Reviewerly/exHarmony huggingface dataset:
Methodology for ISO/IEC 29110 Profile Implementation in EPF Composer

The article presents the ISO/IEC 29110 Profile Implementation Methodology that was developed to manage consistent implementation of individual ISO/IEC 29110 Profiles in the open-source content management tool Eclipse Process Framework Composer. Such an implementation enables effective managing of the standard and its publishing in the form of a web application that can be easily and efficiently used. This methodology represents an example of the usable outputs of the ISO/IEC 29110 standard being utilized in education and research in the Czech Republic. Its main elements described in this article can be also used for implementation purposes in other countries. First, the methodology structure is presented, followed by its individual elements, i.e. General Principles, Profile Structure, Profile Element Mapping, Implementation Conventions, EPF Composer Usage Guidelines, and Implementation Process. The evaluation of this methodology was performed during the implementation of the Entry Profile.
Beyond Scientometrics

Numerous websites are currently being used by researchers for sharing and disseminating research, some of which are CiteULike, BibSonomy, Connotea, Mendeley, ResearchGate, etc. For measuring this data, scientists create alternative indicators related to traditional indicators like bibliometric indicators, scientometric indicators. The main purpose of these indicators is that with such huge amount of information available, some specific tools and techniques are required to filter and evaluate the research outcomes. These indicators reveal the societal and unknown impact of the work that traditional metrics are unable to do. The most prominent indicators for this purpose include Altmetrics or article metrics or alternative metrics. The detailed discussion is provided in this chapter.
Improving Usability of Website Design Using W3C Guidelines

Due to the unceasing growth of web sites and applications, developers and evaluators have interesting challenges not only from the development but also from the quality assurance point of view. The quality assurance was and is one of the challenging processes in software engineering as well as for web engineering, as a new discipline. Although there exist many design guidelines and metrics for the evaluation of web sites and applications, most of them lack a well-defined specification framework and even worse a strategy for consultation and reuse. The main theme of the chapter is to provide optimization techniques to improve the correctness of the website.
First principles

The basic concepts of mathematical morphology are introduced by considering two examples: the search for connected components in an image and the morphological analogue of the convolution operator. A brief history of the subject is included here.
Texture Feature Extraction

ThisFeature extraction chapterTexture feature extraction focuses on another image feature which is texture feature. Two types of texture feature methods are discussed: traditional spatial methods and contemporary spectral methods. The chapter first introduces four spatial or handcrafted methods including Tamura, GLCMGrey level co-occurrence matrices, MRFMarkov random field and FDFractal Dimension (FD). Readers are shown how microstructures of an image can be modeled with handcraft and statistical methods. Pros and cons of spatial methods are also discussed. In the next, four spectral textureSpectral texture methods are described in details including DCTDiscrete Cosine Transform (DCT), Gabor, wavelet and curveletCurvelet. These spectral textureSpectral texture methods not only match theory with data, but also strengthen readers’ understanding of the powerful spectral transforms introduced in Part I. Readers are demonstrated with rich working examples and illustrations.
Kernel Choices in One-class Support Vector Machines for Novelty Detection

Novelty detection plays a fundamental role in the field of statistical classification and has broad applications. The principal of its mechanism is governed by the one-class support vector machine algorithms (OCSVMs), of which the performance varies, dependently. To optimise the performance, this paper compares the choices of kernel functions selected–the deciding factor in the learning and efficiency of the algorithm - in terms of their influence on one -class classification problems, particularly ones involving data of higher dimensions. The decision of the kernel parameters also affects the learning complexity of the SVMs, leading to various levels of validity of the classification outcome, accordingly.
ANALISIS USABILITY SITUS WEB VOCABULARY CONTROL NUSANTARA MENGGUNAKAN METODE WEBUSE

Vocabulary Control Nusantara Website is a voluntary movement that focused work on developing the index language automation system web-based, which is everyone can contribute to the development of site content. From the beginning of the movement until now Vocabulary Control Nusantara Website still in the development stage and no assessment evaluates the level of usability of the website. Therefore, usability analysis is needed to measure the level usability of the Vocabulary Control Nusantara Website and also to predict the potential benefits which will be obtained later when this movement complete. This study does a usability analysis on the homepage and backend page of the Vocabulary Control Nusantara Website by using the WEBUSE method to measure how good the level usability website. The analysis results show on the homepage page get the level usability “Good”, although there are two categories on the backend page which gets the level usability “Moderate”. Besides, there are some unsolved problems that are recommended to further testing and implementation redesign websites.
Efficient Optimization Using Metaheuristics

