Reviewerly/exHarmony huggingface dataset:
Economics-Inspired Neural Networks with Stabilizing Homotopies

Contemporary deep learning based solution methods used to compute approximate equilibria of high-dimensional dynamic stochastic economic models are often faced with two pain points. The first problem is that the loss function typically encodes a diverse set of equilibrium conditions, such as market clearing and households' or firms' optimality conditions. Hence the training algorithm trades off errors between those --- potentially very different --- equilibrium conditions. This renders the interpretation of the remaining errors challenging. The second problem is that portfolio choice in models with multiple assets is only pinned down for low errors in the corresponding equilibrium conditions. In the beginning of training, this can lead to fluctuating policies for different assets, which hampers the training process. To alleviate these issues, we propose two complementary innovations. First, we introduce Market Clearing Layers, a neural network architecture that automatically enforces all the market clearing conditions and borrowing constraints in the economy. Encoding economic constraints into the neural network architecture reduces the number of terms in the loss function and enhances the interpretability of the remaining equilibrium errors. Furthermore, we present a homotopy algorithm for solving portfolio choice problems with multiple assets, which ameliorates numerical instabilities arising in the context of deep learning. To illustrate our method we solve an overlapping generations model with two permanent risk aversion types, three distinct assets, and aggregate shocks.
Classification of Confidential Images Using Neural Hash

Humanity generates considerable information using its devices – smartphones, laptops, and tablets. Users upload images to different platforms, such as social networks, messengers, web services and other applications, which greatly endanger their personal information. User privacy has been exploited on the Internet for a long time. Interested parties lure potential customers into a trap of offers and services using such information as age, weight, nationality, religion and preferences. The sensitive information that may be contained in personal images is sometimes not recognized by their users as dangerous to share and, therefore, can easily be shared online by the owner without a second thought.This article inspects a neural hash algorithm for solving image classification tasks of confidential information and evaluates it via basic metrics. The main idea of the algorithm is to find similar images that will serve as an example for defining classes. The algorithm uses hash codes, ensuring users’ privacy. The evaluation of the algorithm is based on “The Visual Privacy (VISPR) Dataset”. The main components of the algorithm are a neural network that generates vectors of extracted features for images and an indexed set of images (hash tables) that store knowledge about a particular domain.The critical aspect of the algorithm involves collisions of hash codes for similar images due to the similarity of their vectors of extracted features. The resulting hash codes can be identical or differ by a specific value of Hamming distance. Multiple hash tables with different hash functions are used to increase the recall or precision of the results. The effect of imperfect taxonomy was analyzed, which led to further filtration of abstract classes and increasing overall scores.Also, the article investigates the “pseudo-adaptivity” of the algorithm - the ability to classify new classes and add new cases to existing classes that were not included in the training stages. Such ability may be crucial for domains with many image instances or classes.
Development of a Local Area Network-Based Filing System for Outgoing and Incoming Letters in The General Service of The Samboja Sub-District

archives are defined as a place to store important records or documents. Archives are generally written documents in any environment that have historical value or an obligation to keep them. Archiving and recording of letters are still done manually which makes finding documents difficult and time-consuming. Therefore, it is necessary to design an application to assist the head of the study program in finding incoming and outgoing mail reports. The Samboja sub-district office has difficulty managing the filing of incoming and outgoing letters. Work at the sub-district office is work that involves a lot of data or information that must be recorded or processed regularly so that everything related to the data or information has a certain use or value so that the archive is needed to facilitate the search process. By storing this data or information, a record is obtained which is called an archive. It can be said that the current filing system is less efficient and effective because incoming mail and outgoing mail data often do not match the incoming mail and outgoing mail data, and all processes are still carried out manually
Nonmonotone Alternative Direction Method Based on Simple Conic Model for Unconstrained Optimization

In this paper, we propose a nonmonotone Alternative Direction Method (ADM) based on simple conic model for unconstrained optimization. Unlike traditional trust region method, the subproblem in our method is a simple conic model, where the Hessian of the objective function is replaced by a scalar approximation, the trust region subproblem is solved by ADM which was first proposed by Zhu and Ni. When the trial point isn’t accepted by trust region, line search technique is used to find an acceptable point instead of resolving the trust region subproblem. The new method needs less memory capacitance and computational complexity. The global convergence of the algorithm is established under some mild conditions. Numerical results on a series of standard test problems are reported to show the new method is effective and robust.
STUDY OF PSYCHOLOGICAL AND PEDAGOGICAL FEATURESOF THE MODERN JUNIOR SCHOOL STUDENT BY FUTURE TEACHERS IN THE PROCESS OF STUDYING AT THE UNIVERSITY

