Reviewerly/exHarmony huggingface dataset:
Image Segmentation and Defect Detection of Insulators Based on U-net and YOLOv4

The integrity of the insulator on transmission lines directly affects the safety and reliability of power transmission. Deep learning methods are used to explore the problems of insulator image extraction and defect detection. First, the mask image of the insulator region is obtained with the optimized U-net model so as to implement the semantic segmentation of the insulator. Then the YOLOv4 model is built to obtain the position of the defective insulator and achieve the detection of the self-explosive insulator. Aiming at making full use of the pixel information of high-resolution images, the segmentation-recognition-synthesis model is proposed to segment the insulator and obtain the defect area precisely. Multiple groups of experimental comparison are designed to verify the effectiveness of the model. The Dice coefficient of insulator segmentation based on the optimized U-net model is up to 0.92; the accuracy of the self-explosive insulator identification based on the YOLOv4 model is up to 0.96, and the average degree of overlapping IOU reaches 0.88. The research results have high practical value for the realization of intelligent power system operation and maintenance.
The Analysis of The Second Semester Students’ Anxiety In Making Presentation at EnglishEducation Departement at State Islamic University of Alauddin Makassar

The result showed that from 57 students who has fulfill the questioner in the second semester, most of them 82,46% had medium level of speaking anxiety then another finding results are 12,28% of the students has experienced low anxiety and only 5,26% students experienced high anxiety. make a lot of foreign language studens felt nervous and tense so that they could not do their best in speaking.
Neural Language Models vs Wordnet-based Semantically Enriched Representation in CST Relation Recognition.

Neural language models, including transformer-based models, that are pre-trained on very large corpora became a common way to represent text in various tasks, including recognition of textual semantic relations, e.g. Cross-document Structure Theory. Pre-trained models are usually fine tuned to downstream tasks and the obtained vectors are used as an input for deep neural classifiers. No linguistic knowledge obtained from resources and tools is utilised. In this paper we compare such universal approaches with a combination of rich graph-based linguistically motivated sentence representation and a typical neural network classifier applied to a task of recognition of CST relation in Polish. The representation describes selected levels of the sentence structure including description of lexical meanings on the basis of the wordnet (plWordNet) synsets and connected SUMO concepts. The obtained results show that in the case of difficult relations and medium size training corpus semantically enriched text representation leads to significantly better results.
La importancia de la innovación educativa. Tendencias observadas durante los últimos diez años en Colombia

The present work consists of the importance of educational innovation in Colombia, which consists in the implementation of a significant change in the teaching-learning process where through different materials, methods and contents, quality, improvement and improvement can be perceived. optimization of it. Using the different kinds of educational innovation such as: disruptive innovation, revolutionary innovation, incremental innovation and continuous improvement.Throughout the history of the world it has been influenced by education and innovation, in developed countries they have been using educational innovation for several years, because they care more about knowledge and culture, while in Colombia they continue with the ancient thought since students only care about one grade and not what they can learn.According to the above, it is essential for educational innovation the support and connection between the government, teachers, parents and students. Since by means of this, the way of teaching can be evaluated from different perspectives and thus the appropriate measures can be used to improve education in Colombia in an innovative way.
Approaches to HCI Research

The chapter defines the concept of approach, along with its derivation and exemplification. A general definition of approach is proposed. The latter is then applied to HCI research in particular. An approach to HCI comprises the addressing of the topic or problem of human–computer interaction research; the performing of actions to progress the approach to the addressing of the topic or problem of human–computer interaction research; the evaluating of the success of the actions performed to progress an approach to the addressing of the topic or problem of human–computer interaction research; and the cumulating of the successes of whether the topic or problem of human–computer interaction research has been addressed or not. The definition is both explicit and sufficiently well specified for the later application of frameworks to the approaches, retained here – innovation, art, craft, applied, science and engineering.
LibGuides at Priddy Library: ECED 360: Early Literacy: Databases & Articles

Emerging literacy process in children, birth to age five. Examination of strategies, materials, and experiences for literacy development
Ego-network analysis with R (III). Many ego-networks

This text is the third in a series of four that together constitute a workshop on ego-network (and/or personal network) analysis with R. The text is accompanied by data files and the R language scripts needed to perform the proposed activities.
PURE-MAS: Social Intelligent Agents that Perceive their Environment and Act

