Reviewerly/exHarmony huggingface dataset:
Recognition Network of Catenary Dropper State based on Memory-enhanced Convolutional Autoencoder

The application of image processing technology based on deep learning in recognition of dropper state is gradually rising. However, the lack of abnormal samples and the particular shape of the dropper significantly increase the difficulty of training the general classification network. In this paper, we propose a convolution autoencoder aiming at the unbalanced data set based on the VGG classification network. It adopts semi-supervised learning to avoid the disadvantages of unbalanced data sets. We embed the memory mechanism in the network, which improves capability of state identification further, and use lightweight module ghost reduces the model parameter. In the balanced test set, the recognition accuracy of the algorithm is 92%, which has achieved good performance in the related applied research.
Impact evaluation of teleconsultation on gastroenterology during the sars-cov-2 pandemic

Introduction: Lockdown during the SARS-CoV-2 pandemic forced an adaptation of health care delivery system. Teleconsultation (TC) is a measure that allows the continuity of access to medical care, minimizing the risk of infection. This study aims to analyse the impact of TC in Gastroenterology patients. Aims & Methods: Patients followed in the Gastroenterology service of Braga Hospital by TC between March and November 2020 were invited to answer a questionnaire. This was partially adapted from the modified Telemedicine Satisfaction Questionnaire (TSQm), including as well parameters with impact on daily life. Sociodemographic and clinical characteristics of the participants in the study were analysed. Results: One hundred and twenty patients participated, 60 with inflammatory bowel disease (IBD group) and 60 with liver cirrhosis (LC group). Regarding IBD group, 55% were female, with a mean age of 40.7 years. The most frequent level of education was college degree (40.0%), with the majority being employed (78.3%). In the LC group, 66.7% of the patients were male, with an average age of 64.2 years. Of these 81.7% had the basic level of education and 66.7% were retired. The average total TSQm score was 38.23±6.35 in the IBD group and 37.55±6.05 in the LC group, with 68.3% of the cases considering TC as a good experience. Significant impact on absenteeism was observed only in the IBD group, with at least 1 work period saved in 63.3% of the cases. In both groups, no differences were observed in the rate of flares/decompensations, hospitalizations or visits to the emergency department, between the study period of 2020 and the homologous period in 2019. Conclusion: TC is an effective method for follow-up of chronically ill patients, resulting in a high degree of user satisfaction and with an important impact on daily life. It has proven to be clinically safe, without negative consequences on the control of the underlying disease.
Modelling Archival Hierarchies in Practice: Key Aspects and Lessons Learned

An increasing number of archival institutions aim to provide public access to historical documents.Ontologies have been designed, developed and utilised to model the archival description of historicaldocuments and to enable interoperability between different information sources. However, due to theheterogeneous nature of archives and archival systems, current ontologies for the representation ofarchival content do not always cover all existing structural organisation forms equally well. After brieflycontextualising the heterogeneity in the hierarchical structure of German archives, this paper describesand evaluates differences between two archival ontologies, ArDO and RiC-O, and their approaches tomodelling hierarchy levels and archive dynamics.
Content-Based Image Retrieval Using Deep Learning

The problems of content-based image retrieval (CBIR) and analysis is explored in this paper with a focus on the design and implementation of machine learning and image processing techniques that can be used to build a scalable application to assist with indexing large image datasets. The CBIR application will be able to search large image datasets to retrieve digital images that are like predefined specifications such as a given digital image, or a given image type. The search is based on the actual contents of images and not the metadata of these images. Feature extraction techniques are used in this research project to analyze images and extract important features of images. The extracted features reflect the important characteristics of images that are related to contents (such as colors, shapes, edges, and textures) that can identify the image type. Supervised machine learning techniques are used in this project to analyze these extracted features and to retrieve similar images in the form of a convolutional neural network. This network classifies images, and using the statistics that made these classifications, similarities can be drawn between the query image and entities within the database. The developed CBIR algorithms were able to analyze and classify images based on their contents.
Internet i nastava književnosti: digitalni modernizam

