Reviewerly/exHarmony huggingface dataset:
High Capacity Secured Digital Watermarking for Copyright Protection

Computer Network provides a way to share and distribute files and information to the masses within a short span of time. With the advancement in the networking technology, many new ways to share the information is cropping up like mushrooms. With the growth in technology and ways to share information, security and authentication of the information becomes a major issue. There are many ways to secure the information shared, like Cryptography, which makes the information shared unintelligible and Steganography, which hides the data to be shared inside another digital medium like text, image, video or audio files. We propose a high capacity secured digital watermarking steganography by combining cryptography and steganography thus taking advantage of the merits of both Cryptography and Steganography. We achieve high capacity by compression and security by encryption.
Malaria Parasite Identification using Feature Based Recognition

Malaria is one in all the life threatening diseases. Diagnosis of diseases like malaria is very hooked in to the identification of parasites in blood. Various methods are applied for this process. The majority of all method uses machine learning to identify the malarial parasites. This method has shortcomings in long training time and also the must be retrained if a replacement data emerged. Among all of the other various methods that are used, identification using feature based recognition is likely to be rarely used. This method is powerful within the term that it doesn't require training process, but only an image sample from which the feature are visiting be extracted. During this paper, we design an identification process for blood parasites using one all told the famous local feature extraction algorithms, i.e. SURF (Speeded-Up Robust Features). In our experiment, we evaluate the system to spot Plasmodium parasites. During this experiment, we are focusing only on parasite’s gametocyte stage. Here, we use the system to spot whether or not the parasite is Plasmodium falciparum, Plasmodium malariae, Plasmodium ovale, or Plasmodium vivax.
Generating Video from Images using GANs

Generative adversarial networks are a category of neural networks used extensively for the generation of a wide range of content. The generative models are trained through an adversarial process that offers a lot of potential in the world of deep learning. GANs are a popular approach to generate new data from random noise vector that are similar or have the same distribution as that in the training data set. The Generative Adversarial Networks (GANs) approach has been proposed to generate more realistic images. An extension of GANs is the conditional GANs which allows the model to condition external information. Conditional GANs have seen increasing uses and more implications than ever. We also propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models, a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. Our work aims at highlighting the uses of conditional GANs specifically with Generating images. We present some of the use cases of conditional GANs with images specifically in video generation.
Dialogue as A Means of Developing Students' Communicative Literacy

The sphere of communication in general over the past two decades has attracted the attention of researchers. The nature of communication, its age and individual characteristics, mechanisms of course and change have become the subject of study by philosophers and sociologists, psycholinguists, specialists in the field of social child and age psychology. Most scientific research and psychological and pedagogical recommendations on the formation of communication skills are dedicated to childhood. Studies of the communicative skills of preschoolers were devoted to such scientists as A.V. Hawks, E.R. Saitbaev. The approaches to teaching communication, forming a communicative function are felt much more slowly than in other areas of pedagogy and psychology. This is because a child can be taught, for example, to draw (take his hand), but to physically help him speak is much more difficult. For graduates of schools it is necessary to be sociable, contact in various social groups, to be able to work together in different areas, preventing conflict situations or skillfully getting out of them. These skills should provide the young man with mobility, the ability to quickly respond in a changing world with a state of mental comfort, which provides emotional balance. In modern conditions, dialogue takes on a new meaning and quality, acting as the basic principle of the communicative content of education. A multicultural society, saturated with diverse communicative ties, involves not only the establishment of relations of cooperation, mutual understanding, but also the emergence of contradictions, polemic disputes. Therefore, the ability of school graduates to conduct a fruitful, effective dialogue in various fields of the sociocultural sphere, to learn the world not from monological (with a claim to absolute truth), but dialogically, pluralistically becomes the most important and communicative property. Meanwhile, observations of the experience of discussions, political meetings and rallies, business meetings, scientific conferences give reason to conclude that in many speeches there is no deliberation, depth and credibility of arguments, consistency and consistency of reasoning, compliance with ethical standards, flexibility of thinking and speed reactions. They still “see” the monopoly on truth, a special style of communication and belief with its monologue moral teachings and harsh, peremptory judgments. In this regard, communicatively-oriented education departs from the monologic way of teaching and reorientes to the dialogical one, which promotes the development of communicative properties among schoolchildren, namely: the ability to discuss, agree, argue, prove, agree (or disagree) [8]. In order for a modern graduate to possess these skills, it is necessary that he be taught this. This requires appropriate organization of the educational process of modern schools, lyceums and gymnasiums. In connection with the relevance of this problem, a research topic arises - Dialogue, as a means of developing students' communicative literacy.
AN ANALYSIS OF EXPRESSIVE SPEECH ACTS USED IN CRAZY RICH ASIAN MOVIE

