Reviewerly/exHarmony huggingface dataset:
Evaluation of Bethesda System for Reporting Thyroid Cytology with Histopathological Correlation

Background: The Bethesda system of reporting thyroid cytopathology is a standardised system, improving communication between cytopathologists and clinicians, leading to more consistent management approaches.The aim of the research work was to study the utility of Bethesda system in reporting thyroid cytology with histopathological correlation of all the cases undergoing surgical resection.Methods: We studied all the thyroid cytology cases received between January 2017 to June 2018, and classified them according to the Bethesda system.Histopathological correlation was done for all the cases which underwent surgical resection with evaluation of cyto-histological discrepancies.Results: Out of 423 cases studied on FNAC, 152 cases available for histopathological follow-up, cytohistological discrepancies were noted in 11 cases.132 (86.84%) were benign lesions, 13 (8.55%)were Follicular neoplasm/Suspicious for neoplasm, 3 (1.97%)were suspicious for malignancy, and 04 (2.63%) were malignant, 0 case in Unsatisfactory/ Non diagnostic & Atypia of undetermined significance.Statistical analysis of the present study showed that cytological analysis of thyroid lesions by Bethesda system has got high sensitivity (73.68%), high specificity (95.48%) with a positive predictive value of 70% and negative predictive value of 96.24% and a high efficacy (97.55%).Conclusions: Reviewing the thyroid FNAs (fine needle aspirates) using Bethesda system allowed a more specific cytological diagnosis with better inter laboratory agreement.As evidenced by its high sensitivity and specificity, Bethesda system has proven to be a very effective guide for the clinical management of thyroid nodules.
Fuzzy Approach to Short-Term Youth Unemployment Forecast

The practical output of the paper is the prediction of the percentage of the short-term youth unemployment in the following two consecutive years. For this purpose, the non-traditional fuzzy model of the relevant prediction task is formulated in its theoretical-methodological part, which is subsequently applied in the application part. By means of the exact tools of the fuzzy approach (fuzzy sets and inference rules for manipulation with them), the synergy, uncertainty and complexity of the values (terms) of vaguely defined linguistic variables of an economic-psychological nature represented by sets of selected macroeconomic indicators is reflected. Despite the fact that the results of the fuzzy prediction are not significantly different from the results of conventional statistical prediction, the demonstration of the approach of the case of knowledgeable expertise to the choice of the relevant fuzzy sets and the formulation of the set of inference rules can be considered as a secondary benefit.
Reflection on the Reform of Flipped Class Teaching Mode of Art and Design Courses Based on MOOC: Taking the Course “Advertising Creativity and Planning” as an Example

It is an inevitable trend of education development in the Internet plus era to change classroom teaching mode based on MOOC, which will further lead to series of changes transformation undoubtedly.Under the development trend of this era, for art and design courses, how to cultivate applicationoriented talents in accordance with the requirements of the times while following its own laws is a problem that every teacher in the front line of teaching needs to think about.Based on the summary of the author's teaching practice for many years, this paper forms some thoughts on the reform of flipped classroom teaching mode of art and design on the basis of MOOC.
Multi-Sensor Data and Knowledge Fusion -- A Proposal for a Terminology Definition

Fusion is a common tool for the analysis and utilization of available datasets and so an essential part of data mining and machine learning processes. However, a clear definition of the type of fusion is not always provided due to inconsistent literature. In the following, the process of fusion is defined depending on the fusion components and the abstraction level on which the fusion occurs. The focus in the first part of the paper at hand is on the clear definition of the terminology and the development of an appropriate ontology of the fusion components and the fusion level. In the second part, common fusion techniques are presented.
Rocket tracking impact point prediction using α-β, standard Kalman, extended, Kalman, and unscented Kalman filters: a comparative analysis

Accurate information about the impact point (IP) of a suborbital rocket on Earth’s surface during a launch is an important requirement for range safety operations. Four different estimators, i.e., the α-β filter, standard Kalman filter (SKF), extended Kalman filter (EKF), and unscented Kalman filter (UKF), are considered for the suborbital rocket tracking problem, whose data are used specifically for improving the accuracy of the IP prediction (IPP) of these vehicles. This paper presents a comparative analysis between the results of the estimators. Rocket flight data are discussed to demonstrate the advantages and disadvantages of the estimators and to determine the inherent limitations in predicting the aerodynamic effects found in certain flight situations. We discuss the appropriate mathematical model of a filter capable of running the real-time algorithm for the estimation of target position and velocity. This work uses actual data from a radar sensor to evaluate the tracking algorithms. We insert the filter result into the mathematical model developed to predict the rocket IP on Earth's surface. The main goal of this study is to evaluate the performance of four different estimators when specifically applied for the improvement of the IPP of suborbital rockets. It is demonstrated that the UKF outperforms all other tracking algorithms in terms of the accuracy and robustness of IP estimation.
Self-Supervised Fast Adaptation for Denoising via Meta-Learning

Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and these methods can restore an image by exploiting information available from the given input (i.e., internal statistics) at test time. However, self-supervised methods are not yet combined with conventional supervised denoising methods which train the denoising networks with a large number of external training samples. Thus, we propose a new denoising approach that can greatly outperform the state-of-the-art supervised denoising methods by adapting their network parameters to the given input through selfsupervision without changing the networks architectures. Moreover, we propose a meta-learning algorithm to enable quick adaptation of parameters to the specific input at test time. We demonstrate that the proposed method can be easily employed with state-of-the-art denoising networks without additional parameters, and achieve state-of-the-art performance on numerous benchmark datasets.
The Relationship Study for Major Petrochemical Complexes and Liquid Cargo Ports by the Granger and Toda-Yamamoto Causality Test

One of the world's major resources is crude oil, the most fundamental part of the industry. There is no place that does not use crude oil. Petroleum refining products and chemical production industrial products are produced through nearby petrochemical complexes and ports after importing crude oil. There would be a possible relationship among the petrochemical complexes and nearby regional ports working with liquid cargoes. To confirm these relations, Ulsan Port, Daesan Port, and Yeosu Gwangyang Port were selected for this study. A Vector Auto Regressive model using time series data was applied. A Unit Root Test was performed. The relationship was confirmed through the Granger and Toda Yamamoto Causality Test.
Unsupervised Multimodal Image Registration by Polynomial Warping over Correlation-Maximizing Shifts

Abstract Alignment of images from multiple modalities is a very important procedure for many medical and industrial applications. Often times it is not possible to utilize a supervised method due to the lack of labeled data for any specific sensor architecture. In this study, a new unsupervised approach is proposed for a sensor-camera system aligned in one axis, that warps the image-like frames onto each other with a second-degree polynomial sampled from the cross-correlation maximizing segment shifts. This methodology will allow the registration process to adjust for focal differences and varying image modalities between the sensors. Thus, novel architectures utilizing seldom-used sensors will more easily adapt to industrial and medical work environments.
Toward Secure Quantum Key Distribution Protocol for Super Dense Coding Attack: A Hybrid Approach

The essential objectives of security, for example, verification, privacy, respectability and non-denial in correspondence systems are able to accomplish by safe input circulation. Quantum instruments are exceptionally safe methods for conveying mystery key because they are genuinely safe. QKDP is able to adequately counteract different assaults in Quantum Channel (QC) at the same time Traditional Cryptography (TC) is effective into confirmation plus check of mystery key. Through consolidating together QC plus TC safety of correspondences more than systems be able to exist utilized. Inside this manuscript, we suggest another toward secure quantum key appropriation convention for too thick coding assault. Since our plan be recognized because HQKDP.
Mobile Learning Adoption among Tertiary Students

The purpose of this study is to investigate the adoption of mobiles learning among higher institution students and the impact on their on their academic achievement.  Data was collect from students of four higher institutions around Melaka. A questionnaire survey was distribute to selected higher institutions in order to collect the data for the research. The study used convenience-sampling method to select the samples. Two hundred (200) respondents was participated in the study. Regression analysis were conduct to examine the effect of digital mobile learning device elements on the higher institution students' performance. The results of this study indicates that only perceived convenience has a significant positive relationship with student performance. However, there are no significant relationship between perceived information accuracy and perceives mobility and students’ academic achievement. The results also provide practitioners with guidelines for implementing digital mobile learning among tertiary students.
#MeToo on Campus: Studying College Sexual Assault at Scale Using Data Reported on Social Media

Recently, the emergence of the #MeToo trend on social media has empowered thousands of people to share their own sexual harassment experiences. This viral trend, in conjunction with the massive personal information and content available on Twitter, presents a promising opportunity to extract data driven insights to complement the ongoing survey based studies about sexual harassment in college. In this paper, we analyze the influence of the #MeToo trend on a pool of college followers. The results show that the majority of topics embedded in those #MeToo tweets detail sexual harassment stories, and there exists a significant correlation between the prevalence of this trend and official reports on several major geographical regions. Furthermore, we discover the outstanding sentiments of the #MeToo tweets using deep semantic meaning representations and their implications on the affected users experiencing different types of sexual harassment. We hope this study can raise further awareness regarding sexual misconduct in academia.
Studying the Performance of Cognitive Models in Time Series Forecasting

Cognitive models have been paramount for modeling phenomena for which empirical data are unavailable, scarce, or only partially relevant. These approaches are based on methods dedicated to preparing experts and then to elicit their opinions about the variables that describe the phenomena under study. In time series forecasting exercises, elicitation processes seek to obtain accurate estimates, overcoming human heuristic biases, while being less time consuming. This paper aims to compare the performance of cognitive and mathematical time series predictors, regarding accuracy. The results are based on the comparison of predictors of the cognitive and mathematical models for several time series from the M3-Competition. From the results, one can see that cognitive models are, at least, as accurate as ARIMA models predictions.
Deep Weakly-supervised Anomaly Detection

Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples only (i.e.,, seen anomalies), and consequently they fail to generalize to those that are not, i.e., new types/classes of anomaly unseen during training. To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two randomly sampled training instances, in which the pairwise relation can be anomaly-anomaly, anomaly-unlabeled, or unlabeled-unlabeled. Since unlabeled instances are mostly normal, the relation prediction enforces a joint learning of anomaly-anomaly, anomaly-normal, and normal-normal pairwise discriminative patterns, respectively. PReNet can then detect any seen/unseen abnormalities that fit the learned pairwise abnormal patterns, or deviate from the normal patterns. Further, this pairwise approach also seamlessly and significantly augments the training anomaly data. Empirical results on 12 real-world datasets show that PReNet significantly outperforms nine competing methods in detecting seen and unseen anomalies. We also theoretically and empirically justify the robustness of our model w.r.t. anomaly contamination in the unlabeled data. The code is available at https://github.com/mala-lab/PReNet.
Perception and Needs of Teachers for Job Training Program according to NCS-based Chemical Engineering Curriculum of Specialized Vocational High Schools

이 연구는 화공 교사의 NCS 기반 교육과정에 대한 인식과 직무연수 교육요구를 조사 분석하였다. 연구 방법은 설문조사를 실시하였고, 연구 결론은 다음과 같다. 첫째, 화공 NCS 기반 교육과정에 인력양성유형은 화학분석, 위험물 관리, 화학물질 취급 관리 분야, 그리고 바이오와 석유화학 분야의 기술기능인 순으로 적합하거나 타당한 것으로 인식하였다. 둘째, NCS 기반 교육과정의 화공 과목에 대한 교수·학습방법은 강의법, 실습, 실험, 협동학습, 프로젝트학습과 문제해결학습 순으로 적합하며, 평가방법은 선택형, 서답형, 서술형, 포트폴리오와 평가자 체크리스트 순으로 적합하다고 인식하였다. 셋째, NCS 기반 교육과정에 따른 화공 교사연수의 필요성은 평균 4.36으로 대체로 높게 나타났다. 교사 연수내용은 화공 실무과목의 교수·학습방법과 평가방법, 화공 기초과목의 교수·학습방법 적용 사례와 실습, 화공 기술 및 산업체 최신 동향 등이 필요하다고 응답하였다. 화공 기초과목의 교육요구도는 2.47로 분석되었다. 실무과목은 교육요구도가 3.96으로 기초과목보다 높게 분석되었다. 희망하는 실습 주제는 화학분석, 바이오 화학제품 제조, 공정제어, 화학물질관리, 정밀 화학제품 제조 등 과목의 NCS학습모듈에 제시된 실험·실습 내용, 관련 장치(장비) 사용법 등으로 나타났다. 결론적으로 화공 분야 NCS 기반 교육과정을 원활하고 효율적으로 적용하기 위한 교사 연수는 이론적이고 형식적인 교사 연수가 아닌 학교 현장에서의 실제적 수행에 연계된 능력 및 전문성 신장을 도모하는 수요자 중심의 실천적 연수 운영이 이루어져야 한다.This study surveyed and analyzed the perception of NCS-based curriculum and job training education requirements of chemical engineering teachers. This study carried out a survey targeting chemical engineering teachers and the results were as follows. First, with regard to the human resource development type in the NCS-based curriculum, the traditional field of chemical engineering such as chemical analysis, hazardous materials management, chemical treatment management, and technology functions in the bio and petrochemical fields were appropriate or appropriate in order. Second, it was recognized that teaching and learning methods for chemical engineering courses in NCS-based curriculum are appropriate in order of lecturers, practices, experiments, cooperative learning, project learning, and problem-solving learning. And for evaluation method, it is considered that the evaluation methods are appropriate in the order of selection type, supply type, narrative type, portfolio, and evaluator checklist. Third, the need for chemical engineering teacher training according to NCS-based curriculum was generally high at 4.36 on average. With regard to the contents of teachers’job training, they considered that current trends in chemical engineering and industry, teaching evaluation method for chemical engineering subjects, and applicable cases practice of primary subjects were needed. The education requirements for basic chemical engineering subjects were analyzed as 2.47. The education requirement for practical subjects was 3.96, which was analyzed to be higher than the basic subjects. Desired lab subjects were presented in the NCS learning module for subjects such as chemical analysis, bio-chemicals products manufacturing, process control, chemical management and precision chemical products manufacturing, etc., and how to use related devices (equipment). In conclusion, teacher training for efficient application of NCS-based curriculum in the chemical engineering field should be demander-centered practical training operation to promote ability and professionalism connected to actual fulfillment at school, not just theoretical and formal teacher training.
Collaborative Training of Balanced Random Forests for Open Set Domain Adaptation

