Reviewerly/exHarmony huggingface dataset:
A Study to Strengthen the Child Care System in Gyeong-Gi-Do: Focused on the Current State and Problem Solving of Elementary School Care Classes

본 연구의 목적은 경기도 어린이돌봄서비스(초등학생 중심)의 현황을 파악하고, 그 안에서 나타날 수 있는 문제점을 진단함으로써 경기도의 어린이돌봄체계가 기존보다 강화됨과 동시에 안정적으로 운영될 수 있는 기반을 모색하는 것이다. 이를 위해 본 연구는 경기도의 어린이돌봄체계, 그중에서도 초등돌봄교실의 현황을 통계적으로 점검하여 효과적 운영을 위한 개선 및 정책방안을 제시하였다. 본 연구는 ‘경기도어린이돌봄중앙통합관리시스템’을 지자체 소속으로 구성할 것과 구성된 기관을 통해 다음과 같은 사항을 제언하였다. 첫째, 경기도 어린이돌봄중앙통합관리시스템을 마련하여 어린이돌봄체계의 전체적인 수요-공급의 균형을 모색한다. 둘째, 통합관리시스템은 각 돌봄시설 간 유기적 체제를 마련한다. 셋째, 통합관리시스템은 돌봄사각지대 극복을 위한 어린이돌봄등록체계의 통일화를 시행하고, IT 프로그램 및 앱을 개발해야 한다. 넷째, 코로나19와 같은 사회적 긴급상황 안에서 돌봄 서비스가 운영되도록 유연한 제도를 마련해야 한다. 다섯째, 사회적 긴급사태를 대비하기 위하여 돌봄 긴급서비스 제공을 위한 자원을 확보해야 한다. 여섯째, 돌봄인력의 자격기준을 검토하고 통일해야 한다.The purpose of this study is to identify the current state of Gyeonggi-do child care service (focused on elementary school students) and to diagnose problems that may appear in it, in order to find a basis for the Gyeonggi-do child care system to be strengthened and operated stably. For it, this study statistically examined the current state of the child care system in Gyeonggi-do, especially elementary school care classes, and suggested improvement and policy measures for effective operation. In conclusion, this study suggested that the ‘Gyeonggi-do central integrated management system for child care’ be organized under the jurisdiction of local governments. In addition, this study suggested the following matters through the institution. First, by establishing a central integrated management system for child care in Gyeonggi-do, the overall demand-supply balance of the child care system is sought. Second, the integrated management system establishes an organic system between each care facility. Third, the central integrated management system should unify the child care registration system to overcome the blind spots in care, and develop IT programs and apps. Fourth, a flexible system should be established so that care services can be operated in social emergency situations such as COVID-19. Fifth, it is necessary to secure resources for providing emergency care services in order to prepare for social emergencies. Sixth, qualification standards for care workers should be reviewed and unified.
Efficientdet Based Visial Perception for Autonomous Driving

In the realm of big data and cloud computing, the autonomous vehicle is an extremely promising and complex research topic, which is driven primarily by computer systems that employ AI technology, radar, GPS, visual computing, cloud computing, and other technologies to operate. autonomous vehicles use LIDAR to sense road information, and highly accurate maps are integrated with V2X (vehicle networking technology) to recognize relevant information such as traffic lights and speed limit signs. However, in some remote areas where map information needs to be completed, recognition of traffic lights and traffic signs cannot rely on positioning to achieve this. This paper uses the Efficientdet-d1 target detection algorithm built on Pytorch to simulate autonomous vehicles sensing pedestrian, vehicle, traffic light, and traffic sign information. This target detection algorithm uses EfficientNet-B1 as the backbone network and enhances the feature extraction process using four stacked BiFPN modules. The method involves using the open source big dataset LISA traffic light dataset and GTSRB German traffic sign dataset to train the model. Considering the uneven distribution of samples in the dataset, these classes are distributed into three Efficientdet-d1 target detection frameworks for pedestrians and various types of vehicles, traffic lights, and traffic signs, respectively. A multi-threaded approach allows the three detection processes to be executed simultaneously. The detection results are stored in a queue before aggregation for mapping to improve the speed of single-image execution. The experiments show the three prediction networks used achieved better results overall. The method proposed in this paper is a practical guide for autonomous vehicles to make road condition judgments during driving in remote areas.
Development of Mobile Application to Report Criminal Acts in Young Areas of Peru