This research studies about expressive speech act in Crazy Rich Asian movie, the objectives of the research are to describe (1) to analyze the type of expressive speech act found in Crazy Rich Asian movie and (2) to describe the S-P-E-A-K-I-N-G model used in Crazy Rich Asian movie. This research used theory from Searle (1985) and Hymes (1974) in analyzing the data. There are twelve expressive speech act mentioned by Searle; apologize, thank, condole, congratulate, complain, lament, protest, deplore, boast, compliment, greet, and welcome. This research used descriptive qualitative method. The researcher collected expressive speech act utterances as the data to be analyzed; in analyzing the data the researcher used S-P-E-A-K-I-N-G model. The results showed that there were 52 data of expressive speech act and only ten types of expressive speech found in Crazy Rich Asian movie, some of the expressive types appeared except expressive act of condole and boast. the researcher used SPEAKING model is to know how the meaning of the social context, the purpose of the interaction in detail and describe them into analysis text. From the data analysis it shows that the types of expressive speech act that oftenly come up are apologize, thank and compliment. It shows that the characters in the Crazy Rich Asian movie more showed politeness and friendly attitude to others.
Design and Implementation of Web-Based Personal Shopper Marketplace Application

The goal of the research was to simplify the process of buying and selling using personal shopper in the community more controlled, facilitate the community to find reliable personal shopper, as well as make the transaction with personal shopper more secure with third parties.The research method used is divided into problem analysis and evaluation.Problem analysis was obtained by the case study method.Evaluation is done by two approaches that are questioner and comparison with a similar application.The results achieved from this research is a web-based application that will facilitate the process of finding personal shopper and transaction with a personal shopper.The conclusions of this research can make the community more comfortable to find personal shopper also increase the level of trust in personal shopper transactions
A Conceptual Perspective for Identifying and Preventing Phishing Attacks

Phishing is the blend of social building and specialized ability intended to persuade an unfortunate casualty to give individual data, as a rule for the aggressor's fiscal increase. Phishing assaults demonstrate existing vulnerabilities in the frameworks because of the human factor. Phishing has turned into the most prominent practice among web offenders. Phishing assaults are progressively visit and refined. The effect of phishing is extreme and critical, as it can prompt the danger of fraud and money related misfortune. Numerous digital assaults are spread through instruments that abuse the shortcomings found in end clients, which makes clients the weakest component in the security chain. The issue of phishing is wide and there is no single slug answer for adequately moderate all vulnerabilities, so more procedures are regularly executed to alleviate explicit assaults. Phishing tricks have turned into an issue for clients of web based banking and web based business. In this record, we propose another way to deal with distinguish and avoid phishing assaults.
Mining and YouTube Data Analysis using Hadoop

Analysis of structured and consistent data has seen remarkable success in past decades. Whereas, the analysis of unstructured data in the form of multimedia format remains a challenging task. YouTube is one of the most popular and used social media tool. It reveals the community feedback through comments for published videos, number of likes, dislikes, number of subscribers for a particular channel. The main objective of this work is to demonstrate by using Hadoop concepts, how data generated from YouTube can be mined and utilized to make targeted, real time and informed decisions. In our paper, we analyze the data to identify the top categories in which the most number of videos are uploaded. This YouTube data is publicly available and the YouTube data set is described below under the heading Data Set Description. The dataset will be fetched from the Google using the YouTube API (Application Programming Interface) and going to be stored in Hadoop Distributed File System (HDFS). Using MapReduce we are going to analyze the dataset to identify the video categories in which most number of videos are uploaded. The objective of this paper is to demonstrate Apache Hadoop framework concepts and how to make targeted, real-time and informed decisions using data gathered from YouTube.
Detection of Crimes Using Unsupervised Learning Techniques

Data mining can be used to detect model crime problems. This paper is about the importance of datamining about its techniques and how we can easily solve the crime. Crime data will be stored in criminal’s database.To analyze the data easily we have data mining technique that is clustering. Clustering is a method to group identicalcharacteristics in which the similarity is maximized or minimized. In clustering techniques also we have different typeof algorithm, but in this paper we are using the k-means algorithm and expectation-maximization algorithm. We areusing these techniques because these two techniques come under the partition algorithm. Partition algorithm is oneof the best methods to solve crimes and to find the similar data and group it. K-means algorithm is used to partitionthe grouped object based on their means. Expectation-maximization algorithm is the extension of k-means algorithmhere we partition the data based on their parameters.
A Blockchain System For Digital Payment Transaction

