Reviewerly/exHarmony huggingface dataset:
The Construction of Reality in an AI: A Review

AI constructivism as inspired by Jean Piaget, described and surveyed by Frank Guerin, and representatively implemented by Gary Drescher seeks to create algorithms and knowledge structures that enable agents to acquire, maintain, and apply a deep understanding of the environment through sensorimotor interactions. This paper aims to increase awareness of constructivist AI implementations to encourage greater progress toward enabling lifelong learning by machines. It builds on Guerin's 2008 "Learning Like a Baby: A Survey of AI approaches." After briefly recapitulating that survey, it summarizes subsequent progress by the Guerin referents, numerous works not covered by Guerin (or found in other surveys), and relevant efforts in related areas. The focus is on knowledge representations and learning algorithms that have been used in practice viewed through lenses of Piaget's schemas, adaptation processes, and staged development. The paper concludes with a preview of a simple framework for constructive AI being developed by the author that parses concepts from sensory input and stores them in a semantic memory network linked to episodic data. Extensive references are provided.
Analysis of framework networks for sign detection in deep learning models

This paper analyzes and compares modern deep learning models for the classification of MRI images of the knee joint. An analysis of modern deep computer vision architectures for feature extraction from MRI images is presented. This analysis was used to create applied architectures of machine learning models. These models are aimed at automating the process of diagnosing knee injuries in medical devices and systems. This work is devoted to different types of feature detection framework networks for machine learning architectures that perform magnetic resonance imaging (MRI) image classification of the knee. The resulting models were evaluated on the MRNet validation dataset, calculating the metrics (ROC-AUC), prediction accuracy, F1 score, and Cohen’s K-Kappa. The results of this work also show that Cohen's Kappa metric is important for evaluating models on the MRNet architecture because it provides a deeper understanding of the classification decisions of each model.
Distorted character recognition by an incompatible single-layer dipole neural network

This paper solves two problems: the first problem is devoted to the recognition of distorted symbolic images by a single-layer incompatible dipole neural network, and the second - the optimization of computing resources in the recognition of distorted symbolic images. In particular, the architecture of an incompatible single-layer network with dipole neurons is proposed. Incompatibility of synaptic connections between neurons is based on the fact that significant interaction between dipole neurons exists in their immediate environment. Synaptic connections between dipole neurons are taken into account only between the nearest neighboring neurons, because the synaptic tensor λij between the i -th and j -th dipole neurons is inversely proportional to the distance rij between neighboring i -th and j -th dipole neurons, therefore λij+1&lt;&lt;λij . An algorithm for recognizing incoming distorted symbolic images using an incompatible dipole neural network has been developed and implemented in the Matlab application system. It is shown that for the recognition of input symbol images by an incompatible dipole neural network the computational resource time is shorter compared to a fully connected neural network by n(n+1)/4 times ( n is the number of pixels in columns and rows, respectively, used for encoding of input images). Numerical experiments have shown that the computational time to recognize 0,4n2 distorted characters, which is described by a 5×5 matrix, is 7,5 times less than the recognition time of a fully connected neural network.
Preparedness of Tsunami Disaster in Pandeglang Region Due to The Activity of Mount Krakatau

Pandeglang Regency is one of the areas with the most building damage in The 2018 Anak Krakatau Tsunami. The tsunami in 2018 was caused by the activity of Anak Krakatau Volcano in the form of an avalanche of material on the volcano's cliffs. The subdistricts in Pandeglang Regency affected by the tsunami were Carita Subdistrict, Labuan Subdistrict, and Panimbang Subdistrict. This research evaluates potential damage to buildings to determine the condition of the existing land, determines an evacuation route to a temporary evacuation site (TES), and simulates a tsunami evacuation using this evacuation route. Parameters of run-up height and building type are used as parameters for assessing building damage. The determination of TES is influenced by run-up height, elevation, and distance from the shoreline. Evacuation route planning and evacuation simulation are based on the assumed number of evacuees and the scenario of a tsunami evacuation. The results showed moderate damage to buildings in Carita Subdistrict, Labuan Subdistrict, and Panimbang Subdistrict. The examination of existing land as TES, namely Carita Vacant Land, Carita 1 Middle School, LDII Labuan Mosque, Labuan Shelter Building, Panimbang Vacant Land. According to the tsunami evacuation scenario during the day, evacuation time results for 25-30 minutes with an average speed of 1 m/s. The tsunami evacuation time at night is free of obstacles or with obstacles for 50-85 minutes with an average speed of 1 m/s. The tsunami evacuation time at night is full of free and obstacle-free tours for 60-100 minutes with an average speed of 0.5 m/s. Evacuation time based on simulation results is compared with evacuation time calculated by ETA and other studies as data validation to determine the probability of community preparedness in the Pandeglang Regency. The preparedness community in Pandeglang Regency is in the ready category by 25% in the Carita subdistrict, Labuan subdistrict, and Panimbang Subdistrict.
CURIOSITY AND LEARNING MOTIVATION TOWARD SELF-REGULATED LEARNING AMONG UNDERGRADUATE STUDENTS

