Reviewerly/exHarmony huggingface dataset:
Pre-Processing and Image Segmentation Techniques

image are known as image analysis. First step in image analysis is to segment an image. Image segmentation is a key step for partioning an image into several segments to find objects, borders &amp;meaningful objects for an image. In image processing for any application when the work is done on an image, the initial step is to divide the image in order to solve the difficulty. Divided images are widely used in a multitude of different applications like density of tissue volumes, pathology, Satellite imaging, Extracting features &amp; recognizing objects, treatment planning &amp; few computer integrated surgeries etc. This paper gives an overview of different image pre-processing techniques&amp; image segmentation techniques. Before we do segmentation of an image, we need to pre-process an image to remove noise.
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No AccessC++ programmierenOct 2020StringUlrich BreymannUlrich BreymannSearch for more papers by this authorhttps://doi.org/10.3139/9783446465510.031SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2020Pages: 923-934Print ISBN: 978-3-446-46386-8eISBN: 978-3-446-46551-0 Copyright & Permissions© 2020 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
Upcoming Meetings

Artificial OrgansVolume 45, Issue 4 p. 435-436 UPCOMING MEETING Upcoming Meetings First published: 21 March 2021 https://doi.org/10.1111/aor.13939Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume45, Issue4April 2021Pages 435-436 RelatedInformation
Automatic Repair of Semantic Defects Using Restraint Mechanisms

Program defect repair faces serious challenges in that such repairs require considerable manpower, and the existing automatic repair approaches have difficulty generating correct patches efficiently. This paper proposes an automatic method for repairing semantic defects in Java programs based on restricted sets which refers to the interval domains of related variables that can trigger program semantic defects. First, the program semantic defects are summarized into defect patterns according to their grammar and semantic features. A repair template for each type of defect pattern is predefined based on a restricted-set. Then, for each specific defect, A patch statement is automatically synthesized according to the repair template, and the detected defect information is reported by the static detection tool (DTSJava). Next, the patch location is determined by the def-use chain of defect-related variables. Finally, we evaluate the patches generated by our method using DTSJava. We implemented the method in the defect automatic repair prototype tool DTSFix to verify effect of repairing the semantic defects detected by DTSJava in 6 Java open-source projects. The experimental results showed that 109 of 129 program semantic defects were repaired.
Research on virus diffusion prevention method for computer singularity in complex sensor networks

In current virus propagation defence methods, immunisation failures, as well as combination influence of infectious vector and propagation delay in propagation are not considered. Therefore, propagation of computer singularity virus is hard to control. A new SIS propagation model is proposed based on mean field theory with infectious vector and propagation delay. The influence of computer virus in complex sensor networks on the propagation property is analysed. A new cellular automata model is built to simulate computer virus propagation. With abstraction layer from computer singularity virus detection model based on immunisation principle, a fusion method for singularity virus detection is proposed. Through fusion of different antigen presentation gene library, ability of defence of computer singularity virus propagation is improved. Experimental results show that target immune network connectivity factor ratio of our method is only 0.18, lower than 0.37 multiples of nearest neighbour immunity.
The Method for Determination of Bone Marrow Cells in Photographic Images

The automated segmentation of cells in microscopic images is an open research problem that has important implications for studies of the developmental and cancer processes.Known methods for the selection of contours and their main disadvantages are analyzed.A method for isolating the contours of bone marrow cells is proposed.The method includes two stages.The first stage is the selection of contours by the Canny edge detection algorithm.The second stage is the selection of geometric primitives using the Hough transform.The color image of the bone marrow cells was processed.The results of the method at each intermediate stage are given.The assessment of the quality of the selection of the bone marrow cells carried out by visual means.
Developer Personas

After you have completed your segmentation, you can start to develop personas. Personas are a way to personalize your targets (i.e., put a name and face) to help you and your teams think more personally about your users.
Pattern Matching

The chapter deals with the most classical subject in text algorithm, namely text searching and string matching. There are several problems related to special tables occurring in fast patternmatching techniques: tables for borders, strict borders, good-suffixes, prefixes and short borders. Are also presented some versions of classical methods known as Knuth-Morris-Pratt and Boyer- Moore algorithms. Pattern matching is closely related to the computation of periods, maximal suffixes and critical positions in texts. Three problems are related to so-called non-standard stringology: parameterised and order-preserving pattern-matching. Also considered are pattern matching with errors and the related 2D-matching.
On Computing Cluster Centers of Trapezoidal Fuzzy Numbers

