Reviewerly/exHarmony huggingface dataset:
Modeling Others as a Player in Non-cooperative Game for Multi-agent Coordination

An modeling other agents (MOA) constructs a model of other agents in every agent. It enables the agents to predict the actions of other agents and achieve coordinated and effective interactions in multi-agent systems. However, the relationship between the executed and predicted actions of agents is vague and diverse. To clarify the relationship, we proposed a method by which an agent through communications constructs its MOA using the historical data of other agents and asymmetrically treats itself and its MOA in a non-cooperative game to obtain Stackelberg equilibrium (SE). Subsequently, the SE are used to choose actions. We experimentally demonstrated that, in a partially observable and mixed cooperative-competitive environment, agents using our method with reinforcement learning could establish better coordination and engage in behaviors that are more appropriate compared to conventional methods. We then analyzed the coordinated interaction structure generated in the trained network to clarify the relationship between individual agents.
Discrete simulation optimization for tuning machine learning method hyperparameters

An important aspect of machine learning (ML) involves controlling the learning process for the ML method in question to maximize its performance. Hyperparameter tuning (HPT) involves selecting suitable ML method parameters that control its learning process. Given that HPT can be conceptualized as a black box optimization problem subject to stochasticity, simulation optimization (SO) methods appear well suited to this purpose. Therefore, we conceptualize HPT as a discrete SO problem and demonstrate the use of the Kim and Nelson (KN) ranking and selection method, and the stochastic ruler (SR) and the adaptive hyperbox (AH) random search methods for HPT. We also construct the theoretical basis for applying the KN method. We demonstrate the application of the KN and the SR methods to a wide variety of machine learning models, including deep neural network models. We then successfully benchmark the KN, SR and the AH methods against multiple state-of-the-art HPT methods.
Adversarial Multi-task Learning for Efficient Chinese Named Entity Recognition

Named entity recognition (NER) is a fundamental task for information extraction applications. NER is challenging because of semantic ambiguities in academic literature, especially for non-Latin languages. Besides word semantic information, recognizing Chinese named entities needs to consider word boundary information, as words contained in Chinese texts are not separated with spaces. Leveraging word boundary information could help to determine entity boundaries and thus improve entity recognition performance. In this article, we propose to combine word boundary information and semantic information for named entity recognition based on multi-task adversarial learning. Specifically, we learn commonly shared boundary information of entities from multiple kinds of tasks, including Chinese word segmentation (CWS), part-of-speech (POS) tagging, and entity recognition, with adversarial learning. We learn task-specific semantic information of words from these tasks and combine the learned boundary information with the semantic information to improve entity recognition with multi-task learning. We then propose a compression method based on improved clustering to accelerate the proposed model. We conduct extensive experiments on four public benchmark datasets and two private datasets, compared with state-of-the-art baseline models, and the experimental results demonstrate that our model achieves considerable performance improvements on various evaluation datasets.
Keamanan Informasi Data Dalam Pemanfaatan Teknologi Informasi Pada PT Bank Central Asia (BCA)

In this era of very rapid growth of information systems, the security of information is something that must be considered, because if information can be accessed by unauthorized or irresponsible people, then the accuracy of the information will be doubted, it will even become information that is healed. Basically a secure system will protect the data in it such as user assistance (user identification), user authentication (user authentication), user authorization (user authorization). Several possible attacks (Hacking) that can be carried out, such as Intrusion, denial of services, joyrider, vandals, piracy, sniffing, spoofing and others. There are many kinds of threats to information systems, including: data theft, illegal use of systems, illegal data destruction, illegal data modification, system failures, human error (HR-human resources), natural disasters. The purpose of information security is prevent threats to the system and detect and repair damage that occurs to the system.
English pronunciation quality evaluation system based on data mining algorithm

Chinese English learners are constrained by factors such as the English learning environment and educational level, and have encountered many problems in oral learning. For the evaluation of English pronunciation quality, some existing CAT systems both domestically and internationally focus on vocabulary and grammar learning in oral teaching. The evaluation of English oral exams is still conducted manually, with issues such as high subjectivity, uneven standards, slow speed, repeatability, and instability. With the continuous improvement of big data, cloud technology, and language teaching methods, the English speech recognition and pronunciation quality evaluation system based on DM algorithm will bring new opportunities for English oral teaching, correcting pronunciation errors, and improving English oral proficiency.
How the Concrete Diplodocus paper came to be