The wide range of questions that this topic raises lead us to think about the training of teachers, their continuous work and improvement. Following this paragraph, we conclude that a textbook/manual, which would deal with the relationship between the Internet and teaching, or the continuous organization of seminars and trainings, is a necessity for literature teachers. In addition to the teacher's knowledge of the coexistence of literature and new media, it is necessary to pay special attention to the critical attitude of students towards Internet content and to point out the purposeful use of newspapers in the teaching process. The role of teachers in modern timesis not favorable because things related to their profession are rapidly developing: new genres appear, tendencies in study and creation change, communication media are continuously changing, and on the other hand teaching it self must undergo changes and transformations. Nevertheless, proven methodological principles always exist, at least as the essence of teaching, regardless of which teaching aids were used and in what way in class. The Internet content and the opportunities it provides have proven to be stimulating and close to the students, so the teacher can use them, with the awareness that the teaching aid must not overshadow the content that is being processed.
Unsupervised Domain Adaptation for the Restoration-Classification network by Minimizing discrepancy

We propose a novel method for unsupervised domain adaptation which can be achieved by restoration-classification network by minimizing the discrepancy. So, we design a model which can take the labelled source domain for supervised data and unlabeled target domain for unsupervised data. This model will encode useful data from the target or output domain and can be enhanced by usage of back propagation method which is similarly used while training of neural networks. We have used this model to estimate the performance for object recognition task which can bring the transformed features closer since we have performed transformations on the labelled and unlabeled data. Then we are training the classifier on the both transformed features and have achieved a better accuracy such that our model performed accurately during the test time and thus by minimizing the loss of the features by 0.03.
Rancang Bangun Perangkat Komunikasi Adaptif Untuk Pengembangan QoS (Quality of Service) Infrastruktur Internet of Vehicle (IoV)

The communication network is an important and vital component in the implementation of the Internet of Vehicle (IoV). The characteristics of IoV related to mobility, load, coverage area is very complex. The movement of connected nodes, communication load and wide coverage require reliable infrastructure support. Coupled with a high level of Quality of Service (QoS) for Internet of Vehicle (IoV) implementation which has a high risk if a communication system failure occurs. In this research, a system that has adaptive capability has been built in choosing a good connection infrastructure at the point where the unit is connected. Created a system that has the ability to connect to several communication network infrastructure. The system can switch to another provider when there is a connection that decreases its QoS level. The tests carried out resulted in better connection dynamics because there was an infrastructure backup. Although there are still many weaknesses because the distribution of network availability is still problematic. Anticipation of network overload can be anticipated with this system. The test results show that there is an increase in the percentage of lines connected to the new system. There is an increase in the percentage of connectivity around 10% to 20% compared to systems without connection backups.&#x0D;
Identifying Spammers on Twitter Using Machine Learning: A Survey

Abstract: The emergence of social media like twitter has reduced the size of the world from a humungous oblate ellipsoid to a complex network wherein individuals are just a few clicks away. Twitter, a free micro-blogging social media platform has become ever more popular not only as a social site for communication but also it has become a global source for stance on controversial politics, business advertising and follow up on current news. However, this has also lead to the rise of undesirable activities like spamming and presence of fake accounts. Today, a total of 1.3 billion accounts have been created on twitter and it estimates approximately 23 million of its active users are actually bots. One popular approach suggest to tackle these problems are employing Machine Learning tools to detect spam and subsequently remove them. In this paper, we have presented a detailed analysis on identifying spam accounts by key-features and latest methods, also by using machine learning algorithms and prepared comprehensive review for the best algorithm to implement based on inferences drawn from previous research papers and articles published in this field with discussion on recent features implemented by twitter to eradicate this sophisticated but tough to obliterate issue. Also, we have highlighted some common spammers’ behavior that has been observed over the years. Index Terms: Twitter spam; spam detection; machine learning
A consolidative evaluation of extracted EGG speech signal for pathology identification

Speech as a natural and effective way of communication needs an appropriate analysis. This assessment is mainly used to quantify specific signals with various irregularities. The electroglottograph...
Automated Deep Learning based Video Summarization Approach for Forest Fire Detection

Due to the exponential increase in video data, an automated examination of videos has become essential. A significant requirement is the capability of the automated video summarization process, which is helpful in vast application areas from surveillance to security. It assists in monitoring the user application with reduced memory and time. Therefore, this paper designs an automated deep learning-based video summarization approach for forest fire detection (ADLVS-FFD). The ADLVS-FFD technique aims to summarize the captured videos and detects the existence of forest fire in it. In addition, the ADLVS-FFD technique involves different subprocesses such as frame splitting, feature extraction, and classification. Besides, a merged Gaussian mixture model (MGMM) is used to extract keyframes and features. Moreover, the long short-term memory (LSTM) model is employed to detect and classify input images into normal and forest fire images. To ensure the better performance of the ADLVS-FFD technique, a comprehensive experimental validation process takes place on a benchmark video dataset. The resultant experimental validation process highlighted the supremacy of the ADLVS-FFD technique over the recent methods.
Event Flow - How Events Shaped the Flow of the News, 1950-1995.