In this paper we compute cluster centers of trapezoidal fuzzy numbers through fuzzy c means clustering algorithm and kernel based fuzzy c means clustering algorithm. A new complete metric distance between the trapezoidal fuzzy numbers is used to compute the cluster centers on the set of trapezoidal fuzzy numbers.
APSoC-Based Implementation of an EEG Classifier using Chaotic Descriptors

This chapter discusses the use of chaotic descriptors, such as dimension correlation, Lyapunov exponent and Hurst exponent, to train support vector machines. The World Health Organization, WHO, describes epilepsy as a chronic brain disease characterized by recurring seizures that affects people worldwide. Seizures are brief episodes of muscle contractions that could affect some part of the body or all of it, which sometimes are accompanied by loss of consciousness and control of bowel or bladder functions. Pattern recognition on electroencephalogram signals is a complex process that consumes an enormous amount of analysis time, even for professionals in the area. The extraction of certain features requires a previous processing involving the reconstruction of the dynamics of the system in the state space from a time series. Lyapunov exponents are the divergence rates of two close trajectories within an attractor with respect to time.
Implementation of Decision Tree for Making Decision of Claim Product from Steel Production

Product Claims is requests from consumers for products purchased from suppliers in accordance with agreements agreed by both parties. Products that have been claimed from consumers produce historical data sets that can be used as evaluations for producers to produce higher quality products. This study aims to process production data and shipment data then classify the types of products claimed based on the results of claim report from consumers. Data mining can be extracted information from a very large amount of data with specific methods to obtain information or new science. The method used in this study is the C4.5 algorithm method using the production code attribute as a claim or non-claim label attribute. This study produced a decision tree of 4 variables, there are thick of product, width of product, weight of product, destination of product, and type of product claim as label. This decision tree concept collects data which then calculates the value of entropy and gain to determine the rule. The conclusion from this study is the C4.5 algorithm helps classify the product claims and form a decision tree that can provide information about production results and can ensure with consumers related to product limits that may be claimed according to the agreed agreement. Evaluation of the results obtained that the algorithm C4.5 is 99.9% accuracy.
A New Deep Model for Detecting Multiple Moving Targets in Real Traffic Scenarios: Machine Vision-Based Vehicles

When performing multiple target detection, it is difficult to detect small and occluded targets in complex traffic scenes. To this end, an improved YOLOv4 detection method is proposed in this work. Firstly, the network structure of the original YOLOv4 is adjusted, and the 4× down-sampling feature map of the backbone network is introduced into the neck network of the YOLOv4 model to splice the feature map with 8× down-sampling to form a four-scale detection structure, which enhances the fusion of deep and shallow semantics information of the feature map to improve the detection accuracy of small targets. Then, the convolutional block attention module (CBAM) is added to the model neck network to enhance the learning ability for features in space and on channels. Lastly, the detection rate of the occluded target is improved by using the soft non-maximum suppression (Soft-NMS) algorithm based on the distance intersection over union (DIoU) to avoid deleting the bounding boxes. On the KITTI dataset, experimental evaluation is performed and the analysis results demonstrate that the proposed detection model can effectively improve the multiple target detection accuracy, and the mean average accuracy (mAP) of the improved YOLOv4 model reaches 81.23%, which is 3.18% higher than the original YOLOv4; and the computation speed of the proposed model reaches 47.32 FPS. Compared with existing popular detection models, the proposed model produces higher detection accuracy and computation speed.
Penggunaan Metode Guide Note Taking Berbantu Media Video dalam Pembelajaran Muatan Bahasa Indonesia di Sekolah Dasar pada Masa Pandemi Covid-19

This research aims to know the video-assisted note-taking guide method to improve the listening skills of elementary school 1st graders during the covid-19 pandemic. This study is a class action study. Teachers and elementary school 1st graders were the data sources for the study. The data collection techniques used are observations, interviews, and tests. The research procedures in each cycle are (1) action planning, (2) implementation of actions, (3) observations, (4) reflections. To analyze data using qualitative descriptive techniques and comparative descriptive. Based on the research that has been carried out, the research results can be known; namely, the improvement of listening skills of elementary school 1st-grade students after implementing the guide note-taking method assisted video. It is known that before being given the action, there are still many students who completed by 20%. After being given the action in cycle 1, students' grades increased by 75%. In this second cycle, all students have received complete grades.
Evaluation of Micro and Macro-level Coherence of Students Argumentative Essays in ESAWEEK Writing Competition

