Reviewerly/exHarmony huggingface dataset:
Analyzing the Causes of Maternal Death in Postpartum Period Using a Fuzzy Inference System Approach

Analysis of the causes of post-partum maternal death is very important in reducing maternal mortality. This study aims to reduce post-partum maternal mortality by looking at vital signs and physical signs in the form of 19 parameters. At this time the analysis of maternal mortality only focuses on the factor of vital signs without looking at the physical signs that can be analyzed. The post-partum maternal mortality analysis system using the Fuzzy Inference System is very relevant to overcome the problem of postpartum maternal mortality because it can manage the post-partum maternal mortality indicator values into values that are vague in the form of limits such as low, normal, and high. The results of using the fuzzy Inference System model are used to predict postpartum maternal mortality from the first day to the third day based on vital signs Blood Pressure with a status of &#x201C;High&#x201D; with an Membership function (MF) value of 0.44 and factors that influence maternal mortality based on physical signs are Anogenital Lochea Rubra and Anogenital Lochea Sanguinolenta with &#x201C;Medium&#x201D; status with the highest MF on the fourth day with an MF value of 0.84, for Anogenital Lochea Sanguinolenta with &#x201C;High&#x201D; status with the highest MF on the fifth day with an MF value of 0.46.
Seasonal Birth Rate according to the Level of Academic Ability

In this study, students in the upper and lower grades were selected based on the basic academic assessment for 3rd, 4th, and 5th students of elementary school. After that, it was analyzed what pattern the seasonal birth rates of these two groups each showed. Through this, we tried to find out how the problem of the monthly gab in the same grade caused by the birth time has a relationship with their academic ability formation. As a result of the analysis, it was found that the number of spring births was significantly higher in the upper grade group of Korean language and total score. But in the lower grade group, all of the spring, summer, autumn, and winter groups did not show any significant difference in birth rates by season. It can be seen that children born in spring who are the oldest in a school year are more likely to enter the upper grade group than those born in other seasons. The results of this analysis are judged to be directly related to the age at which children enter the elementary school. In other words, in Korea's elementary school entrance system, where a new school year begins in March, spring children group born in March, April, and May has the oldest age, while children born in December, February, or winter, had the youngest age. Spring born students who are up to 12 months older than winter born students enter school with more physical and mental maturity. This degree of maturity also affects the student's academic ability. Therefore, it will be necessary to minimize the impact of monthly gab existing between classmates by variously changing the current school entrance system. Through this, it can be expected that new educational attempts will be introduced into the school education field and useful results will be derived by verifying the effectiveness.
Discrete optical Zeno effect for polarization of light

Abstract Quantum Zeno effect concerns deterministic dynamics of a quantum system induced by a series of projective quantum measurements. Applying this effect in optics, one can achieve an arbitrary lossless transformation of linear polarization of light with help of linear polarizers. However, to demonstrate this effect in practice, we have to take into account unavoidable losses in each polarizer that limits probability of successful transformations. In this work, we theoretically study a realistic quantum Zeno effect with an optimal discrete set of polarizers and find the maximum success probability
Systematic choice of video game benchmarks in Deep Reinforcement Learning

Deep Reinforcement Learning has gained much attention due to results obtained by its methods to problems of high dimensionality, which were previously intractable or difficult to solve. In this context, video games have been widely used as experimental environments and benchmarks for the evaluation of reinforcement learning algorithms, as well as guiding the development of new methods. Although a lot has been done in Deep Reinforcement Learning since the proposal of its seminal work, little has been discussed about proper methodologies for constructing such evaluation benchmarks. This paper proposes to systematize the choice of video games to be used as a benchmark guaranteeing representativeness and diversity of learning environments based on the use of video game typologies proposed in the area of Game Design Research.
The analysis of UAV detection performance using rotating cameras

With the development of the drone industry, various technologies to detect the increasing illegal intrusion drones are being developed to reduce the damage caused by illegal intrusion drones. Deep learning-based image sensing techniques are useful for detecting and classifying drones at close range, but this requires the use of a large number of cameras. To solve this problem, this paper intends to analyze the detection performance of the unmanned aerial vehicle using the image sensing technology that rotates the camera. To this end, a method of constructing a UAV protection area and a method of rotating the camera in a spiral to increase the opportunity of UAV detection are proposed, and performance analysis based on rotating camera is performed.
Artificial Intelligence Driven Monitoring, Prediction and Recommendation System (AIM-PRISM)