Cyberpsychology, Behavior, and Social NetworkingVol. 24, No. 4 CyberEuropePURE-MAS: Social Intelligent Agents that Perceive their Environment and ActCompiled by: Giuseppe Riva and Maurizio MauriCompiled by: Giuseppe RivaAddress correspondence to: Dr. Giuseppe Riva, Humane Technology Lab, Catholic University of Milan, Largo Gemelli 1-20123 Milan, Italy E-mail Address: giuseppe.riva@unicatt.itHumane Technology Lab, Catholic University of Milan, Milan, Italy.Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.Search for more papers by this author and Maurizio MauriAddress correspondence to: Dr. Maurizio Mauri, Faculty of Psychology, Catholic University of Milan, Largo Gemelli 1-20123 Milan, Italy E-mail Address: maurizio.mauri@srlabs.itFaculty of Psychology, Catholic University of Milan, Milan, Italy.Search for more papers by this authorPublished Online:9 Apr 2021https://doi.org/10.1089/cyber.2021.29212.ceuAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"PURE-MAS: Social Intelligent Agents that Perceive their Environment and Act." Cyberpsychology, Behavior, and Social Networking, 24(4), p. 288FiguresReferencesRelatedDetails Volume 24Issue 4Apr 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:Compiled by: Giuseppe Riva and Maurizio Mauri.PURE-MAS: Social Intelligent Agents that Perceive their Environment and Act.Cyberpsychology, Behavior, and Social Networking.Apr 2021.288-288.http://doi.org/10.1089/cyber.2021.29212.ceuPublished in Volume: 24 Issue 4: April 9, 2021PDF download
Multilingual Negation Scope Resolution for Clinical Text

Negation scope resolution is key to high-quality information extraction from clinical texts, but so far, efforts to make encoders used for information extraction negation-aware have been limited to English. We present a universal approach to multilingual negation scope resolution, that overcomes the lack of training data by relying on disparate resources in different languages and domains. We evaluate two approaches to learn from these resources, training on combined data and training in a multi-task learning setup. Our experiments show that zero-shot scope resolution in clinical text is possible, and that combining available resources improves performance in most cases.
Adaptive Tuning Noise Estimation for Medical Images Using Maximum Element Convolution Laplacian

Noise in medical images can adversely affect the outcome of clinical diagnosis. In analyzing medical images, noise estimation is necessary to ensure consistency and performance quality ofimage processing techniques. In this study, we present a noise estimation method, namely Adaptive Tuning Noise Estimation (ATNE) that implements convolution Laplacian noise estimation. ATNE is based on subtraction of Gabor wavelet detected edges of images, and involves the relation element based on the parameters of the input image. This method allows a fast estimation of the image noise variance without a heavy computational cost. To assess the effectiveness of ATNE, 1000 mammograms are used. We pre-process these images to be Rician distributed with various noise variances. ATNE is used to estimate the noise level of the resulting images. We compare ATNE with other noise estimation methods, and the results show that ATNE outperforms other related methods with a lower percentage of error for noise variance estimation.
Giving up Privacy for Security: A Survey on Privacy Trade-off During Pandemic Emergency

While the COVID-19 pandemic continues to be as complex as ever, the collection and exchange of data in the light of fighting coronavirus poses a major challenge for privacy systems around the globe. The disease’s size and magnitude are not uncommon but it appears to be at the point of hysteria surrounding it. Consequently, in a very short time, extreme measures for dealing with the situation appear to have become the norm. Any such actions affect the privacy of individuals in particular. In some cases, there is intensive monitoring of the whole population while the medical data of those diagnosed with the virus is commonly circulated through institutions and nations. This may well be in the interest of saving the world from a deadly disease, but is it appropriate and right? Although creative solutions have been implemented in many countries to address the issue, proponents of privacy are concerned that technologies will eventually erode privacy, while regulators and privacy supporters are worried about what kind of impact this could bring. While that tension has always been present, privacy has been thrown into sharp relief by the sheer urgency of containing an exponentially spreading virus. The essence of this dilemma indicates that establishing the right equilibrium will be the best solution. The jurisprudence concerning cases regarding the willingness of public officials to interfere with the constitutional right to privacy in the interests of national security or public health has repeatedly proven that a reasonable balance can be reached.
Evaluation of Implications of Deceptive Advertising on Consumer Behaviour and Culture

