Reviewerly/exHarmony huggingface dataset:
Special empirical processes of independence indexed by classes of measurable functions

When analyzing statistical data in biomedical research, insurance, demography, as well as in other areas of practical research, random variables of interest take on certain values depending on the occurrence of certain events, e.g., when testing physical systems (individuals), the values of their operating time depend on the failures of subsystems; in the insurance business, the payments of insurance companies to their customers depends on insured events. In such experimental situations, the problems of studying the dependence of random variables on the corresponding events become rather important thus entailing the necessity to study the limiting properties of empirical processes indexed by classes of functions. The modern theory of empirical processes generalizes the classical results of the laws of large numbers, central and other limit theorems uniformly over the entire class of indexing under the imposition of entropy conditions. These theorems are generalized analogs of the classical theorems of Glivenko – Cantelli and Donsker. Special empirical processes are proposed in the study to check the independence of a random variable and an event. The properties of convergence of empirical processes to the corresponding Gaussian processes are analyzed. The results obtained are used to test the validity of the random right censoring model.
Creating the information-cartographic system “Educational institutions of Moscow”

The article deals with a study in the field of mapping general education in Moscow and exploringits structure. It is an important topic in studying the modern lifestyle of the city and it needs constant updating data, creating interactive maps with the most accessible information and proper design. If there is a certain amount of sites providing such materials, it is not always easy to get a comprehensive assessment (a set of information) on all secondary educational institutions in Moscow. The principles and methods of creatingtheir database presented in the article, just like information and cartographic system created on their basis enabled solving this task. The developed information and cartographic system “Educational institutions of Moscow” is represented inan interactive map, the information on the school system structure and the process of rearranging the educational institutions; their lists for each administrative district, cartographic images and additional statistical information on them. As a result, information was collected on 750 schools in the city of Moscow, a set of indicators characterizing the infrastructure of a secondary educational institution as a whole was formed, and a series of maps was created for a number of city districts.
Feature Reaggregation Network for Crowd Counting

In this paper, we propose a novel end-to-end network named Feature Reaggregation Network (FRNet) for crowd counting, which focuses on fusing the multi-scale features in the hierarchy for generating high-quality density maps. Two level and three level feature reaggregation modules are developed between the backbone network and the next feature extraction modules in the hierarchy so that the low-layer spatial feature and the high-layer semantic information can be multi-combined by element-wise addition. In addition, these two modules are placed behind the backbone network to extract global features. Furthermore, we also introduce the extra deformable mixture regression module which donates deformable convolution to extract features so that we can generate the high-quality estimated density map. We have evaluated our experiments on three popular crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF_QNRF datasets), and the experiments demonstrate that the superiority of the proposed method over the other excellent approaches.
Statistical modeling of dynamic eye-tracking experiments: Relative importance of visual stimulus elements for gaze behavior in the multi-group case

This paper presents a model that allows group comparisons of gaze behavior while watching dynamic video stimuli. The model is based on the approach of Coutrot and Guyader (2017) and allows linear combinations of feature maps to form a master saliency map. The feature maps in the model are, for example, the dynamically salient contents of a video stimulus or predetermined areas of interest. The model takes into account temporal aspects of the stimuli, which is a crucial difference to other common models. The multi-group extension of the model introduced here allows to obtain relative importance plots, which visualize the effect of a specific feature of a stimulus on the attention and visual behavior for two or more experimental groups. These plots are interpretable summaries of data with high spatial and temporal resolution. This approach differs from many common methods for comparing gaze behavior between natural groups, which usually only include single-dimensional features such as the duration of fixation on a particular part of the stimulus. The method is illustrated by contrasting a sample of a group of persons with particularly high cognitive abilities (high achievement on IQ tests) with a control group on a psycholinguistic task on the conceptualization of motion events. In the example, we find no substantive differences in relative importance, but more exploratory gaze behavior in the highly gifted group. The code, videos, and eye-tracking data we used for this study are available online.
Study of Comparison on Efficient Malicious URL Detection System Using Data Mining Algorithms

Detecting malicious URLs is an essential task in network security intelligence. Nowadays, lot of methods is available for intrusion detection systems. The intrusion detection method is mainly used for identifying malicious URLs from dataset and then reject. In this paper, we analyze a lot of malicious URL detection methods and compare the performance of the system. The experimentation is analyzed based on two types of the dataset such as URL reputation dataset and phishing websites dataset. The comparative analysis is carried out based on accuracy measures and based on accuracy we find out the best method.
The Faculty and the Dean/President