&lt;div class="WordSection1"&gt;&lt;p class="AfiliasiCxSpFirst"&gt;Di era pandemi, kegiatan pembelajaran &lt;em&gt;online&lt;/em&gt; menuntut siswa untuk memiliki kemandirian belajar. Beberapa faktor yang mempengaruhi kemandirian belajar adalah rasa ingin tahu dan motivasi belajar. Penelitian ini bertujuan untuk mengetahui pengaruh 1) rasa ingin tahu dan motivasi belajar secara simultan terhadap kemandirian belajar, 2) rasa ingin tahu terhadap kemandirian belajar, dan 3) motivasi belajar terhadap kemandirian belajar. Sampel penelitian ini adalah 45 mahasiswa Pendidikan Matematika. Teknik pengumpulan data menggunakan angket. Data yang diperoleh dianalisis dengan analisis regresi linier berganda. Hasil penelitian ini adalah rasa ingin tahu dan motivasi belajar secara simultan berpengaruh positif terhadap kemandirian belajar. Hubungan antara rasa ingin tahu dan motivasi belajar dengan kemandirian belajar berada pada kategori kuat. Pengaruh rasa ingin tahu dan motivasi belajar terhadap self-regulated learning sebesar 41,4%, sedangkan 58,6% ditentukan oleh variabel lain yang tidak diteliti. Namun, rasa ingin tahu tidak mempengaruhi kemandirian belajar siswa, sedangkan motivasi belajar mempengaruhi kemandirian belajar. Dengan demikian, rasa ingin tahu harus disertai dengan motivasi belajar agar siswa dapat memiliki kemandirian belajar. Pemberian pembelajaran yang meningkatkan rasa ingin tahu siswa harus diserta dengan pemberian motivasi untuk meningkatkan kemandirian belajar siswa.&lt;/p&gt;&lt;p class="Afiliasi"&gt;&lt;strong&gt;&lt;em&gt;Abstract&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;In the pandemic era, online learning activities require students to become independent learners. Several factors that influence learning independently are curiosity and motivation to learn. This study aims to see the effect of 1) learners' curiosity and motivation on independent learning, 2) learners' curiosity on independent learning, and 3) learners' motivation on independent learning. The samples of this research include 45 students of Mathematics Education. Questionnaires were to collect the data. The data obtained were analyzed by using multiple linear regression analysis. The results indicate that curiosity and learning motivation have a positive effect on independent learning. The relationship between curiosity and learning motivation with independent learning is in a strong category. The effect of curiosity and learning motivation on self-regulated learning is 41.4%, while 58.6% is determined by other variables which were not examined. However, curiosity does not affect student independent learning, while learning motivation affects independent learning. Thus, curiosity must be accompanied by learning motivation so that students can become independent learners. The provision of learning that increases students' curiosity must be accompanied by the provision of motivation to increase students' independent learning.&lt;/em&gt;&lt;/p&gt;&lt;/div&gt;
An Anomaly Detector Using Filtering Stockwell Transform and Siamese Convolutional Neural Network in Radio Monitoring (AnoFSTSCNN)

Detecting anomalous behavior in the radio spectrum is a demanding task due to high levels of interference behaviors caused by massive wireless devices.Stockwell transform (ST) has grown in popularity for the analysis of nonstationary and nonlinear signals, but it is yet to be adequately explored in the radio monitoring domain.An approach that consists of FST (filtering Stockwell transform) and SCNN (siamese convolutional neural network) for radio spectrum anomaly detection (AnoFSTSCNN) is proposed in this paper.Four types of anomaly behaviors, including tone, chirp, pulse, and noise frequency modulation (FM) are simulated using MATLAB.Simulation results show that the AnoFSTSCNN has a 13.18% and 29.52% improvement in detection performance over short-time Fourier transform (STFT) and ST, respectively.The proposed approach provides a possible reference solution for radio monitoring and anomalous signal detection.
Towards Model-Agnostic Federated Learning over Networks

