Reviewerly/exHarmony huggingface dataset:
A Framework to Secure Medical Image Storage in Cloud Computing Environment

Nowadays, modern healthcare providers create massive medical images every day because of the recent progress in imaging tools. This is generally due to the increasing number of patients demanding medical services. This has resulted in a continuous demand of a large storage space. Unfortunately, healthcare domains still use local data centers for storing medical data and managing business processes. This has significant negative impacts on operating costs associated with licensing fees and maintenance. To overcome these challenges, healthcare organizations are interested in adopting cloud storage rather than on-premise hosted solutions. This is mainly justified by the scalability, cost savings and availability of cloud services. The primary objective of this model is to outsource data and delegate IT computations to an external party. The latter delivers needed storage systems via the Internet to fulfill client's demands. Even though this model provides significant cost advantages, using cloud storage raises security challenges. To this aim, this article describes several solutions which were proposed to ensure data protection. The existing implementations suffer from many limitations. The authors propose a framework to secure the storage of medical images over cloud computing. In this regard, they use multi-region segmentation and watermarking techniques to maintain both confidentiality and integrity. In addition, they rely on an ABAC model to ensure access control to cloud storage. This solution mainly includes four functions, i.e., (1) split data for privacy protection, (2) authentication for medical dataset accessing, (3) integrity checking, and (4) access control to enforce security measures. Hence, the proposal is an appropriate solution to meet privacy requirements.
Computer Big Data Technology and Mathematical Model Applied in Korean Network Learning System

In natural language processing systems (including machine translation systems), grammatical and semantic information dictionaries are essential components. From the perspective of language engineering, the article discusses the theoretical basis of the establishment of a language knowledge expression system for Korean information processing, the design concept of the system, the mathematical model of the system library structure, and the technology and strategy of how to use VC language to implement it. The interface legend of the program.
A Wearable Warning System Design for Mask Recognition via AR Smart Glasses

Under the influence of COVID-19, various studies have shown that the most important transmission of the epidemic is droplet infection, it is the most effective way to control the epidemic by wearing a mask in a safe range. To confirm the situation of masks-wearing in public, a useful way is to use image-recognition technology to detect the people in the field. On the other hand, with the continued development of wearable devices, smart glasses have been widely used in many files such as handicapped person support. Based on the previous researches, it is already possible to incorporate facial recognition technology into smart glasses. Especially, the application of Augmented Reality (AR) technology on smart glasses can provide users with a lot of additional information, for example, to highlight the targets who been identified. Therefore, to identify the people who are not wearing masks more effectively, in this paper we try to design and wearable mask recognition & warning system by using the AR smart glasses. The system can supply the warning messages about the person without masks in both visual and auditory way to the user to support the users including the handicapped persons who not being able to hear or see. The results of this study may provide guidelines to develop the epidemic prevention system and offers useful insights for the supporting of handicapped persons.
Finding Explanations

In the previous chapter we discussed methods that find patterns of different shapes in data sets. All these methods needed measures of similarity in order to group similar objects. In this chapter we will discuss methods that address a very different setup: Instead of finding structure in a data set, we are now focusing on methods that find explanations for an unknown dependency within the data. Such a search for a dependency usually focuses on a so-called target attribute, which means we are particularly interested in why one specific attribute has a certain value. In case of the target attribute being a nominal variable, we are talking about a classification problem; in case of a numerical value we are referring to a regression problem. Examples for such problems would be understanding why a customer belongs to the category of people who cancel their account (e.g., classifying her into a yes/no category) or better understanding the risk factors of customers in general.
INTERNET RESOURCES IN RESEARCH ACTIVITY TEENAGERS

The article reveals the urgency of using Internet resources in the research activity of teenagers. The main directions of the study of the problem of informatization of education also the use of information resources in research activities. The place of Internet resources in the structure of information resources and in the system of informational of education is defined. The analysis of Internet resources is conducted and the necessity of their use is determined. The possibilities of using the Internet resources at different stages of preparing the research work of schoolchildren are shown. The possibilities of a differentiated approach in the use of Internet resources in the research work of schoolchildren are explored. Methodical recommendations on the organization of scientific research work using Internet resources have been developed. Prospects for the development and use of some Internet resources, such as digital libraries, online laboratories, virtual museums, have been identified. The methods of working with each of the types of resources in the process of solving the research problem are described. In detail, the use of online libraries at the stage of choosing the problems of research also theoretical study of the problem. The use of virtual museums as a motivation for adolescents to research. The functions of virtual laboratories are considered on the stage of experimental testing of the hypothesis in research work. The possibilities of some Internet resources in certain areas of knowledge are highlighted, namely: biology, physics, mineralogy, culturology, art. Examples are given of the use of electronic libraries, virtual museums, virtual laboratories in the research activities of adolescents. The role of the supervisor in the organization of work with Internet resources in the process of research is identified.
Deep learning of Hash Method for Fast Image Retrieval