System Performance Monitoring and Management of Enterprise Information systems is an exhaustive work that every organization confronts, and it consumes a lot of effort and cost. Different organizations spend significant cost and efforts in maintaining their applications. This paper presents a system that automates the monitoring, predictions and recommendation of performance issues, bottlenecks, trends and system failures in enterprise information systems, using state-of-the-art machine learning algorithms. This system will help the organizations in maintaining the overall health of different applications intelligently as centralized control with distributed access and control of applications.
Sentiment Analysis Using Simple K-Means to Cluster Tourist in Ecotourism Destination in Sumatera – Indonesia

Sentiment analysis in this particular field is applied to help ecotourism producers to evaluate and improve their performance. Using that, digital footage will be useful to be some source of data that the researcher can use in ecotourism fields. Before starting the sentiment analysis, data preprocessing is needed to be done first. Data preprocessing is a process to clean the data, so it will be suitable to proceed further. Data preprocessing include case folding, tokenization, stop words, and stemming. The next step will be the implementation text clustering technique, also known as a sentiment analysis technique. In this research, sentiment analysis is done by using the Waikato Environment for Knowledge Analysis . Basically, sentiment can be identified or traced through someone’s affective interactions with the object or entity .
Research on foreground target extraction and image processing of surveillance video

Surveillance videos have been deployed throughout the "Safe City" construction to employ large-scale surveillance video information to cope with problems in security and other sectors. So how to study into foreground target extraction and image processing in surveillance video, might give useful help to generic video processing problems. Aiming at extracting foreground targets from surveillance videos with various backgrounds and targets, a matching foreground target extraction model was developed. First, a static background extraction model based on the enhanced three-frame difference technique was created using the adaptive iterative threshold method. Second, by combining the standard twoframe difference approach with the Gaussian mixture model, a dynamic background extraction model based on the neighborhood search method was developed. Finally, a dynamic backdrop extraction model for unstable shooting was created using the enhanced three-frame difference approach, corner feature matching, and linear affine transformation. The experimental results demonstrated that the model can efficiently recognize and extract the foreground targets of surveillance video, which solves the problem of extracting complicated targets.
Fundamental Science, Technological Innovation, and Business. A Brief History of Their Interrelation

The paper considers the major theoretical concepts of the interrelation between higher education systems, science, technological system, and business in creating and disseminating innovations. The purpose of the paper is to consider motivations of the main actors of these systems and problems of interaction between them. Each of such systems creates its own institutions and types of organizations based on their missions. Initially, science had minor effect on creating and disseminating innovations, while higher education and scientific activities were closely linked to each other in European universities. Along with the spread ofhigher education, the links between science and the development of technology are becoming closer because of wider opportunities for information dissemination and appearance of new relevant institutions. It was the state who played a special role in the formation of our modern innovation system that brought new problems to the development of science and business. However, it is business who would play a main role in the creation of an effective innovation system. The development of science requires new subject areas, staff rotation, and discussions, as well as to enlighten the public.
A Survey of Stochastic Computing in Energy-Efficient DNNs On-Edge

In recent years, "edge intelligent computing power deployment" has become a key and difficult problem in key areas such as system structure and artificial intelligence. Some independent decision-making scenario requires the edge devices to have the ability to handle high energy efficient and high throughput tasks. However, restricted by the traditional accurate computing mode and memory-computation separation structure, the edge devices often fail to provide the computing power guarantee of high throughput and high energy efficiency, which seriously hinders the edge intelligent deployment. This paper summarizes the new computing model integrating memory and computation of deploying stochastic circuits around memory, and constructs an edge intelligent system taking stochastic computing as a core from three levels of microstructure of circuit, architecture of near-memory stochastic computing and system-level software/hardware cooptimization. It strives to break through the power consumption and performant bottleneck of traditional computing model, and greatly improves the deploying ability of complex intelligent computing on edge equipment.
Analysis of the aspects and issues of student participation in curriculum development of high school credit system

