Reviewerly/exHarmony huggingface dataset:
Machine Learning Strategies for the Implementation of a Surveillance Drone

Drone technology is utilized for a variety of reasons, including military, agricultural, aerial photography, surveillance, remote sensing, and more. Based on real-time processing techniques, a drone plane is presented for monitoring and targeting public area crime theft in this proposed work. Previously crime prediction model was developed using Artificial Neural Network (ANN) and Regressive Neural Network (RNN), as they suffer from inappropriate accuracy levels and long-time computation. Thus, to overcome this drawback, Cat boost machine learning has been implemented as it uses tree-shaped primitives for the prediction that makes classification faster for the IoT environment. Buffalo-based Cat boosts Crime Prediction System (BCPS) initially collects crime data, preprocessing them, and then extracting environmental features and context features, the features are given to cat boost machine learning. The features are combined and give results as trees, and to improve accuracy, African Buffalo optimization (ABO) has been employed here. By estimating the predictors, a result has been obtained that was used for learning purposes and the testing side shows the result of crime theft detection. Thus BCPS is evaluated for results and compared with previous techniques to show the supremacy of the proposed model.
Analysis and Design of Physical Education Option Course Selection Subsystem Based on UML

The application of object-oriented analysis and design technology in software system development is becoming more and more popular. This paper shows how to develop subsystem with specific functions in large-scale system by taking the educational administration subsystem of physical education option course developed by us as an example, so as to realize seamless enhancement of large-scale system functions.
Development of Massive Open Online Course on Coexistence in Multicultural Society to Enhance Knowledge Construction and Awareness of Cultural Values for Undergraduate Students

The purpose of this research was to 1) develop the massive open online on coexistence in multicultural society to enhance knowledge construction and awareness of cultural values for undergraduate students, 2) compare the awareness of cultural value of undergraduate students before and after studying with MOOC on coexistence in multicultural society, and 3) study the students' satisfaction with MOOC on coexistence in multicultural society to enhance knowledge construction and awareness of cultural values for undergraduate students. The sample consisted of 60 students. The research instruments consisted of the cultural value awareness inventory and the student satisfaction with MOOC scale. The findings revealed that: 1) The students' multicultural learning awareness was significantly higher in the post-test than in the pre-test at .05 level, 2) the students' satisfaction with MOOC was ranked at high level.
Effect of applying problem-based learning to practice classes of cooking healthy snack for pre-service elementary school teachers

이 연구는 예비교사의 실습지도 능력을 향상시키기 위하여 문제중심학습(PBL)을 적용하여 효과를 분석하였다. 본 연구에서 예비교사들이 해결해야 할 문제는 건강한 간식 만들기 실습을 구안하고, 실습수업을 위한 계획을 세워 직접 실행해보고 그에 적합한 지도안을 작성하는 것이다. 수업은 1) 문제제시 및 확인 2) 문제해결을 위한 자료 수집 3) 문제 해결안 도출 4) 실습진행 5) 문제해결안 수정 6) 결과 발표로 진행하였다. 연구대상자는 C 교육대학교 3학년 학생 중 초등 실과교육 과목을 수강하는 학생 154명이다. 창의적 문제해결력, 자기효능감, 수업만족도 등에 대하여 통계적 검증을 진행하였다. 연구결과 예비교사의 창의적 문제해결력과 교사효능감은 Paired t-test 결과 통계적으로 유의미한 차이가 나타났으며(P<.001), 수업 만족도는 4.7로 높은 편으로 나타났다. 예비교사의 문제중심학습에 대한 성찰지를 분석한 결과를 살펴보면 첫째, 문제중심학습을 진행하면서 교사로서 실습수업에 대한 이해도가 높아졌다. 둘째, 비대면과 대면으로 진행된 조별활동을 통해서 의사소통 및 배려의 역량을 기를 수 있었다. 셋째, 교사에게 주어진 문제를 해결하는 역량을 기를 수 있었던 것으로 나타났다.This study analyzed the effectiveness of applying problem-based learning to improve the practice guidance ability of pre-service teachers. The problem that pre-service teachers need to solve in this study is to devise a healthy snack cooking practice, make a plan for the practice class, implement it themselves, and prepare a suitable guidance plan. Classes were conducted by 1) presenting and confirming problems 2) collecting information for problem solving 3) deriving problem solving plans 4) conducting practice 5) revising problem solving 6) presenting results. This study used the one group pretest and posttest design, sampling 156 students who were in the 3rd grade of C University of Education. Statistical verification was conducted on creative problem-solving skills, self-efficacy and class satisfaction. As a result of the study, there was a statistically significant difference in the creative problem-solving ability and self-efficacy of pre-service teachers(P<.001), and the class satisfaction was high at 4.7. The results of analyzing the reflection papers on problem-based learning of pre-service teachers show that, first, the understanding of practical classes as teachers increased while conducting problem-based learning. Second, it was possible to develop the ability to communicate and be considerate through non-face-to-face group activities. Third, it was found that it was possible for teachers to develop the ability to solve problems given to them.
Multi-View-Projection-Based Saliency Detection for Meshed 3d Models with Textures

