Reviewerly/exHarmony huggingface dataset:
INTELLECTUAL ARCHITECTONICS OF REGIONAL INFORMATION AND EDUCATION SPACES IN THE NEW REALIA OF PROFESSIONAL POTENTIAL DEVELOPMENT

This study is a continuation of longitudinal research in the problem area of humanistic activities of the regional information and education environment, which is currently undergoing significant changes due to the actual value aspects of the polyphony of innovative theoretical research and pedagogical findings. The authors see the mission and goals of this article in presenting some novel results of the investigation of regional peculiarities in the aspect of integrated modeling and design. Based on theoretical, methodological and practical search of modern realities the article focuses on the environmental and cultural approaches as the leading ones. The latter helps to formulate the following methodological message: the constructive self-development of the region professional potential is possible if focused on leadership and communicative strategic guidelines. Modeling and designing of the humanitarian trends of the regional information and educational spaces are to develop the research array in terms of the use of systemic humanistic, humanitarian and communicative strategies. It means the reflexive activity on cultural self-determination of a personality who turns/goes professional, and then is in need of the basic intellectual skill set as a priority. As a result of developing the axiology context of the current target study, this project reflects the innovative dominant value trends integrated into the sociocultural and professional system of the region within the threefold intellectual architectonics covering the findings for professional potential development in terms of a) psychological balance; b) the intellectual component of a goer professional c) the communication management of professionals. In conclusion, the authors state the significance of the target goals and devices to systematically develop, test and implement optimal models of communication models for the existence of the participants of a secure information and communication environment, taking into account regional specifics.
SIGN-SYMBOLIC MEANS IN MODELING STUDENTS' MEANING-LIFE ORIENTATIONS

The article discusses the application of the modeling method in the educational process on the problem of the formation of students' life orientations with the help of symbolic means. The relevance of the problem under study is due to the need to form students' life orientations that contribute to their self-improvement and awareness of their contribution to the development of society. Understanding the meaning of life orientations allows a meaningful approach to planning their life as a whole, as a result of which young people take responsibility for their lives With the help of general scientific theoretical methods of analysis, synthesis, generalization, the problem of research is analyzed. In the process of preparing the article, a psychological and pedagogical approach was used, considering the scientific concepts of applying the modeling method in the educational process. The authors of the article reveal the understanding of the concept of “meaning-life orientations”, “sign-symbolic means”, “modeling method”. The article analyzes the principles of the educational process, with the help of which there is an active formation of meaningful life orientations of young people in the pedagogical process. Attention is also paid to the problem of social education of young people, and the structure of the subconscious mind affecting the assimilation of information is considered. The results of the theoretical analysis formed the basis for the development of the elective course “Correction of personal self-attitude”, which is implemented in the educational process of higher education. The course structure is described and the content part of one of the classes is revealed. For example, training exercises are described, including sign-symbolic means of the modeling method, as well as questions that are discussed during the implementation of the exercise task. The conclusions summarize the systematization of the material presented, it is noted that theoretical knowledge on understanding life orientations is effectively assimilated in the process of understanding life situations.
Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat

ML model design either starts with an interpretable model or a Blackbox and explains it post hoc. Blackbox models are flexible but difficult to explain, while interpretable models are inherently explainable. Yet, interpretable models require extensive ML knowledge and tend to be less flexible and underperforming than their Blackbox variants. This paper aims to blur the distinction between a post hoc explanation of a Blackbox and constructing interpretable models. Beginning with a Blackbox, we iteratively carve out a mixture of interpretable experts (MoIE) and a residual network. Each interpretable model specializes in a subset of samples and explains them using First Order Logic (FOL), providing basic reasoning on concepts from the Blackbox. We route the remaining samples through a flexible residual. We repeat the method on the residual network until all the interpretable models explain the desired proportion of data. Our extensive experiments show that our route, interpret, and repeat approach (1) identifies a diverse set of instance-specific concepts with high concept completeness via MoIE without compromising in performance, (2) identifies the relatively ``harder'' samples to explain via residuals, (3) outperforms the interpretable by-design models by significant margins during test-time interventions, and (4) fixes the shortcut learned by the original Blackbox. The code for MoIE is publicly available at: \url{https://github.com/batmanlab/ICML-2023-Route-interpret-repeat}
Requirements For the Digital Pedagogical Competences of Technology and Entrepreneurship Teachers

