Reviewerly/exHarmony huggingface dataset:
A Hybrid Neural Filter (HNF) Based on Adaptive Median and Weiner Techniques for Reducing Speckle Noise of Ultrasound Liver Tumor Images

Pre-processing of image is considered to be an important aspect medical image analysis in order to enhance its quality. Noise reduction is performed for improvement in the visual quality and removing redundant image values. Ways of pre-processing of image are a necessity, for removing the noise and for quality enhancement of the image. Before applying any approach on medical images, measures used to pre-process seem to be vital for limiting the abnormalities’ findings with no influence from background of the medical image. In this work, a filter is proposed based on Adaptive Median and Weiner Hybrid Neural Filter for noise reduction. The review of filtering techniques is implemented using MATLAB platform, followed by comparison of results with different filtering techniques to show the system effectiveness.
Deep Learning for Conversational Agent via Context Question Answering Model

The usage of a conversational device communicate knowledge on behalf of humans has attracted popularity and has been widely employed.As a consequence, this study seeks to analyze and improve the current business platform by providing the ability to offer immediate feedback by integrating Deep Learning to the conversational agent.Our research focuses on Artificial Intelligence (AI) to include Natural Language Processing (NLP) by offering a paradigm focusing on Deep Learning via Context Question Answering Model.This model enables the conversational agent to make wiser and stronger choices to be able to understand and generate their own answers to unknown queries.The testing will be performed in the quantitative approach using Black-box Testing and User Experience (UX) Testing.The Black-box Testing findings show that the average time taken for Deep Learning to produce a response is approximately two seconds.As far as accuracy is concerned, the conversational agent is capable of attaining the accuracy of 67.65 percent.The accuracy could be improved from time to time by providing more training to the model.Apart from this, the test results from User Experience Testing indicates that 84.3 percent of the respondents are satisfied with the conversational agent, whereas 86 percent of respondents would use this conversational agent again.The experiments provide positive outcomes and evidence of the progress of this research, with tremendous contributions to the area of artificial conversational agent.
Attention towards privacy notifications on web pages

A significant number of Internet users is unaware of privacy threats and they might not pay much attention to privacy notifications. It is imperative to create effective but, at the same time, non-distracting notifications about privacy policy of different web services. The article presents an eye-tracking study (N = 16) testing the effectiveness of different common types of privacy notification in capturing and retaining users' attention. Results showed that an non-intrusive notification presented at the top of the web page may be as effective as intrusive notifications in capturing attention. Results are discussed in terms of well-known effects of visual attention distribution on web pages. Present findings also offer practical conclusions for web service developers and publishers.
Implementation Success of an Indoor Navigation with Location-Based Augmented Reality

Confirming the adoption and use of information technology is central to human-computer interaction. User experience (UX) and Usage Continuance (UC) which latently signifying implementation success are vocal upon this mechanism. The advent of smart phones and mobile technologies such as Geographical Positioning System (GPS) has seen great proliferation in positioning outdoor location. However, GPS is incapable to work in an indoor environment (Hub, 2008) properly. Hence, for navigating indoor location one need to combine the existing mobile technologies and most of their components with location-based augmented reality. In this paper, implementation success of Indoor Navigation with Location-Based Augmented Reality named ‘GuideMe” is studied. The factors considered to evaluate implementation success are adopted from information systems and mobile computer interface literature. The objectives of this paper are to determine users’ experience (satisfaction) and usage continuance of GuideMe. Prior to that, GuideMe has been successfully designed and developed using IOS with tools (Unity engine, Placenote SDK and XCode to set up IOS packages), User feedbacks are gathered via questionnaire forms taken from 35 respondents who volunteer to experiment GuideMe. The volunteers are free to choose and navigate offices at buildings of University of Jeddah (UJ), with the help of GuideMe. The findings of the study conclude that: GuideMe has facilitated users to navigate and seek indoor location independently, conveniently and efficiently since they did not disturb or asked others for directions. Hence, this has improved users’ experience which indicates users’ satisfaction. The high value of mean for “behavioral intention to use” has shown users intend to continue using GuideMe. This is further verified and confirmed by “expectation confirmation” analysis. These findings have the potential to deploy GuideMe to large complexes, such as airports, shopping malls, schools, hospitals and libraries in a cost-effective manner.
Facial Recollection System in Live Stream