One of the most important aspects of writing a meaningful text is to achieve its coherence within the text. This study evaluated students' argumentative essays' micro and macro-level coherence in the ESA WEEK writing competition. This research is a qualitative study in the form of a discourse analysis approach by using the theory through the lens of micro and macro-level coherence (Thornburry, 2005). This study reveals several findings. Almost all the texts are sufficient to have good logical relations. The most frequent connections are additive and causal ties. In terms of logical relationships, the most frequent pattern in the texts was the zig-zag pattern, and the rarely used was the multiple rheme pattern. Even though all patterns were in texts, some rogue sentences were still found in each text. The keywords could represent the texts' topic or theme in almost every paragraph. It made readers guess the main issue quickly. Some writers could build a schema well, but others could not. Most of the texts organized in argumentative essays structure has Introduction-Body-Conclusion. This study can be used as a consideration to write meaningful and coherent text and references in the same field of discourse analysis.
A Paradox of Information Aggregation

After briefly exposing the great benefits that can be derived from information aggregation, the author makes the case that there is a paradox in how humans deal with it: They are good at aggregating information but bad at thinking about it. People are good at taking communicated information into account, using reliable cues to decide whom to trust and what to believe, and aggregating information from multiple sources. However, people have no abstract understanding of how this process of information aggregation works, and they tend to underestimate how well it works. Even though many institutions have evolved that take advantage of people’s ability to aggregate information, the author argues that these institutions appeared in spite of, rather than thanks to, the understanding of information aggregation.
WEBIST 2021 conference report

The International Conference on Web Information Systems and Technologies (WEBIST) was held online from the 26th to the 28th of October 2021. WEBIST is an annual conference that has been running for 17 years, bringing together researchers, engineers and practitioners interested in the technological advances and business applications of web-based information systems.
Prelims

Citation (2022), "Prelims", Chandra Das, R. (Ed.) Globalization, Income Distribution and Sustainable Development, Emerald Publishing Limited, Bingley, pp. i-xxiii. https://doi.org/10.1108/978-1-80117-870-920221046
Count-Based Exploration via Embedded State Space for Deep Reinforcement Learning

Count-based exploration algorithms have shown to be effective in dealing with various deep reinforcement learning tasks. However, existing count-based exploration algorithms cannot work well in high-dimensional state space due to the complexity of state representation. In this paper, we propose a novel count-based exploration method, which can explore high-dimensional continuous state space and combine with any reinforcement learning algorithms. Specifically, by introducing the embedding network to encode the state space and to merge the states with similar key characteristics, we can compress the high-dimensional state space. By utilizing the state binary code to count the occurrence number of states, we generate additional rewards which can encourage the agent to explore the environment. Extensive experimental results on several commonly used environments show that our proposed method outperforms other strong baselines significantly.
Two-Stream Retentive Long Short-Term Memory Network for Dense Action Anticipation

Analyzing and understanding human actions in long-range videos has promising applications, such as video surveillance, automatic driving, and efficient human-computer interaction. Most researches focus on short-range videos that predict a single action in an ongoing video or forecast an action several seconds earlier before it occurs. In this work, a novel method is proposed to forecast a series of actions and their durations after observing a partial video. This method extracts features from both frame sequences and label sequences. A retentive memory module is introduced to richly extract features at salient time steps and pivotal channels. Extensive experiments are conducted on the Breakfast data set and 50 Salads data set. Compared to the state-of-the-art methods, the method achieves comparable performance in most cases.
Toward total-system trustworthiness

Considering how to achieve the long-term goal to systemically reduce risks.
An Assistance System for Deaf, Dumb, Blind, and Learning Disability Individuals

There is always some kind of barrier to impaired individuals in performing daily chores. It is difficult for them to perform and carry out even the simplest of tasks with ease. Whether it may be reading, writing, communicating, or studying, it becomes an impossible job for them to execute. Systems are separately designed for people with different types of impairments, but there lacks a system that can solve all problems as a whole. The purpose of our research work is to create one single system which is able solve all issues for deaf, dumb, blind, and learning disability individuals, which is not only simple to use but also provides accurate results. By using the developed system, people with different disabilities can perform simple daily activities with much ease than before. The developed system, which is a website, helps impaired individuals to read, write, communicate, etc. through various application programming interfaces (APIs). Key Words: Assistance system, application programming interface, deaf, dumb, blind, learning disability
Hiding health report in X-ray images to protect people privacy