Last time, I told you about my new paper, The Concrete Diplodocus of Vernal (Taylor et al. 2023), and finished up by saying this: “But Mike, you ask — how did you, a scientist, find yourself writing a history...
Improved Drosophila Food-Search Algorithm for Structural and Mechanical Optimization Problems

The metaheuristic optimization methods are non-gradient-based search techniques that wildly utilized for solving various optimization cases in the different disciplines. Drosophila food-search algorithm (DFO) is a metaheuristic search approach that models the food-search mechanism of the Drosophila Melanogaster incest in the nature. It applies a three-phase search pattern to explore the desired problem’s search domain. To provide a proper level of population diversity during the optimization process, it applied the quadratic approximation search approach. In this chapter, an improved version of the DFO is utilized for solving structural optimization problems. The attained outcomes are reported and compared with five other well-stablished methods as differential evolution (DE), firefly algorithm (FA), teaching and learning-based optimization (TLBO), Drosophila food-search optimization (DFO), and integrated particle swarm optimization (iPSO). The acquired results demonstrate that the proposed IDFO, specially compared with the standard DFO, provides an appropriate dynamic balance between exploration and exploitation search behaviors during the optimization process. Also, the proposed method in comparison with all other selected methods has the most stable behavior with the lowest standard deviation value.
Neural Network Bootstrap Forecast Distributions with Extreme Learning Machines

This paper proposes and discusses a new procedure to estimate the forecast distribution for nonlinear autoregressive time series. The approach employs a feed-forward neural network estimated using extreme learning machines (ELMs) to approximate the original nonlinear process and the pair bootstrap as a resampling device. Compared with conventional neural network algorithms, ELMs have substantial advantages such as fast learning speed and ease of implementation. Moreover, they are particularly useful in all cases which require real-time retraining of the network, significantly reducing the computational problems of the bootstrap procedure. The proposed approach is instrumental in all applications where time series should be longer to justify using complex neural network models, such as LSTM or other deep learning approaches. This is the case, for example, of economic time series, where it is rare to find time series longer than a few hundred-time points. The results of a Monte Carlo simulation experiment show that ELMs can significantly reduce the computational burden of the overall procedure while preserving the good accuracy of completely tuned neural networks.
Towards learning-based denoising of light fields

In recent years, new emerging immersive imaging modalities, e.g. light fields, have been receiving growing attention, becoming increasingly widespread over the years. Light fields are often captured through multi-camera arrays or plenoptic cameras, with the goal of measuring the light coming from every direction at every point in space. Light field cameras are often sensitive to noise, making light field denoising a crucial pre- and post-processing step. A number of conventional methods for light field denoising have been proposed in the state of the art, making use of the redundant information coming from the different views to remove the noise. While learning-based denoising has demonstrated good performance in the context of image denoising, only preliminary works have studied the benefit of using neural networks to denoise light fields. In this paper, a learning-based light field denoising technique based on a convolutional neural network is investigated by extending a state-of-the-art image denoising method, and taking advantage of the redundant information generated by different views of the same scene. The performance of the proposed approach is compared in terms of accuracy and scalability to state-of-the-art methods for image and light field denoising, both conventional and learning-based. Moreover, the robustness of the proposed method to different types of noise and their strengths is reviewed. To facilitate further research on this topic, the code is made publicly available at https://github.com/mmspg/Light-Field-Denoising
Combining Frameworks to Improve Military Health System Quality and Cybersecurity