This chapter provides an insight into the main concepts, theoretical advances, and experimental results in the field of metaheuristics, when applied for efficiently solving real-world optimization problems. A general view of the most well-known metaheuristic methods is presented. After that, relevant applications of metaheuristics in nowadays real-world problems from several domains are described, highlighting their capabilities to solve complex problems with high efficiency. Finally, the main current and future research lines in the field are also summarized and commented on.
Design Considerations

This chapter beginsBegins with the goals and objectives of wearableWearability Technology technologies, describing the different disciplinesDisciplines that contribute to constitute and advanceAdvance wearableWearability computingComputing. This chapter includes also multiple dimensionsDimensions that must be considered in theDesign design, development, and evaluationEvaluation of wearableWearability technologiesTechnology. Concerning hardwareHardware Aspect aspects, six implementation layersLayer are defined and illustrated with examplesExample from the literature. Concerning softwareSoftware Aspect System aspects, systems, andApplication applications, this chapter provides the objectives, features, andFeatures functionalitiesFunctionality implemented with wearableWearability computersComputer. Concerning networkNetwork andConnectivity connectivity, this chapter provides examplesExample of protocolsProtocol and approachesApproaches for physicalPhysical and virtualVirtual connectionsConnection that enable dataData sharingSharing and deviceDevices synchronizationSynchronization. Additional topics discussed in the chapter include energyEnergy requirementsRequirement and considerationsConsiderations forWearability wearables, contextsContext of use where wearersWearer interactInteract with theirDevices devices, and commonCommon tasksTask they execute, emphasizing the advantagesAdvantage of wearableWearability computersComputer to support activitiesActivity of daily living. This chapter highlights the diversity of usersUser who benefitBenefit from wearablesWearability and the inherent trade-offsTrade-offs that emerge when universalUniversal designDesign and customizationCustomization must be considered altogether to ensure acceptabilityAcceptability among diverse usersUser’ profilesProfile. The chapter endsEnds with an in-depth discussion of the main challengesChallenge faced by usersUser when interacting with a wearableWearability technologyTechnology and by stakeholdersStakeholders when creating those technologiesTechnology during the implementation phases. The main challengesChallenge highlighted cut across six different concernsConcern: usabilityUsability andWearability wearability, implementation, power, networkPower Network andSensor sensors, safety, andSafety privacyPrivacy aspectsAspect.
Personalized Recommendation Mechanism Based on Collaborative Filtering in Cloud Computing Environment

With the advent of cloud computing era and the dramatic increase in the amount of data applications, personalized recommendation technology is increasingly important. However, due to large scale and distributed processing architecture and other characteristics of cloud computing, the traditional recommendation techniques which are applied directly to the cloud computing environment will be faced with low recommendation precision, recommended delay, network overhead and other issues, leading to a sharp decline in performance recommendation. To solve these problems, the authors propose a personalized recommendation collaborative filtering mechanism RAC in the cloud computing environment. The first mechanism is to develop distributed score management strategy, by defining the candidate neighbors (CN) concept screening recommended greater impact on the results of the project set. And the authors build two stage index score based on distributed storage system, in order to ensure the recommended mechanism to locate the candidate neighbor. They propose collaborative filtering recommendation algorithm based on the candidate neighbor on this basis (CN-DCF). The target users are searched in candidate neighbors by the nearest neighbor k project score. And the target user's top-N recommendation sets are predicted. The results show that in the cloud computing environment RAC has a good recommendation accuracy and efficiency recommended.
Explainability as an Optimal Stopping Problem: Implications for Human-AI Interaction

Explainability is at the core of several phenomena related to the integration of knowledge held by different actors (including the currently popular case involving machine intelligence and humans). We propose that there are inescapable limits to explainability that arise from the joint effects of limits on knowledge (non-omniscience), the need for absorptive capacity for an explanation to be understood, and finite time. We propose a formalization of the process of explanation as traversal across overlapping knowledge graphs, with the explainer facing an optimal stopping problem. This conceptualization helps to understand why honest, optimizing, actors motivated to cooperate that can communicate perfectly may nonetheless fail to convince one another of something that is true- i.e., fail to explain what they know. This limit to explainability, we suggest, is a fundamental cause of irreducible differences in perspectives across actors, which in turn helps to understand the roles of trust and authority even in the absence of any conflict of interests.
Semantic genetic programming

tutorial Share on Semantic genetic programming Authors: Alberto Moraglio University of Exeter, Exeter, UK University of Exeter, Exeter, UKView Profile , Krzysztof Krawiec Poznan University of Technology, Poznan, Poland Poznan University of Technology, Poznan, PolandView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 877–900https://doi.org/10.1145/3377929.3389884Published:08 July 2020Publication History 0citation39DownloadsMetricsTotal Citations0Total Downloads39Last 12 Months5Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Improving the Teaching of Estimation through an Analysis of Textbook Activities