The chapter starts with the changing roles of the faculty as well as the Dean/President in a fast, flexible, networked setting, with outsourcing and a slimmer core. This network reality is seen as key to cope effectively with contextual macro shifts, especially when it comes to implementing key curricular change. The Dean/President is seen as critical here, in the sense that he/she represents a “top-down” dimension, which might not only be key when it comes to driving key changes, but also for effective faculty mentoring and for quality assurance. This top-down orchestrator role is likely to become even more central in the business schools of the future. The choice of new Dean/President is thus becoming even more critical, discussed at the end of the chapter.
FEATURES OF LABOR PROTECTION IN EDUCATIONAL INSTITUTIONS

The main features of labor protection in educational institutions are considered on &#x0D; the example of the Angarsk technical school of building technologies.
A research on prediction of bat-borne disease infection through segmentation using diffusion-weighted MR imaging in deep-machine learning approach

The theme of this study is to provide a detailed description of its recent improvements in image segmentation and lesion classification in disease prognosis. Previous studies have shown that gray-white matter hyperintensities (GWMH) is one of the hallmarks of Nipah encephalitis, which sometimes occurs during the incubation period. Predicting this type of inflammation is a challenging task because it involves some unknown medical risk factors. A typical Magnetic Resonance Imaging (MRIs) is the best non-invasive system to analyze the anatomical structure of the brain. In-depth analysis of the defined pathological structure from isolated MR imaging leads to a reduction in the processing time of the prognostic model. Modern learning techniques such as Machine Learning, Computer Vision, and Deep Learning are the most promising techniques for determining the optimal outcome, computer can able to learn and extract useful information from historical data using various algorithms. Disease prognosis based on deep learning is sophisticated, so it can handle a variety of difficult tasks including image processing, classification, and feature extraction, noise and object detection. Diffusion-weighted imaging (DWI) in MRIs is a clinical prototype that can be used to diagnose brain abnormalities and to evaluate the microscopic architectural and molecular function of human organs or tissues. In this study, we summarize the results of diagnosing Nipah encephalitis using some publicly available brain encephalitis and encephalopathy databases.
Collaborative robot manipulator control in dynamic environment using computer vision system

Abstract This paper provides the trajectory planning and motion control schema for collaborative robot manipulator in a dynamic environment using the computer vision system. The key result is a real-time motion control algorithm using potential fields approach. The simulation results illustrate the performance of the proposed algorithm. As a test platform, the KUKA iiwa manipulator is used.
CNN-Based Approach to Control Computer Applications by Differently Abled Peoples Using Hand Gesture

In today’s world, the computer and digital technology have revolutionized every aspect of human life. From connecting with each other in a split second to sharing ideas sitting miles apart, the Internet and digital technology have changed the way humans used to look at things. But the advancement of technology has not been revolutionary for every part of human community, especially differently abled people. The learning curve of this new technology has been designed in such a fashion that it is near impossible for differently abled people to learn it, use it and interact with it. There is always a void between the differently abled and the modern digital technology. It has been always difficult for them not to only use the modern technology and derive its benefits, but also to interact with other people and share their ideas and thoughts with them.
INTELLECTUAL GRAPH MODELS FOR RELATED DATA PROCESSING

Abstract In connection with the wide spreading of various intelligent sensors, IoT devices, smartphones, autonomous transport systems, various industrial and home automation systems, an unprecedented amount of data is generated, including those intelligently linked to each other. Linked data allows you to build complex and varied relationships between objects and subjects of the real world. Unfortunately, modern big data processing systems and machine learning models are extremely poorly suited for working with such dynamically linked data, especially in the case of real-time systems. We discuss current and future-proof approaches to working with such data using graph analysis models.
Trust-but-Verify in Cyber-Physical Systems

Cyber-physical systems span a wide spectrum, from long-lived legacy systems to more modern installations. Trust is an issue that arises across the spectrum, albeit with different variants of goals and constraints. On the one end of the spectrum, legacy systems are characterized by function-based designs in which trust is an implicitly in-built concept -- the operation is historically designed, implemented, and optimized in a benign stance with respect to intended use. On the other end of the spectrum, modern systems are characterized by offerings from manufacturers, vendors, and system installers -- the devices and deployments use a variety of security features that offer promises of increased trust. All along this spectrum of cyber-physical systems, extending trust beyond the traditional cyber portions to the arteries that connect the physical portions to the cyber portions is a major challenge. Here, we identify a Trust-but-Verify approach that spans this spectrum in addressing trust.
Opti-Soft: Decision Guidance on Software Release Scheduling to Minimize the Cost of Business Processes

Many approaches have been developed to increase the return on a software investment, but each one has drawbacks due to subjectivity or imprecision. This paper proposes a novel approach that addresses this problem for a particular class of information systems that improve business processes. The approach, called Opti-Soft, models a software development project as a mixed integer linear programming problem, where the objective function maximizes the return on investment and the corresponding decision variables produce an optimal release schedule. The uniqueness of Opti-Soft is in accurately modeling the business process and its improvement due to new software features, which leads to cost reduction. The approach includes a formal model, a methodology and a decision-guidance system.
USER CONTENT – ANALYSIS AND CLASSIFICATION

