Reviewerly/exHarmony huggingface dataset:
Graph-Based Concept Discovery

Multi-relational data mining (MRDM) is concerned with discovering hidden patterns from multiple tables in a relational database. One of the most commonly addressed tasks in MRDM is concept discovery in which the problem is inducing logical definitions of a specific relation, called target relation, in terms of other relations, called background knowledge. Inductive logic programming-based and graph-based approaches are two main competitors in this research. In this chapter, the authors aim to introduce concept discovery problem and compare state-of-the-art methods in graph-based concept discovery by means of data representation, search method, and concept descriptor evaluation mechanism.
The Field, the First, and Latest Court of Appeal: An Australian Cratonic Landscape and Its Wider Relevance

Fieldwork involves the interplay between eyes (observations) and mind (background knowledge), the possibilities offered by both changing in time. Recognition of anomalies between field realities and received wisdom is critical. The method of multiple working hypotheses produces the most reliable results. Repeat observations and monitoring of sites are rewarding. These issues are addressed in the context of certain Australian landscapes and their global implications. Structure, tectonics, deep weathering, etching, unequal erosion, and reinforcement are significant factors in understanding and interpretation of these field situations, as is the dating of forms and events. Field studies raise as many questions as answers.
Real-Time Haze Removal Using Filter Function

Haze, fog and smoke are main explanations of street mishaps. Fog lessens differentiate neck and neck of the picture that stimuluses the chromatic nature of appearance. In arena of PC pictorial superiority and perceivability levels of a pic is influenced via air light and weakening marvels. In flight subdivisions, which were present in climate influence the perceivability glassy of some artifact, termed commotion or undesirable sign among spectator and item. For improvising the perceivability level of a pic and decreasing haze and clamor different upgrade strategies are utilized. After improvement is again reestablished, upgraded image by rebuilding techniques. For enlightening the perceivability level 4 significant advances are utilized. Initial phase is securing formula of unclear imageries. Second is approximation method (gauge dissipating wonders, perceivability level). Third is upgrade progression (advance perceivability, decrease Smog or clamor). Preceding advance is rebuilding practice (reestablish improved pic). The principle point of the journal is to audit condition; workmanship picture improvement and rebuilding strategies for refining the eminence and perceivability of an image which give clear focus in awful climate circumstance.
System Output

In this Sect. 4.1 classify the analyzed swarm systems into five categories based on their type of output. First, I review visual artworks including digital drawing, image rendering, and painting. This is followed by a discussion of moving images including generative videos and movies.
Introduction

Cloud computing has become a catchphrase in the academia and industry. The technical definition of the Cloud is very important due to its ability to support different types of software and network services. Cloud computing, also known as on-demand computing, is a kind of Internet-based computing, where shared resources and information are provided to computers and other devices on-demand.
Counterfactual communication

Counterfactual communication, transferring bits and even qubits without particle travelling in the transmission channel is a bizarre quantum effect. It is also a very controversial topic. Here I will try to clarify the meaning of various “counterfactual" quantum protocols.
Wiley Online Library

The Wiley Online Library allows access to multidisciplinary resources, such as journals, reference works, and e-books under one platform with nine overarching subject discipline areas including health and medical sciences. Users have several methods of searching and discovery, can download e-resources in PDF format, and can produce citations in many formats. Users are also able to track citations, add items to a favorites list, add certain e-resource alerts, and use a built-in copyright permission/usage tool named RightsLink®. An administrator platform offers many acquisition packages types to choose from according to institutional needs. Subscribers also have access to other services, trainings, and support.
ABlockchain-enabled Efficient and Fair Energy Trading Schemes in Industrial Internet of Things

As the technical support of the Industrial Internet of Things, blockchain technology has been widely used in energy trading, data transactions, and Internet of Vehicles. However, among the existing energy trading models, most of them only address the transaction security and transaction privacy issues that arise in the energy trading process, ignoring the fairness of resource allocation and transaction equity in the trading process. In order to tackle those problems, an energy trading scheme called Ho-trade is proposed in this paper to improve the efficiency of model trading while ensuring the fairness of energy trading. We propose a new trading strategy in the Ho-trade energy trading scheme that guarantees fairness in the allocation of trading re-sources by introducing an entity's active-reputation value. Based on the identity verification foundation of the consortium chain, the scheme enhances the existing PBFT consensus algorithm and improves the efficiency of model transactions. The experimental simulation indicates that the scheme requires less transaction time and has higher transaction fairness and security.
A Digital Approach to Access and Manage Distributed Data