Advertising as an age long concept that has its roots deep into the economy, is employed to help promote an organization's products and services with an increase in sales as the ripple effect. Advertising to an extent has contributed immensely to the growth of various businesses and organizations by increasing their sales volume,  organizations have been able to diversify into other industries and advertising in some cases has helped to create apositive and lasting impression on the minds of the audiences about the advertised products or services. It is in line with this that advertisers try to do everything within their means to ensure that their advertisement meet up with its goal, thereby engaging in deceptive advertising. Deceptive advertisement is fast becoming an acceptable trend in thesociety as many advertisements through the media has an element or two of deception. Owing to the fact that advertising and culture are connected, the implication of deceptive advertisement affects not only consumer behavior but also the culture of the people. Hence, it is the aim of this study to critically evaluate the implication deceptive advertising has on consumer behaviour and culture. present some findings, conclusion and recommendations
Quantum Machine Learning for Power System Stability Assessment.

Transient stability assessment (TSA), a cornerstone for resilient operations of today's interconnected power grids, is a grand challenge yet to be addressed since the genesis of electric power systems. This paper is a confluence of quantum computing, data science and machine learning to potentially resolve the aforementioned challenge caused by high dimensionality, non-linearity and uncertainty. We devise a quantum TSA (qTSA) method, a low-depth, high expressibility quantum neural network, to enable scalable and efficient data-driven transient stability prediction for bulk power systems. qTSA renders the intractable TSA straightforward and effortless in the Hilbert space, and provides rich information that enables unprecedentedly resilient and secure power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of qTSA. qTSA underpins a solid foundation of a quantum-enabled, ultra-resilient power grid which will benefit the people as well as various commercial and industrial sectors.
PocketNet: A Smaller Neural Network for 3D Medical Image Segmentation.

Overparameterized deep learning networks have shown impressive performance inthe area of automatic medical image segmentation. However, they achieve thisperformance at an enormous cost in memory, runtime, and energy. A large sourceof overparameterization in modern neural networks results from doubling thenumber of feature maps with each downsampling layer. This rapid growth in thenumber of parameters results in network architectures that require asignificant amount of computing resources, making them less accessible anddifficult to use. By keeping the number of feature maps constant throughout thenetwork, we derive a new CNN architecture called PocketNet that achievescomparable segmentation results to conventional CNNs while using less than 3%of the number of parameters.
E-Commerce AF Parfum System Website Based Using Design Thinking Method

Perfume is a fragrance that is produced from the extraction process of aromatic ingredients that are used to provide a fragrant aroma to the human body. E-commerce that develops in the field of information technology that provides convenience and advantages, if compared to conventional sales such as consumers can buy goods online and make it easier for consumers to transact and sellers get large profits because the merchandise is more familiar with the public and can manage data systematic sales.The development method in building AF Perfume E-commerce System is based on the theory of model design thinking. Design thinking is a software development methodology that proposes approaches to solve problems and gather ideas in order to get solutions. The purpose of research writing is to produce a Website-Based AF Perfume E-Commerce System. With the application of this system will facilitate and benefit many parties both consumers and sellers.Keywords: E-Commerce, Parfume, Design Thinking, Website
APLIKASI PEMBELAJARAN BERBASIS WEB PADA TK MANGGA DI BAROS (Studi Kasus : TK Mangga, Desa Sukamanah, Kecamatan Baros, Kabupaten Serang, Banten)

The development of technology is increasingly advanced, computers have a very big role. TK Mangga has a process of teaching and learning activities consisting of letter recognition, reading iqro, counting, coloring and drawing pictures that still use 2 (two) dimensional drawing paper and a blackboard. The large number of students and the small number of teachers has an effect on reducing children's concentration in teaching and learning activities, so it is necessary to use web-based learning applications that interact to support teaching and learning activities between students and web-based teachers that are attractive and interactive. The benefits of this application are divided into 2 (two), namely the benefits for users which can increase the enthusiasm of students in teaching and learning activities and benefits for institutions. The waterfall model based on UML objects (usecase diagrams, activity diagrams, sequence diagrams and class diagrams) is used as a tool. Making information systems using PHP as a web programming language, Adobe Flash as an animation and audio manager, MySQL as a database server and testing of beb-based learning applications using black box testing. The result of the research is a web-based learning application that produces output in the form of teacher data reports and student data reports in the form of PDF and the results of the practice scores of each learning material that has been done by students who are not connected to the database, and teachers and parents of students can interact using the application Web Based Learning at TK Mangga in Baros.
Analisis Kesuksesan Implementasi Sistem Informasi dengan Menggunakan Model Delon & McLean

