Reviewerly/exHarmony huggingface dataset:
Improving the detection of noisy labels in image datasets using modified Confidence Learning

The effectiveness of machine learning algorithms, including deep neural networks (DNN) for classifying image data, depends on proper preparation of the training dataset. Erroneously labeled images in the training data will degrade algorithmic efficiency and cause unpredictable model behavior, thus reduce its safety. Verifying labels in the numerous available databases remains a complicated and laborious task. In this article, we present a MultiNET approach that allows for efficient verification of labeled image datasets. We adapt a state-of-the-art technique, namely Confidence Learning, extending its flexibility and improving the effectiveness by combining outcomes from various DNN architectures. Thanks to the proposed modification, it is possible to automatically detect incorrect labels while minimizing the number of false positives, thus making the verification process much less burdensome. The technique may be of use for researchers and software engineers dealing with externally supplied image datasets.
Construction and Implementation Path of College English Teaching Mode in Big Data Environment

As a basic course for cultivating innovative and applied foreign language talents in the new era, college English audio-visual courses are facing huge opportunities and challenges in the era of big data. The transformation of the teaching environment under the background of big data is mainly reflected in the role of teachers, the dissemination of the information age, and the improvement of students’ self-learning ability. The role of students in teaching has been strengthened, and the education system has undergone certain changes. This paper analyzes the current teaching status of college English audio-visual courses. According to the actual situation, a feasible reform plan is proposed to reconstruct the teaching mode of college English audio-visual courses, to improve the teaching quality of college English audio-visual courses, and to promote the improvement of students’ comprehensive ability of college English.
Application of Fuzzy Clustering in Higher Education General Management Based on Internet Environment

General education is an important part of university education aiming at cultivating students’ comprehensive quality and sense of responsibility. As professional sports colleges and universities, higher sports colleges implement the curriculum reform of general education relatively late, and there is a problem of curriculum system construction in the process of implementing general education. The purpose of this paper is to research and discuss the application of general education management in higher education based on fuzzy cluster analysis in the Internet environment. This paper first discusses the teaching mode in the Internet environment and then analyzes the application of fuzzy cluster analysis in general education. Through fuzzy cluster analysis, the students’ understanding of general education before and after learning general education courses is compared, which paves the way for the promotion of general education in colleges and universities. Finally, this paper takes the general education curriculum system of higher physical education colleges as the research object and conducts fuzzy cluster analysis on it. The experimental results show that before studying general courses, 66.7% of the students hope to improve their language communication and expression skills, and 52.8% and 46% of the students hope to improve their practical ability and logical thinking ability. After receiving general education, only 23.5% of students have improved logical thinking ability, and only 13.7% of students have improved language communication and expression skills. The results show that general education does not meet the expectations of students, general education in higher physical education colleges is not effective, and the realization of general education needs to be further improved. To this end, schools can purchase network information resources, cooperate with well-known universities at home and abroad, develop general education courses with a global perspective, train students’ spirit of independent thinking, and master correct thinking methods.
HumanRoboLab: Experiments with Chatbots in Management Education at Universities

Digital dialog systems, known as “chatbots,” are an application sub-area in automation which is increasingly used in both work and business contexts. The growing use, functionality, and benefits of chatbots are therefore also of interest for university teaching in economics. Students at the University of Bremenʼs Department of Economics are developing their own transdisciplinary chatbot project. Working together with partners from different companies within the framework of the HumanRoboLab (HRL), they explore human–machine interaction from an application-oriented perspective. The main objective of the HRL is to test a didactic concept for transdisciplinary human–machine interaction projects in order to subsequently integrate this concept into business studies and business psychology degree programs. The purpose of such a module is to provide students without prior experience in programming with the knowledge, tools, and competences to research, apply, and question AI technologies in a self-effective manner; in other words, the HRL aims to help students develop expertise with regard to digitization, benefiting them in both social and economic contexts. The concept of the HRL, as well as some examples of the projects and the role of AI, is outlined in this chapter in order to provide a template for university teachers who might wish to adapt these concepts.
Medical Prescription Traceability Using Blockchain-Based Decentralized Application

Illegal movement or the flow of drugs in the supply chain has always been the root cause of various criminal activities involving pharmaceutical products. One such concern is the black-marketing of drugs by erasing the record of the drug from the registered supply chain and selling it on various channels for many times the price with an alteration made to the drug itself. To counteract such methods, we have proposed a solution using one of the most prominent characteristics, i.e., traceability, using which we can back-trace the route taken in the supply chain for the targeted drug on which the medical prescription is made.
Persistence of Fuzzy RDF and Fuzzy RDF Schema