With the advent of Information Technology in to-days time, there is no dearth of increasing requirements for storing and retrieving data. As the data produced per year increases at an explosive rate, storing this data and representing it in a well structured format and in a timely manner is one of the biggest issues today for the organizations and institutions. This paper proposes an approach to manage and access large amounts of distributed unstructured data of an organization in an efficient manner. Hence, a web-application has been designed to efficiently access and manage the data and to save precious time spent in accessing the distributed data. Also the paper proposes the methodology which describes the features provided by the web-application.
Cover

Meteoritics & Planetary ScienceVolume 54, Issue 5 p. i-i CoverFree Access Cover First published: 18 April 2019 https://doi.org/10.1111/maps.13156AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract Volume54, Issue5May 2019Pages i-i RelatedInformation
Free Speech, Press Freedom, and Democracy in Ghana

Theoretically portrayed as monolithic in the African communication scholarship, Ghana media system calls for a conceptual explanation of its position both as apparatus of political suppression and as a weapon of people's advocacy and liberation. Notwithstanding availability of scholarly works regarding press development in Ghana; there is the need to situate its differing roles in the contemporary democracies as a watchdog, mediator, and advocate and, ideological state apparatus within a specific theoretical context. This paper chooses exploratory method to discuss free speech, press freedom, and media ownership in Ghana, with focus on the roles of the colonialists, anti-colonial activists, post-independence democratic government, and business conglomerates. While exploring the identified subject matter, this paper discusses specific factors that affect free speech and press activities within the framework of diverse political and legal settings that operate in Ghana.
Market Prediction in Criptocurrency: A Systematic Literature Mapping

This article examines the main published works on predictability of cryptocurrencies from a point of view of identifying techniques, algorithms and characteristics that can improve price predictability in cryptocurrencies. This paper presents a systematic mapping of papers and classifies them into categories on type of analysis and techniques. This work can help the scientific community to better identify and structure a fairly emerging area that is the cryptocurrencies and how their markets can be studied.
Explainable software systems

Abstract Software and software-controlled technical systems play an increasing role in our daily lives. In cyber-physical systems, which connect the physical and the digital world, software does not only influence how we perceive and interact with our environment but software also makes decisions that influence our behavior. Therefore, the ability of software systems to explain their behavior and decisions will become an important property that will be crucial for their acceptance in our society. We call software systems with this ability explainable software systems . In the past, we have worked on methods and tools to design explainable software systems. In this article, we highlight some of our work on how to design explainable software systems. More specifically, we describe an architectural framework for designing self-explainable software systems, which is based on the MAPE-loop for self-adaptive systems. Afterward, we show that explainability is also important for tools that are used by engineers during the development of software systems. We show examples from the area of requirements engineering where we use techniques from natural language processing and neural networks to help engineers comprehend the complex information structures embedded in system requirements.
Mission Planning of MAV/UAV Cooperative Combat based on Improved Genetic Algorithm

Manned/unmanned aerial vehicles (MAV/UAV) cooperative combat mission planning is an extremely complex decision-making and optimizing problem. The purpose of this is to maximize the overall combat benefit with minimum cost by allocating reasonable targets for different types of aircraft, and proposing rough path planning for coordinated combat formations. Based on the background of OODA integrated combat cycle, this paper establishes the combination optimization model of MAV/UAV cooperative combat, and solves the problems of heterogeneity and resource limitation of MAV/UAV cooperative combat task planning by improving multi genotype genetic algorithm. Finally, the feasibility and effectiveness of this method are proved by the combat mission of MAV/UAV formation cooperating against ground fixed targets.
Exploration Media of Muhammadiyah University of Sidoarjo with Mixed Reality

