Reviewerly/exHarmony huggingface dataset:
Spam Detection in Online Comments Based on Feature Weight Breakdown

The user reviews posted online by the Internet users about a product plays a vital role in determining its success in the market. The reviews also influence the purchase decision of the consumers. The chances of getting cheated by fake reviews are very high because detecting spams in reviews is not an easy task either manually or automatically. Hence there is a need to evolve new techniques and methods to outperform the smartness of spammers. In this paper, we propose a Heterogeneous Feature Weight Analysis framework for extracting various features related to the review and certain parameters are calculated from these features to form a pattern for deceptive reviews. The features associated with the review are review content, review rating and user centric characteristics which are pulled out from the dataset retrieved from Amazon. This analysis has helped us to categorize reviews into normal and suspicious reviews. We have executed our algorithm in Python software and were able to achieve an accuracy of 71.6% inprediction.
An Information Extraction Method for Sedimentology Literature with Semantic Rules

Because of the high demand for text information, the semantic network is under tremendous pressure to improve semantic accuracy. The domain data set expands the types of entities in order to ensure that entity analysis is performed directly between contexts, thereby saving the time consumption of information search and improving the quality control of key data. However, literature data in the field of geological sedimentology relies mainly on manual annotation, which consumes considerable time. It is still a big problem in reducing human error and dynamically expanding entity classification. For solving these problems, this paper proposes a batch document information extraction method based on sentence part of speech rules (ESM). Technically speaking, nltk (Natural Language Toolkit) is used to identify specific sentence components by adding sedimentology specific semantic rules and Bi-LSTM + CRF (bidirectional Long Short-Term Memory network and conditional random field). Therefore, this paper makes an experimental evaluation to prove the efficiency of ESM.
Container description ontology for CaaS

Besides its classical three service models (IaaS, PaaS, and SaaS), container as a service (CaaS) has gained significant acceptance. It offers without the difficulty of high-performance challenges of traditional hypervisors deployable applications. As the adoption of containers is increasingly wide spreading, the use of tools to manage them across the infrastructure becomes a vital necessity. In this paper, we propose a conceptualisation of a domain ontology for the container description called CDO. CDO presents, in a detailed and equal manner, the functional and non-functional capabilities of containers, Dockers and container orchestration systems. In addition, we provide a framework that aims at simplifying the container management not only for the users but also for the cloud providers. In fact, this framework serves to populate CDO, help the users to deploy their application on a container orchestration system, and enhance interoperability between the cloud providers by providing migration service for deploying applications among different host platforms. Finally, the CDO effectiveness is demonstrated relying on a real case study on the deployment of a micro-service application over a containerised environment under a set of functional and non-functional requirements.
Opinion Mining and Trend Analysis on Twitter Data

With rise in Internet use across the global, there has been a trending increase in the online data. Every person from different profession gives their view from politics to entertainment, sports or economics. The web’s current evolution is a major pacesetter as it generates an effectual methodology to embed “smart data” into web pages and hence result in easy content implementation for authors. The web 2.0 has changed the way of communication on the web. Using Social Networks (SNs) they have become active participants by connecting, producing and sharing information, experiences and opinions with each other [1]. Public opinions extracted in the form of trends are interesting for researchers, sociologists, news reporters, marketing professionals and opinion tracking companies. The aim of this project is opinion mining and the analysis of the trends of the public statements gathered from different social media sources (specifically twitter). Here Binary sentiment analysis is performed on currently fetched data from twitter over various emotional quotients. W have also performed (i) Comparison between two users based on public reaction in the form of likes, shares and number of re-tweets; (ii) Visualization of comparison results by plotting graphs over popularity of social media (likes/re-tweets/shares).
Five Academic Years of Activated Third-Party and Custom-Coded Applications on an LMS Instance

On Canvas's data portal, the “external_tool_activation_dim” data table showcases applications activated on the LMS instance through an LTI or other integration mechanism. The “apps” include those by third-party content providers, publishers, software makers, social media platforms, as well as in-house developers. The linked resources include e-books, simulated labs, inter-communications tools, digital content hosting services, assessment supports, proctoring services, work management tools, micro-credentialing services, and others. Understanding which third-party and customized applications are activated may shed light on the interests of the online instructors, the gaps between activated applications and available ones, local custom-coded applications, and others. This chapter captures activated app data through the full lifespan of the LMS instance at Kansas State University to the present moment and encapsulates five academic years: Fall 2013 – Summer 2014, Fall 2014 – Summer 2015, Fall 2015 – Summer 2016, Fall 2016 – Summer 2017, and Fall 2017 – Spring 2018.
Information metaphors