This research work entitled "Design of a mobile application for complaints in real time to the national police", being that the police officer must be in his office or in front of a computer with an Internet connection so that he can see the complaints that are in charge, or manually search for documents, cases, folders.For this reason, a mobile application called "mobile application" was proposed and developed; as a solution proposal. Its purpose is to solve different problems without having to be in the police station and / or in the office. We want to help the population to carry out any complaint in a more EASY AND QUICK way, and this will be achieved through the application "MOBILE APPLICATION", with Android operating system, resulting in a friendly and easy-to-use mobile complaint management application.For the present project, XP Extreme Programming (eXtreme programming) methodology was used, which adapts to the necessary characteristics required for the application to have optimal performance, in addition to facilitating the development and design work of the application. We made use of a series of technologies and tools for the development and design work of the application, we will use IDE (integrated development environment) Android Studio that provides us with comprehensive services, which facilitates the task in time of programming our mobile application.
PELATIHAN PENGGUNAAN GAFE DAN KAHOOT SEBAGAI MEDIA PEMBELAJARAN DI SMP YPPK YOANES XXIII MERAUKE

The COVID-19 pandemic forces us to innovate in learning. However, the lack of awareness of teachers to use technology in learning, especially using Google Apps For Education and Kahoot as Gamification-based learning media. There is no special training related to learning using technology, especially using Google Apps For Education and Kahoot. There is not enough knowledge and skills regarding Google Docs, Google Sheets and Google Slides as a medium to work together on a virtual worksheet effectively and efficiently. Teachers still use quizzes, assignments, and written tests on paper that are printed and distributed manually to students so that they are not accustomed to working on these instruments using computers or cellphones based on Android or IOS even though this is very much needed at this time. The method used for this service activity is socialization, training, mentoring, monitoring, and evaluation. The results of the service showed that the teachers experienced an increase in knowledge and skills after participating in the service.&#x0D; Keywords: Training, GAFE, Kahoot, Learning Media
A Combination Of A Rail Fence Cipher And Merkle Hellman Algorithm For Digital Image Security

Image is a combination of planes, points, lines and colors to create a physical or human object. Images can be in the form of 2-dimensional images, such as photographs and paintings. 3-dimensional image like a statue. The use of image media information has several weaknesses, one of which is the ease with which it can be manipulated by certain parties with the help of increasingly developing technology. In this study, the Rail Fence Cipher and Merkle Hellman methods were applied which aimed to obtain a stronger cipher by utilizing two key levels where an asymmetric algorithm was used to protect the symmetric key. The asymmetric algorithm used is Merkle Hellman and the symmetrical algorithm used is Rail Fence Cipher. The results of this study indicate that applying the Rail Fence Cipher and Merkle Hellman algorithms can secure image files and secure keys for data integrity. Encryption and description processing time is affected by the size and resolution of the image file.
An Interactive Visualization System for Streaming Data Online Exploration

The practices of understanding real-world data, in particular the high dynamic streaming data (e.g., social events, COVID tracking), generally relies on both human and machine intelligence. The use of mobile computing and edge computing brings a lot of data. However, we identify that existing data structures of visualization systems (a.k.a., data cubes) are designed for quasi-static scenarios, thus will experience huge efficiency degradation when dealing with the ever-growing streaming data. In this work, we propose the design and implementation of an enhanced interactive visualization system (i.e., Linkube) based on novel structure and algorithms support, for efficiently and intelligibly data exploration. Basically, Linkube is designed as a multi-dimensional and multi-level tree with spatiotemporal correlated knowledge units linked into a chain. Interested knowledge aggregations are thus attained via efficient and flexible sequential access, instead of dummy depth-first searching. Meanwhile, Linkube also involves a smart caching mechanism that adaptively reserves some beneficial aggregations. We implement Linkube as a web service and evaluate its performance with four real-world datasets. The results demonstrate the superiority of Linkube on response time ( $$\sim $$ 25% $$\downarrow $$ ) and structure updating time ( $$\sim $$ 45% $$\downarrow $$ ), compared with state-of-the-art designs.
A Survey of Empathetic Generative Chatbots