&lt;p&gt;We present a model-agnostic federated learning method for decentralized data with an intrinsic network structure.The network structure reflects similarities between the (statistics of) local datasets and, in turn, their associated local models. Our method is an instance of empirical risk minimization, using a regularization term that is constructed from the network structure of data. In particular, we require well-connected local models, forming clusters, to yield similar predictions on a common test set. In principle our method can be applied to any collection of local models. The only restriction put on these local models is that they allow for efficient implementation of regularized empirical risk minimization (training). Such implementations might be available in the form of high-level programming frameworks such as scikit-learn, Keras or PyTorch.&lt;/p&gt;
The effect of interest rates on facilities granted by the country's banks in accelerating Iran's economic growth

The effect of interest rates on facilities granted by the country's banks in accelerating Iran's economic growth
The Students and Faculty Perception of Digital Citizenship Practices in Distance Learning Environment – The Case Study of Al-Quds Open University

COVID-19 pandemic has changed the education landscape and increased the acceptance and adoption of distance educations by both individuals and academic institutions. The rise in e-learning and distance education and the increased access to the Internet and learning technology, it becomes critical that provide positive, safe, legal, and ethical behavioural online. While COVID 19 acerated the use and adoption of online learning, it also highlighted the importance of fostering effective digital citizenship given the level of misinformation and conspiracy theories generated spread on the Internet and social media about the pandemic. In this study, the researcher examined the students and faculty perception of digital citizenship practices in distance learning environments at Al-Quds Open University. The study used a mix method approach with 8 faculty participating in the qualitative interview and 559 students participating in the quantitative survey. The study's findings revealed that while students and teachers were aware of digital citizenship principles, they lacked in-depth understanding and knowledge of topics like digital rights, digital security, and digital ethics. Faculty and students both believed that digital citizenship is vital and required for protecting students' privacy and cultivating ethical technology use. Students and faculty both agreed on the importance of incorporating digital citizenship skills like digital rights, digital security, and digital ethics within e-learning curricula.
A Review Report on Software Quality Measurement and Estimation

&lt;p&gt;Software quality definition encompasses six key factors. These are low levels of defects, high reliability, efficient structure that minimizes insertion of new defects during defect repairs, effective customer support, high user satisfaction, and a high defect repair rate, especially for high-severity defects. Several methods have been proposed for measuring software quality. These include fuzzy logic based frameworks, neuro fuzzy models, unsupervised learning methods, queuing models, and software quality metrics. This research paper reviews different frameworks, models, metrics, and evaluation techniques that have been published in various research papers for estimating and assessing software quality.&lt;/p&gt; &lt;p&gt; &lt;/p&gt; &lt;p&gt;Research publications related to software quality measurement, estimation, and assessment were collected for the literature review. These publications were accessed from online research databases that consist of open access (OA) journals. &lt;/p&gt;
Graph Embedding-based Matching Multilingual Out-of-Vocabulary Terms on Social Media

&lt;p&gt;Our study aims to detect multilingual Out-of-Vocabulary (OOV) and matching among multilingual OOV. Based on the original OOV issue, many multilingual OOVs also emerged at the same time. In order to solve this problem, this paper proposes a graph embedding-based matching among multilingual OOV. The method is divided into two parts. The first part is to extract OOV from the network corpus and understand it. In the second part, the OOV in the first part is taken as the target node, and the understood part is taken as the feature node of the target node to construct the graph and embed the graph. Our study uses Chinese, Korean, and Japanese for the experiment. The result of the method that Our study proposed is that F1- score reached 93.94%. Our study also compares this method with other embedding algorithms, and the F1-score is higher than the average of other algorithms F1-score by 9.62%.&lt;/p&gt;
Re-Label Is All You Need

In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. In this paper, we illustrate our idea for a broad set of deep learning tasks, includes classification, sequence tagging, object detection, sequence generation, click-through rate prediction. The experimental results and human evaluation results verify our idea.
Yes-no questions in Balkar

This paper investigates yes-no questions in Balkar. We show the distribution and attachment options for the Balkar question particle mI in single-clause matrix questions and in questions with embedded complements. We also show that the Balkar mI-particle can scope out of island structures. We consider movement and non-movement accounts for Balkar yes-no questions. We conclude that the mI particle in Balkar is an adjunct focus particle attaching to the source of alternatives.
A Scale-Adaptive Time-Efficient Depth Map Fusion Algorithm

