Reviewerly/exHarmony huggingface dataset:
AUTOMATED TECHNIQUE FOR CAROTID PLAQUE CHARACTERIZATION AND CLASSIFICATION USING RDWT IN ULTRASOUND IMAGES

In this paper we proposed a Rational-Dilation Wavelet Transform (RDWT) technique to characterize plaques recorded from high-resolution ultrasound images and develop a Computer-Aided Diagnosis (CADx) model. The image acquisition and preprocessing, feature extraction and ensemble classifiers are automated for the classification of plaque. The transition bands are constructed by using the transition function. From the sub-bands mean, standard deviation, skewness, Renyi entropy and energy these statistical features are extracted. Salp Swarm Algorithm (SSA) is used for optical features, the fundamental inspiration is the swarming behavior of slaps when navigating and foraging in oceans. K Nearest Neighbor (k-NN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) classifiers are used in the Plaque Classification these techniques are compared in the classifier comparison. Experimental results show the accuracy, specificity and sensitivity of proposed method in terms of algorithm and classifiers. The percentage of accuracy in our method is 93%, the percentage of sensitivity in our method is 90% and the percentage of specificity in our ultrasound images. A texture feature analysis and classifiers for the automated carotid method is given as 94%.
Acquisition and use of meta-knowledge for more efficient training sample selection

The field of artificial intelligence has been throughout its history repeatedly inspired by human cognition. In this master’s thesis, we take a high-level view of meta-cognition and implement a system with similar characteristics. Our motivation for this is of two kinds: the first stems from the desire to contribute to machine learning methods, more specifically semi-supervised classification, and the second from the ability to compare human learning and artificial systems. According to the literature, our system is divided into object and meta parts, with the former solving the classification problem, and the latter selecting learning examples based on its knowledge by determining the appropriate confidence thresholds. Similarly to humans, we learn learning strategies in our system through the accumulation of knowledge about solving a particular problem, for which we use reinforcement learning. While designing our system, one of the important guidelines is generality, which is why we perform experiments by varying the architectures of classifiers (neural networks) and datasets. We train the system both from the beginning and by transferring knowledge from one problem to another. We obtain mixed results that depend largely on the effectiveness of our approach to semi-supervised learning. When comparing our method with “naive” approaches, we get at most 1% worse, but often better results than using confidence thresholds found by random search. By transferring learning strategies from one problem to another, we reduce the time required to solve the sample selection problem by 80% and obtain comparable results as when learning from the beginning. Our work shows that it is possible to learn a curriculum within a given framework, that it accelerates learning and that the selection of learning samples using meta-knowledge is one of the effective approaches for successful classifier training. The listed properties are also key similarities of the implemented system when compared to human learning.
다문화 관련 한국어교육 연구 동향 분석

The purpose of this research is to analyze trends in Korean language education studies related to multi-culture. Therefore, the trends in Korean language education research were examined through analysis of multi-culture related Korean language education research published from 2000 to the present. This study examines 685 dissertations and 442 academic journals, a total of 1127, and analyzes them for yearly trends, type-specific trends, and subject-specific trends. Based on the analysis of research trends, the following is a look at some points of discussion regarding multi-culture research that will be dealt with in the field of Korean language education in the future. First, since 2001, research on Korean language education related to multi-culture has steadily increased in quantity, and the objects of research have been expanding. However, it was found that it has not deviated from the existing research trends that have been focused on elementary school children from multi-culture families. Second, it was confirmed that research areas are expanding through diversification of academic journals and graduate schools. Third, studies have shown that they are not being conducted evenly by subject. In particular, it was found that less research was conducted on ‘grammar’, ‘pronunciation’, and so on. Therefore, various forms of basic research are needed.
Georgian students digital competences

Al Khateeb, A. A. M. (2017). Measuring Digital Competence and ICT Literacy: An ExploratoryStudy of In-Service English Language Teachers in the Context of SaudiArabia. International Education Studies , 10(12), 38.Castells, M. (2010). The rise of the network Society (2nded.). Oxford:Wiley-Blackwell, pp.656Dijk, J. (2006). Network Society, Social Aspects of the New Media. SAGE p.301DiMaggio, P., Hargittai, E., Neuman, W. R., & Robinson, J. P. (2001). Social implications of theInternet. Annual review of sociology , 27(1), pp. 307-336.DiMaggio, P., & Hargittai, E. (2001). From the ‗digital divide‘to ―digital inequality‖: StudyingInternet use as penetration increases. Princeton: Center for Arts and CulturalPolicy Studies, Woodrow Wilson School, Princeton University , 4(1), 4-2.Ferrari, A. (2012). Digital competence in practice: An analysis of frameworks. Sevilla: JRCIPTS.(DOI: 10.2791/82116) .
Pengembangan repositori data pada lembaga riset dengan status Pusat Unggulan Ilmu Pengetahuan dan Teknologi