In this paper, we introduce a collaborative training algorithm of balanced random forests with convolutional neural networks for domain adaptation tasks. In real scenarios, most domain adaptation algorithms face the challenges from noisy, insufficient training data and open set categorization. In such cases, conventional methods suffer from overfitting and fail to successfully transfer the knowledge of the source to the target domain. To address these issues, the following two techniques are proposed. First, we introduce the optimized decision tree construction method with convolutional neural networks, in which the data at each node are split into equal sizes while maximizing the information gain. It generates balanced decision trees on deep features because of the even-split constraint, which contributes to enhanced discrimination power and reduced overfitting problem. Second, to tackle the domain misalignment problem, we propose the domain alignment loss which penalizes uneven splits of the source and target domain data. By collaboratively optimizing the information gain of the labeled source data as well as the entropy of unlabeled target data distributions, the proposed CoBRF algorithm achieves significantly better performance than the state-of-the-art methods.
Efficient Semantic Enrichment Process for Human Trajectories in Surveillance Videos

Nowadays, it becomes very convenient to collect large-scale videos that record trajectories of human mobility behavior in various situations in cities, due to the increasing availability of surveillance camera. Obviously, surveillance videos became a new data source of spatiotemporal trajectories. However, a typical trajectory semantic enrichment process receives as input spatiotemporal trajectories. The process methods cannot be applied to video data directly. In this paper, we propose a semantic enrichment process framework for human trajectories in surveillance videos. It includes trajectory identification in videos, trajectory transformation, sub-traj ectory segmentation, segment annotation. We can derive semantic trajectories from surveillance videos through the four phases. Having observed the common occurrence of the similarities between individual trajectories, we propose a grid index-based method to search similar pre-annotated sub-trajectory segments in pixel space for retrieving semantic trajectories in order to enhance the performance of this approach. Finally, we demonstrate the effectiveness and efficiency of our proposed approach by using a real world data set.
Algorithms in Multi-Agent Systems: A Holistic Perspective from Reinforcement Learning and Game Theory

Deep reinforcement learning (RL) has achieved outstanding results in recent years, which has led a dramatic increase in the number of methods and applications. Recent works are exploring learning beyond single-agent scenarios and considering multi-agent scenarios. However, they are faced with lots of challenges and are seeking for help from traditional game-theoretic algorithms, which, in turn, show bright application promise combined with modern algorithms and boosting computing power. In this survey, we first introduce basic concepts and algorithms in single agent RL and multi-agent systems; then, we summarize the related algorithms from three aspects. Solution concepts from game theory give inspiration to algorithms which try to evaluate the agents or find better solutions in multi-agent systems. Fictitious self-play becomes popular and has a great impact on the algorithm of multi-agent reinforcement learning. Counterfactual regret minimization is an important tool to solve games with incomplete information, and has shown great strength when combined with deep learning.
COMMUNICATIVE PRACTICES IN THE SOCIO-CULTURAL SPACE OF THE REGIONAL LIBRARY

The article highlights the issues of organizing social and communicative practices by means of library activities. The experience of the State Universal Scientific Library of the Krasnoyarsk Territory on forming communicative interaction of various social groups through information services, offices and the library communication system is considered.
E-material Formatting Application Prototype 2.0 Development Through Usability Testing of Prototype 1.0

The increase of digital device use is a reality. The idea about the automated e-material formatting application development came as a possible solution for the improvement of e-material reading comfort and text perception from the screen. It is needed as there are no complete recommendations for e-material formatting, and users are having complains after work behind screens. After prototype 1.0 development, it is understandable that there is a possibility of idea realization. Preliminary prototype usability testing was conducted in a small group. It showed places for improvement. Prototype 2.0 is focused on improvements. A correctly created prototype must give the possibility to conduct an alpha testing of the application in a larger user group.
Italian court orders Amazon not to sell luxury cosmetics subject to a selective distribution network

Abstract Case 50977/2018 Sisley Italia et al v Amazon Core Europe et al (Sisley v Amazon) (2019) District Court of Milan, Commercial Law Chamber, Decision of 28 March 2019 In upholding an application for an interlocutory injunction, the District Court of Milan ordered Amazon to refrain from marketing, selling, promoting or advertising on its Amazon Marketplace Sisley high - quality cosmetics. The court considered that offering luxury products alongside low-quality goods on the same Internet site: (i) is incompatible with the qualitative standards, which the members of the selective distribution system covering the said cosmetics have to respect; (ii) constitutes trade mark infringement as the existence of a lawful selective distribution network is a legitimate reason for the non-application of the exhaustion principle.