Internet negativity has always been a hot topic. Moderators of online discussion forums often struggle with controlling extremist comments on their platforms.We also assess the class imbalance issues associated with the dataset by employing sampling techniques and loss. The models we applied yield high overall accuracy with relatively low cost.In order to reduce the negative impact of toxic comment in day-to-day life, we have attempted to design a toxic comment detector
Prediction and Causality Visualization in Speech Recognition

Speech Recognition is a rapidly developing aspect of modern Deep Learning research. Many up-to-date technologies, such as Google Assistant or Alexa, apply it. They change the way people interact with everyday devices, i.e., phones, cars. Despite being so influential, current AI models are complex black-boxes, and few people comprehend what lies beyond them. This paper aims to feature a Deep Learning architecture for Speech Recognition and visualizations of the model state when making the predictions. We incorporated class activation maps and the attention mechanism to increase the accuracy of the presented algorithm. The introduced model can analyze audio sequences of various lengths. Preliminary results suggest that the proposed method provides a satisfactory outcome. It can be successfully applied to get insight into the state of the model during predictions. This work might, therefore, help Data Scientists detect flaws, such as overfitting in their algorithms.
Automatic Assistance Method for Disease Diagnosis Based on a Deep Learning Fusion Model and Chinese Electronic Medical Record

Extracting disease characteristics from large-scale Electronic Medical Records and achieving disease-assisted diagnoses have significant research value. Due to the complex multi-feature items and unbalanced data distribution of Electronic Medical Records, feature representation and disease diagnosis are difficult. Our study proposes a deep feature fusion (DFF) model based on the feature partition and deep feature extraction. First, the feature partition is performed, and different feature representation algorithms are adopted for different types of data. The discrete feature items are directly mapped into real-valued vectors, and the continuous feature items are represented by GCNN-based VAE. Then, the two parts are fused. Finally, the assisted diagnosis results are output through a supervised learning classification method based on the XGBoost framework. The dataset of our study is from the 18,590 real and effective clinical Electronic Medical Record of Huangshi Central Hospital. The experimental results show that the method can perform clinical Assisted diagnosis accurately and efficiently, which are superior to some other state-of-the-art approaches, can better meet the needs of practical clinical diagnosis applications.
DESIGN EXPLORATION ON VARIETY USE OF STRUCTURAL STEEL FOR TEMPORARY EVACUATION SHELTER

Indonesia has the second-longest coastline in the world and known as a country that is located in the Pacific Ring of Fire, which has frequent earthquakes, volcanic eruptions, and tsunamis. Tsunamis often occur in Indonesia and cause quite a lot of casualties such as the events in Aceh in 2004 (220,000 killed) and the latest, namely the Banten tsunami in 2018 (killed 446). The population that inhabits along the coast in Indonesia, a total of 3.8 million people, is considered a population that lives with a high threat from tsunamis. In order to minimize the number of victims and losses, innovations for rescue strategy are needed â€“ one of which is the design of Temporary Evacuation Shelter (TES). TES is a safe house and a vertical protection from tsunami hazards. Generally in Indonesia, TES is built made of reinforced concrete structures. Although it has high compressive strength but has weaknesses that it tends to be heavier and larger in dimensions and also brittle, it has a high risk of damage due to earthquake shocks that usually proceed with the tsunami. This research paper is intended to utilize steel material as a TES structure. The advantage of using structural steel is that it has a good tensile capacity, high compressive strength, with relatively slimmer and lighter dimensions compared to concrete, and, lastly, it has high ductility measure. This becomes the advantage of steel as a TES structure because it can withstand large deformations without collapse caused by an earthquake. It is expected that the performance of the structural steel TES will be preferable than the reinforced concrete TES structure. This research explores the idea of designing a variety of multi-function TES built made of steel structures, in addition to having excellence and resilience as a safe house for tsunami evacuation. The design shall have the flexibility to respond to various contextual site conditions and shall suffice the function of tourism, markets, sports, worship, and education facilities when there are no disaster threats. Designs of TES aim for an integrated solution as a means of saving residents' lives as well as improving the quality of life for coastal communities in Indonesia.
Enhancement of UAV-Aerial Images Using Weighted Differential Evolution Algorithm

