information at local and field scale due to inhomogeneous characteristics of the soil and vegetation. To effectively exploit existing knowledge at different scales we thus need to answer the following questions: How to downscale the global water cycle products to local scale using multiple sources/scales of EO data? How to explore and apply the downscaled information at the management level for understanding soil-water-vegetation-energy processes? And how to use such fine-scale information to improve the management of soil and water resources? An integrative information aqueduct (iAqueduct) is proposed to close the gaps between global satellite observation of water cycle and local needs of information for sustainable management of water resources. iAqueduct aims to accomplish its goals by combining Copernicus satellite data (with intermediate resolutions) with high resolution Unmanned Aerial System (UAS) and in-situ observations to develop scaling functions for soil properties and soil moisture and evapotranspiration at high spatial resolution scales.&amp;lt;/p&amp;gt;
The Regulation and Use of Artificial Intelligence and 5G Technology to Combat Cybercrime and Financial Crime in South African Banks

Artificial intelligence (AI) and fifth generation network technology (5G) are now being utilised by some companies and financial institutions such as banks to enhance their competitiveness and expand their businesses. The general types of AI include functional AI, interactive AI, text AI, visual AI and analytic AI. The key components of AI include machine learning, fast Internet connectivity, deep learning, neural networks and advanced data analysis. These components may be complemented by the adoption and use of standard 5G cellular networks. 5G utilises broadband Internet access and Internet connection, and is now employed by some banking institutions, especially in developed countries. It is not clear whether South African banking institutions have adopted 5G for their Internet connectivity and operations. AI and 5G may be used to detect and combat cybercrimes in banking institutions. On the other hand, AI and 5G may also be abused by cybercriminals to commit financial crimes such as money laundering and insider trading. In this regard it is submitted that South African policy makers should carefully revise the Cybersecurity Bill B6-2017 (Cybercrimes Bill) to embrace the use of AI and 5G to detect and combat cybercrimes in South African banks. Accordingly, this article examines the adequacy of the Cybercrimes Bill. It also explores the regulation and use of 5G and AI to detect, prevent and combat cybercrimes in banks and other financial institutions in South Africa.
Song Recommendation Application Using Speech Emotion Recognition

This research was conducted to facilitate the interaction between radio broadcasters and radio listeners during the song request process. This research was triggered by the difficulty of the broadcasters in monitoring song requests from listeners. The system is made to accommodate all song requests by listeners. The application produced in this study uses speech emotion recognition technology based on a person's mood obtained from the spoken words. This technology can change the voice into one of the mood categories: neutral, angry, sad, and afraid. The k-Nearest Neighbor method is used to get recommendations for recommended song titles by looking for the closeness of the value between the listener's mood and the availability of song playlists. kNN is used because this method is suitable for user-based collaborative problems. kNN will recommend three songs which then be offered to listeners by broadcasters. Based on tests conducted to the broadcasters and radio listeners, this study has produced a song request application by recommending song titles according to the listener's mood, the text message, the searching songs, and the song requests and the song details that have been requested. Functional test that has been carried out has received 100 because all test components have succeeded as expected.
A Recipe for Global Convergence Guarantee in Deep Neural Networks

Existing global convergence guarantees of (stochastic) gradient descent do not apply to practical deep networks in the practical regime of deep learning beyond the neural tangent kernel (NTK) regime. This paper proposes an algorithm, which is ensured to have global convergence guarantees in the practical regime beyond the NTK regime, under a verifiable condition called the expressivity condition. The expressivity condition is defined to be both data-dependent and architecture-dependent, which is the key property that makes our results applicable for practical settings beyond the NTK regime. On the one hand, the expressivity condition is theoretically proven to hold data-independently for fully-connected deep neural networks with narrow hidden layers and a single wide layer. On the other hand, the expressivity condition is numerically shown to hold data-dependently for deep (convolutional) ResNet with batch normalization with various standard image datasets. We also show that the the proposed algorithm has generalization performances comparable with those of the heuristic algorithm, with the same hyper-parameters and total number of iterations. Therefore, the proposed algorithm can be viewed as a step towards providing theoretical guarantees for deep learning in the practical regime.
Language-based Decisions