The article reveals the content and methods of studying the psychological and pedagogical characteristics of younger students by future primary school teachers. The necessity of a research approach to the organization of student learning has been substantiated, which allows to identify the current state of social and mental development of younger students. The received information can be used to improve the efficiency of the process of professional training of future primary school teachers.
Natural Language Processing in Translation of Relational Languages

Methods of data manipulation in combination with sturcture of the data and integrity constraints define data model used in the relational databases. This article focuses on the methods and processes of operation sets which are used for selection of data from relational database and translation between various formats of this manipulation. The article presents the design, implementation and experimental evaluation of tool for translating between relational algebra, tuple relational calculus, Structured Query Language and unrestricted natural language in all directions. Presented software tool translates from natural language with the use of artificial intelligence methods for natural language processing, syntactic analysis and parsing. Generative pretrained transformer of third generation (GPT3) based Codex – Davinci translation model was used as an artificial intelligence method used in the workflow of translation from English language to relational languages.
Analysis of Secure Data Sharing Techniques Using Blockchain

The demand for cloud computing has increased immensely, and its security is becoming challenging. The enormous growth in cloud computing adaptation has been observed, but the information security concerns have not been addressed thoroughly. The security issues related to cloud computing are a concern. The emergence of Blockchain as a key security provider has increased the hope for the availability of a secure cloud computing environment. The data-sharing technique based on the cloud scenario relies on the network's storage and architecture; however, the storage providers are considered trusted third parties for data-sharing and storage purposes. The associated limitations such as security, high operational cost, centralized storage capability, and data availability have become a challenging task, which leads to the development of a trusted data management system for secure data sharing through the Blockchain. This study presents an analysis of secure data-sharing techniques using Blockchain. The related research articles were elicited from several sources such as Springer, IEEE, Elsevier, and other online sources. The primary studies have been categorized into four types: healthcare data sharing, vehicular communication-based data sharing, IoT-based data sharing, and other miscellaneous techniques. The techniques have been analyzed based on various performance metrics. The analysis and findings of this study can pave a way for the future development of safe data-sharing techniques using Blockchain technology.
Positioning Method of Four-Wheel-Steering Mobile Robots Based on Improved UMBmark of Michigan Benchmark Algorithm

To reduce the error of the odometer positioning system and improve the positioning accuracy of four-wheel-steering mobile robots, three types of coupling errors are considered, based on the University of Michigan Benchmark (UMBmark) method: unequal track width, unequal wheel diameter, and speed difference of ipsilateral wheels. A “dual direction square path experiment” is designed to decouple the error, a new system error model is defined, and an improved UMBmark method for a four-wheel mobile robot is proposed. In the mobile robot positioning system, a laser tracker is used to measure the absolute positions of the starting and ending points of the robot. The positioning test results of the robot using the improved UMBmark method show that the odometer system error is 69.103 mm, which is 2.6 times less than that in the traditional UMBmark method. Hence, the improved UMBmark can better compensate for the system error of four-wheel-steering mobile robots.
Super-resolution of large field of view infrared image based on convolutional sparse auto-encoder

Based on the auto-encoder, shallow and deep auto-encoders with residual concept are constructed. The effects of different depth auto-encoders on large-field of view infrared image reconstruction are discussed under four conditions: no sparse constraint, regularization constraint, KL divergence sparse constraint and both constraints. From the reconstructed image quality and index parameters, it can be concluded that with the increase of the auto-encoder depth, sparse constraints have a greater impact on the reconstruction effect of the network.
Image dehazing based on mist concentration estimation and non-local similarity constraint

The atmospheric light scattering and absorption from the aerosols usually cause visible light imaging quality decline. The dark channel prior method is a dehazing method to estimate transmittance or background light, which is suitable for foggy image restoration with the influence of suspended particles. Based on the DCP model, the proposed method combines mist concentration level estimation and nonlocal similarity constraint to improve transmittance estimation accuracy. Firstly, the mist concentration level is calculated from natural scene statistical characteristics of the image. Then the corresponding regularization factor is chosen for transmittance estimation. The local structure similarity constraint is introduced into simplified DCP method to decreasing the uneven transmission estimation error. Weighted quadtree searching method is used to estimate the background light. Finally, the image dehazing model is conducted with the transmission map and background light. The comparative experiments are carried out among the original DCP method, the classical image enhancement method MSRCR and the neural network defogging method PDR-NET. The experimental results show that the mist concentration level prior and nonlocal similarity constraints can optimize the solution of the transmittance. The proposed method has significant effect on increasing the image contrast, reducing mist concentration, keeping texture clarity and edge intensity.
Skew Constacyclic Codes over a Non-Chain Ring

