Reviewerly/exHarmony huggingface dataset:
Solar Power Plant Performance

A key part in the development of any project for deployment a solar power plant is the analysis of the expected energy yield production. The system energy production depends on the plant design, the technology used for power conversion, the solar resource, and the characteristics of the site. Due to the intrinsic variability of the solar resource, the prediction of long-term electricity production is also crucial for the financial evaluation of solar power plants. The energy yield performance is thus the process of predicting the annual average energy output for the lifetime of the solar power plant. For that purpose, a number of system performance models and tools have been developed; many of them are updated regularly. In addition, several international programs deliver recommendations and guidelines for yield performance analysis. Thus, in the case of photovoltaic (PV) plants the PVPS program from the International Energy Agency (IEA) publishes regularly updated reports on many aspects of PV generation ( http://www.iea-pvps.org/ ). In addition, the Sandia National Laboratories is facilitating a collaborative group called PV Performance Modeling Collaborative (PVPMC) with regular activities focused on improving the accuracy of PV performance analysis ( https://pvpmc.sandia.gov/ ). On the other hand, in the case of Concentrated Solar Power (CSP), the SolarPACES program of the IEA ( http://www.solarpaces.org/ ) is developing guidelines for solar thermal energy (STE) yield assessment. This chapter summarizes the main aspects included in the tools and software for estimating yield performance of PV and CSP power plants and the long-term characterization of yield energy, risk analysis, and uncertainty quantification.
Widely Linear Kernels for Complex-valued Kernel Activation Functions

Complex-valued neural networks (CVNNs) have been shown to be powerful nonlinear approximators when the input data can be properly modeled in the complex domain. One of the major challenges in scaling up CVNNs in practice is the design of complex activation functions. Recently, we proposed a novel framework for learning these activation functions neuron-wise in a data-dependent fashion, based on a cheap one-dimensional kernel expansion and the idea of kernel activation functions (KAFs). In this paper we argue that, despite its flexibility, this framework is still limited in the class of functions that can be modeled in the complex domain. We leverage the idea of widely linear complex kernels to extend the formulation, allowing for a richer expressiveness without an increase in the number of adaptable parameters. We test the resulting model on a set of complex-valued image classification benchmarks. Experimental results show that the resulting CVNNs can achieve higher accuracy while at the same time converging faster.
The bring‐your‐own‐device unintended administrator: A perspective from Zimbabwe

Abstract As bring your own device (BYOD) becomes part of workplace tools for employees in Zimbabwe, the responsibility to implement information security management methods, which was traditionally confined to the information technology (IT) employees, has extended to all the employees, who now become unintended administrators because of the usage of their devices. The purpose of this paper is to show how banks can mitigate the information security risks caused by the unintended administrator using the BYOD information security behavioural (BISB) model. A literature review of the BYOD information security and organisational information security culture was conducted. A questionnaire was developed from the literature and sent to 270 bank employees in Zimbabwe. A total of 205 employees participated, and 179 completed the questionnaire. An expert review consisting of chief information officers (CIOs) at banks in Zimbabwe was conducted to evaluate the proposed model. From the literature review, individual traits of attitude, knowledge, and habit, as well as organisational traits of the environment, governance, and training, were identified as key traits that constituted the constructs of the BISB model. The overall theme of this paper is that banks can mitigate the BYOD information security challenges by using of the BISB model.
Big Data Analytics in Smart Living Environments for Elderly Monitoring

Today, data collected in smart-living environments are constantly increasing in the dimensions of volume, velocity and variety, which characterize any big data application. In such a way, it makes sense to investigate big data analytics for elderly monitoring at home. The aim of this study is to conduct a preliminary investigation of state-of-the-art algorithms for abnormal activity detection and change prediction, suitable to deal with big data. The algorithmic approaches, under evaluation and comparison, belong to the three main categories of supervised, semi-supervised and unsupervised techniques. At this purpose, specific synthetic data are generated, including activities of daily living, home locations in which such activities take place, as well as physiological parameters. All techniques are evaluated in terms of abnormality-detection accuracy and lead-time of prediction, using the generated datasets with various kinds of perturbation. The achieved results, even though preliminary, are very encouraging, showing that unsupervised deep-learning techniques outperform traditional (machine learning) ones, with detection accuracy greater than 96% and prediction lead-time of about 15 days in advance.
Designing Behavioral Changes in Smart Cities Using Interactive Smart Spaces