The purpose of Chatbots also known as Dialogue Systems or Conversational Agents is to have natural conversations indistinguishable from human ones. Perception and expression of emotion are key factors to the success of Dialogue systems or conversational agents. In this review article, we focus on the literature on Empathetic Chatbots. The main goal of this review is to serve as a comprehensive guide to research and development on Empathetic Generative Chatbot and to suggest future directions in this domain.
Information and analysis service and technology for monitoring potentially hazardous areas from satellite multi-spectral sensing data

An information and analytical service is presented, created on the basis of the practical implementation of innovative technologies in the processing of multispectral satellite data and the provision of monitoring results for potentially hazardous areas in various environments. Monitoring technologies and methods for identifying various types of underlying surfaces, including the layer-by-layer method of monitoring, are considered. Descriptions are given of specially developed methods for automatically determining the state parameters of given objects and phenomena according to multispectral aerospace sensing data. It is proposed to use digital maps of geospatial layers of information as analog models of the parameters of objects and natural areas controlled from space. The initial data for building models are aerospace information of various spatial resolutions, as well as route survey materials. A geoportal technology for presenting the results of recognition of potentially hazardous areas has been created. The developed service can be used to solve various industry problems of monitoring hazardous phenomena and background parameters of natural environments, comparable with the spatial and temporal resolution of aerospace sensing data.
Non-intrusive temperature rise fault-identification of distribution cabinet based on tensor block-matching

In this study, a novel non-intrusive temperature rise fault-identification method for a distribution cabinet based on tensor block-matching is proposed. Two-stage data repair is used to reconstruct the temperature-field information to support the demand for temperature rise fault-identification of non-intrusive distribution cabinets. In the coarse-repair stage, this method is based on the outside temperature information of the distribution cabinet, using tensor block-matching technology to search for an appropriate tensor block in the temperature-field tensor dictionary, filling the target space area from the outside to the inside, and realizing the reconstruction of the three-dimensional temperature field inside the distribution cabinet. In the fine-repair stage, tensor super-resolution technology is used to fill the temperature field obtained from coarse repair to realize the smoothing of the temperature-field information inside the distribution cabinet. Non-intrusive temperature rise fault-identification is realized by setting clustering rules and temperature thresholds to compare the location of the heat source with the location of the distribution cabinet components. The simulation results show that the temperature- field reconstruction error is reduced by 82.42% compared with the traditional technology, and the temperature rise fault- identification accuracy is greater than 86%, verifying the feasibility and effectiveness of the temperature-field reconstruction and temperature rise fault-identification.
E.K. Malinovskaya and Ballet

Elena Konstantinovna Malinovskaya (1875–1942) played a significant, albeit, at a first glance, not a particularly distinguishable role in the history of the Russian stage. After the revolution, she directed a number of Moscow State (which held the appellations of Academic starting from 1919) theaters until 1924, during the years 1930–1935 she was the director of the Bolshoi Theater. For eleven years, Malinovskaya's personality, artistic views and administrative tactics influenced in various degrees the development of one of the two largest ballet companies in the country. She was compelled to take into account the tastes of high-ranking “curators” of the theater (Anatoly Lunacharsky, Avel Enukidze et al.), to comply with the demands of a powerful trade union organization, adapt to Communist Party censorship, struggle against the desires of choreographers and dancers for tours abroad, which frequently turned into one–way trips. Malinovskaya had to deal with, and not unsuccessfully, not only management of theaters, and also provide for the living conditions of artists. On the basis of materials introduced into scholarly circulation for the first time (first of all, from Malinovskaya's extensive personal funds in the Russian State Archive of Literature and Art and the A.A. Bakhrushin State Central Theater Museum), this article examines the complex relationships of Elena Malinovskaya, the Bolshoi Theater Ballet Troupe and various public figures from the world of art and politics, involved in ballet in various degrees, during the years 1918–1935. Overall, Malinovskaya's artistic activities have been evaluated highly in the history of Russian ballet.
Continual Graph Convolutional Network for Text Classification