Existing conceptual frameworks and commercially available technology could be considered to rapidly operationalize the use of Quality Measures (QM) within military health systems (Costantino et al. 2020). Purchased healthcare as well as digital healthcare services have paved the way for data collection from multiple information systems thus offering stakeholders actionable intelligence to both guide and measure healthcare outcomes. However, the collection of data secondary to Smart Devices, disparate information systems, cloud services, and the Internet of Medical Things (IOMT) is a complication for security experts that also affect clients, stakeholders, organizations, and businesses delivering patient care. We have combined three conceptual frameworks: (1) Donabedian’s Quality Attributes Framework (DQA), (2) The National Academy of Medicine (NAM) framework, and the (3) Healthcare and Public Health (HPH) Cybersecurity Framework (HCF). Each of these frameworks is well-tested, widely accepted, and referenced as the gold standard in delivering quality care (DQA and NAM) and cybersecurity (HCF) for healthcare. Separately, each framework provides long-used analysis, guidance, and application of quality reliable measures of healthcare services. The combined frameworksmay enhance data integrity (collection and standardized safety of data in regard to regulatory compliance). The combined frameworks also support identifying gaps in quality of care, support the prioritization of cost-effective methodologies that predictably improve patient outcomes, and support DHA’s quality measures. Finally, we offer the importance of understanding the components of quality measures in a more precise manner while maintaining information technology (IT) governance compliance.
QRIS and GOST: A Symbiotic Approach for Secure QR Code Transactions

This study investigates using the GOST encryption algorithm to enhance security within the QRCode Indonesia Standard (QRIS).Given the ubiquity of QR Codes in digital transactions, particularly for Micro, Small, and Medium Enterprises (MSMEs), robust security measures are paramount.We evaluated the GOST algorithm's ability to secure data within the QR Code, converting the code into an encrypted data carrier decipherable only via a specially-equipped QR Code reader.Our findings reveal that the GOST algorithm presents a potent defence mechanism against potential malicious attacks, offering a promising path for QR Code security enhancement.However, the comprehensive application of this measure within QRIS poses challenges, including the need for specialized reader applications and a broader understanding of encryption procedures.Future research opportunities lie in optimizing encryption processes, exploring alternative encryption algorithms, and developing user-friendly, secure QR Code reader applications, which could ultimately support a more secure standard for QR Code usage.
System characterization report on the BlackSky Global multispectral sensor

First posted June 6, 2023 For additional information, contact: Director, Earth Resources Observation and Science CenterU.S. Geological Survey47914 252nd StreetSioux Falls, SD 57198Contact Pubs Warehouse This report addresses system characterization of the BlackSky Global satellites and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.The BlackSky Global satellites are three-band multispectral imagers (red, green, and blue multispectral bands plus a panchromatic band) with a 0.8- to 0.9-meter (m) pixel ground sample distance for the assessed satellites. BlackSky Global satellites 9 and 12–17 were launched in March and December 2021, respectively, into a Sun-synchronous orbit of 430–450 kilometers with an inclination of 42–53 degrees and a swath width of 6 kilometers at nadir. Each Global satellite has an expected lifetime of about 3 years. More information on the BlackSky Global satellites is available in the “Land Remote Sensing Satellites Online Compendium” (https://calval.cr.usgs.gov/apps/compendium) and from BlackSky at www.BlackSky.com.The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior) and spatial performances. Results of these analyses indicate that the assessed BlackSky Global satellites have an interior geometric performance in the range of −0.011 m (−0.012 pixel) to 0.007 m (0.008 pixel) in easting and −0.018 m (−0.020 pixel) to 0.012 m (0.013 pixel) in northing in band-to-band registration; an exterior geometric performance using ground control points of 8.0-m circular error (95-percent certainty) for orthorectified products and 10.7- to 17.4-m circular error (95-percent certainty) for nonorthorectified products, depending on the geolocation metadata used; and a spatial performance in the range of 1.70 to 2.43 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.032 to 0.084.
Probabilistic Programming with Stochastic Probabilities

We present a new approach to the design and implementation of probabilistic programming languages (PPLs), based on the idea of stochastically estimating the probability density ratios necessary for probabilistic inference. By relaxing the usual PPL design constraint that these densities be computed exactly, we are able to eliminate many common restrictions in current PPLs, to deliver a language that, for the first time, simultaneously supports first-class constructs for marginalization and nested inference, unrestricted stochastic control flow, continuous and discrete sampling, and programmable inference with custom proposals. At the heart of our approach is a new technique for compiling these expressive probabilistic programs into randomized algorithms for unbiasedly estimating their densities and density reciprocals. We employ these stochastic probability estimators within modified Monte Carlo inference algorithms that are guaranteed to be sound despite their reliance on inexact estimates of density ratios. We establish the correctness of our compiler using logical relations over the semantics of λ SP , a new core calculus for modeling and inference with stochastic probabilities. We also implement our approach in an open-source extension to Gen, called GenSP, and evaluate it on six challenging inference problems adapted from the modeling and inference literature. We find that: (1) ‍can automate fast density estimators for programs with very expensive exact densities; (2) convergence of inference is mostly unaffected by the noise from these estimators; and (3) our sound-by-construction estimators are competitive with hand-coded density estimators, incurring only a small constant-factor overhead.
Data Mapping using Combining Clustering Methods and C.45 Classification