This paper explores potentials of interactive media technology, public spaces, and interactive media art and design as potential drivers for behavioral changes and social innovations within future Smart City ecosystems. Design for Behavior Change as an approach is already accepted in several key areas such as ecology, safety, health, and well-being as well as widely adopted in social design. In this paper two interactive installations created by Predrag K. Nikolic, InnerBody and Before & Beyond, are used to explore the possibilities of employing interactive media art and design in the conceptualization of public spaces and the way it can affect and provoke user behavioral changes. Mostly considering importance of increasing user’s consciousness related to sustainable community development.
An instance of productive overabundance: The plural of some Italian VN compounds

This paper investigates a case of overabundance in the plural cell of an open subclass of Italian VN compounds. The empirical basis includes: (i) a 163-item list of relevant compounds, for which the relative frequency of cell mates has been estimated by means of Web data; (ii) a naming questionnaire based on visual input, with 30 images submitted to about 200 informants, including those of several objects whose names are scarcely established in the lexicon; (iii) a further questionnaire, adapted to each informant, asking for acceptability judgements to detect overabundance at the single speaker's level. Results show that the given subclass of VN compounds provides an instance of systematic and productive overabundance in the Italian morphological system, differently from the examples usually discussed for this language.
Segmentation of Panels in d-Comics

For over a hundred years, comics is presented on paper-based carriers such as magazines and books. With the development of new technologies, the comics industry has the opportunity to embrace a new carrier—the digital environment in electronic devices. However, due to the difference of the carrier, there exist differences between d-Comics (digital comics) and printed comics. One main difference is how the carrier creates segmentation of a sequence of comic panels: the segmentation created by paper is static and exclusive, while the segmentation created by screen-based electronic device may not be static nor exclusive because the same comics can be accessed through electronic devices with different screen sizes. This article describes an online experiment conducted to investigate how panel sequences are segmented in d-Comics. By analyzing the collected data from 80 participants with 4 panel sequences, two types of segmentation of panels in d-Comics were identified and discussed. This finding will further contribute to the design of d-Comics.
Free-Improvised Rehearsal-as-Research for Musical HCI

The formal evaluation of new interfaces for musical expression (NIMEs) in their use by ensembles of musicians is a challenging problem in human-computer interaction (HCI). NIMEs are designed to support creative expressions that are often improvised and unexpected. In the collaborative setting of a musical ensemble, interactions are complex and it can be almost impossible to directly evaluate the impact of interface variations. The evaluation environment also needs to be carefully considered. In the wild, concert pressures and practicalities limit experimental control. In the laboratory, studies may not sufficiently reflect real-world usage to make their conclusions relevant. To address some of these issues, we propose a methodology of rehearsal-as-research to study free improvisation by ensembles of NIME performers. In this methodology, evaluation sessions are structured to mirror established practices for improvisation training and performance development. Such sessions can allow controlled, order-balanced studies with extensive data collection in the style of factorial HCI experiments while preserving the artistic setting of a rehearsal. Experiment design, questionnaires, and objective measures such as session duration will be discussed along with two case studies.
The Lifecycle of a User Transaction in a Hyperledger Fabric Blockchain Network Part 1: Propose and Endorse

The paper describes the happy and the less happy flows of a user transaction through a Hyperledger Fabric Blockchain Network. The full cycle of a user transaction originates from the Client Application (an actor outside the Hyperledger Ledger Network but inside an organization that is part of a set that runs the Hyperledger Fabric Blockchain Network). In the process of making the Hyperledger Fabric Blockchain network knowledge explicit, essential parts of FBM were applied in cooperation with the developers of the Fabric Blockchain platform. The approach was given the name Visual Vocabulary by the Documentation Working Group of Hyperledger Fabric, a visual vocabulary to represent instances of facts.
Deep Multi-Modal Encoder-Decoder Networks for Shape Constrained Segmentation and Joint Representation Learning

Deep learning approaches have been very successful in segmenting cardiac structures from CT and MR volumes. Despite continuous progress, automated segmentation of these structures remains challenging due to highly complex regional characteristics (e.g. homogeneous gray-level transitions) and large anatomical shape variability. To cope with these challenges, the incorporation of shape priors into neural networks for robust segmentation is an important area of current research. We propose a novel approach that leverages shared information across imaging modalities and shape segmentations within a unified multi-modal encoder-decoder network. This jointly end-to-end trainable architecture is advantageous in improving robustness due to strong shape constraints and enables further applications due to smooth transitions in the learned shape space. Despite no skip connections are used and all shape information is encoded in a low-dimensional representation, our approach achieves high-accuracy segmentation and consistent shape interpolation results on the multi-modal whole heart segmentation dataset.
Workshop on Barriers to Interactive IR Resources Re-use (BIIRRR 2019)