X-rays images are a popular medical diagnoses method. These images are hard to read and to be understandable outside the medical community. However, the radiologist writes a report of the X-ray. This report can be understandable easily which, can impact the privacy of the patients’ medical records. To tackle this issue, in this work, X-ray image steganography algorithm based on Sudoku mathematical game is proposed. The method converts the image into triangular shaped blocks. Subsequently, the data is encoded and segmented two bits at the time to be embedded in the image. To evaluate the algorithm, eight different X-ray images from all X-ray types have been utilized for data hiding process. The algorithm has been compared to the lest significant bit (LSB) and the turtle shell algorithms using the mean square error (MSE) and peak signal to noise ratio (PSNR) performance metric. The proposed algorithm obtained higher bit per pixel embedding capacity with 2 bpp which is higher than LSB and turtle shell. Moreover, the algorithm has higher PSNR compared to turtle shell algorithm.
Esclerose múltipla e SARS - COV2 (trigger ou coincidência?): relato de caso/ Multiple sclerosis and SARS - COV2 (trigger or coincidence?): case report

Introduction: The new coronavirus, SARS-CoV-2, represents a strong global threat to public health in the 21st century.Even after two years since the first recorded case, knowledge about all the mechanisms of action of SARS-CoV-2 is still insufficient.Among the symptoms already reported in the literature, the most common neurological symptoms were encephalopathy, headache, anosmia and dysgeusia.It is recognized that the occurrence of viral infections can act as triggers for immunological diseases, as in the case of Multiple Sclerosis.For this reason, latent and central nervous system resident viruses, such as Epstein-Baar, Varicella and Herpes virus, have been implicated in the genesis of multiple sclerosis.Objective: To report a case of a patient who SARS-COV2 acted as a trigger for the triggering of secondary immunological events that culminated in the diagnosis of Multiple Sclerosis.Method: This is a descriptive study with retrospective data collection from the medical records of an elderly patient, admitted to a Tertiary Hospital in the Federal District -Brazil, with a previous diagnosis of COVID-19 infection.The present study was approved by the Research Ethics Committee of the Instituto de Gestão Estratégica de Saúde do Distrito Federal (IGESDF), under CAAE number 56465022.4.0000.8153.Being respecting before, during and after the development of the study the norms that deal with the development of research with human beings of the National Health Council.Case Report: The patient was 74 years old, male, brown, with a previous diagnosis of COVID-19 infection in August 2020, and the RT-PCR test was carried out at the time, which confirmed the diagnosis.The patient also previously had chagasic megaesophagus and chronic gastritis, with no other comorbidities.About seven days after admission, the patient developed mental confusion, progressive decline in motor and cognitive functions, requiring help to perform basic self-care activities.On the fifteenth day of hospitalization, the patient presented motor aphasia and intermittent sleepiness, being transferred to a tertiary care hospital for investigation of the case, 47 days after the sudden episode of right hemiparesis.After clinical discussion, pulse therapy with methylprednisolone was chosen for three days.After this therapy, the patient evolved with cognitive and motor improvement, corroborating hospital discharge with follow-up at the neuroimmunology outpatient clinic of the tertiary hospital in question.
Metric Learning-Based Unsupervised Domain Adaptation for 3D Skeleton Hand Activities Categorization

First-person hand activity recognition plays a significant role in the computer vision field with various applications. Thanks to recent advances in depth sensors, several 3D skeleton-based hand activity recognition methods using supervised Deep Learning (DL) have been proposed, proven effective when a large amount of labeled data is available. However, the annotation of such data remains difficult and costly, which motivates the use of unsupervised methods. We propose in this paper a new approach based on unsupervised domain adaptation (UDA) for 3D skeleton hand activity clustering. It aims at exploiting the knowledge-driven from labeled samples of the source domain to categorize the unlabeled ones of the target domain. To this end, we introduce a novel metric learning-based loss function to learn a highly discriminative representation while preserving a good activity recognition accuracy on the source domain. The learned representation is used as a low-level manifold to cluster unlabeled samples. In addition, to ensure the best clustering results, we proposed a statistical and consensus-clustering-based strategy. The proposed approach is experimented on the real-world FPHA data set.
Misleading Video Detection Using Deep Image Retrieval and Dual-stage Confidence Filtering

Computer vision technologies have recently been maliciously used to spread misleading information.Because of the low cost of video production, misleading videos have been used for attack ads, criminal fraud, and even political manipulation, which could undermine social progress.Hence, it is important to develop a system for detecting misleading videos that can help a fact-checking center detect misleading videos more efficiently.In this research, we propose a novel video retrieval system based on a deep convolutional neural network that extracts deep visual informatics to retrieve visually alike videos from annotated misleading videos.Moreover, we propose dual-stage confidence filtering that considers both video-and image-level retrieval.This is one of the latest studies on misleading video detection using video-level retrieval, and preliminary experiments demonstrate its superior retrieval performance, enabling it to be applied in real-world applications.