School participation is measured by the Pure Participation Rate (APM).This study examines whether data mining can generate new knowledge.The Central Sumatra Statistic Central Agency (BPS-North Sumatra) provided secondary statistics on APM by city/district (2011-2019) for elementary, junior high, high school, and PT.Data mining uses clustering (k-means) and classification (Decision tree).This cluster maps the APM.Mapping clusters are utilized again for categorization.Cluster value ranges indicate classification.C1 was the high APM cluster, and C2 was the low APM cluster.RapidMiner aids processing.The study found 18 high-cluster (C1) cities and 15 low-cluster cities (C2).Based on the clustering results obtained, classification results show that SMA and PT become influential attributes in mapping the area based on the Decision tree method, resulting in 3 rules: if SMA has a percentage <68,085% (high cluster); if SMA has a percentage> 68,085% and PT has a presentation <18,730% (low cluster); and if SMA and PT have a presentation> 18,730%.(high cluster).Classification and clustering have yielded new data.
Simple questions on simple associations: regularity extraction in non-human primates

Abstract When human and non-human animals learn sequences, they manage to implicitly extract statistical regularities through associative learning mechanisms. In two experiments conducted with a non-human primate species (Guinea baboons, Papio papio ), we addressed simple questions on the learning of simple AB associations appearing in longer noisy sequences. Using a serial reaction time task, we manipulated the position of AB within the sequence, such that it could be either fixed (by appearing always at the beginning, middle, or end of a four-element sequence; Experiment 1) or variable (Experiment 2). We also tested the effect of sequence length in Experiment 2 by comparing the performance on AB when it was presented at a variable position within a sequence of four or five elements. The slope of RTs from A to B was taken for each condition as a measurement of learning rate. While all conditions differed significantly from a no-regularity baseline, we found strong evidence that the learning rate did not differ between the conditions. These results indicate that regularity extraction is not impacted by the position of the regularity within a sequence and by the length of the sequence. These data provide novel general empirical constraints for modeling associative mechanisms in sequence learning.
Trends in Research on Language of Infants

Objectives The purpose of this study was to examine research trends related to infant language published in Korea from 2007 to 2020, in order to suggest directions and implications for research in the field of infant language. Methods A total of 165 studies related to infant language were analyzed for trends by research period, research subject, research method, and research topic. Results As a result of this study, by research period, it was published at a similar level from 2007 to 2011, then increased from 2012 to 2015, and then gradually decreased. By research subject, studies targeting infants were the most common, followed by mixed studies involving infants and adults, literature studies, and studies involving adults. By research method, quantitative research was the most common, followed by qualitative research, literature research, and mixed research. By research subject, language development was the most common, followed by vocabulary development, communication, instructional media, parental environment, developmental factors, reading, program, physical environment, teaching method, and teacher environment. Conclusions Based on these results, the research direction and educational implications for the future research and development in the field of language of infants were presented.
A Study on Literature and Literature Education in the Era of Ecological Crisis

Objectives The purpose of this study is to explore the direction of literature and literary education on how to approach the issue of the ecological crisis facing our society today in terms of literature and literary education. Methods In this study, recent theoretical searches related to the ecological crisis were reviewed, literary directions were confirmed through analysis of specific texts, and literary educational measures were derived from them. Results As the main orientation of literature in the era of ecological crisis, it was first suggested that literature is actively responding to the ecological crisis as a problem of ‘here and now’. Second, it was pointed out that literature aims to connect and ally with non-human beings in the era of ecological crisis, and this was analyzed in relation to Bruno Latour's theory of Actor-Network Theory. Third, literature of our time revealed that it is trying to respond to the ecological crisis in a way that urges the transition of ‘affect’ to the ecological crisis. Conclusions Based on this, this study proposed classrooms that read and write ecological problems of “Now Here,” seek connection and symbiosis, and experiment with thinking and finding solutions to the ecological crisis as literary education measures in the era of the ecological crisis.