Depending on technological developments, the use of Unmanned Aerial Vehicles (UAVs) is increasing day by day and is a valuable source of data for different applications. Generally, low-cost and lightweight non-metric digital cameras are used in UAV systems. During the data collection phase, exposure parameters such as camera shutter speed, aperture value, ISO value, and various weather and light conditions have significant effects on image quality. Image enhancement methods can be used to increase image quality in accordance with the desired purpose. In this study, image enhancement is considered as an optimization problem and Weighted Differential Evolution (WDE) Algorithm is used to solve it. The image quality is enhanced by using an objective function in which performance measures of entropy value, sum of edge density and number of edge pixel are maximized. In the proposed color image enhancement method, aerial images defined in RGB color space are transformed into HSV color space images. the brightness component (V) of HSV color space is modified for image improvement with WDE algorithm. The performance of the proposed method has been compared with other existing techniques such as histogram equalization, linear contrast stretching and evolutionary computing-based image enhancement method like Artificial Bee Colony (ABC) Algorithm in terms of fitness value and image quality.
Research on gesture recognition preprocessing technology based on skin color detection

In the process of gesture recognition, human hand detection based on skin color is a method with high recognition rate. When the acquired gesture information is converted into digital images that can be processed by computer, the gesture images will be interfered and affected by various factors in the process of generation, transmission or transformation, and the quality of the gesture images will be distorted to different degrees due to noise, so the image needs to be preprocessed first. The process of image preprocessing includes image binarization and image smoothing. In this paper, the smoothing technology and binarization method are respectively studied and compared in experiments, providing effective data samples for gesture segmentation and feature extraction in gesture recognition.
An Online Planning Agent to Optimize the Policy of Resources Management

Reinforcement learning-based systems have received a lot of attention in various domains in recent years. In such domains, an autonomous agent learns from environment to provide a solution. Resource scheduling is considered as research challenge where such autonomous agent optimizes the solutions. This work is presented as an investigation on the effectiveness of various algorithms which drives actions associated with autonomous agent. We give a detailed contention between three differing algorithms—Q-learning, Dyna-Q, and deep-Q-network, given the task of effectively allocating the resources in an online basis. Among the mentioned algorithms, the Q-learning and deep-Q-network, which are model free algorithms, have remained in wide use for planning. However, this paper focuses on highlighting the effectiveness of lesser-known model-based algorithm, Dyna-Q. The experiment results show agent-based policy derived by the Dyna-Q algorithm which provides optimized resource scheduling for the current environment.
Effects of Leisure Satisfaction on Mental Health of Young People in the covid-19 Era: Mediating Effects of Stress Coping

본 연구는 청년층을 대상으로 covid-19 이후의 변화된 여가생활에 따른 여가만족도와 정신건강의관계에서 스트레스 대처방식의 매개효과를 분석하여 정신건강을 향상시키는 요인을 파악하는 데 목적이 있다. 이와 같은 연구목적을 달성하기 위하여 15~29세 청년층을 대상으로 온라인 설문지 통해 총226부의 자료를 수집하여 연구를 수행하였다. 수집된 자료를 기초로 SPSS 25.0을 이용하여 탐색적요인분석과 신뢰도 분석을 하였으며, 매개효과를 파악하기 위하여 다중회귀분석을 시행하였고, Baron 과 Kenny(1986)의 3단계 매개효과 검증 절차를 이용하여 분석하였다. 연구결과 청년층의 여가만족은 스트레스 대처의 하위요인인 문제중심적 대처방식, 소망적사고 대처방식, 사회적지지 추구 대처방식이 정신건강 간의 관계에서 완전 매개효과가 있었다. 이에 따라 코로나 판데믹과 같은 환경적 여가제약의 상황에서 청년들의 정신건강 특히 사회부적응 등에 어려움을 호소할 때, 문제를 해결하기 위하여 스트레스에 직면하고 원인을 밝혀내려는 노력을 증진 시키거나 사회적인 지지 또는 현 상황에 몰입함을 중재시켜 올바른 생각의 방향으로 나아갈 수 있는 상담전략 및 예방책이 필요함을 시사한다.This study aims to identify factors that improve mental health by analyzing the mediating effects of stress coping methods in the relationship between leisure satisfaction and mental health due to changed leisure life after covid-19. In order to achieve this research goal, a total of 226 copies of data were collected and studied through online questionnaires for young people aged 15 to 29. Based on the collected data, exploratory factor analysis and reliability analysis were conducted using SPSS 25.0, multiple regression analysis was performed to identify the mediating effect, and three-step mediating effect verification procedures by Baron and Kenny (1986). According to the study, young people s leisure satisfaction was fully mediated in the relationship between mental health, with problem-centered coping methods, wishful thinking coping methods, and social support seeking coping methods. This suggests that when complaining of difficulties in environmental leisure constraints such as Corona Pandemic, counseling strategies or precautions are needed to improve stress and identify causes, or mediate social support or immersion in the current situation.