TSDF (truncated signed distance function) is a classical algorithm for real-time 3D reconstruction, which is simple and parallelizable. Still, the memory consumption and cost of time consumption limit the expansion of the reconstructed scene. To solve these problems, we proposed a new depth map fusion system. We open up a dynamically adjustable memory space, the size of which can be adaptively adjusted according to the coordinate distribution interval obtained from the GMM(Gaussian Mixture Model). When the scene exceeds the voxel coverage, a loop switch is triggered, and the mode of recycling the cache space is turned on. This system has a scene-adaptive memory management mechanism interspersed with CPU-GPU cooperative operations. It can improve the time efficiency without sacrificing the reconstruction accuracy and make it possible to reconstruct high-scale changing scenes. We have created a dataset of natural aviation scenes for extensive experiments. The experimental results show that our system is suitable for any scale of scenes. In terms of time, our method improves the time to fuse each depth map frame by order of magnitude. In evaluating the quality of the reconstructed surface, we compared our reconstructed surfaces with commercial 3D software, and the results show that more than $92 \%$ of the fractions differed from Metashape's results by less than 0.6m. In general, our system is more suitable for scenes with dynamic changes in scene scale and faster task requirements, which will promote the expansion of 3D reconstruction in practical applications.
Cybersecurity Awareness: Investigating Students’ Susceptibility to Phishing Attacks for Sustainable Safe Email Usage in Academic Environment (A Case Study of a Nigerian Leading University)

With the advancement in information communication technology (ICT), cyber-attacks have become a global phenomenon, with email phishing at the topmost. Academic institutions' ICT infrastructures are one of many targets, thus the need to facilitate cybersecurity awareness among students. This research is aimed at investigating students’ susceptibility to phishing attacks for sustainable safe electronic mail (email) usage in the academic environment. Two email phishing tests were carried out during this research work to discover how students reacted to phish emails and understand how students respond to phish emails where all group members are recipients. Finally, questionnaires are administered to participants after completing the exercise to ascertain the students' awareness of phishing attacks based on received emails. The results show that 70.6% of college students surveyed are susceptible to this form of attack due to unawareness. In conclusion, recommendations are outlined on securing the academic community and ICT infrastructures to achieve a sustainable and Safe email usage environment.
THE PROGRAM OF FORMATION OF THE LEADERSHIP POSITION PSYCHOLOGICAL FEATURES OF THE PERSONALITY OF THE SOCIAL SERVICES SPECIALIST

The article presents the program of formation of the leadership position psychological features of the personality of the social services specialist. The publications of scientists on the formation of a leadership position, its value as one of the components of professional development in the field of social work are analysed. The purpose, main tasks, features of leadership position formation and scope of the program are revealed. Three sessions of the program are described in detail. The first session is aimed at forming a value attitude to the leadership position of a social services specialist, meaningful life prospects; the second - to understand and realize the essence of the leadership position and experience a positive attitude, the formation of an active-positive type of attitude towards other people in the activities of social services; third - the ability to show leadership and act constructively in problematic situations of professional activity, etc. The components of the sessions are described, which will allow achieving the results of forming a leadership position and form values, meanings, understanding the essence and experience of a positive attitude to the leadership position, to promote the ability to self-expression of the leadership position, the value of the desire to lead, meaningful life perspective, the idea of him/herself as a leader, cognitive-reflexive - awareness of the essence of leadership and leadership position, formed understanding of the role of leadership in the professional activities of social professionals, reflection, affective-evaluative - formed attitude to other people, active-positive type of attitude towards other people, developed motives for leadership in professional activities, the ability to empathize and regulatory-behavioural - formed the ability to act constructively in problematic situations of professional activity, to show social courage, communicative tolerance, use and generate innovative technologies, initiate and manage changes in the field of social services.
Research of Construction Methods for Cloud Services and Overview of the Implementations TOSCA Standard

This paper overview and compares various tools for automating resource management in the cloud. Changes in software architecture and development approaches require automation of deployment management processes and further maintenance of software in different environments. Chapter 2 provides a detailed overview of the tools with sample configurations, as well as a breakdown of relevant articles that look at various automation tools and the effectiveness of their implementation. Chapter 3 presents a draft solution for combining orchestrators developed at ISP RAS to obtain a tool with functionality that competitors do not have.
VCX Version 2023 The latest transparent and objective mobile phone test scheme

VCX or Valued Camera eXperience is a nonprofit organization dedicated to the objective and transparent evaluation of mobile phone cameras. The members continuously work on the development of a test scheme that can provide an objective score for the camera performance. Every device is tested for a variety of image quality factors while these typically based on existing standards. This paper presents that latest development with the newly released version 2023 and the process behind it. New metric included are extended tests on video dynamics, AE and AWB, dedicated tests on ultra wide modules and adjustments to the metric system based on a large scale subjective study.
Imprint: Geomechanics and Tunneling 1/2023

Geomechanics and TunnellingVolume 16, Issue 1 p. 119-119 ImprintFree Access Imprint: Geomechanics and Tunneling 1/2023 First published: 15 February 2023 https://doi.org/10.1002/geot.202370107AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume16, Issue1February 2023Pages 119-119 RelatedInformation