In the current information society, specific requirements are imposed on the competence profile of teachers. A defining element of the pedagogical culture of modern pedagogues is the scope of their digital pedagogical competences. These competences are a prerequisite for the effectiveness of supervised learning and an important factor for personal and social development and professional realization. The article defines the content parameters and the specific requirements for the digital pedagogical competences of the teachers in the subject of technology and entrepreneurship studied in the Bulgarian school. Based on the TPACK (Technological Pedagogical and Content Knowledge) model, the standards developed by ISTE (International Society for Technology in Education) for the preparation of teachers in the digital age and the normative Bulgarian educational standards and curricula, the necessary basic digital knowledge and skills for effective planning and management of lower secondary stage technological training. The results of an empirical study conducted with technology and entrepreneurship teachers at the lower secondary stage (grades 5-7) are presented. The levels of certain digital competences formed through self-assessment and the teachers' attitudes towards professional improvement were analyzed. Development guidelines are outlined in accordance with the European reference frameworks. This document presents the formatting instructions for the Proceedings of International Conference on Recent Academic Studies. This document can serve as the base template for a Microsoft Word based typesetting system. The abstract should state briefly the purpose of the research, the approach used, the principal
Traffic Sign Recognition Based on Bayesian Angular Margin Loss for an Autonomous Vehicle

Traffic sign recognition is a pivotal technology in the advancement of autonomous vehicles as it is critical for adhering to country- or region-specific traffic regulations. Defined as an image classification problem in computer vision, traffic sign recognition is a technique that determines the class of a given traffic sign from input data processed by a neural network. Although image classification has been considered a relatively manageable task with the advent of neural networks, traffic sign classification presents its own unique set of challenges due to the similar visual features inherent in traffic signs. This can make designing a softmax-based classifier problematic. To address this challenge, this paper presents a novel traffic sign recognition model that employs angular margin loss. This model optimizes the necessary hyperparameters for the angular margin loss via Bayesian optimization, thereby maximizing the effectiveness of the loss and achieving a high level of classification performance. This paper showcases the impressive performance of the proposed method through experimental results on benchmark datasets for traffic sign classification.
Higher-order Bloch sphere: geometric representation of Larmor precession of the higher-order spin states

We propose an analogous sphere, herein referred a higher-order Bloch sphere (HOBS), to visualize the Larmor precession of higher-order spin. Our proposed HOBS is potentially applied to advanced quantum information processing.
Deep Reservoir Computing Based on Injection-Locked Quantum Dot Lasers

This work proposes a deep reservoir computing architecture based on cascading injection-locked quantum dot lasers. It is proved that the four-layer reservoir computing performs better than the single-layer one on multiple benchmark tasks.
Miner ID: Facilitating bitcoin as a service

Block production in Bitcoin, often referred to as mining, is becoming increasingly industrialized. Many nodes in the network are represented by registered business entities. The concept of Bitcoin as a Service is also pushing the industry to become more customer oriented. Services such as transaction validation, transaction status query or notification, and blockchain data indexing are in high demand among blockchain application providers and users. As service providers, nodes need to distinguish themselves from others and be identifiable. In this article, we introduce an efficient self-established identity system, called Miner ID, to enable nodes to be publicly identifiable. It is based on economic investment and active participation in the blockchain network. Moreover, Miner ID is optional for nodes, and it does not affect the consensus mechanism of the network. We explore use cases including instant transaction confirmation, blockchain attestation, public key infrastructure, and token recovery. Miner ID can also be used for secure communication with applications, services, users and peers.
Multi-Oriented Enhancement Branch and Context-Aware Module for Few-Shot Oriented Object Detection in Remote Sensing Images

For oriented object detection, the existing CNN-based methods typically rely on a substantial and diverse dataset, which can be expensive to acquire and demonstrate limited capacity for generalization when faced with new categories that lack annotated samples. In this case, we propose MOCA-Net, a few-shot oriented object detection method with a multi-oriented enhancement branch and context-aware module, utilizing a limited number of annotated samples from novel categories for training. Especially, our method generates multi-oriented and multi-scale positive samples and then inputs them into an RPN and the detection head as a multi-oriented enhancement branch for enhancing the classification and regression capabilities of the detector. And by utilizing the context-aware module, the detector can effectively extract contextual information surrounding the object and incorporate it into RoI features in an adaptive manner, thereby improving its classification capability. As far as we know, our method is the first to attempt this in this field, and comparative experiments conducted on the public remote sensing dataset DOTA for oriented object detection showed that our method is effective.
Prediction and influencing factors of photovoltaic system power generation of a single house based on continuously measured data

