Reviewerly/exHarmony huggingface dataset:
Structure of CloudSim Toolkit with Cloud

Cloud computing is a worldview and works on huge scale with circulated registering, that is a repackage of currently using different ideas/advances, for example, utility processing, Grid figuring, Autonomic figuring, IoT innovations, etc. Cloud computing works on the IoT innovations for conveyance the assets to clients on the basis of their interests. As, it creates innovation, different type of issues, for example, encryption, vitality the executives, asset provisioning, dependability should be tended. The goal of said work is to think about the reenactment toolbox is known as CloudSim and a portion of exploration which already addressed the previous issues and utilizing its toolbox. Main portion of the examinations after analysis depend on reproduction in Cloud and utilizing genuine frameworks on the cloud, for example, Amazon EC2, Google, which cost with tedious assignments and furthermore enormous of cloud assets. That requires for accomplish practical outcomes and unreasonable for further analysts. This paper surveys some reenactment apparatuses and thinks about them as far as their hidden structure, programming language, GUI, accessibility, cost demonstrating, vitality displaying, reproduction time, league strategy and its respective communication model.
Human Behavior Recognition Based on Deep Learning

With the continuous advancement of technology, human behavior recognition, as an important scientific research in the field of computer vision, has important research in many fields such as intelligent surveillance, smart home, virtual reality. In the current complex environment, traditional manual methods have been difficult to meet the requirements of high recognition accuracy and applicability. The introduction of deep learning has brought new development directions for behavior recognition. This article mainly summarizes behavior recognition algorithms based on deep learning. Firstly, the research background and significance of behavior recognition are introduced, and then the traditional learning methods and deep learning methods of behavior recognition are discussed and analyzed respectively, and then the structure of algorithmic models and commonly used public data sets are introduced, and finally, the advantages and disadvantages of the various research directions of human behavior recognition methods based on deep learning are analyzed and some suggestions are given in the future research and expansion directions.
Segmentation of thyroid nodules using Improvised U-Net Architecture

Thyroid nodules are considered as most common disease found in adults and thyroid cancer has increased over the years rapidly. Further automatic segmentation for ultrasound image is quite difficult due to the image poor quality, hence several researcher have focused and observed that U-Net achieves significant performance in medical image segmentation. However U-net faces the problem of low resolution which causes smoothness in image, hence in this research work we have proposed improvised U-Net which helps in achieving the better performance. The main aim of this research work is to achieve the probable Region of Interest through segmentation with better efficiency. In order to achieve that Improvised U-Net develops two distinctive feature map i.e. High level feature Map and low level feature map to avoid the problem of low resolution. Further proposed model is evaluated considering the standard dataset based on performance metrics such as Dice Coefficient and True positive Rate. Moreover our model achieves better performance than the existing model.
Uncertainty-Aware Model for Reliable Prediction of Sepsis in the ICU

Predicting the onset of sepsis from clinical data is challenging, as physiological and laboratory measurements are sampled at different frequencies and missing data are not randomly distributed.Our team ("CRASHers") propose a two-model approach, where the first predicts a probability of sepsis and the second estimates the uncertainty of these predictions.We then optimize a "decision rule" using both the probability and uncertainty to make the final prediction.A set of derived features was used to train a Gradient Boosting Machine (GBM) classification model to predict sepsis (within 6 hours).A second GBM regression model was trained to estimate the uncertainty of those predictions using a different set of derived features.Optimal hyperparameters for both models were determined using Bayesian optimisation with 5-fold cross validation (using 70% records from each training set).The outputs from both models were then combined using logistic regression (using 15% of records available) to re-calibrate the probability of sepsis.Due to an error in setting up the test environment for our entries, we did not obtain a valid score in the hidden test set.The combined model was evaluated on the remaining 15% of records available for training (i.e., our validation set).Our uncertainty-aware approach achieved a Utility score of 0.412 on our validation set.
Pose Invariant Hand Gesture Recognition using Two Stream Transfer Learning Architecture

The hand gesture detection problem is one of the most prominent problems in machine learning and computer vision applications. Many machine learning techniques have been employed to solve the hand gesture recognition. These techniques find applications in sign language recognition, virtual reality, human machine interaction, autonomous vehicles, driver assistive systems etc. In this paper, the goal is to design a system to correctly identify hand gestures from a dataset of hundreds of hand gesture images. In order to incorporate this, decision fusion based system using the transfer learning architectures is proposed to achieve the said task. Two pretrained models namely ‘MobileNet’ and ‘Inception V3’ are used for this purpose. To find the region of interest (ROI) in the image, YOLO (You Only Look Once) architecture is used which also decides the type of model. Edge map images and the spatial images are trained using two separate versions of the MobileNet based transfer learning architecture and then the final probabilities are combined to decide upon the hand sign of the image. The simulation results using classification accuracy indicate the superiority of the approach of this paper against the already researched approaches using different quantitative techniques such as classification accuracy.
The DAO Hacked