Share on Workshop on Barriers to Interactive IR Resources Re-use (BIIRRR 2019) Authors: Toine Bogers Aalborg University Copenhagen, Copenhagen, Denmark Aalborg University Copenhagen, Copenhagen, DenmarkView Profile , Samuel Dodson University of British Columbia, Vancouver, Canada University of British Columbia, Vancouver, CanadaView Profile , Luanne Freund University of British Columbia, Vancouver, Canada University of British Columbia, Vancouver, CanadaView Profile , Maria Gäde Humboldt-Universität zu Berlin, Berlin, Germany Humboldt-Universität zu Berlin, Berlin, GermanyView Profile , Mark Hall Martin-Luther-Universität Halle-Wittenberg, Halle, Germany Martin-Luther-Universität Halle-Wittenberg, Halle, GermanyView Profile , Marijn Koolen Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands Royal Netherlands Academy of Arts and Sciences, Amsterdam, NetherlandsView Profile , Vivien Petras Humboldt-Universität zu Berlin, Berlin, Germany Humboldt-Universität zu Berlin, Berlin, GermanyView Profile , Nils Pharo Oslo Metropolitan University, Oslo, Norway Oslo Metropolitan University, Oslo, NorwayView Profile , Mette Skov Aalborg University, Aalborg, Denmark Aalborg University, Aalborg, DenmarkView Profile Authors Info & Claims CHIIR '19: Proceedings of the 2019 Conference on Human Information Interaction and RetrievalMarch 2019 Pages 389–392https://doi.org/10.1145/3295750.3298965Published:08 March 2019Publication History 1citation75DownloadsMetricsTotal Citations1Total Downloads75Last 12 Months8Last 6 weeks1 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
“What Is Optical Flow For?”: Workshop Results and Summary

Traditionally, computer vision problems have been classified into three levels: low (image to image), middle (image to features), and high (features to analysis) [11]. Some typical low-level vision problems include optical flow [7], stereo [10] and intrinsic image decomposition [1]. The solution to these problems would then be combined to solve higher level problems, such as action recognition and visual question answering.
Machine learning: Great opportunities, but will it replace nurses?

International Journal of Nursing PracticeVolume 25, Issue 1 e12725 EDITORIAL Machine learning: Great opportunities, but will it replace nurses? Lin Perry PhD, MSc, RN, Corresponding Author Lin Perry PhD, MSc, RN Professor, Editor-in-Chief Lin.Perry@uts.edu.au orcid.org/0000-0002-8507-1283 International Journal of Nursing PracticeSearch for more papers by this author Lin Perry PhD, MSc, RN, Corresponding Author Lin Perry PhD, MSc, RN Professor, Editor-in-Chief Lin.Perry@uts.edu.au orcid.org/0000-0002-8507-1283 International Journal of Nursing PracticeSearch for more papers by this author First published: 08 February 2019 https://doi.org/10.1111/ijn.12725Citations: 1Read the full textAboutPDF 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 No abstract is available for this article.Citing Literature Volume25, Issue1February 2019e12725 RelatedInformation
Clustering Mixed Data Based on Density Peaks and Stacked Denoising Autoencoders

With the universal existence of mixed data with numerical and categorical attributes in real world, a variety of clustering algorithms have been developed to discover the potential information hidden in mixed data. Most existing clustering algorithms often compute the distances or similarities between data objects based on original data, which may cause the instability of clustering results because of noise. In this paper, a clustering framework is proposed to explore the grouping structure of the mixed data. First, the transformed categorical attributes by one-hot encoding technique and normalized numerical attributes are input to a stacked denoising autoencoders to learn the internal feature representations. Secondly, based on these feature representations, all the distances between data objects in feature space can be calculated and the local density and relative distance of each data object can be also computed. Thirdly, the density peaks clustering algorithm is improved and employed to allocate all the data objects into different clusters. Finally, experiments conducted on some UCI datasets have demonstrated that our proposed algorithm for clustering mixed data outperforms three baseline algorithms in terms of the clustering accuracy and the rand index.
A Chaotic Cryptosystem for Color Images Using Pixel-Level and Bit-Level Pseudo-Random Permutations