In order to deal with imprecise and uncertain information in many real-world applications, fuzzy RDFFuzzy RDF has been extensively investigated as introduced in Chap. 3 . However, RDF brings some limitations, e.g., it may be difficult to store various data in a semantics way because of the semi-structured characteristic of RDF. As we have known, fuzzy databases such as fuzzy relational databasesFuzzy relational database and fuzzy object-orientedLogical databaseobject-oriented database databasesFuzzy object-oriented database can store a large set of semantic information. Therefore, there is an increasing need to reengineer fuzzy RDFFuzzy RDF into fuzzy database models, which may satisfy the needs of storing fuzzy RDF data in fuzzy databases. In this chapter, we focus on reengineeringReengineer(ing) fuzzy RDF into fuzzy database models, including fuzzyLogical databaserelational database relational databaseFuzzy relational database models, fuzzy object-orientedLogical databaseobject-oriented database databaseFuzzy object-oriented database models, and HBase databaseHBase database. Moreover, RDF has a graph structure and uses the existing graph database to support the storage and queryQuery of RDF data. Therefore, we also introduce the uncertain RDF graph data storage method based on Ne4j graph data in this chapter.
RDF Data and Management

The Resource Description Framework (RDF) is a flexible model for representing information about resources on the Web. As a World Wide Web Consortium (W3C) Recommendation, RDF has rapidly gained popularity. With the widespread acceptance of RDF on the Web and in the enterprise, a huge amount of RDF data is being proliferated and becoming available. Efficient and scalable management of RDF data is therefore of increasing importance. This is creating a new set of data managementData management requirements involving RDF, and RDF data management has attracted attention in the database and Semantic Web communities. In this chapter, RDF data and techniques for data management will be introduced. We start by providing a formal definition of RDF that includes the model and semantics, and then focuses on the RDF query language SPARQLSPARQL. We provide an algebraic syntaxAlgebraic syntax and a compositional semantics for this language. Finally, we introduce RDF data storageRDF data storage and present an overview of the current state of the art in RDF data storageRDF data storage strategy.
AuditTrust: Blockchain-Based Audit Trail for Sharing Data in a Distributed Environment

There has been a significant recent interest in trust-building technologies for decentralized environments, especially for sharing data between mutually distrusting entities. One of the critical challenges in this context is to ensure that shared data cannot be tampered with, and that access to this data can always be traced and audited in a secure and trustworthy way, e.g., by using an access log to detect tampering. However, for audit trail data to be useful, it must be correct, immutable, and tied with access control mechanisms. We present AuditTrust, a blockchain-based secure audit trail for data sharing in a distributed environment. We prototype AuditTrust using several technologies, such as Hyperledger Besu, IPFS, the Intel SGX TEE, and Vault. Our evaluation of AuditTrust examines the latency costs of auditing and access control and shows the effectiveness of the approach.
Performance Improvement of Logistic Regression for Binary Classification by Gauss-Newton Method

This paper proposes a new approach to optimizing cost function for binary logistic regression by the Gauss-Newton method. This method was applied to the backpropagation phase as a part of the training process to update the weighted coefficients. To show the performance of the approach, we used two data sets to train the logistic regression model for binary classification problems. Our experiment demonstrated that the proposed methods could perform better than gradient descent for both examples, as we expected. Furthermore, the performance of our approach is more advanced than the classical method, either in speed or accuracy.
Convolving Hyper-Erlang with Hyper-Exponential Distributions Using Linear Algebra

In this paper, the sum of a hyper-Erlang and a hyper-exponential distributed random variables is analyzed. Although tedious, the resulting random variable’s probability density function (PDF) can be obtained through convolution of its summands’ PDFs. Alternatively, the resulting distribution can be stated directly in terms of a phase-type distribution. However, computing its PDF can still be very costly and this representation gives little insight on the distribution. It can be shown that the sum of both random variable is again hyper-Erlang distributed of incremented order and can therefore be described without requiring the matrix exponential function. We derive a closed form linear algebra expression for the probability weights of the sum’s hyper-Erlang distribution, which significantly reduces the computational complexity of evaluating its distribution.
Indonesian Finance News Sentiment from Hybrid Deep Learning and Support Vector Machine

One common action to keep aware of the current investment progress is by updating finance news continuously. Indeed, we can read a bunch of news relating to finance from social media, which is often difficult to figure out at a glance. Hence, this work aims to propose hybrid models that can help us to classify whether the finance news is positive to follow. Also, we may sort a few articles containing neutral ones. More specifically, we incorporate deep neural networks: deep convolutional neural networks and long short term memory, to draw diverse word representations, and support vector machines to categorize them as a multi-class classification case. In this work, we evaluated the proposed models on Indonesian finance news that was officially reported from the Bank of Indonesia around 2019 before the pandemic started. In the evaluation results, we showed the DCNN-SVM better accuracy compared to others.
Design and Implementation of a Web Editing and Publishing System Based on a Semantic Network Generation Algorithm