In Chapter 5 , we discussed the concept of decentralized organizations and the modus operandi of a DAO. Here, we want to highlight a historic moment leading to the creation of the first DAO, and how it eventually got hacked. Our discussion begins with a fresh perspective on decentralized organizations from Buterin, and leads into the story of Slock.it, the company at the heart of the DAO revolution. Then, we present some code that made The DAO dysfunctional: pieces of the smart contract relevant to the vulnerability, the conditions that allowed repetitive withdrawals from The DAO, and the exploit itself. We conclude the chapter by talking about the consequences of this hack: the debate about hard versus soft forks, and the creation of Ethereum Classic.
Quantum Walks in Quasi-Periodic Photonics Lattices

We present construction rules for a new class of quasiperiodic photonics lattices (QPL) to realize localized quantum walks (LQWs) deterministically.The new quasiperiodic structures are constructed symmetrically with Fibonacci, Thue-Morse, and other quasiperiodic sequences.Our construction rules allow us to build the symmetrical quasiperiodic photonics lattices.As a result, LQWs with symmetrical probability distributions can be realized in these QPLs.Furthermore, the proposed QPLs are composed with different waveguides providing both on-and off-diagonal deterministic disorders.We show LQWs in the proposed QPLs are highly programmable and controllable.
Object-specific face authentication system for liveness detection using combined feature descriptors with fuzzy-based SVM classifier

The object face liveness detection for genuine face recognition and user authentication is a difficult task and day to day it becoming an interesting tricky in real time vision and security applications. Since many decades, various authors have proposed new technique and methods and developed but still the system has to improve to recognise the genuine object faces from spoofing objects with increasing in accuracy. However, in account of existing methods were fails in finding of genuine objects from sample objects and individual differences between them. The ordinary classifier cannot simplifies well to various kind of objects in different directions especially in case of blur images. In order to overcome his problem, we proposed an object-specific face authentication system for liveness detection using combined feature descriptors with fuzzy-based SVM classifier, allows to select specific area from whole object, extract features from specific area of object leads reduction in processing time and complexity in feature extraction. Later the system recognise respective faces, finally it checks for live objects with the help of fuzzy logic-based SVM classifier. With these proposed system, makes it practical to train well performed individual object to its certain face with liveness detection and achieved improvement in performance and accuracy.
Developing Efficient Web-Based Xml Tool

The “XML TOOL” is used to design and develop the efficient web based application. The main objective of this application is to design and develop web based software for creation of XML, DTD, Schema and Validation of XML against a DTD or Schema, thereby reducing the discrepancies of the existing manual system which is quite inefficient and time consuming. The user can create XML by giving the name of the file, the names of the tags, description of tags, its sub tags, the attributes and the various other values. The user can create DTD and XSD by providing values for the tag names, the sub tags names, the attribute names and the various rules associated with it. The XML, DTD and XSD files are created automatically based on the values provided by the user. The user can check if the XML is well formed or not. The user can manually edit the XML, DTD or XSD that is auto created. The user can validate the XML against either a DTD or an XSD. If the XML is valid then a success message is be displayed in green color. If the XML is either not well formed or not valid, then the associated errors are displayed in red color.
A novel LSB-based RDH with dual embedding for encrypted images

A novel reversible data hiding technique for encrypted images is proposed in this paper. Encryption helps achieving privacy which is a necessity for sensitive imagery such as medical and military images. In encrypted domain data embedding capacity remains a big challenge. A dual embedding scheme is proposed to enhance the additional data hiding capability. The general architecture includes a content owner, a data hider, and a receiver. This scheme is subjected to work on 512 × 512 grey-scale image. The encryption is done using a random 512 × 512 key matrix followed by a two phase data hiding technique to embed bits differently in odd and even blocks. Experiments are conducted in different block sizes in the embedding phase to achieve maximum data hiding potential. Experimental results show a huge increase in embedding capability. The error rate has significantly reduced over 50% in the recovered image and visual quality and image recovery is better than the existing methods.
Hate Speech