In this paper, we investigate the algebraic structure of the non-local ring Rq=Fq[v]/⟨v2+1⟩ and identify the automorphisms of this ring to study the algebraic structure of the skew constacyclic codes and their duals over this ring. Furthermore, we give a necessary and sufficient condition for the skew constacyclic codes over Rq to be linear complementary dual (LCD). We present some examples of Euclidean LCD codes over Rq and tabulate the parameters of Euclidean LCD codes over finite fields as the Φ-images of these codes over Rq, which are almost maximum distance separable (MDS) and near MDS. Eventually, by making use of Hermitian linear complementary duals of skew constacyclic codes over Rq and the map Φ, we give a class of entanglement-assisted quantum error correcting codes (EAQECCs) with maximal entanglement and tabulate parameters of some EAQECCs with maximal entanglement over finite fields.
Understanding the role of automation in society

Automation is the procedure of operating technology to manage and operate tasks, machines, and processes without the need for human input. It employs control systems and advanced technology to enable self-sufficiency in the functioning these systems.We have conducted this study to examine the effects and ramifications of automation on contemporary society, considering its impact on productivity, employment, morality, and economic and social development. It provides an overview of the current state of automation and its potential impact on society. Our study has found that automation has positive and negative consequences for society, including a decline in the job market and a change in the skills set for employment. It was a comprehensive qualitative study that included a literature review, 16 online interviews with experts from 16 countries, and online surveys. We categorized the findings by analyzing the data using thematic analysis. Our results suggest that there should be laws and regulations on the use of automation in society to minimize its adverse impacts on society. This study suggests that automation should improve the quality of life and create more opportunities for people in the market. This research provides insights into the implications of automation on society and its potential to improve efficiency, labour, ethics, and economic and social progress. This research will inform policymakers and other stakeholders about automation's potential benefits and risks.
The Effect of Motivation Factors on Employee Performance: Performance Appraisal Using Data Mining

Intellectual capital is a company’s most precious asset, and human resource management is responsible for how people are treated in businesses. The purpose of this article was to investigate the impact of motivating variables on employee performance in the higher education industry. A structured questionnaire was provided to 206 selected workers from public and private Lebanese institutions for the study. In this study, data mining techniques will be used to highlight the representational new forms where incentive influences the performance of personnel in institutions, must understand how to support management as well as engage with their staff. The results revealed a robust and statistically significant positive link between motivating variables and employee performance. A model is used in the study to elucidate the link between motivational elements and employee performance. As a result, the goal and challenge of staff development is to help individuals, teams, and organizational units acquire and adapt abilities and competencies.
Basics – Service-Oriented Architecture

To me, a service is literally something that serves a need – hence, a service-oriented architecture is a way to guide the purpose, structure, story, and so on of all those things in the enterprise that serve a need. Yet if we read the existing literature on service-oriented architecture, we’ll usually find much the same as we saw with enterprise architecture: it seems it’s almost all about detail-level IT such as microservices and very little else. As with enterprise architecture, though, that’s not the whole story: the IT is only one small part of the enterprise and represents only a small part of its architecture, too. However, the IT is often the only part of the architecture that people know, so it’s probably the best place to start; we just have to remember to translate any IT-centric assumptions to the broader whole-of-enterprise scope.
Deep Learning-Based Model for Face Mask Detection in the Era of COVID-19 Pandemic

Recent advancements in the growth of classification tasks and deep learning have culminated in the worldwide success of numerous practical applications. With the onset of COVID-19 pandemic, it becomes very important to use technology to help us control the infectious nature of the virus. Deep learning and image classification can help us detect face mask from a crowd of people. However, choosing the correct deep learning architecture can be crucial in the success of such an idea. This study presents a model for extracting features from face masks utilizing pre-trained models ConvNet, InceptionV3, MobileNet, DenseNet, ResNet50, and VGG19, as well as stacking a fully connected layer to solve the issue. On the face mask 12 k dataset, the study assesses the effectiveness of the suggested deep learning approaches for the task of facemask detection. The performance metrics used for analysis are loss, accuracy, validation loss, and validation accuracy. The maximum accuracy is achieved by DenseNet and MobileNet. Both the models gave a comparable and good accuracies in terms of training and validation (99.89% and 99.79%), respectively. Further, the paper also demonstrates the deployment of deep learning architecture in the real-world using Raspberry Pi 2B (1 GB RAM).
An Exploratory Study to Develop Education Contents for Workers Supporting Children’s Self-reliance