The term information has been known for centuries with many different definitions and interpretations in use. But there is still lack of commonly accepted one and the question what actually stands for the term information is open. This is why some new concepts appear. Among them, the approach based on the analysis of its properties and functions enables better understanding of the phenomenon of information. In the literature one can find a suggestion that the concept of metaphor can be useful in this ap-proach. The main aim of the paper is to give some examples of such metaphors called information metaphors, showing simultaneously their role in explaining some features of information by comparison to other concepts used in daily lives.
Visualization and Plotting

Visualizing data is usually a great way to convey important engineering and/or science concepts and information, especially if the information is made up of many, many numbers. The ability to visualize and plot data quickly and in many different ways is one of Python's most powerful features. Python has numerous graphics functions that enable you to efficiently display plots, surfaces, volumes, vector fields, histograms, animations, and many other data plots. By the end of this chapter, you should be familiar with the most commonly used graphics functions in Python and have enough background to explore the rest.
Minerals Yearbook, volume II, Area Reports — Domestic

For additional information, contact: Director, National Minerals Information Center U.S. Geological Survey 12201 Sunrise Valley Drive 988 National Center Reston, VA 20192 Email: nmicrecordsmgt@usgs.gov The U.S. Geological Survey (USGS) Minerals Yearbook discusses the performance of the worldwide minerals and materials industries and provides background information to assist in interpreting that performance. Content of the individual Minerals Yearbook volumes follows:Volume I, Metals and Minerals, contains chapters about virtually all metallic and industrial mineral commodities important to the U.S. economy. Chapters on survey methods, summary statistics for domestic nonfuel minerals, and trends in mining and quarrying in the metals and industrial mineral industries in the United States are also included.Volume II, Area Reports: Domestic, contains a chapter on the mineral industry of each of the 50 States and Puerto Rico and the Administered Islands. This volume also has chapters on survey methods and summary statistics of domestic nonfuel minerals.Volume III, Area Reports: International, is published as four separate reports. These regional reports contain the latest available minerals data on more than 180 foreign countries and discuss the importance of minerals to the economies of these nations and the United States. Each report begins with an overview of the region’s mineral industries during the year. It continues with individual country chapters that examine the mining, refining, processing, and use of minerals in each country of the region and how each country’s mineral industry relates to U.S. industry. Most chapters include production tables and industry structure tables, information about Government policies and programs that affect the country’s mineral industry, and an outlook section.The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the Minerals Yearbook are welcome.
AN OVERVIEW OF SWARM INTELLIGENCE IN ARTIFICIAL INTELLIGENT SYSTEMS

Swarm intelligence refers to a kind of problem-solving ability that emerges in the interactions of simple information-processing units. The concept of a swarm suggests multiplicity, stochasticity, randomness, and messiness. Advancement of technology has led to problems that are complex and more challenging.Swarm intelligence techniques were mostly developed for solving optimization problems.
Cost Estimate Model for Software Projects using GREYWOLF Algorithm and COCOMO Model

Cost estimation analysis for the software system project is the foremost difficult tasks in software organizations. In this paper, a comparison between an estimate and actual effort was done by applying the grey wolf’s algorithm to predict the effort and time of this software system for a given archive. The intermediate semi-detached COCOMO model was used with the grey wolf’s algorithm by taking the KLOC of the dataset as input, additionally to fifteen cost drivers and giving effort and time as output. The recommended model of the cost estimation helps the project manager by offering a fast and truly estimates the hassle and time of software system project which is nearer to the actual cost.
Optimization of Robust Image Watermarking using Ensemble-Based Classifier

The secure transmission of digital multimedia over the internet is a challenging task. For secure transmission of digital multimedia uses various methods such as encoding of data and digital watermarking in this paper proposed optimized watermarking methods based on the ensemble-based classifier. The ensemble-based classifier used two classifier decision tree and KNN. The process of ensemble classifier optimized the value of features and reduce the gap of the pixel difference between the source image and watermark image. The feature-based watermarking process used a discrete wavelet transform. The wavelet transforms more dominated feature of textures. The diverse space of texture features applied ensemble classifier to optimized the features of watermarking. The proposed algorithm implemented in MATLAB software and estimate standard parameters such as PSNR and NC. The proposed algorithm also tested on the geometrical attack to measure the strength of the proposed algorithm.
Energy Efficient Low Resource Consumption Lightweight Block Ciphers for Internet of Things