With the development of photovoltaic (PV) power generation systems in single houses, research has recently focused on the prediction of PV power generation to match PV power generation with building energy consumption characteristics. However, prediction models for PV power generation under different weather conditions based on the actual monitoring data of the PV power generation system in a single house are still lacking. The present study is based on the actual monitoring of PV power generation data of a single house with a PV system that was installed in Beijing 5 years ago. We analysed the main weather factors affecting PV power generation and power conversion efficiency (PCE). The results showed a positive correlation between power generation and solar radiation, with the ambient temperature (T a ) and total cloud cover (TCC) being the main weather factors affecting power generation and PCE. Based on the analysis results, a support vector regression (SVR) model was established to predict PV power generation. In addition, the mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of variation (R 2 ) of the SVR model were 3.39, 34.7% and 0.86, respectively, which can accurately be used to predict PV power generation under different weather types.
Improved Differential Evolutionary Algorithm Based on Adaptive Scaling Factor

The differential evolution algorithm is a meta-heuristic algorithm with the advantages of a simple structure, few control parameters, and robustness. However, in the face of different problems, if the factor F controlling the difference amplification is fixed, it will be difficult for the algorithm to adapt to complex and changeable problems. In order to enable the differential evolution algorithm to deal with many problems with the optimal F parameter, this paper proposes a differential evolution algorithm with Adaptive Scaling Factor. The algorithm continuously explores the optimal F parameters for the current problem while ensuring that it does not converge prematurely, which eventually leads to an improvement in the search efficiency of the algorithm.
Grief Is Where We Begin: On Robert D. Richardson’s Last Biography

ABSTRACT In many ways his most personal work to date, Robert D. Richardson’s latest, posthumously published biography, Three Roads Back: How Emerson, Thoreau, and William James Responded to the Greatest Losses of Their Lives, charts the formation of his three subjects’ literary and philosophical identities through the personal tragedies that ended up working as catalysts for some of their biggest intellectual and creative achievements. A valuable work of biography and intellectual history in its own right, Richardson’s final book equally serves as a valediction on a celebrated scholar’s lifelong commitment to writing about the minds of some of America’s greatest, foundational thinkers.
The Need for Biometric Anti-spoofing Policies: The Case of Etsy

Effective, safe, and fast identity recognition is crucial in today’s rapidly growing society. As a convenient and reliable alternative to traditional identification methods, biometric technologies are increasingly adopted for security applications, such as the verification of ID cards or passports and the authentication of computer and mobile devices. However, if spoofed, such technologies can create serious privacy and security risks, and the proliferation of high quality multimedia content on social media platforms facilitates such spoofing attacks. Unfortunately, many users are unaware of the risks of posting their biometric information online and social media companies are not taking appropriate action to protect them. In this paper, we make the case for biometric anti-spoofing policies by examining the social media enabled marketplace of Etsy. We demonstrate that biometric information can be collected from social media users and that the level of privacy concerns is not a predictor of a user’s biometric information sharing behavior.
An Improvement to the Nonparametric Regression Models Using the Nonsmooth Loss Functions

This work addresses the supervised learning problem using a smooth and accurate approximation of the absolute loss function. The supervised learning problem is reformulated as an unconstrained minimization problem and solved using an accelerated and fast gradient descent method. Finally, we present some numerical results from real-life data sets to demonstrate the effectiveness of the proposed method and confirm that it is better in terms of stability and convergence speed compared to sub-gradient descent and Newton methods.
Solving nonlinear filtering problems with correlated noise based on Hermite–Galerkin spectral method

Nonlinear filtering problem has important applications in various fields. One of the core issues in nonlinear filtering is to numerically solve the Duncan–Mortensen–Zakai (DMZ) equation, which is an evolution equation satisfied by the unnormalized conditional density of state process under noisy observations, in a real-time and memoryless manner. When the noise in observations is correlated to the state process, the DMZ equation we need to deal with is a second-order stochastic partial differential equation. In this paper, we will propose an algorithm to solve the DMZ equation in this case, based on Hermite–Galerkin spectral method. According to this method, the DMZ equation is converted into a system of linear stochastic differential equations generated by the observation process. The effects of different discretization schemes on this stochastic differential system will also be discussed. Moreover, rigorous convergence analysis of the algorithm is given under mild conditions. Numerical results show that the method proposed in this paper can provide an instantaneous and accurate estimation to the state process of the system.
No. 52947. International Bank for Reconstruction and Development and Egypt