The purpose of this study is to devise the on-the-job education contents for the workers supporting children's self-reliance. Literature on self-reliance support and its education system were reviewed, and the focus group interview was conducted to the three types of study participants including youth preparing self-reliance, current self-reliance workers at child welfare facilities, and the newly recruited self-reliance workers at the self-reliance centers. The MAXQDA software developed for the qualitative analysis was utilized. As a result, three subjects were identified: current environment in relation to self-reliance, worker competencies required for its provision, and education programs needed for self-reliance support. In addition, 6 categories and 15 sub-categories were selected. Based on the findings, the on-the-job education contents containing 34 subjects have been developed. Lastly, three policies were proposed: the systematic designing of education every year, understanding the youth deeply who prepare self-reliance, and improving the worker capacities in designing education plans.
Technology - Blog. Bizarre Tech - Thin Ice 2.0; TapFresh; KRUSH ice cream maker

I am currently a frozen popsicle in my living room, dreaming of warmer times. Inspired by my chilly surroundings, I decided to look up 'cold' gadgets, just to make me feel more Baltic.
Infrared linear polarization small target enhancement algorithm in the cloudy background

With the development of infrared polarization sensors, image enhancement algorithms have been developed. Although using polarization information quickly distinguishes man-made objects from natural backgrounds, cumulus clouds would become detection noise because of their similar characteristics to targets in the sky scene. In this paper, we propose an image enhancement algorithm based on polarization characteristics and the atmospheric transmission model. The algorithm utilizes the principle of polarization imaging and atmospheric transmission theory to enhance the target in the image while suppressing the interference of clutter. We compare with other algorithms through the data we collected. The experimental results show that our algorithm significantly improves the target brightness and reduces clutter at the same time with real-time performance.
Lessons Learned from Penetration Testing Hands-on Training during COVID-19 Pandemic

Because of the increase in cyberattacks in the Internet era, cybersecurity is a crucial area that still requires many more human resources. Security handson training plays an important role in educating a skilled workforce. However, delivering hands-on training with a large number of participants during the COVID-19 pandemic was a big challenge. In this paper, we discuss differences between onsite and online penetration testing hands-on training, during the COVID-19 pandemic in 2020 and 2021. We share our teaching experiences and lessons learned and provide recommendations for preparing and delivering online hands-on training efficiently.
An Improved CatBoost Algorithm for Red Fox Optimization in the Field of Anomaly Detection

Anomaly detection is an important research direction for machine learning in the data era, it has been a popular technology enhancement in medica, financial and Internet fields. For anomaly detection algorithms, accuracy is an important criterion. Unbiased Boosting with Categorical features(CatBoost) is an algorithm proposed by Yandex in 2018, which claims to be superior to XGBoost and LightGBM in terms of algorithmic accuracy and other aspects. Optimization algorithms are often used instead of tedious parameter tuning when choosing hyperparameters for building models. In this paper, the Red Fox Optimization Algorithm(RFOA) is improved and great optimization results are obtained when compared with other optimization algorithms. Based on this, a CatBoost algorithm based on Improved Red Fox Optimization(IRFCB) is proposed to find the most suitable hyperparameters to build CatBoost models by training the optimization algorithm without using application scenarios. Experimental validation was conducted using anomaly detection datasets on Kaggle, and IRFCB showed good results in terms of accuracy, recall, auc, and other metrics.
Research on Intelligent Prediction of Power Transformation Operation Cost Based on Multi-dimensional Mixed Information

Operation cost is an important link in the operation of power enterprises. In the process of intelligent prediction of power transformation operation cost, there is a problem of low accuracy. Therefore, an intelligent prediction method of power transformation operation cost based on multi-dimensional mixed information is designed. Evaluate the fixed cost of power grid, determine the budget amount in different budget periods, extract the life cycle of power grid substation equipment, establish the cost estimation relationship, use multi-dimensional mixed information to build the cost control model, refine the project category, and optimize the intelligent prediction mode of operation cost according to the different nature of each link cost. Test results: the average prediction accuracy of the intelligent prediction method of power grid substation operation cost in this paper and the other two intelligent prediction methods of power grid substation operation cost are 79.357%, 71.066% and 69.313% respectively, indicating that after using multi-dimensional mixed information, the application effect of the designed intelligent prediction method of power grid substation operation cost is more prominent.