National regulations in science and technology mandate researchers, research institutions, and research funders to store and share research data. However, research institutions in Indonesia have not widely practiced research data management by utilizing institutional repositories. This study aimed to determine the development of repositories in research institutions under the Center of Excellence (PUI) supervision program from three aspects: policy, infrastructure, and organizational culture. This research used a quantitative approach through descriptive methods. Data was collected from the management of the research institute repository under the supervision program of the Center of Excellence (PUI), a program of the Ministry of Research and Technology/the National Research and Innovation Agency. The data were analyzed descriptively to see how the repository's development supported research activities. The study results indicated that most institutions had policies for managing institutional repositories on the policy side and needed to be reinforced by policies for managing research data. On the infrastructure aspect, most research institutions did not yet have a repository for research data management. While on aspects of cultural organization, most researchers realized that collaborative research activities could benefit from the institutional repository. This study concludes that research institutions can develop existing institutional repositories for research data management by reinforcing policies, improving the infrastructure function of institutional repositories, and building a culture of data management through campaigns and outreach.
Improving Cloud Data Security by hybridization of Zero- Knowledge Proof and Time-Based One-Time Password

assword has been the major vulnerability and challenge in cloud computing environment due to adversarial threat sources. Zero-Knowledge Proof (ZKP) becomes the excellent optional strategy and current anticipation for Cloud Data security and assurance as it gives the server zero knowledge of passwords. However, the VERIFIER, despite having no knowledge of the password must verify that the password entered by the PROVER is correct. A major challenge that occurs is when the user disremembers the password, the cloud environment becomes inaccessible and the cloud service provider has no any replica of the password as a backup. Eavesdropping/snooping is a major threat which provides full access to the cloud environment. Adding Time-based One-time Password (TOTP) will be an elucidation to password delinquency as it expires after a certain splits of seconds or minutes which would render the password unserviceable to the snooped. This research examined cloud services in relation to the security challenges faced by organizations and proposed the hybridization of the Zero-Knowledge Proof and Time- Based One-Time Password algorithms for a better security of the cloud environment.
Log File Data Extraction or Mining

Log records contain data generally Client Title, IP Address, Time Stamp, Get to Ask, number of Bytes Exchanged, Result Status, URL that Intimated, and Client Chairman. The log records are kept up by the internet servers. By analysing these log records gives a flawless thought to the client. The wide Web may be a solid store of web pages that gives the Net clients piles of information. With the change in the number and complexity of Websites, the degree of the net has gotten to be massively wide. Web Utilization Mining may be a division of web mining that consolidates the application of mining strategies to web server logs in coordination to expel the behaviour of clients. Log records contain basic data around the execution of a framework. This data is frequently utilized for investigating, operational profiling, finding quirks, recognizing security dangers, measuring execution,
Anticollusion Attack Strategy Combining Trust Metrics and Secret Sharing for Friendships Protection

Online social networks provide users with services such as online interaction, instant messaging, and information sharing. The friend search engine, a new type of social application, provides users with the service for querying the list of other individuals’ friends. Currently, the existing research focuses on independent attacks for friend search engines while ignoring the more complicated collusion attacks, which can expose more friendships that users are not willing to share. Compared with independent attackers, collusion attackers share query results by cooperating with each other. In this article, we propose a resistance strategy against collusion attacks to protect the friendship privacy. The proposed trust metric is based on users’ behaviors and is combined with Shamir’s secret sharing system, which can transform friendships into secrets. Through secret distribution and reconfiguration, only the participants who meet the query requirements can successfully reconstruct the secret, while the participants who do not meet the query conditions cannot successfully obtain the secret fragments even if they obtain the secret fragments. Experiments are conducted to verify the effectiveness of the proposed strategy and proved that this strategy can greatly limit the number of malicious attackers, greatly reduce the probability of successful collusion attacks, and reduce the number of victims.
Implementation of Visitor Analysis for Covid-19 using Machine Learning and IOT