In Savage's classic decision-theoretic framework, actions are formally defined as functions from states to outcomes. But where do the state space and outcome space come from? Expanding on recent work by Blume, Easley, and Halpern (BEH), we consider a language-based framework in which actions are identified with (conditional) descriptions in a simple underlying language, while states and outcomes (along with probabilities and utilities) are constructed as part of a representation theorem. Our work expands the role of language from that of BEH by using it not only for the conditions that determine which actions are taken, but also the effects. More precisely, we take the set of actions to be built from those of the form "do(phi)", for formulas phi in the underlying language. This presents a problem: how do we interpret the result of do(phi) when phi is underspecified (i.e., compatible with multiple states)? We answer this using tools familiar from the semantics of counterfactuals: roughly speaking, do(phi) maps each state to the "closest" phi-state. This notion of "closest" is also something we construct as part of the representation theorem; in effect, then, we prove that (under appropriate assumptions) the agent is acting as if each underspecified action is first made definite and then evaluated (i.e., by maximizing expected utility). Of course, actions in the real world are often not presented in a fully precise manner, yet agents reason about and form preferences among them all the same. Our work brings the abstract tools of decision theory into closer contact with such real-world scenarios.
Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene

The major paradigm of applying a pre-trained language model to downstream tasks is to finetune it on labeled task data, which often suffers instability and low performance when the labeled examples are scarce.One way to alleviate this problem is to apply post-training on unlabeled task data before fine-tuning, adapting the pre-trained model to target domains by contrastive learning that considers either tokenlevel or sequence-level similarity.Inspired by the success of sequence masking, we argue that both token-level and sequence-level similarities can be captured with a pair of masked sequences.Therefore, we propose complementary random masking (CRM) to generate a pair of masked sequences from an input sequence for sequence-level contrastive learning and then develop contrastive masked language modeling (CMLM) for post-training to integrate both token-level and sequence-level contrastive learnings.Empirical results show that CMLM surpasses several recent post-training methods in few-shot settings without the need for data augmentation.
HUBUNGAN KESIAPAN ORANG TUA DENGAN KEMAMPUAN PSIKOMOTORIK SISWA DALAM PEMBELAJARAN DARING DIMASA PANDEMI COVID-19

COVID-19 is a type of virus that attacks the respiratory system, this virus is a family with SARS and MERS. The impact of this virus makes some countries force online learning, namely learning through internet social media, online learning has a very beneficial effect on the psychomotor aspects of students. The purpose of this study was to determine the relationship of parental readiness in online learning with students' psychomotor abilities. Subjects in this study were Mohammad Hatta elementary school students grade 3 and parents of students with 30 each. The parameters studied were online readiness including the provision of infrastructure tools, application systems, and online content. The parameters used for the psychomotor aspects of art and cultural materials and craft making a miniature house include the preparation stage, the process stage, the product. Data collection technique is observation using a Likert scale questionnaire. Data analysis using Pearson's task test. The results showed that the average readiness of parents during online learning in the provision of infrastructure tools was 73%, application systems 62%, online content 62%. The average value of psychomotor ability is 81%. The findings of this study are the correlation between parents 'readiness in online learning and students' psychomotor abilities (0.638 0.361)
Planning Multimodal Exploratory Actions for Online Robot Attribute Learning

Robots frequently need to perceive object attributes, such as "red," "heavy," and "empty," using multimodal exploratory actions, such as "look," "lift," and "shake."Robot attribute learning algorithms aim to learn an observation model for each perceivable attribute given an exploratory action.Once the attribute models are learned, they can be used to identify attributes of new objects, answering questions, such as "Is this object red and empty?"Attribute learning and identification are being treated as two separate problems in the literature.In this paper, we first define a new problem called online robot attribute learning (On-RAL), where the robot works on attribute learning and attribute identification simultaneously.Then we develop an algorithm called information-theoretic reward shaping (ITRS) that actively addresses the trade-off between exploration and exploitation in On-RAL problems.ITRS was compared with competitive robot attribute learning baselines, and experimental results demonstrate ITRS' superiority in learning efficiency and identification accuracy. 1
Analisis Fundamental Cryptocurrency terhadap Fluktuasi Harga: Studi Kasus Tahun 2019-2020