Internet of Things (IoT) refers the system of interrelated heterogeneous uniquely identifiable devices with the ability to collect data and communicate with each other to enable applications/services. The security and privacy are the two major issues as IoT applications are providing personalized services by collecting users' private, sensitive and confidential data. The traditional cryptographic methods are not appropriate for most of the IoT applications due to resource-constraints. The lightweight block ciphers are the most common solution to provide security and privacy in IoT. In this paper, various popular lightweight block cipher algorithms and their performance metrics are discussed in detail. The comparative performance analysis of the block ciphers are investigated through experimental study in terms of various metrics and their results are presented. Based on the study, we made recommendations to choose a suitable cryptographic algorithm for an IoT environment depending upon the desired level of security and privacy requirements. The detailed directions are also given which can be considered as guidelines for designing new security solutions.
Comitative P

This chapter investigates the diachronic development of the Italian comitative preposition <italic>con</italic> “with” and its pronominal complement. The authors verify the predictions of the Directional Asymmetry Principle, an independently motivated developmental universal, in the diachrony of Italian and argue that symmetry breaking reduces the choice between a valued and an unvalued variant of a functional feature associated with a functional head, here comitative P. As predicted by the hypothesis, this choice, which was available in earlier stages of Italian, is gradually reduced in Modern Italian. The authors relate variation in language to variation in evolutionary developmental biology. Because language variation is biologically grounded, and variation is a central concept in biology, a deeper understanding of linguistic diversity can be foreseen, that is, an understanding that goes beyond explanatory adequacy.
Negation-Instance Based Evaluation of End-to-End Negation Resolution

In this paper, we revisit the task of negation resolution, which includes the subtasks of cue detection (e.g."not", "never") and scope resolution.In the context of previous shared tasks, a variety of evaluation metrics have been proposed.Subsequent works usually use different subsets of these, including variations and custom implementations, rendering meaningful comparisons between systems difficult.Examining the problem both from a linguistic perspective and from a downstream viewpoint, we here argue for a negation-instance based approach to evaluating negation resolution.Our proposed metrics correspond to expectations over per-instance scores and hence are intuitively interpretable.To render research comparable and to foster future work, we provide results for a set of current state-of-the-art systems for negation resolution on three English corpora, and make our implementation of the evaluation scripts publicly available.
Reading *

The teacher desiring advice on the teaching of reading may well be deterred by the number, scope, and technicalities of the books dealing with the subject. An ideal medium for the former purpose, that is of giving daily practice in the closely related skills of reading and writing, is the recording of daily happenings and observations by means of a News Sheet and the use of diaries. Various exercises, based on suitable paragraphs taken from the basic readers, could be devised. The teacher herself should remember to emphasize direction by making some indicative gesture when reading to beginners. The child must be encouraged to read and reread the rest of the sentence to see if help can be obtained in finding the unknown word. Cards are made resembling dominoes but with words taking the place of spots.
Effectively Diagnosing Malaria by Optimizing the Hyperparameters of CNN using Genetic Algorithm on the Multi core GPU

Image classification is an important task in computer vision involving a large area of applications such as object detection, localization and image segmentation. When it comes to image classification, the most adopted methods are based on deep neural network and especially convolutional Neural Networks(CNN). Selection of hyperparameters plays a crucial role in performance of model and it comes by experience. So, in this paper, we will use the genetic algorithm(GA) to automate and build the CNN model for higher accuracy on GPU which is provided by Google Collaboratory cloud. The best architecture of CNN after several generations of the genetic algorithm is then compared to the state-of-the-art CNN. We have used the malaria cell images dataset to find out whether the person is normal or if they are suffering from malaria. We trained two types of malaria cells, which are uninfected and parasitized on Tesla P100 multi core GPU. We got a high training accuracy of 97% and got a testing accuracy of about 95% on the multicore GPU that boosted the speed of execution of training time period and testing time period.
Metadata to the rescue – enabling understanding of data and fitness for use through quality descriptions that also trace its history

Earth and Space Science Open Archive posterOpen AccessYou are viewing the latest version by default [v1]Metadata to the rescue – enabling understanding of data and fitness for use through quality descriptions that also trace its historyAuthorsIrinaBastrakovaiDSee all authors Irina BastrakovaiDCorresponding Author• Submitting AuthorGeoscience AustraliaiDhttps://orcid.org/0000-0002-4643-7289view email addressThe email was not providedcopy email address
On the problem of preserving the cultural-humanistic measurement of innovative education