The corona-virus COVID-19 pandemic is a global health crisis. Covid-19 virus is transferred between two people through the airborne transmission. One of the effective protection methods is wearing a face mask in public areas, which has been imposed as a compulsory measure to prevent the transmission all over the world . Also, temperature and oxygen level monitoring are the additional ways of monitoring of visitors. Manual monitoring of the face mask, temperature and oxygen level in congested and thronged places is a tedious task. The corona-virus epidemic has given rise to an extraordinary degree of worldwide scientific cooperation. Machine learning and Deep Learning can help to tackle Covid-19 pandemic in many ways. . Existing face detection system needs more enhancement and modifications to solve real world problems. In this paper we have implemented face-mask detection using the CNN algorithm and MobilenetV2 architecture, and we have also implemented face-recognition over the mask in our system. We are going to use Open-CV to do real-time face detection from a live stream via a camera, and then we are going to pass these frames(images) to our mask detector classifier to find out if the person is wearing a mask or not. After performing mask detection, the system will perform facerecognition over the mask to identify the visitor. Then we have used MLX90614 infrared temperature sensor for temperature check and MAX30100 Pulse oximeter for oxygen level check. ViAC will be highly beneficial for contact-less monitoring purpose and thus reducing the risk of guards to get infected and eliminating the tedious task of monitoring visitors. We have achieved 98% accuracy in face-mask detection.
Analisis Kasus Gugatan Medis di Rumah Sakit Harapan Bunda Batam

Background This Tesis study about factors that influence medical accusation in the hospital. Purpose. This study purposed to analyze influence factor related with medical accusation in Harapan Bunda Batam Hospital. Metode. This research is qualitatif research with descriptive design. Processing of data collection by primary data and secondary data. Primary data collected by medical record of patients who claim to hospital and deep interview with interrelated party. Secondary data collected by investigation of library document. Result. This study give strategic suggestion and operational suggestion to minimalized medical accusation in hospital and give solution to minimalized hospital loss by medical accusation.
Multi-Descriptor Random Sampling for Patch-Based Face Recognition

While there has been a massive increase in research into face recognition, it remains a challenging problem due to conditions present in real life. This paper focuses on the inherently present issue of partial occlusion distortions in real face recognition applications. We propose an approach to tackle this problem. First, face images are divided into multiple patches before local descriptors of Local Binary Patterns and Histograms of Oriented Gradients are applied on each patch. Next, the resulting histograms are concatenated, and their dimensionality is then reduced using Kernel Principle Component Analysis. Once completed, patches are randomly selected using the concept of random sampling to finally construct several sub-Support Vector Machine classifiers. The results obtained from these sub-classifiers are combined to generate the final recognition outcome. Experimental results based on the AR face database and the Extended Yale B database show the effectiveness of our proposed technique.
PRESENT AND FUTURE OF PHYSIOLOGICAL MONITORING TO EVALUATE USER EXPERIENCE

User Experience (UX) is a key factor and an opportunity for improvement in digital products. Traditionally, it has been evaluated retrospectively through surveys and interviews. However, retrospective and subjective evaluation is not in all cases the optimal approach, as it does not evaluate the UX at the time of human-machine interaction, and therefore may not project reality due to inaccurate recall.
An Application of Fuzzy Value Assignment In A Relation In Two Wheeler Road Accident Non Wearing Helmet Death Details

The most important feature of data manipulation is the valve assignment in relation to the similarity method. When it comes to changing values and specifying direction, this method is extremely accurate. The primary source of data for this study is the 2019 two-wheeler non-helmet death list. Every day, there is a new accident. On the other hand, bicycle injuries are at an all-time high among this generation's riders. In this article, we'll examine the number of non-helmet two-wheeler accidents each month, month by month, and rotation by rotation.
Does Learning Interest Mediate Computer Self-Efficacy and Internet Use on Learning Achievement? (Accounting Class student overview)

Learning achievement is an achievement that has been achieved by students during the learning process according to the target time which is manifested in the form of numbers, letters, symbols and certain sentences as a standard of student completeness in learning. Achievement of learning achievement among students can vary because of many factors that influence it. For example, internal factors in the form of computer self-efficacy and internet use and external factors in the form of interest in learning. This study aims to determine whether interest in learning is able to mediate computer self-efficacy and internet use on learning achievement. By using a quantitative approach, the sample was chosen randomly (proportionate stratified random sampling) obtained 108 students of Class XI Accounting. Data were collected using an online questionnaire (on a Likert scale). Data were analyzed using descriptive statistics, hypotheses were analyzed multiple regression, and path analysis and single test (for indirect effects). The test results conclude that interest in learning can mediate computer self-efficacy and the use of the internet on learning achievement significantly, which means that it can have a direct and indirect effect on the improvement of student achievement in accounting in the form of partial mediation. Researchers recommend that student achievement in accounting will increase if the students' literacy of Computer Self-Efficacy and Internet Use can be mastered properly.Keywords: Computer Self Efficacy, Internet Usage, Study Interest, Learning Achievement, Accounting Students, Vocational High School
Vocational Education in Ensuring Successful Economic Socialization of High School Students in U. S. Secondary Schools