Many methods exist for detecting saliency of 3D models, but they work poorly in predicting human eye fixation on textured 3D models. Inspired by an observation that the saliency maps of two overlapping projected 2D images of a 3D model could be unified , this work proposes a novel method which detects salient regions of a textured model based on several 2D images projected from a 3D textured model. Furthermore, a dataset is built to assess the performance of our method. The obtained experimental results show that our method performs better than the state-of-the-art method by 44% and 41% in terms of Linear Correlation Coefficient (CC) and Similarity Metric (SIM), and demonstrates that texture information is a factor that needs to be taken into account when predicting human fixation on a textured model.
BNC Position Estimation for an Automated Testing Facility in the Production of Oscilloscope

Oscilloscopes may contain manufacturing defects in their BNC ports and need to be checked and tested one-by-one before packaging them. The testing process is still done manually by plugging a BNC connector into the BNC ports. Therefore, this work aims to develop an automatic inspection system that can be installed in the testing area in the plant of oscilloscope production. To do so, it needs to find the accurate position of each BNC and its distance from the installed camera on the tester robot arm. This information can be given to the robot arm to do the correct movement. A prototype system is developed based on Hough Circle Detection algorithm to identify the BNC ports. The experimental results show that the developed method is able to determine the distance from the center of the BNC port to the center of the camera, and the according angle between them.
Cognitive Environment at “Bansko Cultural Heritage Promotion Center”

The present research is based on “Methodics for Preservation and Promotion of the Cultural Heritage with Artistic Interactions and Educational Impacts through Information and Communication Technologies (Nenov 2019), and the application of “the possibilities to promote cultural heritage with artistic interactions and educational impacts through the application of modern information and communication technologies (ICT), applying an interdisciplinary analysis of the scientific information for the cultural heritage sites” (Nenov 2019). Analysis of the activities for preservation and promotion of the cultural heritage has been made in the context of the concept, prepared by author team headed by architect Nenov – “Concept for activities for revitalization of the tourist attractions for promotion of intangible cultural heritage sites, under category “national” significance in the town of Bansko. (Nenov 2019a). The concept explores the impact of the environment in the “House of Arts Bansko” and the prerequisites for its reconstruction in “Cultural Heritage Promotion Center”. The applicability of notions, such as “cognition”, “cognitive architecture”, “interpretation of the cultural heritage”, “media façade”, educational “STEM” method and “authentic artistic interactions”, related to the research, is being analyzed as new approach for preservation and promotion of the cultural heritage. The significance of the interpretation of the cultural heritage in the context of ICT capabilities for the promotion of tangible and intangible cultural values is substantiated. Present examples for application of the Augmented reality (AR) are examined. The study presents the “possibilities for interpretation of the cultural heritage through visual research with information and communication technologies (ICT) in a real “in situ” and museum environment in the context of the cultural heritage sites and using the cited methodics, which was introduced in the dissertation “Conservation of the cultural heritage of Bulgaria in the context of the synthesis of architecture and fine arts “(Nenov 2016–2019). The possibilities for building cognitive environment are explored in the context of introduction of new technologies and good practices for popularization of the cultural heritage.
PathSAGE: Spatial Graph Attention Neural Networks with Random Path Sampling

Graph Convolutional Networks (GCNs) achieve great success in non-Euclidean structure data processing recently. In existing studies, deeper layers are used in CCNs to extract deeper features of Euclidean structure data. However, for non-Euclidean structure data, too deep GCNs will confront with problems like "neighbor explosion" and "over-smoothing", it also cannot be applied to large datasets. To address these problems, we propose a model called PathSAGE, which can learn high-order topological information and improve the model's performance by expanding the receptive field. The model randomly samples paths starting from the central node and aggregates them by Transformer encoder. PathSAGE has only one layer of structure to aggregate nodes which avoid those problems above. The results of evaluation shows that our model achieves comparable performance with the state-of-the-art models in inductive learning tasks.
CRYPTOCURRENCIES AND CENTRAL BANKS

Today’s economy is being transformed by digitisation, prompting central banks to seriously consider the issuance of central bank digital currencies (CBDCs’. In that context, many central banks are examining the relevant economic and political factors, as well as necessary regulatory reforms in order to weigh the benefits and disadvantages of issuing CBDCs. In addition to these factors, legal issues related to CBDCs and the possible need to adopt new legislation to address them, also deserve consideration. This chapter examines the legal characteristics of CBDCs by reference to the features of sovereign currencies and cryptocurrencies. It concludes that the issuance of CBDCs will require adapting national legal and regulatory frameworks, in order to ensure clarity around the status and legal characteristics of CBDCs.
Correlated geometric models of order <i>k</i> and its application to intensive care unit and leprosy data