Abstract This chapter highlights hate speech, which is abusive speech that targets members of certain groups—typically minority groups—including racial groups, ethnic groups, religious groups, and groups defined on the basis of sexual orientation. Most groups protected by hate speech legislation are groups that have been subjected to past discriminatory treatment. However, the restrictions on hate speech legislation have often been expanded and the proper scope of the groups that deserve protection have been subject to a fierce debate. While much of hate speech can be legitimately prohibited by standard recognized legal exceptions to free speech, such as fighting words or incitement to violence, other forms of hate speech cannot. To address the prevalence of hate speech as such, many legal systems prohibit some forms of hate speech and impose criminal or civil sanctions for violating such prohibitions. Those systems need to address the serious challenge of defining the category of hate speech, draw its boundaries, and determine the sanctions. The chapter then explores the moral and political arguments for and against the legal regulation of hate speech as such or, at least, some sub-categories of hate speech.
Priority Analysis of Community Complaints through E-Government Based on Social Media

Now days in this modern era of globalization, the development of information and communication technology has offered solutions to improve public service performance. This also provides new innovations for the government to be able to offer the latest solutions in improving its services. This report discusses research based on Presidential Instruction No. 3 of 2003 concerning national policies and strategies for the development of e-Government, various regions began to develop the application. The case is discussed about the application of e government in Tangerang city. LAKSA is a feature for the people of Tangerang City to convey their aspirations, aim to the government can effectively and efficiently fulfill the needs of the city of Tangerang. With this prosperity, the government response is needed in accordance with the level of urgency of the complaints conveyed by the community. This research will compare the methods that can be used to solve this problem, namely Naive Bayes and decision trees.
Web Page Information Extraction System by Using Deep Learning

In many companies, business units that aim to online sell, need every type of referential data about the market. In order to collect this data which can be group of price, content, survey etc. with a predefined format, websites which sell similar products can be used. The methods used in the data collection process are generally categorized by 3 main groups: 1- Manual 2-Half Manual 3-Auto. Statically coded data collectors (type 1 and type 2) are unable to collect healthy data in the long term and require continuous development and maintenance effort, as internet pages are dynamic and changes would happen frequently in their page designs. In this study, a data scraping application (type 3) which is not affected by structural changes in web pages was developed. This study aims to obtain data from images of web pages using Deep CNNs.
Action

Actions can be either physical, virtual, or mental and act on either physical, virtual, or mental objects. For instance, Maria Montessori constructed educational materials that enabled students to learn by manipulation. The materials required physical actions on physical objects, such as combining beads to depict operations on numbers. Nintendo’s Wii video game supported physical actions on virtual objects. Gestures are actions that often apply to imaginary objects. Virtual actions involve manipulating computer consoles such as those used in robotic surgery to operate on physical objects. Virtual actions on virtual objects occur in many video games and instructional software. Virtual actions on mental objects occur in computer systems that use audio feedback to help the blind learn to navigate. Mental actions can be captured in brain–computer interfaces to control both physical robots and information on a computer screen. Mental actions on mental objects produce mental simulations. The increasing popularity of augmented reality will require more research on the pairing of physical, virtual, and mental actions and objects.
Detection of Malicious uniform Resource Locator

With the growing use of internet across the world ,the threats posed by it are numerous. The information you get and share across the internet is accessible, can be tracked and modified. Malicious websites play a pivotal role in effecting your system. These websites reach users through emails, text messages, pop ups or devious advertisements. The outcome of these websites or Uniform Resource Locators (URLs) would often be a downloaded malware, spyware, ransomware and compromised accounts. A malicious website or URL requires action on the users side, however in the case of drive by only downloads, the website will attempt to install software on the computer without asking users permission first. We put forward a model to forecast a URL is malicious or benign, based on the application layer and network characteristics. Machine learning algorithms for classification are used to develop a classifier using the targeted dataset. The targeted dataset is divided into training and validation sets. These sets are used to train and validate the classifier model. The hyper parameters are tuned to refine the model and generate better results.
Ameliorated Methodology for Base Design in Information System

The pragmatic Information retrieval technique is providing the responses to the users’ query depending on their choice. The clients are struggling hard to comprehend the semantic within the reports. The data recovery within common language content isn't organized and could be semantically equivocal. The unstructured data may contain non-key attributes. The joint operation takes place between the primary key and foreign key of different tables. The foreign key of one table must be the primary key of another table is the most common in a database and heavily optimized. Join is used to connect rows in another table (or even in the same table) based on the arbitrary condition for structured data but what it for unstructured data to retrieve an information? Information containing an unstructured data and Non-key attributes needs the establishment of relation between Non-key attributes as well as Non-key joins. This paper proposes a data base design to retrieve information with Non-key attributes and representing the knowledge with decision tree. From the decision tree the semiotic is extracted i.e. path from root node to leaf node.
An Exploration on Privacy Concerned Secured Data Sharing Techniques in Cloud