The article examines the importance of studying the role of vocational education in solving the problem of economic socialization of high school students and its possible adaptation in domestic pedagogical practice. The article provides a comparative analysis of the content of the work of secondary schools in the United States in order to identify the role of vocational education in ensuring the process of successful economic socialization of high school students. The authors reveal the most effective methods, tools and strategies implemented in the studied US schools to ensure the successful economic socialization of high school students. The research was carried out on the basis of: analysis of scientific and methodological literature on the problem of economic socialization of high school students; content analysis of regulatory and software documentation; comparative analysis of initiatives and innovative projects implemented in the studied US schools.
Effect of photovoltaic (PV) module mounting angle on PV module power output

Solar energy is a renewable resource that is environmentally friendly. Unlike fossil fuels, solar energy is available just about everywhere on earth. This source of energy is free and immune to rising energy prices. Solar energy can be used in many ways - to provide electricity, heat, lighting, and mechanical power. The objective of this project is to determine the effect of mounting orientation of a photovoltaic panel on power output. Based on the simulation results, the report proposes alternative energy management techniques to characterize the unstable nature of the photovoltaic power generating system. A typical power generating system using photovoltaic technology would have many components. The software used for determining the effects of photovoltaic module mounting angle on the PV module power output is the PV-DesignPro. This software is designed to simulate photovoltaic energy system operation on an hourly basis for one year, based on a user selected climate and system design.
Human Resources Management and its Role in The Criteria of the Organization Seeking for Learning from the Perspective of the Employees in Al-Hussein IBN Talal University

This study aimed at identifying the human resources management and its role in the criteria of the organization that seeks learning from the perspective of the employees in Al-Hussein Ibn Talal University. The study used the analytical descriptive approach to demonstrate the concepts relating to the study topic, analyze them and come up with the results based on the opinions of (174) employees of the middle administration in Al-Hussein Bin Talal University. In order to achieve the study objectives, a questionnaire was designed and consisted of (18) items to collect the primary data from the study sample individuals. Accordingly, the data were collected and analyzed and the hypotheses were tested using (SPSS). The results revealed that the level at which the employees (the study sample) evaluate the dimensions of the human resources management and the organization that seek learning was high. The results also revealed that there is a statistically significant impact for human resources management on the organization that seeks learning.
Machine-Learning-Based Closed-Set Text-Independent Speaker Identification Using Speech Recorded During 25 Hours of Prolonged Wakefulness

We performed machine learning for text-independent speaker identification using speech recorded during the day, evening, and night, from subjects undergoing 25 hours of prolonged wakefulness. Subjects answered casual questions lasting approximately 3 minutes and described pictures presented to them for 0.5 minutes. We extracted 12,515 vocal features using OpenSmile software. For generalization of the training scheme, we segmented the 20 subjects into training and testing sets (10 subjects for each) and repeated testing four times with different subsets. Specifically, we used one set of 10 subjects to find the best feature-sets and the optimal machine-learning method, and the other set of 10 subjects was used to test the trained model. With trained machine-learning models using three speech sessions recorded throughout the day for speaker identification, we obtained 95% and 98.8% for balanced accuracies for daytime and evening speech, respectively, but 84.2% for nighttime-testing speech. With training data from all times of day-daytime, evening, and nighttime-we obtained 97.5%, 98.8%, and 98.1% for balanced accuracies for test data from daytime, evening, and nighttime speech, respectively; the overall accuracy was 98.1%. Prolonged wakefulness deteriorates the performance of machine-learning based speaker identification. This work suggests that machine-learning based speaker identification should be trained using speech data from both daytime and nighttime speech sessions for better overall accuracy. Machine learning can potentially be used for identifying a speaker's voice even when it is affected by tiredness and fatigue which are frequently encountered in scenarios such as the emergency rooms and long-duration repetitive task operations.