Mixed reality technology is a combination of Augmented Reality (AR) and Virtual Reality (VR) technology. Augmented reality and virtual reality are often used for various fields in terms of education, media promotion, even used as games to make it more interesting, creative and innovative. With the existence of these two advanced technologies, the advertising media as well as promotion and entertainment media really need this kind of technological innovation in introducing the media one step further with technology. Mixed Reality can be used to make it easier for prospective students or the public to explore campuses at the University of Muhammadiyah Sidoarjo through the virtual world without having to come to the location. For this reason, the contribution of this research is to provide alternative digital information media services and access to information available at the Muhammadiyah University of Sidoarjo as a means of educating the campus community on the community and the academic community.
A Framework for Image Encryption on Frequency Domain

In this chapter, the authors propose an improved image encryption algorithm based on digital watermarking. The algorithm combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) together in a DWT-DCT-SVD framework to improve the robust watermarking technique. The secret image is embedded into both high-frequency and low-frequency sub-bands of the host image; this makes it difficult to be attacked in all the sub-bands. To reduce the size of a secret key, the authors use a logistic map to generate random images so as to replace the host images. They tested the algorithm by using five types of attacks and the results indicate that the proposed algorithm has higher robustness than traditional chaotic scrambling method and the DRPE method. It shows strong resilience against the five types of attacks as well as statistical attacks.
Emergent Technologies Shaping Instructional Design

This chapter discusses emergent technologies that are currently shaping or expected to shape the field of instructional design in the near future. The discussion begins with a brief overview of instructional design as a professional field over the past century, then focuses on current and promising trends for the field based on advances in technologies supporting instructional development. This chapter intends to provide a centralized literature review of multiple pathways currently being carved in the field, encompassing several parallel trending areas, such as adaptive learning, digital storytelling, gamification, simulation technologies, augmented and virtual reality, cybernetics, the xAPI standard, mobile and ubiquitous learning, and offer a glimpse of how they are shaping or expected to shape the future of all those involved in designing and delivering learning or effecting human behavior and performance change.
A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques

Smishing attack is generally performed by sending a fake short message service (SMS) that contains a link of the malicious webpage or application. Smishing messages are the subclass of spam SMS and these are more harmful compared to spam messages. There are various solutions available to detect the spam messages. However, no existing solution, filters the smishing message from the spam message. Therefore, this article presents a novel method to filter smishing message from spam message. The proposed approach is divided into two phases. The first phase filters the spam messages and ham messages. The second phase filters smishing messages from spam messages. The performance of the proposed method is evaluated on various machine learning classifiers using the dataset of ham and spam messages. The simulation results indicate that the proposed approach can detect spam messages with the accuracy of 94.9% and it can filter smishing messages with the accuracy of 96% on neural network classifier.
Helping Language Learners Put Concordance Data in Context

While corpus tools provide several different ways to display relationships between words within texts and across texts, the main format for viewing concordance data is Key Word in Context (KWIC). In Computer Aided Language Learning, concordance lines in KWIC format may be accessed inside a concordancer or within other software through links to corpus data. Language learners can and do gain useful insights from exploring concordance data in KWIC format, but some kinds of information may be harder to see, some patterning of use may not be so obvious, and reading of complete examples may not be very easy. The Prime Machine was developed for language learners and aims to make corpus data easier to access and interpret. This paper introduces the design of the Cards Tab, which provides an additional way of viewing concordance data. Results from three evaluations with language learners and teachers show positive attitudes towards this display.
An Incentive Mechanism for Resource Allocation in Coded Distributed Computing

Coded distributed computing (CDC) has emerged as a promising approach because it enables computation tasks to be carried out in a distributed manner while mitigating straggler effects, which often account for the long overall completion times. However, incentive issues have not been studied systematically for the edge servers to complete the CDC tasks. In this paper, we propose a tractable two-level game-theoretic approach to incentivize the edge servers to complete the CDC tasks. Specifically, in the lower level, a hedonic coalition formation game is formulated where the edge servers share their resources within their coalitions. In the upper level, given the CPU power of the coalitions of edge servers, an all-pay auction is designed to incentivize the edge servers to participate in the CDC tasks. Simulation results show that the edge servers are incentivized to allocate more CPU power when multiple rewards are offered, i.e. there are multiple winners, instead of rewarding only the edge server with the largest CPU power allocation. Besides, the utility of the cloud server is maximized when it offers multiple homogeneous rewards, instead of heterogeneous rewards.
Literature Survey of Various Techniques of Documents Clustering