In developing the school curriculum, the ߢstudentߣ has been regarded as a resource for decision-making. However, with the recent introduction of the high school credit system raises the meaning of student participation in the school curriculum development. The goal of this study is exploring the meaning and the issues of student participation in the school curriculum development following the implementation of the high school credit system. Data collection was conducted interviews with nine people, including teachers and supervisors. In the high school credit system, students were regarded as an important ߢresourceߣ for decision-making related to the curriculum as in the existing method. Students also had the meaning as important ߢparticipantsߣ in determining the subjects of the school curriculum, and sometimes as ߢchange facilitatorsߣ who make teachers to find alternatives. In this process, there are difficulties in relation to how well students have understanding and competency in subject selection, differences in the voices of students about the demand for opening subjects, and the extent to which teachers should intervene when the needs of students are importantly reflected in the curriculum. Based on the results of this study, this study suggests that a change in the perspective on student participation is necessary to develop curriculum under the high school credit system, and practical support should be prepared to resolve detailed issues related to student participation.
Analysing the importance and impact of cloud computing on organization's performance management during economic crises

Organization performance management have difficulties in providing the required funding for instructional, research and development activities given the present financial crisis and the increasing requirements. The purpose of this article is for colleges to identify options to the usage of IT to make them more agile and save. A careful examination of the current Cloud Computing studies as an alternative to IT, security management were the conceptual framework. In addition to the authors of IT and higher education, it takes into account best practices for cloud computing used in organization management. The paper starts with a brief overview to cloud computing at organization performance management, which refers to the main outcomes that have been achieved. At addition, the proposal for an adoption plan provides a beginning point for cloud computing in organization management. The approach consists of five steps, focusing on information assessment and processes/functions/applications from many major academic organization managements on a range of essential criteria, while at the same time building a match between any of these features and the cloud market concepts. The results achieved encourage and encourage the use of cloud solutions in organization performance management by enhancing knowledge in this sector and offering a gentle introduction suitable for the organization of the university. The suggested model takes into consideration the architectural and criteria of the university, such as mission, availableness, applications significance, and mission, sensitivity, secrecy, integrity and accessibility, in order to be practically usable.
Queryable Compression on Time-evolving Web and Social Networks with Streaming

Time-evolving web and social network graphs are modeled as a set of pages/individuals (nodes) and their arcs (links/relationships) that change over time. Due to their popularity, they have become increasingly massive in terms of their number of nodes, arcs, and lifetimes. However, these graphs are extremely sparse throughout their lifetimes. For example, it is estimated that Facebook has over a billion vertices, yet at any point in time, it has far less than 0.001% of all possible relationships. The space required to store these large sparse graphs may not fit in most main memories using underlying representations such as a series of adjacency matrices or adjacency lists. We propose building a compressed data structure that has a compressed binary tree corresponding to each row of each adjacency matrix of the time-evolving graph. We do not explicitly construct the adjacency matrix, and our algorithms take the time-evolving arc list representation as input for its construction. Our compressed structure allows for directed and undirected graphs, faster arc and neighborhood queries, as well as the ability for arcs and frames to be added and removed directly from the compressed structure (streaming operations). We use publicly available network data sets such as Flickr, Yahoo!, and Wikipedia in our experiments and show that our new technique performs as well or better than our benchmarks on all datasets in terms of compression size and other vital metrics.
A Door Detection System for Convenience Stores in Taiwan

A door is a very substantial element since it enables a person to enter a target place. Though detecting a doorway is an easy task for a regular person, it is challenging for robots or visually impaired people. Although most existing deep learning object detection models have shown promising results, they have a limitation in distinguishing between glass doors and glass walls in a convenience store. To address this issue, we propose an effective door detection system for convenience stores in Taiwan. Our system consists of two main models: 1) the object detection and 2) the door bounding box models. The former model uses the re-train YOLOv4 as the main building box. The latter model uses a fully connected neural network as the main building box. In particular, we utilize the surrounding objects in the scene to improve the performance and robustness of convenient glass door entrance detection. The experimental results demonstrate that our proposed method not only achieves the quantitative accuracy result up to 93&#x0025; but also provides decent qualitative results.
Platform for Comparative Study of Focus and Retention in Virtual Reality and Online Learning