This article relies on information-theoretic measures to examine how events impacted the news for the period 1950-1995. Moreover, we present a method for event characterization in (unstructured) textual sources, offering a taxonomy of events based on the different ways they impacted the flow of news information. The results give us a better understanding of the relationship between events and their impact on news sources with varying ideological backgrounds.
Representing second-order arguments with Adpositional Argumentation (AdArg)

This paper extends a high-precision method for representing ‘first-order’ arguments to the linguistically and pragmatically more complex ‘second-order’ arguments (such as the argument from authority). It thereby contributes to the further development of Adpositional Argumentation (AdArg), an approach to representing argumentative discourse with applications in corpus linguistics and computational argumentation that combines Gobbo and Benini’s linguistic representation framework of Constructive Adpositional Grammars (CxAdGrams) and Wagemans’ argument categorisation framework of the Periodic Table of Arguments (PTA).
Cheating Detection Pipeline for Online Interviews and Exams

Remote examination and job interviews have gained popularity and become indispensable because of both pandemics and the advantage of remote working circumstances. Most businesses and educational organizations use these platforms for recruitment as well as online exams. However, one of the critical problems of the remote examination systems is conducting the exams in a reliable environment. In this work, we present a cheating analysis pipeline for online interviews and exams. The system only requires a video of the candidate, which is recorded during the exam by using a webcam without a need for any extra tool. Then cheating detection pipeline is employed to detect the presence of another person, electronic device usage, and candidate absence status. The pipeline consists of face detection, face recognition, object detection, and face tracking algorithms. To evaluate the performance of the pipeline we collected a private video dataset. The video dataset includes both cheating activities and clean videos. Ultimately, our pipeline presents an efficient and fast guideline for detecting and analyzing cheating actions in an online interview and exam video.
Real-Time American Sign Language Realization Using Transfer Learning With VGG Architecture

Sign language is a barrier between the hearing impaired and the hearing community. In the modern world, despite the technical advancements, it is still a struggle for specially-abled people to communicate and express their thoughts with the rest of the world. A system is built that will reduce the gap for the specially-abled, the system recognizes the hand gesture and translates it into corresponding letters. ASL data set is used. The software is built on Python and inherits the power of deep learning. A convolutional neural network is used for image classification. The system was able to classify all the alphabets from A-Z with an accuracy of 98.89 percent. The software aims to eradicate the communication barrier between the hearing-impaired community and the rest of the world. The software provides a medium for the inarticulate community to express their thoughts to everyone out there.
&lt;i&gt;SASC&lt;/i&gt;: A Simple Approach to Synthetic Cohorts for Generating Longitudinal Observational Patient Cohorts from COVID-19 Clinical Data

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Analysis of important components of rubber extruder

research-article Analysis of important components of rubber extruder Share on Authors: Faguo Huang Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Shouqi Zhang Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Jiafang Pan Guilin University of Technology, China Guilin University of Technology, ChinaView Profile Authors Info & Claims ICIIP 2021: 2021 6th International Conference on Intelligent Information ProcessingJuly 2021 Pages 167–171https://doi.org/10.1145/3480571.3480601Online:29 October 2021Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Annotazioni sulla fenomenologia della tonalità nel pensiero di Giovanni Piana

Giovanni Piana's phenomenology of music was directed against any conventionalist point of view about the musical experience. According to Piana, each compositional system depends on the phenomenological structure of sound that determines the musical perception. Tonal system is not prescribed by natural laws, however it is based on the essential difference between consonance and dissonance. So the perceptual meaning of a tonal piece is not founded on subjective (culturally acquired) habitudes, but on phenomenological data.
Exploration With Task Information for Meta Reinforcement Learning