This Research use success model information system Delone & Mclean (2003).Quality system measures technical quality, information quality measures semantic success, service quality, usage, user satisfaction and net benefits. This study aims to find a positive and significant influence and to find the suitability between each other variable. The methodology of this research is quantitative research, the sample in this study is internal users who live in the region of Medan. Distribution of questionnaires was conducted on February 7 to March 9, 2017 with total questionnaires distributed 50 Respondents and processed with Tools Spss 19 by using multiple linear regression model and path analysis. The results partially have a positive and significant effect because of the value of t table> t arithmetic and sig <tig sig of which tested. And no positive and significant effect due to the number of respondents in the test only slightly or less than 100. Therefore, the solution to solve it by adding data with Transform Data method, which where made changes and additions of data with SPSS Tools Recode menu which means to change / add data on certain variable.
Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand

Sudden steam-driven eruptions strike without warning and are a leading cause of fatalities at touristic volcanoes. Recent deaths following the 2019 Whakaari eruption in New Zealand expose a need for accurate, short-term forecasting. However, current volcano alert systems are heuristic and too slowly updated with human input. Here, we show that a structured machine learning approach can detect eruption precursors in real-time seismic data streamed from Whakaari. We identify four-hour energy bursts that occur hours to days before most eruptions and suggest these indicate charging of the vent hydrothermal system by hot magmatic fluids. We developed a model to issue short-term alerts of elevated eruption likelihood and show that, under cross-validation testing, it could provide advanced warning of an unseen eruption in four out of five instances, including at least four hours warning for the 2019 eruption. This makes a strong case to adopt real-time forecasting models at active volcanoes. In this study, the authors investigate the predictability of sudden eruptions, motivated by the 2019 eruption at Whakaari (White Island), New Zealand. The paper proposes a machine learning approach that is able to identify eruption precursors in data streaming from a single seismic station at Whakaari.
Are Data Subjects Investors

This article explores the status of data subjects in the era of data capitalism. A data subject is a person whose personal data is being collected, held, or processed by data collectors or processors (e.g., Amazon, Google, or Facebook). Data-driven companies rely on data subjects offering personal information for training algorithms for services. We maintain that it is time to seriously investigate whether data subjects can be considered as investors. First, we preview our thesis, followed by a functional deﬁnition of an investor. Then, we develop our argument that data subjects are better understood as investors rather than consumers or labor providers by examining the balance sheet impact of a data contribution to the data ﬁrm and the existing legal regime requiring data subjects to retain an ownership interest in their data even after it has been transferred to the data ﬁrm.
LibGuides: Students' Right to Read and Intellectual Freedom: Home

This guide offers information about students' right to read and intellectual freedoms and how to self-advocate for those rights.
Situation-Specific Multimodal Feature Adaptation

In the next decade, we will see a considerable need for NLP models for situated settings where diversity of situations and also different modalities including eye-movements should be taken into account in order to grasp the intention of the user. However, language comprehension in situated settings can not be handled in isolation, where different multimodal cues are inherently present and essential parts of the situations. In this research proposal, we aim to quantify the influence of each modality in interaction with various referential complexities. We propose to encode the referential complexity of the situated settings in the embeddings during pre-training to implicitly guide the model to the most plausible situation-specific deviations. We summarize the challenges of intention extraction and propose a methodological approach to investigate a situation-specific feature adaptation to improve crossmodal mapping and meaning recovery from noisy communication settings.
21세기 음악 전공생의 진로와 관련한 연구의 현황 분석

The purpose of this study was to analyze the research trends on music career development including classical music, Korean traditional music and popular music majors, and presume the direction of 21st century music majors in Korea. The results of this study indicated that the research on the career development of music majors was very limited while other majors including economics, managements, or technological majors. Even though, the professors and students at collage of music recognized the matter of career development of music majors, professors and students do not have a solution for these matters. In conclusion, the researchers need to study on music career development for 21st music majors in Korea
Amidst a global pandemic, who is AI for?

Global artificial intelligence capability is expanding fast, as is the threat of emergent infectious diseases. However, AI is not always used for the benefit of the people. Track-and-trace apps have produced serious concerns and implications for democracy and transparency during national emergencies, and their rolling out has often failed to protect those most at risk of contracting COVID-19. Stephen Roberts, Audrey Prost, and Lele Rangaka find a significant variance in how different populations and communities either benefit from, or are oppressed and disenfranchised by, AI operations aimed at containing COVID-19. The authors argue that for AI to address these inequalities, it must focus on three factors outside of technology: people, processes, and politics.