In our Facial Recollection system, the facial pattern of a person is being noted during Livestream using LBPH algorithms. Faces can be detected through our camera which then reveals their identity on the screen. Faces can be identified through the collection of the previous image of the person and checking with spatial structure and arrangement. Hence we can then able to track the person of our interest. Simultaneous detection and identification of each and every individual within the frame video boundary can be made possible. Hence the image is then used to detect faces visible in the camera and then training the images and are stored in a database with a name given by the user.
Development of the Creativity of Students in the Context of the Problematic Teaching

This article was written with the aim of the development of creative thinking of students in the educational process in the context of problem-based learning. The article defines the following tasks as to reveal the essence, content, basic structural components of the student’s creative thinking development, to determine their relationship; to develop a pedagogical model for the effective development of creativity and creative thinking of students; identify and justify pedagogical conditions that ensure the optimality of the development of creative thinking of students in the process of problem-based learning; to carry out an experimental verification of the constructed model for the development of creative thinking of students. The article consists of from introduction, literature survey, methodology, recommendation, discussions, and conclusion.
Semi-supervised Learning in Camera Surveillance Image Classification

Recognizing pedestrian attributes in camera surveillance images is a very hard problem, due to the lack of high-quality labeled data. In the field of deep learning the semi-supervised learning paradigm provides a possible answer to this problem. We propose a novel semi-supervised model that we call Binary Mean Teacher, tailored for binary classification task of detecting the presence of wearable objects. We train our model in a traditional scenario with a randomly initialized model, but we also explore fine-tuning a model pretrained on a large-scale image dataset. The performance of our model is compared to strong supervised baselines trained or fine-tuned using our dataset and the same augmentation strategy as in our model. We evaluate the impact of various augmentation strategies commonly used in deep learning on the performance of models in our binary classification task. Using only 1000 labeled training images, randomly initialized Binary Mean Teacher model achieves roughly 90% classification accuracy compared to 75% accuracy of randomly initialized supervised model that does not use any augmentations. When both Binary Mean Teacher and the supervised model are pretrained using the ImageNet dataset, and augmentations are used for both models, the Binary Mean Teacher achieves 92% accuracy compared to 90% accuracy of the supervised model.
University Decission Support System for Promotion Strategy Using Topsis Method

Campus or college is a place of education for people to seek knowledge. One of the essential components of campus sustainability is the existence of students. For this reason, each campus must have the right strategy in carrying out promotions, especially during the Covid-19 Pandemic as it is today. Because in a pandemic like this, many people are inevitably aware of the economy and health. So, the campus must have a strategy where the community continues to respond to the campus's promotions. Among the several promotional strategies to be right on target in this pandemic are conducting webinars, television broadcasts, radio broadcasts, advertisements on social media, placing banners, distributing pamphlets, and distributing campus marches. In carrying out promotions, wherever possible, keep prioritizing health by reducing the risk of covid-19 transmission. The promotion strategy in this pandemic must also pay attention to several factors for its implementation including available costs, human resources involved, the location of the activity implementation, the number of activities, and the risks associated with covid-19. From several promotional strategies and factors that support the smooth running of activities, a decision support system can be made that can help determine the campus promotion strategy. Decision Support Systems made using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method approach.Furthermore, the developing of decision support systems using the waterfall system development method. Based on the results and discussion of this study, it concluded that the TOPSIS method could be used to determine campus promotion strategies during the Covid-19 pandemic by building a decision support system. The results of determining the promotion strategy with a decision support system obtained the greatest value lies in V4, which means that the strategy for using social media is the most suitable for campus promotion during the Covid-19 pandemic.
Methodize of Block Chain Technology

A blockchain is a collection of records with cryptography. All records or exchanging information are checked by all members. Once enter the information’s into the blockchain record means, it will be never changed or deleted. Distributed and Peer authentication technology is used in blockchain technology. A blockchain is maintaining a record in decentralized with high secure manner, so all users are easily accessed. Blockchain technology is mainly used in financial sectors and banking sectors. This new technology mainly used in IoT, Gaming and entertainment sector, Government Public services, online fund transformation, etc. In today world, blockchain technology mainly used exchanging the documents and money. In blockchain technology is based on the bit coin money transaction. This bit of coin technology will be reducing the intermediate of the money transformation. The main scope of the blockchain technology is both money related and non-financial world.
Introduction to Data Mining

This chapter gives a brief overview of the field of Data Mining. The topics covered are the data explosion, the knowledge discovery process, applications of data mining, labelled and unlabelled data, supervised learning: classification and numerical prediction, and unsupervised learning: association rules and clustering.
CULTURA, EXPRESIE A VOCAȚIEI OMULUI CA PREOT AL CREAȚIEI