In this modern times, there are various options for conducting financial transaction or payment.This is related to the rapid development of technology that can make payment transaction more easily and practically.The entry of technology can not be denied to affect all various aspects of human life, including in the case of buying and selling or the use of cash to the bill payment process.If we can see, In Indonesia many digital based financial services are present, such as e-wallet like Sakuku, LinkAja, Go-Pay, OVO, Jenius, Dana, etc.With this service, people can make payment transaction for various purposes with the existing balance in e-wallet.The use of e-wallet is considered more convenient and profitable.It is considered to be more convenient because it is sufficient to carry out all transaction only from mobile phone.It's profitable because usually you will be offered lots of discounts and cashback from merchants who work with e-wallet providers.According to survey conducted by Databooks.co.id, this is the most 10 frequently used e-wallet in Indonesia 2020.
Fuzzy database for medical diagnosis

A challenge of working with traditional database systems with large amounts of data is that decision making requires numerous comparisons. Health-related database systems are examples of such databases, which contain millions of data entries and require fast data processing to examine related information to make complex decisions. In this thesis, a fuzzy database system is developed by integration of fuzzy inference system (FIS) and fuzzy schema design, and implementing it by SQL in three different health-care contexts; the assessments of heart disease, diabetes mellitus, and liver disorders. The fuzzy database system is implemented with the potential of having any form of data and tested with different types of data value, including crisp, linguistic, and null (i.e., missing) data. The developed system can explore crisp and linguistic data with loosely defined boundary conditions for decision-making. FIS and neural network-based solutions are implemented in MATLAB for the mentioned contexts for the comparison and validation with the dataset used in published works.
The Benefits of Cloud Computing for Children with Special Educational Needs in Education or Using Cloud Computing as Assistive Technology

The paper reveals the benefits of cloud-based education technologies and the possibility of using them in inclusive education for equal access to it.Basic concepts related to cloud technologies are discussed.Some strategies for the use of cloud technologies in children and pupils with special educational needs as assistive technologies are mentioned.
Preface

The 6th International Conference on Software Engineering &amp; Computer Systems (ICSECS’19) organised by Faculty of Computing, Universiti Malaysia Pahang , is a biennial conference series that serves as a forum for international community of researchers, practitioners and industries in subfield of Software Engineering and Computer Systems. This year it held in Vistana Kuantan City Center, Kuantan, Pahang, Malaysia from 25-26 September 2019 with the theme of IR 4.0: Eyeing The Future. We are profound to promote the conference to a wider community by contributing the matters of public concern and future applications in the fields of software engineering and computer system with IR 4.0. Three keynote speakers were invited based on their expertise areas. The first keynote speaker was Professor Dr. Ts. Kamal Zuhairi bin Zamli from Universiti Malaysia Pahang. As a well-known professor in software testing , he delivers a speech on “Optimization via Meta/Hyper Heuristics: Achievements &amp; Prospects for the Future”. The second keynote speaker was Professor Dr. Yuko Hatano from University of Tsukuba, Japan. She delivered her keynote speech on “Supercomputing by the Oakforest of University of Tsukuba Regarding the Flow in Porous Media”. The last speaker was Dr. Ishkandar bin Baharin, the President of Malaysia Robotics and Automation Society (MyRAS). His speech ws on “Bridging the Robotics and Automation Gap for IR 4.0 Adoption for local SMEs and the role of Local Academia”. This proceeding book is a compilation of selected papers that are submitted and presented in the conference. The content of the proceeding book is structured based on 4 conference tracks. The track has emphasized on the following topics include, but not limited to: Track 1: Internet of Things, Big Data and Autonomous Robots Track 2: Simulation and Virtual/Augmented Reality Track 3: Additive Manufacturing and System Integration Track 4: Cyber-Security and Cloud Computing The editors would like to extend their gratitude to all reviewers and program committees for the hard works. They would also like to thank to all presenters and participants for sharing their research and innovation outcomes in this conference. In addition, their special appreciation is dedicated to the sponsors, namely Malaysian Communications and Multimedia Commission, UMP Holdings, UMP Advance Education, Purnama Sdn. Bhd., Optik Prima Sdn. Bhd., KARAR Solutions Sdn. Bhd., Brilliant Grid Resources and Kafe Bonda Habsah Universiti Malaysia Pahang. Last but not least, the editors would like to thank to all for their enormous supports in this conference.
On the spatial representativeness of point station measurements for comparison with WRF estimates