In order to solve the problem of web editing data mining effectively, a semantic network generation algorithm is proposed. First of all, on the basis of preprocessing the variant short text, the maximum matching distance between short text is calculated by using the dictionary to expand the semantics of the Chinese words, which is used as an index to measure the formal distance between short text. Finally, a weighted method is used to synthesize formal distance and unit semantic distance into text distance, which is applied to the clustering analysis of online comments. The length of the word list is used to punish the distance. Results show that the most popular query topics on the Internet are shopping 10%, entertainment 10%, pornography 12%, computer 9%, research 9%, healthy life 5%, travel 5%, games 5%, family medical 5%, sports 3%, personal economic plan 3%, holiday 1% and others. It is proved that the improved algorithm proposed in this paper is superior to other methods and the clustering performance is significantly improved.
A Study on the Use of Padlet by Japanese L earners -Focusing on tourism Japanese class in this COVID-19 era-

This study aims to enhance learners’ participation and immersion using Padlet in online tourism Japanese classes provided in this COVID-19 era. After providing the class using Padlet, this researcher conducted a survey among the learners. The results are as follows.BR First, in the questionnaire on the use of Padlet, the strongest scores were given to “I participated eagerly in 〈Padlet〉 during the class.”, and “I could participate in the class actively with the use of 〈Padlet〉”.BR Second, learners who had participated in Padlet activities positively also indicated high satisfaction scores with the use of Padlet.BR Third, regarding learners’ positive opinions about the use of Padlet, “improving concentration, checking the degree of learners’ participation, referring to peer learners’ contents, supporting to enhance self-directed learning ability, reviewing, having interaction based on teacher’s comments, and lessening the task burden” were found.BR Fourth, with the use of Padlet in tourism Japanese classes, it was possible to open the products of learners transparently and induce competition among them. Furthermore, it could be used as a tool to maintain balance in learning by referring to the results of fellow learners.
A Study on a Method for Teaching Indefinite Nouns -Based on Surveys of the Actual Status of Learners’ Use of Indefinite Nouns and their Recognition-

In this article, I aim to propose an effective method for teaching indefinite nouns based on a survey of the learners’ unnatural uses of human-denoting indefinite nouns. Human-denoting indefinite nouns have hardly been described in previous language studies and grammar books, and the learners of Japanese occasionally do not even recognize their meaning.BR First, I surveyed the actual status of the learners’ use of indefinite nouns based on composition data to see how the teachers should teach indefinite nouns to the learners. The results indicate that the indefinite nouns do not frequently appear in the composition data, but I found several unnatural usage-patterns. Based on this result, I conducted a survey about the use of human-denoting indefinite nouns with 32 participants and found that several Korean learners of Japanese do not sufficiently recognize the correct use of indefinite nouns.BR Second, I explored how indefinite nouns are used in novels to propose an effective method for teaching indefinite nouns. The results show that the most frequently used type is daremo, followed by darenimo, daredemo, daremoga, dareyorimo, and darenidemo. Furthermore, each type of indefinite noun co-occurs with predicates featuring some specific morphological properties.BR Based on these results, I compared daredemo and daremoga in the positive-polarity contexts. I also revealed that the indefinite nouns involving a particle co-occur with certain types of predicates and uncovered meaning associated with the particle for each indefinite noun. These findings are reflected in my proposed teaching method. That is, I propose that the teachers should teach the following: the human-denoting indefinite nouns, daremo and darenimo, co-occur with negative predicates, whereas daredemo, daremoga, and darenidemo co-occur with positive predicates.
Joint Adversarial Domain Adaptation with Structural Graph Alignment