Culture, expression of man's priesthood in creation 1 .After God created everything that exists, He placed the first human beings in this environment.So that, the man, was brought to the end into being, as a completeness and sense of all creatures; he become God's co-worker on earth and spokesman creation before the Creator, in solidarity with the whole nature and responsible for it in the face of its Creator.This one, however, has failed in his mission of "priest of creation", because he did not understand that this holy purpose can't be achieved autonomously, without God.Thus Adam altered in the same time with human nature everything that was ontologically related to it, all creation.Alone and exterior to himself, he could no longer restore everything, so the redemption from sin was also added to the mission of the incarnation of the Son of God, and Jesus Christ, succeeded what Adam did not.What's left is for man to unite with the Son of God incarnate, and thus to spiritualize the whole universe.There is, therefore, a natural tendency in man to transfigure the creation and to accommodate it to a higher idea than mere survival.This can be seen from the most rudimentary social settlements to the big metropolises.Man does not simply live in the natural, wild environment, but organizes it, adapts it to live there.More than this, the settlements are not just utilitarian, they also pursue a certain aesthetics.It is precisely this fact that man tends to turn nature into something more than subsistence.
Analisa Data Profil Pelanggan Menggunakan Algoritma FP-Growth

The number of bouquet orders is quite varied sometimes increased and decreased. The number of hikes certainly carries the goodness but the amount of decline certainly has an impact for Wawa Florist because it can not fulfill the number of bouquet order. The purpose of this research is to know how Data Mining techniques with Fp-Growth algorithm methods and designing the grouping of customer data of Wawa Florist with the FP-Growth algorithm method to obtain better and more effective analysis results. The result of the order data of the wreaths in Wawa Florish can be obtained which area information most booked wreaths, the most ordered bouquet of flowers are: D02 (Lubuk Buaya), D04 (Lubuk Minturun), D01 (Pariaman) and D03 (Lubuk Alung ). These results are obtained based on the appearance of the itemset of the bouquet booking data. Meet minimum confidence 60%.
QR Codes &amp; the Library

&lt;p&gt;Outline&lt;/p&gt; &lt;p&gt;· What are QR Codes?&lt;/p&gt; &lt;p&gt;· How do they work?&lt;/p&gt; &lt;p&gt;· Uses of QR Codes&lt;/p&gt; &lt;p&gt;· How Libraries can and do use QR Codes?&lt;/p&gt; &lt;p&gt;· How to create your own QR codes&lt;/p&gt;
Mineralogy of Lower Diyala River Sediments Northeastern Baghdad

The purpose of this study is to determine the mineralogical composition of Lower Diyala River, northeastern of Baghdad, and attempt to define the sources of sediments. 10 samples were collected from Lower Diyala River from the area between Himreen Dam to south Baghdad, where these samples are dealings by the heavy liquid to separated into light and heavy minerals. The light minerals contents are composed mainly of quartz, feldspars, and rock fragments. The main rocks fragments consist of; igneous, metamorphic, carbonate, chert rock fragments, and evaporites. The heavy minerals contents are opaques minerals, chlorite, amphiboles, pyroxenes, epidote, zircon, garnet, muscovite, biotite, kyanite, tourmaline, staurolite, and rutile. The source area of these sediments is the outcrops of Upper Miocene-Pliocene rocks at the northeastern part of the studied area, igneous, and metamorphic rocks in the northeastern Iraq and Iran.
EXPERIENCES OF PILOTING THE LEARNING BY DEVELOPING ACTION MODEL IN A COMPUTING SCIENCE CONTEXT

This article describes the piloting of the Learning by Developing (LbD) action model in the UK.The purpose of the pilot is to study how a pedagogical method based on the LbD can be introduced in computing students in the UK.The LbD action model has long been used in Finland at Laurea University of Applied Sciences (Laurea), but now the LbD has been piloted in a UK-based institution in a projectbased study module.The main purpose was to study, through self-assessment, how the competence of computing students develops during the study module.In addition, the study reviews an action research analysis of the responses of lecturers and the project client.In addition, the pedagogical expert of the institution has been interviewed as background research to obtain sufficient information about the initial situation at Robert Gordon University (RGU).
A road map for digital forensics research: a novel approach for establishing the design science research process in digital forensics