&amp;lt;p&amp;gt;Solar energy proves to be an interesting alternative to conventional sources based on the burning of fossil fuels. However, it shows a high short-term variability that makes its integration into the electricity mix difficult. To facilitate this integration, reliable short- and medium-term forecasts become highly necessary. To respond to this demand, solar radiation forecasting models have emerged. Among them, Weather Research and Forecasting (WRF) has become particularly promising and has shown good performance at different temporal and spatial scales. The performance of these models is usually assessed by comparing their estimates with point measurements at selected stations. This comparison is hampered by the difference in spatial dimensions between the model estimates (representative of a given area) and the station (point) measurements. This difference introduces a certain error in the forecast, mainly related to the short-scale variability of cloudiness. Despite being essential to understand model validation, this issue has not been sufficiently investigated.&amp;amp;#160;In this framework, the present study analyzes the effect of the spatial representativeness of point measurements when used to validate model estimates. For this purpose, a specific one-month measurement campaign was conducted, deploying seven pyranometers in the vicinity of the city of Badajoz, Spain. To ensure their intercomparability, all pyranometers were calibrated with respect to a reference pyranometer previously calibrated by the World Radiation Center in Davos, Switzerland. Solar radiation was measured at a 1-minute basis to record the short-term variability due to cloudiness. Two series were constructed with these data, one corresponding to a selected station and the other to the average of the seven stations.&amp;amp;#160;These series of measurements were compared with the estimates provided by the WRF model for the same period and location. A configuration with two nested domains of 27 km and 9 km was used. Model performance showed better agreement when averaging was used instead of individual measurements, with RMSE improving from 89 W/m&amp;amp;#178; to 77 W/m&amp;amp;#178;. Cloudy cases contributed the most to the differences between station measurements and model estimates, showing an RMSE greater than 100 W/m2, more than three times higher than the RMSE for clear cases (about 33 W/m2). The difference between the stations and the model for cloudy cases is reduced from 125 W/m2 to 107 W/m2 when averaged measurements are considered instead of single station measurements. This study contributes to the understanding of the representativeness of point station measurements for validation and comparison with WRF estimates.&amp;amp;#160;Acknowledgments. This work is partially funded by FEDER/Ministerio de Ciencia, Innovaci&amp;amp;#243;n y Universidades-Agencia Estatal de Investigaci&amp;amp;#243;n of Spain through project RTI 2018-097332-B-C22, and by Junta de Extremadura-FEDER through project GR18097.&amp;lt;/p&amp;gt;
Computed tomography noise reduction based on total variation minimization and morphological component analysis

X-ray Computed Tomography (CT) scans, while useful, emit harmful radiation which is why low-dose image acquisition is desired. However, noise corruption in these cases is a difficult obstacle. CT image denoising is a challenging topic because of the difficulty in modeling noise. In this study, we propose taking an image decomposition approach to removing noise from low-dose CT images. We model the image as the superposition of a structure layer and a noise layer. Total Variation (TV) minimization is used to learn two dictionaries to represent each layer independently, and sparse coding is used to separate them. Finally, an iterative post-processing stage is introduced that uses image-adapted curvelet dictionaries to recover blurred edges. Our results demonstrate that image separation is a viable alternative to the classic K-SVD denoising method.
Review of &amp;quot;Observation and explanation of spurious seismic signals emerging in teleseismic noise correlations&amp;quot;

This study investigates the source of spurious arrivals in ambient noise crosscorrelation functions calculated over teleseismic differences.The authors explain that such spurious seismic arrivals can be the result of the interference between seismic phases that have time delays that are 'quasi-stationary', that is, their arrival time difference does not vary strongly with source distance.This effect can occur even if the phases do not share a ray path.The authors use two seismic arrays to demonstrate an example involving the P and PKPab phases.In general, this discussion paper is a very nice contribution that will be of interest to a wide audience.I have a few comments that I believe should be addressed before publication, but these are probably quite minor.
A robust approach to detect video-based attacks to enhance security

Face authentication has become widespread on smart devices and in various applications these days. Real time detection of human faces in video surveillance systems is challenging due to variations in expression and background conditions. Thus, precise detection of spoofed faces is important to make such security systems robust against potential attacks. Several deep learning-based techniques involving the use of convolutional neural networks have proven to be excellent in detection of spoofed faces. In this paper, we have proposed the combined use of spatial and temporal information from facial images using CNN and long short-term memory networks. We have tested the approach on Idiap Replay Attack benchmark and compared the results with the application of pre-trained models like InceptionV3, VGGNet and ResNet models to detect replay attacks during video surveillance. Our approach proves to be robust and more efficient for detection of security breaches in real time situations.