Meta reinforcement learning (meta-RL) is a promising technique for fast task adaptation by leveraging prior knowledge from previous tasks. Recently, context-based meta-RL has been proposed to improve data efficiency by applying a principled framework, dividing the learning procedure into task inference and task execution. However, the task information is not adequately leveraged in this approach, thus leading to inefficient exploration. To address this problem, we propose a novel context-based meta-RL framework with an improved exploration mechanism. For the existing exploration and execution problem in context-based meta-RL, we propose a novel objective that employs two exploration terms to encourage better exploration in action and task embedding space, respectively. The first term pushes for improving the diversity of task inference, while the second term, named action information, works as sharing or hiding task information in different exploration stages. We divide the meta-training procedure into task-independent exploration and task-relevant exploration stages according to the utilization of action information. By decoupling task inference and task execution and proposing the respective optimization objectives in the two exploration stages, we can efficiently learn policy and task inference networks. We compare our algorithm with several popular meta-RL methods on MuJoco benchmarks with both dense and sparse reward settings. The empirical results show that our method significantly outperforms baselines on the benchmarks in terms of sample efficiency and task performance.
PENDAMPINGAN KETERAMPILAN BERPIDATO PADA NASYIATUL AISYIYAH CABANG PADANGSIDIMPUAN SELATAN

Giving a speech is one of the activities to speak or communicate verbally. The purpose of the implementation of this activity is to train Nasyiatul Aisyiyah's South Padangsidimpuan branch in speaking skills. By conducting this training, Nasyiatul Aisyiyah will be more skilled in making speeches in front of the public, therefore follow-up is needed to continue to practice Nasyiatul Aisyiyah's speech skills. There are 4 stages in providing speech training assistance, namely starting from the preparation stage, the training implementation stage, the evaluation stage and the followup stage. The results of the speech skills assistance to Nasyiatul Aisyiyah received a good response from the partners. The partners play an active role in participating in the speech skills training
MATEMATIKA KROZ IGRU – MOGUĆNOSTI UPOTREBE IKT

Due to its voluntary and unburdening character, game is an excellent medium for learning. Children learn through games, because on the basis of rules, settings and their own skills, they understand the existing and define new concepts and relations between them. The game meets one important requirement of education – straightening multiple skills at the same time. Learn- ing mathematics is a challenge for many students, so the game seem like a good solution to bring mathematics closer to students. Mathematical games encourage strategic thinking, problem solving and develop logical thinking. Combining play as a form of learning with ICT progress, computer games became a new teaching tool. Video games are a much better representative system for learning mathematics than symbolic representations on static pages of textbooks.
Implementing Automatic Ontology Generation for the New Zealand Open Government Data: An Evaluative Approach

Open Government Initiatives are increasingly gaining momentum and becoming important among developed and developing countries. Open government initiatives purpose is to provide transparency and data reuse. Governments around the globe are increasingly releasing data for public consumption, however, the drawback is that these data are disparate and in heterogeneous formats which makes it challenging to consume the data. This has given rise to a need for a framework that can transform these data into a form that is easily consumable and accessible to stakeholders as well as the general public. This paper introduces the design, implementation and usage of an approach driven by an ontology that captures the knowledge on a subset of data released by the government of New Zealand (NZ). This approach uses datasets from agriculture, and land use to generate ontologies where SPARQL queries are imposed to get the desired results. The implementation and evaluation outcomes demonstrated that the proposed approach is realistic to generate an Ontology Web Language (OWL) triple ontology from the given Comma Separated Value (CSV) datasets. In the future, this approach will further ease the semantic linking process of the multiple ontologies of different domains.
Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation

Simulation-based techniques such as variants of stochastic Runge-Kutta are the de facto approach for inference with stochastic differential equations (SDEs) in machine learning. These methods are general-purpose and used with parametric and non-parametric models, and neural SDEs. Stochastic Runge-Kutta relies on the use of sampling schemes that can be inefficient in high dimensions. We address this issue by revisiting the classical SDE literature and derive direct approximations to the (typically intractable) Fokker-Planck-Kolmogorov equation by matching moments. We show how this workflow is fast, scales to high-dimensional latent spaces, and is applicable to scarce-data applications, where a non-parametric SDE with a driving Gaussian process velocity field specifies the model.