Our research objective is to compare the effectiveness of standard online learning methods versus the utilization of virtual reality in education in terms of student focus and information retention. Our proposed platform will have identical lesson plans in virtual reality as our online learning methods. Eye gaze tracking and a recall test will be used on both platforms to measure focus on the screen and retention, respectively. The ultimate goal of the project is to use this data to evaluate the effectiveness of VR as a digital learning environment.
Performance Comparison of Evolutionary Biogeography Based Whale Optimizations with Chaotic Arc Adaptive Grasshopper Optimization Algorithms

Whale Optimization Algorithm and Grasshopper Optimization Algorithm are two heuristic search methods inspired by the intelligence of whales and grasshoppers that are very useful for solving global optimization problems. The Evolutionary Biogeography-based Whale Optimization Algorithm was first introduced in the year 2020 to complete the global optimization function. Chaotic Arc Adaptive Grasshopper Optimization Algorithm is proposed in the year 2021 by integrating chaos sequence into the Grasshopper Optimization algorithm to improve its global search capability. The problem formulation of this research is which algorithm has the best performance of the two types of algorithms (EWOA, and AGOA). This paper aims to compare the performance between the Evolutionary Biogeography-based Whale Optimization Algorithm (EWOA) and the Chaotic Arc Adaptive Grasshopper Optimization Algorithm (AGOA). The performances of the Evolutionary Biogeography-based Whale Optimization Algorithm and Chaotic Arc Adaptive Grasshopper Optimization Algorithm are tested on four benchmark problems, namely Rotated Bent Cigar, Shifted and Rotated Rosenbrock, Shifted and Rotated Schwefel, and Shifted and Rotated Zakharov function. Based on the results, Evolutionary Biogeography-based Whale Optimization Algorithm is more efficient than the Chaotic Arc Adaptive Grasshopper Optimization Algorithm for solving standard test problems and engineering optimization problems.
Soft Lookup False Data Matching Method for Estimating Suspect SCADA Data -NR Grid Case Study

Power System operator's real time decisions depend on available Supervisory Control and Data Acquisition (SCADA) measurement systems. Under external disturbances like failure in Fiber Optic (FO) cables, measurement transducers etc., an operator may get inaccurate data displayed. To ensure reliable operation of the power grid under such cases (including unknown network topology), this paper proposes an interpretable data-driven model Soft Lookup False data Matching (SLFM) through Dynamic Time Warping (DTW) for estimating missing/suspected SCADA data values. Numerical experiments conducted on one of the utilities of the Northern Region of the Indian Grid with real-time data of SCADA and Meter of PoC (Point of Connection) feeders for four different cases and compared the results with LSTM (Long Short Term Memory) Autoencoder model. The experimental results demonstrated the better performance of the proposed model.
The Main Problems of Distance Learning in Vocational Education in the Russian Federation

The article deals with various organizational issues of distance learning in vocational education. The authors applied the theory of distance learning to vocational education in Russia. The research featured teachers of more than 40 educational institutions, who mentioned such problems as the lack of equipment, personal computers, software, and Internet access. Most respondents believed that the quality of vocational education deteriorated during distance learning mostly as a result of poor technical support. Another problem is the absence of personal contact between teaching staff and students. The authors analyzed the current situation and propose various means of improvement, e.g. asynchronous teaching methods.
An Empirical Study on Root Cause Analysis and Prediction of Network Failure Using Deep Learning

This paper proposes a pragmatic deep learning-based method to not only analyze the root cause of network failure, but also predict possible failures in the microwave (MW) network utilizing alarms, status, and performance information of weather, terrain, and network equipment in South Korea. In general, it is incredibly difficult for network engineers to distinguish the temporal MW communication problem, which is originated from bad weather and is usually recovered automatically from the actual network failure caused by physical or software issues of the equipment. First, to solve the problem, we propose a deep learning-based MW Root Cause Analysis System (MW-RCAS) which performs RCA actions whenever a new network failure occurs. The system efficiently utilizes weather and terrain information in addition to data from the network, such as status, alarms, and performance data of each MW equipment. Compared with the 80% RCA accuracy of highly professional network engineers, the proposed MW RCA system shows 95% accuracy. Lastly, we propose the MW Network Failure Prediction System (MW-NFPS) based on an long short-term memory (LSTM) which can forecast future occurrences of MW communication failures at least 15 minutes in advance using weeklong data of network equipment, weather, and terrain.