Measurement involves actual measuring or estimating to generate a quantitative understanding of a phenomenon. Estimation in particular refers to the process of obtaining measurements without a measuring tool. This paper aims to investigate didactical implications for estimation by analyzing the estimation activities of textbooks. To this end, we focused on estimation activities in elementary school mathematics textbooks from the seventh to the 2015 revised national curriculum, analyzing them based on activity quantity, estimation type, and estimation strategy. As regards length, the estimation activities were presented appropriately in terms of quantity, type, and strategy. However, other attributes were insufficient in quantity while some activities showed biases and limitations with few estimation strategies to use in a variety of contexts. Based on these results, we propose the inclusion of achievement standards for time, area, and volume and argue that the textbook should address various types of and strategies for estimation activities.
Impact of Discovery Layers on Accessing E-Resources in Academic Libraries

The paper discusses the implementation of the ‘CUB E-journal One Search' tool as an alternative solution to commercial discovery services, which was designed using Google Custom Search by the Central Library, Central University of Bihar and its impact on the library users. A descriptive survey method was used for the study. The present study found that library users found CUB E-journal One Search as a useful tool to get their desired information out of 9000 subscribed e-resources in the university. Most of the users used CUB E-journal One Search frequently in order to find relevant articles, write their assignments and research articles. The study indicated that the library users were influenced by Google like single search boxes and wished to have same features. Further, users expected features like document recommendation, search filters, RSS and on-screen help from the discovery tool. The paper is a first attempt to study the impact of open source discovery tools on the library users. It will further give confidence to the librarians in developing countries to deploy open source search solutions using Google Custom Search in the libraries.
An Attention-Based Deep Learning Model for Pedestrian Attribute Recognition

Pedestrian attribute recognition, which can be applied in many scenes, has become one of the most popular research fields of computer vision, which can help person re-recognition and pedestrian retrieval. The paper proposes an neural network model with attentional mechanism for the pedestrian attribute recognition. The model consists of two modules: i) an attentional mechanism module to highlight features that are positive to the attribute recognition, which introduces a new weight learning method so that the module adjust the weight of features obtained in the training; ii) an attribute location module adaptively to locate attentional regions from the feature maps, which can localize the attentional region corresponding to individual attribute through the spatial transform module. The experiments achieve mean accuracy of 93.32% and 91.5% on Market1501 dataset and DukeMTMC-reID dataset respectively. The extensive experimental results illustrate the superiority and effectiveness of the proposed method.
NCRG: A Node Classification Method Based on Updated Risk Graph under Hybrid Replica Consistency Strategy

Hybrid replica consistency strategy can effectively deal with the replica consistency problem in dynamic cloud storage. These strategies usually classify nodes according to a feature that affects replica consistency, and then update replica according to the node classification. However, since the replica consistency in dynamic cloud storage is restricted by many factors, the classification method that consider a single factor may affect cloud storage availability. To deal with this problem, this paper proposed a node classification method based on replica update risk map (NCRG for short), which can be used to design a new hybrid consistency method, aiming to improve the replica update timeliness and update probability in dynamic cloud storage. NCRG considers many factors that affect replica consistency, and divides these factors into two types: node service capability and node service willingness. Then, this method builds a replica updating risk map according to this attribute classification. Finally, the nodes are divided into two types: stable nodes and variable nodes according to the position of nodes in the risk map. Extensive experiments show that NCRG can obtain superior performances in replica's probability of receiving updates successfully around 5% on average than other similar methods.
Sentiment Analysis using Deep Learning – A survey

Opinion Mining and Sentiment analysis on textual data are widely carried out. The main aspect is what techniques we use to accentuate the performance of these models. In this paper, we are going to perform multi-class sentiment analysis using Deep Learning models like Long Short Term Memory (LSTM) and C-LSTM on textual data and compare its performance. We also see its results as compared to other Machine Learning models. Performance evaluation technique like parameter tuning using Dropout regularization is carried out to analyze its effect on the accuracy of the model. The models were also trained using three different datasets for observing the results.
Standards and Guides for Implementing Security and Privacy for Health Information Technology