Generative adversarial networks as a powerful technique is also used in domain adaptation (DA) problem. Existing adversarial DA methods mainly conduct domain-wise alignment to alleviate marginal distribution shift between the two domains, while it may damage latent discriminative structure hidden in data feature space and cause negative transfer accordingly. To handle this problem, we propose a joint adversarial domain adaptation method with structural graph alignment to minimize joint distribution bias by further realizing classwise matching (conditional distribution shift) based on a simple sampling strategy except for the domain-wise alignment, and validate that simultaneously considering these two types of shift can approximately reduce the joint distribution bias. To explore transferable structural information and realize more sufficient transfer for DA problem, we propose to align structural graphs between the two domains which is also based on a simple sampling strategy. Notably, the structural graph describes the relationship between each two samples and it is computed on two domains. As such, we can learn new feature representation of the two domains which are more discriminative and transferable to benefit a cross-domain classification task desirably. Finally, we design a number of experiments to evaluate our approach on four public cross-domain benchmark datasets including standard and large-scale ones, and empirical results show that the proposed model can outperform compared state-of-the-art methods. Keywords: joint adversarial domain adaptation, marginal distribution, conditional distribution, joint distribution, structural graph alignment.
The Improvement Strategy for Piped-Water Services During the COVID-19 Pandemic (Case Study: Depok City, West Java, Indonesia)

To anticipate a water crisis that is getting wider and to balance the capacity of clean water supply that cannot be changed immediately, a study of the fulfillment of clean water needs through Regional Drinking Water Company (PDAM) piped-water service in Depok City was carried out during Covid-19 pandemic. Depok City is currently facing a water crisis and has experienced an increase usage of clean water during the pandemic. This study used qualitative and quantitative approach to analyze piped-water service in Depok City before and during the pandemic, and analyze the target of 35% piped-water service coverage in 2021 to see the capacity of PDAM in fulfilling clean water needs, especially during the pandemic. It was found that there was an increase in customer water consumption during the pandemic by 5% from normal condition, which was to 147 L/person/day. PDAM production efficiency was obtained by 80% and the amount of water distribution was around 90% of the production water. From these results with using the 25% non-revenue water (NRW) level based on the national target, PDAM needs to uprate its overall water treatment plants (WTPs) capacity by 2.13 times, from 1,080 L/s to 2,300 L/s, in order to achieve the target of 35% service coverage during the 2021 pandemic. Efforts are being made by PDAM, such as uprating the Legong WTP and the plan to build new WTPs in Cimanggis and Cinere Districts. Others that need to be improved by PDAM include socialization to the public regarding the urgency of using piped water; optimization of real production volume by repairing leaks or overflows in the WTP so that water distribution can be far above 90%; utilization of backup unit system at the WTP so that water distribution to the customers is not disturbed for 24 hours; and controlling the existing 35% NRW level which is still high by repairing and maintaining the distribution network system. This study is expected to provide recommendations for stakeholders, especially PDAM Tirta Asasta Depok, in fulfilling the clean water needs for public both during the pandemic and in the future.
Intelligent Chat-Bot Using AI for Medical Care

This article is about CHATBOT, a new learning tool that uses data collected over time to help students do better in school. A CHATBOT is a computer-programmed artificial intelligence (AI) that can talk to people through interactive text or speech. This CHATBOT uses artificial intelligence and machine learning to talk to people in real time. Here, we'll look at how the CHATBOT was made, what words it uses, and the different platforms it runs on. In this guide, there are also more examples of how CHATBOT can be used in the real world. The CHATBOT tool may be useful in computer-aided design (CAD) programmes, according to this review. to make clear Chatbots with artificial intelligence for medical care.
Psychological Capabilities Required for Continuous Transformations: The Self on Permanent Journeys with a Travelling Organization

This article takes the perspective of developmental and personality psychology to summarize what the personality and the development of its coherent self require—according to current knowledge—to develop a good structural level of flexible, yet stable ego functions and diverse/hybrid capabilities of the self. The latter are considered to be factors that organizations should take into particular account in the question of how the concept of organizations in (permanent) transformation and the concept of persons with their selves fit together as the psychological organization of the individual in the context of a travelling organization. Which types of personality are particularly helpful for organizations undergoing transformations, what diversity should be aimed at from the perspective of developmental and personality psychology? How can travelling organizations with their global requirements today use insights into the differentiated/diverse/hybrid capabilities of the self for optimal efficiency and effectiveness in their transformation journey and, specifically, in connective cooperation and communication across local and global teams? What developmental psychological perspectives are there for personality types who may initially seem less suited to transformations to still engage and drive transformations effectively and efficiently after all? The answers to these fundamental questions are summarized at the end of this article in the chapter Conclusions and Takeaways. So this article provides practical recommendations for action from the perspective of the individual, but also of the travelling organization.
Level Set Methods and Lagrangian Interfaces

This chapter introduces geometric notations and concepts related to curves and surfaces moving in a flow. It defines level set methods as alternatives to Lagrangian methods for the implicit tracking of these surfaces, with illustrations from classical examples in image processing and fluid mechanics. Finally, it discusses stability issues related to the time discretizations of these methods and proposes a semi-implicit method which is both simple to implement and efficient form the point of view of stability.