Geometric models are used to analyse the discrete time until the occurrence of an event of interest (success or consecutive successes). In two real data sets, named leprosy and intensive care unit (ICU), the events correspond, respectively, to abandoning the clinical treatment of leprosy, where abandonment corresponds to four consecutive patient absences from treatment, and the patient's discharge from the ICU. The distribution proposed in this article, called the correlated geometric distribution of order k (or correlated k-order geometric distribution), k≥1 , consists of including a correlation parameter in the geometric distribution of order k, thus considering the dependence between patient responses until the occurrence of the event. This model proves to be a better option for real data analysis where the effect of individual correlation is considered. The model is applied to real leprosy data to estimate the treatment abandonment probability. Bayesian methods are used to determine the parameter estimators of the models and to evaluate regression models. The covariates are related to the probability of the event by an appropriate link function chosen by Bayesian selection criteria. A diagnostic analysis evaluates the models fit by posterior randomized quantile residuals and influential observations by ψ -divergence measures. This methodology is illustrated by simulation studies and real ICU admission data analysis. Studies show a good fit of the proposed model. Real data analyses also find that the probabilities of the event of interest can be overestimated or underestimated when modeled without considering the effect of dependency on the model.
Image Quality Assessment of No Reference JPEG Compressed Images Using Various Spatial Domain Features

In today's world of technological advancement higher data rate and data transmission with minimal memoryrequirement is gaining importance. At the same time it also important to preserve quality of data to be transferredfrom various types of distortions and to assess the quality at the receiving end. Hence perceptual image qualityassessment is becoming more popular. This paper introduces a new approach in domain of no reference imagequality assessment of jpeg compressed images using spatial features .Considering the fact that pixel distortion,blurring and edge information are important aspects as far as distortions are concern. A novel approach is presentedin this paper to address the distortion categories. The results are obtained in conjunction with the subjectiveimage quality assessment of train and test image categories. Also results are compared with full reference imagequality assessment technique and parameters of full reference quality assessment. To achieve better accuracyalgorithm is tested on LIVE Texas' image database and our own developed database. Results are found to be wellcorrelating to the various parameters considered for the comparison purpose.
Study on the tool setting method of micro-milling tool based on in-line holography

Aiming at the various shortcomings of existing tool setting methods, this paper proposes a coaxial holographic tool setting method for tiny tools. Based on the research and analysis of the principle of holographic imaging and the key issues of holographic images, a set of holographic tool setting detection device for micro milling tool was built, and the micro milling tool measurement was carried out on the five-axis machining center using standard tools. experiment. Experimental results show that the tool setting device can efficiently perform tool setting detection of micro-milling tool. Compared with the measurement results of the high-precision external presetting instrument, the relative error of the contact tool setting instrument is 0.033%, and the relative error of the holographic tool setting prototype is 0.007%, which is more effective in realizing the tool setting of tiny tools. Detection. This result verifies the feasibility of the coaxial holographic tool setting method for micro tool, that is, holographic measurement can be used for high-precision tool setting of micro milling tool.
Projective unification in weakly transitive and weakly symmetric modal logics

Abstract We prove that $\textbf {K4}^{\textbf {n+}}\textbf {B}^{\textbf {k+}}$ has projective unification, for any $k,n\geq 1$. It means, in particular, that any weakly transitive and weakly symmetric modal logic is unitary. Some consequences of projective unification concerning structural completeness and passive rules are provided.
Dual Feature Distributional Regularization for Defending Against Adversarial Attacks

In recent years, the security of widely used deep learning models has been threatened by adversarial attacks. In this paper, we aim to incorporate a newly proposed metric H-score with the adversarial training framework to further improve model robustness on classification tasks. Specifically, we propose a novel defense method called Dual Feature Distributional Regularization (DFDR) to give dual-level regularization on feature distribution of both normal and adversarial examples, achieving maximal inter-class and minimal intra-class feature distance in a normalized feature space. The experimental results show that our DFDR can not only outperform many other defense methods against adversarial attacks but also improve the adversarial detection results effectively.
Intelligent Agents for Social and Learning Logistics Systems