In this chapter, the authors survey security standards and guides applicable to healthcare industry including control objective for information and related technologies (COBIT), ISO/IEC 27001:2005 (which has been revised by ISO/IEC 27001:2013), ISO/IEC 27002:2005 (which has been revised by ISO/IEC 27002:2013), ISO 27799:2008 (which has been revised by ISO 27799:2016), ISO 17090:2008 (which has been revised by ISO 17090:2015), ISO/TS 25237:2008, HITRUST common security framework (CSF), NIST Special Publication 800-53, NIST SP 1800, NIST SP 1800-8, and building code for medical device software security. This survey informs the audience of currently available standards that can guide the implementation of information security programs in healthcare organizations, and provides a starting point for IT management in healthcare organizations to select a standard suitable for their organizations.
Software Security—Exploits and Privilege Escalation

Here we consider common methods that exploit vulnerabilities in (typically non-security) software programs, through abuse of features in programming languages, system architectures, and supporting functionality.
Principles of Modeling

After we have gone through the phases of project and data understanding, we are either confident that modeling will be successful or return to the project understanding phase to revise objectives (or to stop the project). In the former case, we have to prepare the data set for subsequent modeling. However, as some of the data preparation steps are motivated by modeling itself, we first discuss the principles of modeling. Many modeling methods will be introduced in the following chapters, but this chapter is devoted to problems and aspects that are inherent in and common to all the methods for analyzing the data.
Impact of Technology Innovation

Cloud enables computing as a utility by offering convenient, on-demand network access to a centralized pool of configurable computing resources that can be rapidly deployed with great efficiency and minimal management overhead. In order to realize the benefits of the innovative cloud computing paradigm, companies must overcome heightened risks and security threats associated with it. Security and privacy in cloud is complex owing to newer dimensions in problem scope such as multi-tenant architectures and shared infrastructure, elasticity, measured services, viability etc. In this paper, we survey existing literature on cloud security issues and risks which then guides us to provide a section on auditing based to address the identified risks. We also provide a discourse on risk assessment frameworks to highlight benefits using such structured methods for understanding risks. The main contribution of the paper is investigation of current innovations in cloud computing that are targeted towards assisting in effective management of aforementioned risks and security issues. The compilation of discussed solutions has been developed to cater to specific cloud security, compliance and privacy requirements across industries by cloud service providers, software-as-a-service (SaaS) application vendors and advisory firms.
Integrating ACT-R Cognitive Models With the Unity Game Engine

The main aim of the chapter is to describe how cognitive models, developed using the ACT-R cognitive architecture, can be integrated with the Unity game engine in order to support the intelligent control of virtual characters in both 2D and 3D virtual environments. ACT-R is a cognitive architecture that has been widely used to model various aspects of human cognition, such as learning, memory, problem-solving, reasoning and so on. Unity, on the other hand, is a very popular game engine that can be used to develop 2D and 3D environments for both game and non-game purposes. The ability to integrate ACT-R cognitive models with the Unity game engine thus supports the effort to create virtual characters that incorporate at least some of the capabilities and constraints of the human cognitive system.
A More Efficient Deep-learning Processing Unit Architecture with Runtime Configurable Parallelism

Typically, the loop dimension varies greatly between the different layers of a Convolutional Neural Network (CNN). However, the loop dimension’s parallelism degree of most Deep-learning Processing Units (DPUs) is not runtime configurable. It results in low theoretical performance utilization for most high parallelism DPUs. To solve the problem, we propose a new DPU architecture, named Dataflow Driven Multicore Architecture (DDMA). It consists of multiple function cores, and the direction of data flow between them can be configured at runtime by a routing module. This design allows DDMA to improve parallelism degree while maintaining computational efficiency. To verify the property of DDMA, -we designed a Basic-DPU (B-DPU) based on it and obtained the Extended-DPU (E-DPU) by improving the parallelism degree of B-DPU. The experimental results show that the peak performance of B-DPU reaches 512 GOPS, and the computational efficiency reaches 91.9%, -which is 1.69 times that of the peer FPGA implementation under the same test algorithm. The peak performance of E-DPU reaches 1024 GOPS, and the actual performance is 1.96 times that of B-DPU. Meanwhile, its computational efficiency is 90.15%, almost the same as B-DPU.