Graph convolutional network (GCN) has been successfully applied to capture global non-consecutive and long-distance semantic information for text classification. However, while GCN-based methods have shown promising results in offline evaluations, they commonly follow a seen-token-seen-document paradigm by constructing a fixed document-token graph and cannot make inferences on new documents. It is a challenge to deploy them in online systems to infer steaming text data. In this work, we present a continual GCN model (ContGCN) to generalize inferences from observed documents to unobserved documents. Concretely, we propose a new all-token-any-document paradigm to dynamically update the document-token graph in every batch during both the training and testing phases of an online system. Moreover, we design an occurrence memory module and a self-supervised contrastive learning objective to update ContGCN in a label-free manner. A 3-month A/B test on Huawei public opinion analysis system shows ContGCN achieves 8.86% performance gain compared with state-of-the-art methods. Offline experiments on five public datasets also show ContGCN can improve inference quality. The source code will be released at https://github.com/Jyonn/ContGCN.
Inferential Knowledge-Enhanced Integrated Reasoning for Video Question Answering

Recently, video question answering has attracted growing attention. It involves answering a question based on a fine-grained understanding of video multi-modal information. Most existing methods have successfully explored the deep understanding of visual modality. We argue that a deep understanding of linguistic modality is also essential for answer reasoning, especially for videos that contain character dialogues. To this end, we propose an Inferential Knowledge-Enhanced Integrated Reasoning method. Our method consists of two main components: 1) an Inferential Knowledge Reasoner to generate inferential knowledge for linguistic modality inputs that reveals deeper semantics, including the implicit causes, effects, mental states, etc. 2) an Integrated Reasoning Mechanism to enhance video content understanding and answer reasoning by leveraging the generated inferential knowledge. Experimental results show that our method achieves significant improvement on two mainstream datasets. The ablation study further demonstrates the effectiveness of each component of our approach.
Memory-Augmented Theory of Mind Network

Social reasoning necessitates the capacity of theory of mind (ToM), the ability to contextualise and attribute mental states to others without having access to their internal cognitive structure. Recent machine learning approaches to ToM have demonstrated that we can train the observer to read the past and present behaviours of other agents and infer their beliefs (including false beliefs about things that no longer exist), goals, intentions and future actions. The challenges arise when the behavioural space is complex, demanding skilful space navigation for rapidly changing contexts for an extended period. We tackle the challenges by equipping the observer with novel neural memory mechanisms to encode, and hierarchical attention to selectively retrieve information about others. The memories allow rapid, selective querying of distal related past behaviours of others to deliberatively reason about their current mental state, beliefs and future behaviours. This results in ToMMY, a theory of mind model that learns to reason while making little assumptions about the underlying mental processes. We also construct a new suite of experiments to demonstrate that memories facilitate the learning process and achieve better theory of mind performance, especially for high-demand false-belief tasks that require inferring through multiple steps of changes.
Towards Optimal Randomized Strategies in Adversarial Example Game