The leading trend in innovative education is technologization. It is generally welcomed and purposefully implemented. There are much fewer people who are not afraid to discuss the contradiction: this process is due to the diminution of its cultural and humanistic aspects. Deprived of a value character, education ceases to fulfill a social role in the development of man as a person. Unlimited technologization turns it into programming, until the trainees reach an “expanded consciousness”, when they may not know anything: “everything is in the computer”. The preservation of the cultural and humanistic dimension of education presupposes the reproduction of all forms of culture in it. School and university should not a cognitive robot, even if innovative, but a personality. Technologization is required, but it must be dosed and regulated in order to preserve upbringing and education.
Cover figure

Adequately localizing pain is crucial to protect the body against physical damage and react to the stimulus in external space having caused such damage. Accordingly, it is hypothesized that nociceptive inputs are remapped from a somatotopic reference frame, representing the skin surface, towards a spatiotopic frame, representing the body parts in external space. This ability is thought to be developed and shaped by early visual experience. To test this hypothesis, normally sighted and early blind participants performed temporal order judgment tasks during which they judged which of two nociceptive stimuli applied on each hand's dorsum was perceived as first delivered. Crucially, tasks were performed with the hands either in an uncrossed posture or crossed over body midline. While early blinds were not affected by the posture, performances of the normally sighted participants decreased in the crossed condition relative to the uncrossed condition. This indicates that nociceptive stimuli were automatically remapped into a spatiotopic representation that interfered with somatotopy in normally sighted individuals, whereas early blinds seemed to mostly rely on a somatotopic representation to localize nociceptive inputs. Accordingly, the plasticity of the nociceptive system would not purely depend on bodily experiences but also on crossmodal interactions between nociception and vision during early sensory experience.
Type Reflection, Late Binding, and Attribute-Based Programming

As shown in Chapter 16, assemblies are the basic unit of deployment in the .NET Core universe. Using the integrated Object Browser of Visual Studio (and numerous other IDEs), you can examine the types within a project’s referenced set of assemblies. Furthermore, external tools such as ildasm.exe allow you to peek into the underlying CIL code, type metadata, and assembly manifest for a given .NET Core binary. In addition to this design-time investigation of .NET Core assemblies, you are also able to programmatically obtain this same information using the System.Reflection namespace. To this end, the first task of this chapter is to define the role of reflection and the necessity of .NET Core metadata.
Iteration

Many tasks in life are boring or tedious because they require doing the same basic actions over and over again, called iterating, but in slightly different contexts. For example, consider looking up the definition of 20 words in the dictionary, populating a large table of numbers with data, alphabetizing many stacks of paper, or dusting off every object and shelf in your room. Thus, it is only natural that programming languages like Python would have direct methods of performing iterative tasks. This chapter teaches you how to program iterative tasks. With branching and iteration, it is possible to program just about any task you can imagine.
Disambiguating the Context of the Concept Terms using Concept Hierarchies

Latent Semantic Analysis (LSA) makes the machine clearly conceptualize the terms of the document by learning the context in which these terms are written. However, LSA suffers from the limitation of input data matrix size in terms of number of terms and number of documents of the considered dataset. When the size of the dataset is huge, LSA becomes inefficient towards learning the correct context and thereby is unable to produce the intended concepts by the machine. To overcome this problem, Context Disambiguation (ConDis) ontology is engineered for a domain which has the capability of evolving itself based on automatic learning of concepts and relations from the ever scaling documents over the web. The concept hierarchies from general to specific concepts combined with corresponding object relations specify the particular context for a term. These object relations based concept hierarchies clearly help disambiguate the context of the concept terms in an effective manner.
Dual Protectıon for Data usıng Steganographıc Technıques with Embedded Framework

As the world is getting digitalized, the rush for need of secured data communication is overtop. Provoked by the vulnerability of human visual system to understand the progressive changes in the scenes, a new steganography method is proposed. The paper represents a double protection methodology for secured transmission of data. The original data is hidden inside a cover image using LSB substitution algorithm. The image obtained is inserted inside a frame of the video producing a stego-video. Stego-video attained is less vulnerable to attacks. After decryption phase, the original text is obtained which is error-free and the output image obtained is similar as the cover image. The quality of stego-video is high and there is no need for additional bandwidth for transmission. The hardware implement is required in order to calculate the corresponding analytical results. The proposed algorithm is examined and realized for various encryption standards using Raspberry Pi3 embedded hardware. The results obtained focuses on the attributes of the proposed model. On comparing with other conventional algorithms, the proposed scheme exhibits high performance in both encryption and decryption process with increase in efficiency of secured data communication.