Clustering means that identical items are held together. Document clustering is an application of clustering, which refers to having related text records together. Document clustering plays a crucial role in the creation of search engines, where a document category is expected to be listed in the minimum answer period as a result of the query. Document clustering is critical in terms of the final objective of tracking membership, rundown, collection of subjects and data retrieval in a competent manner. First the designation is related to the upgrading of data recovery procedures. Recently, clustering techniques have been related in the regions, which involve browsing the gathered knowledge or ordering the findings of the web indices to address the query posed by the clients. This paper elaborates on the idea of document cum text clustering. This paper would include a survey of recent work in the area of text clustering. This paper would also include a critical analysis of current text clustering techniques.
Conclusions

Abstract This final chapter gives a short summary of the contents of the book and represents some important lessons about the security considerations of the cryptography used in blockchain systems.
A Multi-Agent Based Modeling and Simulation Data Management and Analysis System for the Hospital Emergency Department

In the last decades, multi-agent based modeling and simulation systems have become more increasingly used to model the dynamic and the complex healthcare systems which contain many variabilities and uncertainties such as the hospital emergency departments (ED). Modeling and creating virtual societies almost identical and similar to the reality are considered as the strongest advantages of these agents systems. However, during the dynamic development of the artificial societies, a massive volume of data, which generally contains non-express and shrouded information and even knowledge, is involved. Therefore, dealing with this data, to study and to analyze the unclear relationships and the emerging phenomena, is a well-known weakness and bottleneck that the multi-agent systems is suffering from. In conjunction, data mining techniques are the most powerful tools that can help simulation experts to tackle this issue. This paper presents an ongoing research that combines the multi-agent based modeling and simulation systems and data mining techniques to develop a decision support system to improve the operation of the emergency department.
Data Mining Analytics for Crime Security Investigation and Intrusion Detection

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.
The Library at Ryerson University: A Case Study in Relationship-Building and Academic Collaboration

Ryerson University has grown from a relatively small polytechnical institute to a large comprehensive university, with a focus on innovation. The Library has transformed itself over the last decade by closely aligning strategic planning with University planning; embracing the entrepreneurial culture in its physical expansion and collaborative initiatives, and highlighting the expertise of its staff. The study outlines the steady progress through relationship-building in a culture of openness and a focus on common goals to reposition the library in the university’s academic mission. By 2017, a number of faculty and student library initiatives were underway, enhancing the library’s position as an innovator, and ensuring its continued high profile and strong reputation within the university and beyond. Examples include a digital media lab and collaboratory, a convergence of Archives, Special Collections, and the Digital Humanities centre, a leading role in Open Educational Resources, and an association with the University’s digital incubator.
Conclusion

The interdependent relation of clause complexes in SFL is grammatical in nature, and the logico-semantic relation, semantic. In this study, we divided the interdependent relations into parataxis, hypotaxis, and embedding and conducted a corpus-based study on these types of relations. The research findings will answer the research questions.
An Open-Sourced Time-Frequency Domain RF Classification Framework

In this paper we present a machine learning-based approach to solving the radio-frequency (RF) signal classification problem in a data-driven way. To this end, we propose an efficient and easy-to-use graphical user interface (GUI) for researchers to collect their own data to build a customized RF classification system. The GUI operates in the time-frequency (TF) domain, which is achieved by applying short-time Fourier transform to the in-phase and quadrature (IQ) time domain signals. Using the proposed GUI, a radio frequency (RF) dataset is collected from the ultra high frequency industrial, scientific, and medical (ISM) bands using commercial-off-the-shelf (COTS) transceivers, and COTS transceiver modules. We train three different variants of convolutional neural network models, such as VGG and ResNet, using the collected dataset and show that they can perform acceptable test-time classification (up to 95% accuracy) on unseen real-world RF signal recordings. Our experimental results also show that a carefully prepared TF domain without a loss of information can achieve better performance than a magnitude-only representation that loses phase information during the TF transformation. We open-source our project to provide the public with access to the labeled datasets, programming code, and the GUI software that can expedite the labeling process.