Purpose: This study aimed to analyze cryptocurrency fundamentals against price fluctuations (case study 2019-2020). Research Methodology: The data in the research paper were accessed on the coinmarketcap.com website. The sample of this research is Bitcoin, Ethereum, Litecoin, and Bitcoin Cash. Results: The results show that: 1) Bitcoin Market Cap, Ethereum Market Cap, Litecoin Market Cap, Bitcoin Cash Market Cap have a positive and significant effect on price fluctuations. 2) Bitcoin Volume and Bitcoin Cash Volume have a positive and insignificant effect on price fluctuations. 3) Ethereum volume has a positive and significant effect on price fluctuations. 4) Litecoin volume has a negative and significant effect on price fluctuations. Limitations: Further researchers are expected to add variables such as (global macroeconomics, cryptocurrency Blockchain technology changes, psychological factors), different variables (quantitative and qualitative) and combine models to predict cryptocurrency price fluctuations by analyzing them fundamentally and technically. Contribution: This research implies that cryptocurrencies with highly speculative prices have the same fundamental value as the stock price.
Impact of virtual vs. face-to-face learning on 21st-century skills among pre-service bilingual teachers

The so-called ‘twenty-first century skills’ (Dudeney, Hockly and Pegrum, 2014) have been identified as essential literacies for the young generation, who must use creativity and innovation, collaboration and teamwork, critical thinking, problem-solving, autonomy, flexibility and lifelong learning to function effectively. Bilingual pre-service teachers are entitled to develop these skills for both their own education and that of their future students (Savage and Barnett, 2015). This research will analyse the opinions of 45 bilingual pre-service teachers on the development of their twenty-first century skills through either online or face-to-face teaching. Quantitative and qualitative data will be scrutinized under a mixed-methods research, which will throw light on how participants think such skills are better developed. Findings will show that teacher students think that some of these skills are better developed under a face-to-face modality of learning (e.g. teamwork and collaboration), whereas others (e.g. autonomy and innovation) evolve better under an online framework of learning. Twenty-first century literacies push the educational boundaries of bilingual teachers as they are entitled to have a repertoire of communication skills that make us, teacher educators, pursue academic rigour regarding our own teaching and updated training.
Digital education: cultural aspects of communication in the modern educational environment

The article presents an analysis of modern approaches to the concepts of “information and educational environment” and “digital educational environment”, in which modern students -future bachelors-develop competencies in the field of intercultural communication. Special attention is paid to the consideration of the features of professional training, as well as the requirements for future specialists. The problems of modern digital education closely follow from the problems of verbal interaction of communicants in the educational environment. The considered issues of updating the needs for an educational product cannot be separated from the realization of the possibilities of didactic and methodological support for digital education, which is realized in the communicative interaction of a student and a teacher. The authors conducted a number of experimental studies in the field of studying the needs of students in educational services and the opportunities for teachers to effectively realize their educational potential. The authors offer recommendations for improving educational technologies in the context of digitalization, improving the technical and communicative literacy of teachers, conducting trainings and trainings aimed at increasing the stress tolerance of teachers, providing them with comfortable working conditions in remote mode, modernizing the system of technical regulation in the process of providing educational activities, conducting explanatory work with students, motivating them to contact work in remote access, improving the culture of group and interpersonal communication
Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning

A fundamental challenge in reinforcement learning is to learn policies that generalize beyond the operating domains experienced during training. In this paper, we approach this challenge through the following invariance principle: an agent must find a representation such that there exists an action-predictor built on top of this representation that is simultaneously optimal across all training domains. Intuitively, the resulting invariant policy enhances generalization by finding causes of successful actions. We propose a novel learning algorithm, Invariant Policy Optimization (IPO), that implements this principle and learns an invariant policy during training. We compare our approach with standard policy gradient methods and demonstrate significant improvements in generalization performance on unseen domains for linear quadratic regulator and grid-world problems, and an example where a robot must learn to open doors with varying physical properties.
Analisa Tingkat Kepuasan Masyarakat Terhadap Pelayanan Pengadilan Agama Pematangsiantar Menggunakan Algoritma C4.5

The purpose of this study was to determine how much the level of community satisfaction with services in the Pematangsiantar Religious Court. In this study, researchers used the data mining technique C.45 algorithm. The source of research data used is by giving questionnaires to the community. In this case the researchers used the assessment attributes, among others: Service, ability, behavior, cost, and infrastructure. In this study the alternative used as a sample is Pematangsiantar City Community. the results of this study will become input for the Pematangsiantar Religious Court to provide the best possible service so that the community is satisfied with the service.
Structured Logconcave Sampling with a Restricted Gaussian Oracle