The vulnerability of deep neural network models to adversarial example attacks is a practical challenge in many artificial intelligence applications. A recent line of work shows that the use of randomization in adversarial training is the key to find optimal strategies against adversarial example attacks. However, in a fully randomized setting where both the defender and the attacker can use randomized strategies, there are no efficient algorithm for finding such an optimal strategy. To fill the gap, we propose the first algorithm of its kind, called FRAT, which models the problem with a new infinite-dimensional continuous-time flow on probability distribution spaces. FRAT maintains a lightweight mixture of models for the defender, with flexibility to efficiently update mixing weights and model parameters at each iteration. Furthermore, FRAT utilizes lightweight sampling subroutines to construct a random strategy for the attacker. We prove that the continuous-time limit of FRAT converges to a mixed Nash equilibria in a zero-sum game formed by a defender and an attacker. Experimental results also demonstrate the efficiency of FRAT on CIFAR-10 and CIFAR-100 datasets.
Effective Continual Learning for Text Classification with Lightweight Snapshots

Continual learning is known for suffering from catastrophic forgetting, a phenomenon where previously learned concepts are forgotten upon learning new tasks. A natural remedy is to use trained models for old tasks as ‘teachers’ to regularize the update of the current model to prevent such forgetting. However, this requires storing all past models, which is very space-consuming for large models, e.g. BERT, thus impractical in real-world applications. To tackle this issue, we propose to construct snapshots of seen tasks whose key knowledge is captured in lightweight adapters. During continual learning, we transfer knowledge from past snapshots to the current model through knowledge distillation, allowing the current model to review previously learned knowledge while learning new tasks. We also design representation recalibration to better handle the class-incremental setting. Experiments over various task sequences show that our approach effectively mitigates catastrophic forgetting and outperforms all baselines.
Calibration of Robotic Arm for Workstation Installation in Changing Environment - Comparison of Manual, Mechanic, and Automatic Calibration

The aim of this paper is to obtain a complete (manual, mechanic, and automatic) calibration method for robots as digital laboratory technicians in order to boost speed, accuracy, and smooth installation process and setting up of digital pathology workstations in new laboratories or exhibitions. The manual calibration consists of jogging, with burned-in values, the mechanical calibration includes touching a reference point with a head of a calibration tool, meanwhile, the automatic calibration covers the detection of reference aruco markers by computer vision, and image processing techniques. Methods are implemented, tested, and their costs are compared so as to decide which calibration mode is used for which robot tasks and environment.
Selection of Criteria for the Problem of Optimizing Resource Allocation when Implementing an Information Leakage Prevention System at an Enterprise

The article considers the problem of choosing criteria for the task of optimizing resource allocation when implementing an information leakage prevention system in an enterprise. An information leakage prevention system is a software and hardware system designed to detect unauthorized transmission of information. The choice and implementation of an information leakage prevention system in an organization is a time-consuming task that requires an integrated approach and optimal allocation of resources. To obtain the best option for implementing an information leakage prevention system in a particular organization, it is necessary to solve the problem of optimizing the distribution of resources of this organization. The article considers the characteristics of the information leakage prevention system that affect its effectiveness. The boundaries of the optimization problem are determined, the restrictions on the controlled variables are described. As controlled variables for optimization, an economic indicator, human resource, implementation time, structure and technical equipment of the organization were chosen.
THE EFFECT OF SMS REMINDER ON THE BEHAVIOR OF CONSUMING FE TABLETS IN PREGNANT WOMEN

This study aims to determine the effect of SMS reminder on the behavior of consuming Fe tablets in pregnant women. This type of pre-experimental research with one group pretest-posttest design used a questionnaire on the behavior of pregnant women in consuming Fe tablets before and after being given treatment in the form of SMS Reminder. Research carried out BPM Nelli Herawati Bogor Regency with a sample of 32 respondents with a total sample. The data analysis technique used statistical tests with the simple paired t test. Before the SMS reminder was given, from 32 respondents, 17 (53.1%) had negative behavior in consuming Fe tablets and 15 (46.9%) respondents with positive behavior in consuming Fe tablets. Meanwhile, after receiving SMS remainder for 1 week in a row, 13 (40.6%) respondents with negative behavior in consuming Fe tablets and 19 (50.4%) respondents with positive behavior in consuming Fe tablets. The results of the simple paired t test statistic obtained a p value of 0.000 (p value &lt;0.05). There was an effect of SMS reminder on the behavior of consuming Fe tablets in pregnant women