Compared to other well-established scientific fields computer forensics (CF) is still evolving as a new scientific field. CF lacks standardisation in various aspects including process models, datasets, procedures, techniques, and formal research methodologies. Consequently, progress in the establishment of CF as a scientific field has been hindered. Therefore, this paper aims to address one of such issues concerning the lack of standardisation, namely the absence of formal research methods in CF. Our study adds to the body of knowledge by filling the gap that there does not currently exist a well-established research methodology in CF. To this end, we borrow a well-established research methodology from the domain of IS, namely Peffers et al.'s (2006), adapt and extend it and make it relevant to research studies in CF. Our study sets a precedent for other researchers to identify, adapt, extend and apply other well-established research methods to studies in CF.
Innovation in batik tulis with ICT technology for sustainability design

The development of ICT technology (Information and Communication Technology) is currently very rapid, creating tools to support human life.Information and communication technology are devices consisting of hardware, software, processes, and systems used to help the communication process between humans be successful.The problem in creating batik tulis clothing is that there have not been many collaborations with ICT technology so far.Both ICT technology and the creation of batik tulis are still running, respectively.On the other hand, today's human needs for clothing are not limited to body coverings and fashion but also develop as a means of security, communication, and data storage.The method used in this paper is qualitative-experimental using the design creation stage.The finding of this creation is that human needs for batik are also increasing; it is necessary to create a creation that combines ICT technology and batik tulis to answer human needs in the era of 4.0.This creation can be a solution to human needs for security, communication, and data storage that is integrated into a batik tulis cloth.
Camera-Based Bi-lingual Script Identification at Word Level using SFTA Features

Most of the documents in various application areas like Government, Business and Research is available in the form of bi-lingual/multi-lingual text document. The multilingual documents are captured from video/camera for identification of script of the text document for automatic reading and editing. In this paper, an attempt is made to address the problem of script identification from camera captured document images with SFTA features. The input image is decomposed into a group of binary images by applying TTBD with fixing the number of the threshold as t n =3 empirically, on each decomposed binary image, Box Count, Mean Gray Level, and Pixel Count are extracted to form the feature vector. This feature vector is submitted to K-NN classifier to identify the scripts of the input document image. In all 10 scripts of the indian languages are considered along with common English language as bi-lingual documents. The novelty of the paper is that 7 features are selected as potential features to obtain the highest accuracy. Features like Box Count (3), Mean Gray Level (2), and Pixel Count (2) have obtained the 87.02% recognition accuracy for English and Hindi Script combinations for the dataset collected and encouraging results for other combinations. These 7 potential features were selected using the technique named as feed-forward feature selection, from the set all 18 features.
Tweet Acts: A Speech Act Classifier for Twitter

Speech acts are a way to conceptualize speech as action. This holds true for communication on any platform, including social media platforms such as Twitter. In this paper, we explored speech act recognition on Twitter by treating it as a multi-class classification problem. We created a taxonomy of six speech acts for Twitter and proposed a set of semantic and syntactic features. We trained and tested a logistic regression classifier using a data set of manually labelled tweets. Our method achieved a state-of-the-art performance with an average F1 score of more than 0.70. We also explored classifiers with three different granularities (Twitter-wide, type-specific and topic-specific) in order to find the right balance between generalization and overfitting for our task.
Geometry in active learning for binary and multi-class image segmentation

We propose an active learning approach to image segmentation that exploits geometric priors to speed up and streamline the annotation process. It can be applied for both background–foreground and multi-class segmentation tasks in 2D images and 3D image volumes. Our approach combines geometric smoothness priors in the image space with more traditional uncertainty measures to estimate which pixels or voxels are the most informative, and thus should to be annotated next. For multi-class settings, we additionally introduce two novel criteria for uncertainty. In the 3D case, we use the resulting uncertainty measure to select voxels lying on a planar patch, which makes batch annotation much more convenient for the end user compared to the setting where voxels are randomly distributed in a volume. The planar patch is found using a branch-and-bound algorithm that looks for a 2D patch in a 3D volume where the most informative instances are located. We evaluate our approach on Electron Microscopy and Magnetic Resonance image volumes, as well as on regular images of horses and faces. We demonstrate a substantial performance increase over other approaches thanks to the use of geometric priors.
Cryptographic quantum metrology

We develop a general framework for parameter estimation that allows only trusted parties to access the result and achieves optimal precision. The protocols are designed such that adversaries can access some information indeterministically, but only at the risk of getting caught (cheat sensitivity); under the assumption that the adversary can access the channel only once, then the protocol is unconditionally secure. By combining techniques from quantum cryptography and quantum metrology, we devise cryptographic procedures for single-parameter estimation when an arbitrary number of parties are involved.