Loan Agreement (Strengthening Social Safety Net Project) between the Arab Republic of Egypt and the International Bank for Reconstruction and Development (with schedules, appendix and International Bank for Reconstruction and Development General Conditions for Loans, dated 12 March 2012). Washington, 17 April 2015
Quantum recurrent neural networks for sequential learning

Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources. Recurrent neural networks are the most fundamental networks for sequential learning, but up to now there is still a lack of canonical model of quantum recurrent neural network (QRNN), which certainly restricts the research in the field of quantum deep learning. In the present work, we propose a new kind of QRNN which would be a good candidate as the canonical QRNN model, where, the quantum recurrent blocks (QRBs) are constructed in the hardware-efficient way, and the QRNN is built by stacking the QRBs in a staggered way that can greatly reduce the algorithm’s requirement with regard to the coherent time of quantum devices. That is, our QRNN is much more accessible on NISQ devices. Furthermore, the performance of the present QRNN model is verified concretely using three different kinds of classical sequential data, i.e., meteorological indicators, stock price, and text categorization. The numerical experiments show that our QRNN achieves much better performance in prediction (classification) accuracy against the classical RNN and state-of-the-art QNN models for sequential learning, and can predict the changing details of temporal sequence data. The practical circuit structure and superior performance indicate that the present QRNN is a promising learning model to find quantum advantageous applications in the near term.
On Accuracy of Gaussian Approximation in Bayesian Semiparametric Problems

We consider the problem of Bayesian semiparametric inference and aim to obtain an upper bound on the error of Gaussian approximation of the posterior distribution for the target parameter. This type of result can be seen as a nonasymptotic version of semiparametric Bernstein–von Mises (BvM). The provided bound is explicit in the dimension of the target parameter and in the dimension of sieve approximation of the full parameter. As a result, we can introduce the so-called critical dimension $$\,p_n\,$$ of the sieve approximation, the maximal dimension for which the BvM result remains valid. In various particular statistical models, we show the necessity of the condition “ $$\,p_n^2 q / n\,$$ is small”, where $$\,q\,$$ is the dimension of the target parameter and $$\,n\,$$ is the sample size, for the BvM result to be valid under the general assumptions on the model.
Analysis of Transitions in Differences between Frequent Medical-order Sequences for COVID-19

With the increasing use of electronic medical records, medical support from analysis of the accumulated medical information is expected. Currently, new treatment methods and drugs are being developed for the treatment of new diseases, but the transition history of medical orders has yet to be visualized for diseases such as COVID-19. In this paper, we use sequential pattern mining to extract frequent medical orders and then apply the longest common subsequence variant (LCSV) and merged sequence variant (MSV) to analyze the differences in treatment patterns at different times. We also propose three types of sliding window (time interval window, sequence number window, and time-sequence number window) to analyze the transition history of medical orders. As an example, we applied these methods to Japanese electronic medical records covering the first to the fifth waves of COVID-19 and analyzed the differences in medical-order patterns for the five infection waves and the transition history of medical orders. We then visualized the difference with MSV. The results showed that the proposed method can successfully visualize the differences in medical orders between infection waves, and the transition history of medical orders can be revealed. The validity of the results was confirmed by the medical staff involved.
Application of IoT detection based on deep self coding multidimensional feature fusion in sports training

In response to the shortcomings of existing centralized IoT detection activities, this article proposes a new IoT detection scheme based on programmable switches and deep self coding, which combines programmable switches with IoT MQTT protocol and multidimensional feature fusion algorithm. Based on this point, a comprehensive programmable switch and machine learning centralized IoT detection system are designed. This system is different from traditional centralized IoT detection systems. The IoT detection module discussed in this article is located on a programmable switch between IoT nodes and servers. This system utilizes the potential of programmable switches to quickly collect required information from packet data and pre detect fault data from the front end of the server, thereby quickly making IoT node packet processing decisions (i.e. redirecting or directly discarding) based on programmable switches to minimize the delay in transmitting data packets. Finally, in order to apply this technology to the field of sports training, we examined different directions of sports training. Therefore, based on the development of simulation systems, this article greatly enhances the practicality of such projects, effectively increasing the usage time and meeting the actual training needs. Therefore, The deep self coding computer simulation of sports training is an issue that we must pay attention to. The article conducted research on IoT detection based on deep self coding multidimensional feature fusion, and applied the research results to sports training, promoting the rapid development of sports training.