Most of the image retrieval technology are complex and advanced methods to improve the accuracy of retrieval. They also have deep net layers, which costs much time to caculate the result. When we need deal with complex image task, it will be hard to complete the fast image retrieval.This paper presents a convolutional neural network combining with the fast encoding technology under the hash algorithm to improve fast retrieval time and accuracy. In order to keep the existing accuracy and retrieval speed of the network, we present a Hash method path of the network combining witn the dual networks. By clustering analysis and AlexNet,we get lots of high dimensional feature. Then these feature is put into Hash-coding algorithm. The algorithm will output binary feature to match the target image. Through learning similar graph pairs, Hash net work shorten the distance between the similar images, and achieve fast clustering. Then it realizes image retrieval and outputs the most similar picture. Complete the search task.
Foreword to the Special Section on Graphics Interface 2018

• The 44th Graphics Interface conference was held in Toronto, Canada at York University. • This special section presents extended versions of three top graphics papers. • CNNs are adapted for reconstruction of point cloud surfaces with sharp features. • A hierarchical task network planning method can enable complex storyline visualizations. • Timeslice grammars are proposed as a tool for efficiently generating motion graphics.
Time Optimal Trajectory Planning of Unmanned Surface Vessels: A Deep Learning-based Control Approach

This paper presents a paradigm for Zermelo’s navigation problem (ZNP) of an unmanned vessel with the need of avoiding obstacles. The ZNP is first formulated as a typical optimal control problem (OCP) with continuous inequality constraints. In general, solving this typical OCP needs repeated offline optimization. On the one hand, a significant amount of time is consumed; nevertheless, once environmental influence creates a change in the system’s initial circumstances, it is required to re-optimize calculations. To overcome these drawbacks, a bi-level control paradigm based on deep neural network (DNN) is presented to achieve real-time optimal feedback control of the vessel in the presence of environmental uncertainty. Based on homotopy technology, we integrate the solution of OPC under the assumption of a limited range of current resistance as a training dataset. Due to the dataset incorporates all trajectory with different disturbance, once the DNN have been trained on a sufficient amount of dataset, the relationship between the optimal state variable and control instructs can be utilize as the optimal feedback control laws for realizing the real-time optimal control. The time-consuming re-planning process due to changes of initial conditions and the weakly robustness to control process in movement with environment disturbance can be effectively avoided in the paradigm. Numerical experimental results show the potential for real-time optimal control of more complex nonlinear models.
Asian CHI Symposium: HCI Research from Asia and on Asian Contexts and Cultures

The Asian CHI symposium 2021 is the joint event organized by the researchers and practitioners in Asia. The symposium aims to bring together young and senior researchers from the academics and industries in one forum to exchange ideas and foster social network in the field of HCI. The symposium showcases the latest HCI work from Asia and those focusing on incorporating Asian sociocultural factors in their design and implementation. In addition to circulating ideas and envisioning future research in human-computer interaction, this symposium aims to foster social networks among academics (researchers and students) and practitioners and grow a research community from Asia.
Introduction

With respect to Bayesian approach to nonparametric inference, a prior distribution is desirable whose support includes many continuous distributions. Ferguson’s Dirichlet process satisfies this property, while a distribution being the Dirichlet process is discrete almost surely. Many works have been done on the Dirichlet process and the related processes. Because the purpose of this monograph is to give some topics associated with the Dirichlet process, we illustrate the process with two related process in this chapter. They are normalized random measure and process neutral to the right.
Complaint Processing