Abstract The digitalization of logistics processes is often based on distributed models and decentralized control. As these logistics models constitute an important part of Industrie 4.0 concepts they must be powerful enough to cover dynamic processes and must enable a host of functions such as goal-oriented, reactive, pro-active, communicative, cooperative, competitive, and learning behaviors. In addition, these distributed models must allow for simulating, planning, allocating, scheduling, and optimizing logistics tasks. This implies that they must be able to act through communication channels with each other thus establishing logistics social communities. Multiagent Systems (MAS) have been around for more than 30 years and lend themselves to the implementation of these distributed models needed for autonomous and cooperating logistics processes. It will be described and also demonstrated by three case studies why MAS are well suited for social and learning logistics systems. It will be shown how the resulting distributed MAS models provide the required functionalities for production and transportation logistics including the handling of dynamic local events as an essential feature for the successful planning, scheduling, optimizing, monitoring, and control of global logistics processes.
Breast Cancer Detection Using Deep Convolutionnal Neural Networks and Raspberry Pi 3 Board

Approximately 1 in 8 women (13%) will be diagnosed with invasive breast cancer in their lifetime and 1 in 39 women (3%) will die from breast cancer [1]. The high mortality rate can be decreased by early and accurate screening. As the data shows the early detection correlates heavily with low mortality rates. Screening measures and guidelines differ from country to country, but can fit into two big groups, imaging techniques and biopsy. While generally women are guided to fast but less accurate imaging techniques, doctors generally refer to biopsy when the imaging techniques don’t give clear results. This paper uses convolutional neural networks, trained on a biopsy dataset neatly run on single board computer (Raspberry Pi) to advance computer aided diagnosis which would help doctors get a second opinion without referring to multiple doctors, doctors conjointly provide high false positive rates wherever healthy women area unit subject to false diagnosing, and might suffer from psychological and economic issues. The doctor would introduce some pictures of the biopsy cells to the system, which would return a report for the doctor, giving them estimation that the patient has malignant cells (deadly cancerous cells) or benign cells (not cancerous). Pathologists nowadays have 85% accuracy; creating a system that would increase the accuracy to 90% and decrease the false negatives would greatly increase early detection and survival chances for the patients.
Room Layout Estimation by Learning Depth Maps of Planes from 2D Layout Labels

Room layout estimation is a basic mission in understanding the indoor scene and has a wide range of applications. Specifically, the task of room layout estimation is to segment an indoor RGB image with semantic surface labels, i.e., ceiling, floor, and walls. A straightforward solution for the problem is to estimate the geometry of the dominant indoor planes. However, since the ground truth depth maps of the layout is not easy to obtain, research in this direction is rare. In this paper, we focus on the cuboid rooms and propose an effective learning framework that can learn the depth maps of planes from 2D layout labels without ground truth depth maps. We employ a deep network to learn the surface parameters, which can be used to produce depth maps of planes and layout results. Then we propose stereo supervision mechanism that encourages the generated layout to be consistent with the ground truth layout segmentation along Z axis and layout edge on the image plane simultaneously, so that the learned surface parameters and layout results are reasonable. Experimental results show that our method can produce high-quality layout estimates and corresponding depth maps on benchmark datasets.
Searching for Explainable Solutions in Sudoku

Explainable AI is an emerging field that studies how to explain the rationality behind the decisions of intelligent computer-based systems in human-understandable terms. The research-focus so far has though almost exclusively been on model interpretability, in particular, on trying to explain the learned concepts of (deep) neural networks. However, for many tasks, constraint- or heuristic-based search is also an integral part of the decision-making process of intelligent systems, for example, in planning and game-playing agents. This paper explores how to alter the search-based reasoning process used in such agents to generate more easily human-explainable solutions, using the domain of Sudoku puzzles as our test-bed. We model the perceived human mental effort of using different familiar Sudoku solving techniques. Based on that, we show how to find an explanation understandable to human players of varying expert levels, and evaluate the algorithm empirically on a wide range of puzzles of different difficulty.
An Adversarial Objective for Scalable Exploration

Collecting new experience is costly in many robotic tasks, so determining how to efficiently explore in a new environment to learn as much as possible in as few trials as possible is an important problem for robotics. In this paper, we propose a method for exploring for the purpose of learning a dynamics model. Our key idea is to minimize a score given by a discriminator network as an objective for a planner which chooses actions. This discriminator is optimized jointly with a prediction model and enables our active learning approach to sample sequences of observations and actions which result in predictions considered the least realistic by the discriminator. Comparable existing exploration methods cannot operate in many prediction-planning pipelines used in robotic learning without hardware modifications to standard robotics platforms in order to accommodate their large compute requirements, so the primary contribution of our adversarial exploration method is scalability. We demonstrate progressively increased performance of our adversarial exploration approach compared to leading model-based exploration strategies as compute is restricted in simulated environments. We further demonstrate the ability of our adversarial method to scale to a robotic manipulation prediction-planning pipeline where we improve sample efficiency and prediction performance for a domain transfer problem.