Cloud service is the most popular environment utilized by millions of users for different kind of service utilization. Cloud services needs to be provided with the concern of satisfying user requirements to increase their popularity level. The main issues associated with cloud service handling are maintaining integrity of data, user management, secured data transmission and so on. These issues are addressed by various authors by introducing the different methodologies. In this analysis work, discussion about those research techniques has been given with the detailed explanation. This paper provides the overview about the different research techniques whose main goal is to achieve the increase the data integrity level of the shared data. This paper also provides the discussion about various dynamic grouping techniques which tends to provide more flexible access to the user revocation. Different research techniques proposed by various authors has been described and compared their performance in terms of merits and demerits each research technique. The overall evaluation of the research technique is done based on numerical outcome present in each research technique and best research technique has been found
Uses of Augmented Reality for Development of Natural Literacy in Pre-Primary Education

The chapter focuses on the educational potential of augmented reality for support of scientific literacy in pre-primary education. Development of scientific abstraction in preschool children (aged 3–7) is one of the goals of scientific pre-literacy (Jančaříková, 2015). The chapter presents the opportunities that augmented reality brings to development of scientific literacy. The authors give a brief overview of the area and of selected applications suitable for preschool children. They then focus on two of them, 4D animal and Quiver Vision, which were used in a research project involving two Czech and two Slovak kindergartens as well as 5 preschool children’s parents. Improvement of the learning process and of teaching methods contributes to a child’s complex development in the contemporary socio-cultural context. That is why the goal of the presented research was to identify the impact of the use of augmented reality on the development of children’s scientific literacy as well as on the teachers’ professional development. This action research project was conducted within the frame of science education context and included semi-structured interviews with teachers from a learning/play community of children involved in the research, and preschool children’s parents, as well as direct and indirect observations of children while involved with augmented reality in educational activities. The research showed that many teachers were not familiar with the technology of augmented reality, and some teachers expressed worries about how to employ augmented reality with preschool-age children. Additionally, the teachers shared creative ideas on how these technologies can be included in the offered activities.
An optimal dimension reduction-based feature selection and classification strategy for geospatial imagery

Driven by the explosive growth on the available data nowadays and advancement of technologies, the strong need arises for utilising and maintaining the available data. However, while building an expert prediction system, the inconsistency present in the information system, incompleteness of available knowledge base, continuous natured attribute values and noise present in the system (especially in case of spatial image data handling), are prime factors which may degrade the process of classification with available traditional methods. Our proposed construction adopts an efficient strategy for classification. Here we explore the problem of classifying remote sensing satellite images. Image data pre-processing and its categorisation refers to the labelling of individual pixel object instances into one of a number of predefined categories. Although this is usually not a much intractable task for humans, it has proved to be an extremely difficult problem for machines. We performed experimental analysis for classification using NWPU-RESISC45 dataset. Experiment result shows the improvement in classification by adopting our proposed strategy over other significant state of the art.
Similarity Effects on the Emergence of Default Inflection in Jordanian Arabic

This study investigates the similarity effects that determine the default inflection in the nominal system of Jordanian Arabic (JA). Unlike the single route hypothesis (e.g. Rumelhart and McClelland 1986), the dual-route account assumes that regular and irregular forms are stored and processed in two autonomous systems (e.g. Kim et al. 1994; Pinker 1999, Pinker and Prince 1998, Pinker and Ullman 2002, Clahsen et al. 2015). A Paired Sample t-test was used to measure the effects of similarity on agreement between the inflected forms and the base forms. We used 40 triplicate words designed by analogy to 20 pairs of existing JA nouns (base nouns) given to 20 JA native speakers. The results showed that JA speakers manifested tight matching of inflection among irregular forms which was not observed with regularly inflected forms. The results of the study support the idea that the regular inflection is a default; such observation indicates that the regular inflection applies regardless of the similarity of the target to existing regular tokens. The default inflection is triggered by an ‘elsewhere condition’ upon the failure to activate a similar irregular token in associative memory (Kiparsky 1973). This research also provides evidence on the regular inflection application to non-word targets that show phonological distance from their base nouns. Keywords: Jordanian Arabic, default, similarity, sound feminine, regular inflection.