For systematic planning of the complaint processing it is first necessary to identify the relevant process types. Depending on the extent of responsibility for the operative complaint processing, three basic types can be differentiated: independent processes, cooperative processes and transfer processes. A second classification based on the criterion of reaction urgency distinguishes between routine and non-routine processes. The analysis of all current complaint processing procedures must be performed from the company’s perspective as well as from the customers’ perspective in order to achieve both efficiency and complaint satisfaction among customers. On the basis of this analysis the logical sequence of processing steps must be defined and the responsibilities in each step determined. There are three types of responsibility: the “process owner” for the entire management process, the “complaint owner” for the individual case processing, and the “task owner” for the individual phases of complaint processing. Furthermore, it is necessary to determine clear deadlines for all identified complaint processing procedures as well as for their sub-processes and processing steps. Compliance with the deadlines must be monitored by an employee-oriented reminder system and an associated escalation system, that can extend over several hierarchical levels. With regard to the communication in cross-departmental processes, it is important to determine which information is to be delivered between which units involved, on which paths and within which time limits. The individual processing steps must be documented in the complaint-processing history as well as in the customer-contact and complaint history.
Holmes

Co-location of latency-critical services with best-effort batch jobs is commonly adopted in production systems to increase resource utilization. Although memory and CPU isolation have been extensively studied, we find Simultaneous Multi-Threading (SMT) technology imposes non-trivial interference on memory access which jeopardizes efficient co-location and performance assurance of latency-critical services. However, there is not an existing metric to quantitatively measure and lacks a deterministic approach to tackle SMT interference on memory access.
Performance Evaluation of Simple K-Mean and Parallel K-Mean Clustering Algorithms: Big Data Business Process Management Concept

Data is the most valuable asset in any firm. As time passes, the data expands at a breakneck speed. A major research issue is the extraction of meaningful information from a complex and huge data source. Clustering is one of the data extraction methods. The basic K-Mean and Parallel K-Mean partition clustering algorithms work by picking random starting centroids. The basic and K-Mean parallel clustering methods are investigated in this work using two different datasets with sizes of 10000 and 5000, respectively. The findings of the Simple K-Mean clustering algorithms alter throughout numerous runs or iterations, according to the study, and so iterations differ for each run or execution. In some circumstances, the clustering algorithms’ outcomes are always different, and the algorithms separate and identify unique properties of the K-Mean Simple clustering algorithm from the K-Mean Parallel clustering algorithm. Differentiating these features will improve cluster quality, lapsed time, and iterations. Experiments are designed to show that parallel algorithms considerably improve the Simple K-Mean techniques. The findings of the parallel techniques are also consistent; however, the Simple K-Mean algorithm’s results vary from run to run. Both the 10,000 and 5000 data item datasets are divided into ten subdatasets for ten different client systems. Clusters are generated in two iterations, i.e., the time it takes for all client systems to complete one iteration (mentioned in chapter number 4). In the first execution, Client No. 5 has the longest elapsed time (8 ms), whereas the longest elapsed time in the following iterations is 6 ms, for a total elapsed time of 12 ms for the K-Mean clustering technique. In addition, the Parallel algorithms reduce the number of executions and the time it takes to complete a task.
Impact of mobile learning in engineering mathematics under 4-year undergraduate curriculum

Mathematics is one of the core subjects in engineering education and forms the foundation of all disciplines in engineering. In Hong Kong, a significant number of students are admitted to engineering programmes without sufficient mathematical background. Engineering mathematics is essential for teaching and learning many fundamental and advanced engineering subjects. Due to this reason, a mobile application (app) is developed as one of the core components of engineering mathematics education by offering students flexible and self-initiated learning. In this study, a hypothesis model is proposed to investigate the effectiveness of student’s learning in engineering mathematics with the developed mobile app. The results showed that the ease and convenience of using the mobile app have a positive effect on students’ autonomous learning, and motivation and have a positive relationship with the learning effectiveness of the mobile learning (M-learning) app. It was found that the average marks of the assessment tests were significantly better than the screening test by using the app and notes. The current study has important implications in that the M-learning app should be used as a supportive role to supplement traditional teaching approaches.
Theory of Knowledge Based on the Idea of the Discursive Space