We give algorithms for sampling several structured logconcave families to high accuracy. We further develop a reduction framework, inspired by proximal point methods in convex optimization, which bootstraps samplers for regularized densities to improve dependences on problem conditioning. A key ingredient in our framework is the notion of a "restricted Gaussian oracle" (RGO) for $g: \mathbb{R}^d \rightarrow \mathbb{R}$, which is a sampler for distributions whose negative log-likelihood sums a quadratic and $g$. By combining our reduction framework with our new samplers, we obtain the following bounds for sampling structured distributions to total variation distance $\epsilon$. For composite densities $\exp(-f(x) - g(x))$, where $f$ has condition number $\kappa$ and convex (but possibly non-smooth) $g$ admits an RGO, we obtain a mixing time of $O(\kappa d \log^3\frac{\kappa d}{\epsilon})$, matching the state-of-the-art non-composite bound; no composite samplers with better mixing than general-purpose logconcave samplers were previously known. For logconcave finite sums $\exp(-F(x))$, where $F(x) = \frac{1}{n}\sum_{i \in [n]} f_i(x)$ has condition number $\kappa$, we give a sampler querying $\widetilde{O}(n + \kappa\max(d, \sqrt{nd}))$ gradient oracles to $\{f_i\}_{i \in [n]}$; no high-accuracy samplers with nontrivial gradient query complexity were previously known. For densities with condition number $\kappa$, we give an algorithm obtaining mixing time $O(\kappa d \log^2\frac{\kappa d}{\epsilon})$, improving the prior state-of-the-art by a logarithmic factor with a significantly simpler analysis; we also show a zeroth-order algorithm attains the same query complexity.
Virtual field experiences in a web-based videogame environment: Open-ended examples of existing and fictional field sites

Abstract. We present two original, videogame-style field-geology experiences designed to allow flexible, open-ended exploration for geologic mapping and structural geology. One simulation features the Whaleback anticline, a site in central Pennsylvania (USA) with three-dimensional exposure of a 30-m-high fold, based on a terrain model that was acquired through structure-from-motion photogrammetry. The second example is a fictional location with simplified geology, built with digital modeling software and inspired by the geology of northwestern Washington. Users move through the terrain, as if in the field, selecting where to make observations of the geologic structure. Additionally, these virtual field experiences provide novel visualization opportunities through tools like a geodetic compass that instantly plots data to a stereonet, and a jetpack simulation which allows the user to interrogate geologic surfaces in hard-to-reach locations. We designed the virtual field experiences in a widely-used videogame-creation software and published the field simulations for access via the internet and common web browsers, so that no special hardware or software is required to play. We implemented these field simulations to partially replace field and lab exercises in two different courses offered remotely through the University of Washington Department of Earth and Space Sciences, with assignments that address many of the learning goals of traditional in-person exercises. Because the virtual field experiences are open-ended, other instructors can design different exercises to meet different learning goals. While this game environment currently serves as an enhancement to remote education, this format can also augment traditional educational experiences, overcoming several challenges to accessing the field or particular outcrops and thereby broadening opportunities for participation and scientific collaboration.
Unstable Throughput: When the Difficulty Algorithm Breaks

In Proof-of-Work blockchains, difficulty algorithms serve the crucial purpose of maintaining a stable transaction throughput by dynamically adjusting the block difficulty in response to the miners' constantly changing computational power. Blockchains that may experience severe hash rate fluctuations need difficulty algorithms that quickly adapt the mining difficulty. However, without careful design, the system could be gamed by miners using coin-hopping strategies to manipulate the block difficulty for profit. Such miner behavior results in an unreliable system due to the unstable processing of transactions.We provide an empirical analysis of how Bitcoin Cash's difficulty algorithm design leads to cyclicality in block solve times as a consequence of a positive feedback loop. In response, we mathematically derive a difficulty algorithm using a negative exponential filter which prohibits the formation of positive feedback and exhibits additional desirable properties, such as history agnosticism. We compare the described algorithm to that of Bitcoin Cash in a simulated mining environment and verify that the former would eliminate the severe oscillations in transaction throughput.
Professional development of a teacher of preschool education: realizing individual abilities

И. Б. Бичева Т. Г