This paper discusses the theory of knowledge based on the idea of dynamical space. The goal of this effort is to comprehend the knowledge that remains beyond the human domain, e.g., of the artificial cognitive systems. This theory occurs in two versions, weak and strong. The weak version is limited to knowledge in which retention and articulation are performed through the discourse. The strong version is general and is not limited in any way. In the weak version, knowledge is represented by the trajectories of discourses in time, in a dynamical space called the discursive space, which has an arbitrary number of dimensions. Given space is used to represent a given part of knowledge. A manifold is introduced to represent knowledge with a wider scope (all knowledge). The strong version is an extrapolation of the weak version to cover all forms of knowledge, not necessarily human or manifesting in language. The use of dynamical space construction allows one to formalize knowledge as such. Such an effort requires us to initially consider knowledge as mainly a social and linguistic phenomenon, which also could be presented as a result of the evolution of the understanding of knowledge that took place in the 20th century.
Research on Performance Management Evaluation of Public Sports Venues Based on GFAHP

The importance of public stadiums has become increasingly prominent, but the efficiency of its performance management operation mechanism is low, which affects the overall operation efficiency of the whole stadium. However, due to the diversity and incompleteness of its data types, the traditional analytic hierarchy process is difficult to construct its index system. This article introduces the grey fuzzy analytic hierarchy process. Based on the analysis of its performance management problems, this article constructs the index system and makes an application case analysis. The results show that the GFAHP method (group fuzzy analytic hierarchy process) can ensure the integrity of information data. In data classification, the highest accuracy of sample classification is 40.13%, and the relative error rate is basically less than 6%. The performance effect is much higher than other classification algorithms. At the same time, the total score of the evaluation index system constructed by GFAHP is more than 3, the overall operation is good, and the risk evaluation effect of each influencing index factor is also good. Using the grey analytic hierarchy process to evaluate the operation and performance management of public stadiums and gymnasiums will help managers do a good job in risk control and quality dynamic adjustment, so as to improve the operation efficiency and benefit of stadiums and gymnasiums.
Indecomposability of entanglement witnesses constructed from symmetric measurements

We propose a family of positive maps constructed from a recently introduced class of symmetric measurements. These maps are used to define entanglement witnesses, which include other popular approaches with mutually unbiased bases and mutually unbiased measurements. A particular interest is given to indecomposable witnesses that can be used to detect entanglement of quantum states with positive partial transposition. We present several examples for different number of measurements.
Multiple dynamic penalties based on decomposition for constrained optimization

When solving constrained optimization problems by constraint-handling techniques, the core idea is to balance constraint violation and objective function during the search process. Although many constraint-handling techniques have proposed to deal with this problem, it is difficult to reduce the risk of falling into the local optimums and maintain good population diversity. To tackle these issues, this paper proposes an evolutionary algorithm using multiple dynamic penalties based on decomposition for constrained optimization. Specifically, a constrained optimization problem is decomposed into a number of unconstrained optimization subproblems, and each subproblem is assigned a penalty coefficient to balance constraint and objective function. Then, these subproblems are solved simultaneously, which can improve global search ability and population diversity. In order to generate proper penalty coefficients, a penalty coefficient adjusting strategy is designed, and the penalty coefficient value increases with the number of evolutionary generations to balance the exploration and exploitation. The experimental results on two benchmark test suites and six real-world application problems demonstrate the proposed algorithm is efficient and competitive. • Multiple dynamic penalties based on decomposition are proposed for constrained optimization. • We convert a constrained optimization problem into a set of unconstrained optimization problems by assigning a penalty coefficient to each subproblem. • We design a penalty coefficient adjusting strategy to balance the exploration and exploitation.
Open research data: A case study into institutional and infrastructural arrangements to stimulate open research data sharing and reuse

This study investigates which combination of institutional and infrastructural arrangements positively impact research data sharing and reuse in a specific case. We conducted a qualitative case study of the institutional and infrastructural arrangements implemented at Delft University of Technology in the Netherlands. In the examined case, it was fundamental to change the mindset of researchers and to make them aware of the benefits of sharing data. Therefore, arrangements should be designed bottom-up and used as a “carrot” rather than as a “stick.” Moreover, support offered to researchers should cover at least legal, financial, administrative, and practical issues of research data management and should be informal in nature. Previous research describes generic institutional and infrastructural instruments that can stimulate open research data sharing and reuse. This study is among the first to analyze what and how infrastructural and institutional arrangements work in a particular context. It provides the basis for other scholars to study such arrangements in different contexts. Open data policymakers, universities, and open data infrastructure providers can use our findings to stimulate data sharing and reuse in practice, adapted to the contextual situation. Our study focused on a single case and a particular part of the university. We recommend repeating this research in other contexts, that is, at other universities, faculties, and involving other research data infrastructure providers.