This paper, we present a novel color image encryption algorithm based on pseudorandom permutations intra and inter pixels. Firstly, confusion by modification of pixel values in the image is applied by a permutation via a bijective function in the ring $$ {\mathbb{Z}}/8{\mathbb{Z}} $$ . Secondly, permutation is applied to all pixels in the image by the same function but this time in the ring $$ {\mathbb{Z}}/\varvec{n}{\mathbb{Z}} $$ to dispel the redundancy on the entire image. Finally, a second chaotic confusion is applied to the different pixels in the image. The results show that the algorithm effectively reduces the correlations between plain and ciphered image and therefore can encrypt the color image more effectively.
Dynamic Determinants of Access to Weapons: Global Evidence

This study investigates the determinants of and persistence in access to weapons using a global sample of 163 countries for the period 2010–2015. The empirical evidence is based on the generalized method of moments. Hysteresis in access to weapons is consistently more apparent in countries with below-median levels in access to weapons compared to their counterparts with above-median levels in access to weapons. The hysteresis hypothesis within this context is the propensity of past values of access to weapons to influence future values of access to weapons. Factors that consistently drive access to weapons are: perceptions of crime, criminality, conflict intensity, political instability, military expenditure, violent demonstrations and terrorism. The effects of these drivers are contingent on initial levels of access to weapons. Policy recommendations for managing access to weapons are discussed. JEL: H56, L64, K42, P50
Statistical Comparison of Opinion Spam Detectors in Social Media with Imbalanced Datasets

Sentiment analysis is a growing research area that analyzes people’s opinions towards a specific target using posts shared in social media. However, spammers can inject false opinions to change sentiment-oriented decisions, e.g. low quality products or policies can be promoted or advocated over others. Therefore, identifying and removing spam posts in social media is a crucial data cleaning operation for text mining tasks including sentiment analysis. An inherent problem related to spam detection is the imbalanced-class problem. In this paper, we explore the impact of imbalance ratio on the performance of Twitter spam detection using multiple approaches of single and ensemble classifiers. Besides ensemble-based learning (Bagging and Random forest), we apply the SMOTE oversampling technique to improve detection performance especially for classifiers sensitive to imbalanced datasets.
Temporary Book Storage Using a Document Management Company

Finding an appropriate temporary storage location for books and providing access to them is a significant challenge for libraries undergoing renovations. The current article describes the use of a document management company by an academic library to store 430,000 books for 8 months and provide a retrieval-on-demand service. Aspects covered include the selection of a commercial storage provider with its own retrieval service; details of the move to and from offsite storage; the integration of the service with library processes; communications and user feedback; book usage during the storage period; overall costs; and lessons learned from the experience.
Visualising the<i>Titanic</i>Disaster

Abstract The sinking of the Titanic has inspired books, movies and documentaries. But it has also motivated data visualisation designers to tell the story of the tragedy in new ways. Michael Friendly, Jürgen Symanzik and Ortac Onder review the first graph of the disaster and some recent history
Enhancing Semantic Word Representations by Embedding Deep Word Relationships

Word representations are created using analogy context-based statistics and lexical relations on words. Word representations are inputs for the learning models in Natural Language Understanding (NLU) tasks. However, to understand language, knowing only the context is not sufficient. Reading between the lines is a key component of NLU. Embedding deeper word relationships which are not represented in the context enhances the word representation. This paper presents a word embedding which combines an analogy, context-based statistics using Word2Vec, and deeper word relationships using Conceptnet, to create an expanded word representation. In order to fine-tune the word representation, Self-Organizing Map is used to optimize it. The proposed word representation is compared with semantic word representations using Simlex 999. Furthermore, the use of 3D visual representations has shown to be capable of representing the similarity and association between words. The proposed word representation shows a Spearman correlation score of 0.886 and provided the best results when compared to the current state-of-the-art methods, and exceed the human performance of 0.78.
Motion Segmentation Using Spectral Clustering on Indian Road Scenes

We propose a novel motion segmentation formulation over spatio-temporal depth images obtained from stereo sequences that segments multiple motion models in the scene in an unsupervised manner. The motion segmentation is obtained at frame rates that compete with the speed of the stereo depth computation. This is possible due to a decoupling framework that first delineates spatial clusters and subsequently assigns motion labels to each of these cluster with analysis of a novel motion graph model. A principled computation of the weights of the motion graph that signifies the relative shear and stretch between possible clusters lends itself to a high fidelity segmentation of the motion models in the scene.
Huggy Pajama: Remote Hug System for Family Communication

Touch is a quintessential expression of love and intimacy. It comprises a large part of non-verbal human communication, and plays a signification role in conveying emotions, building relationship bonds, and improving mental and physical well-being. A major technical challenge in making robots and virtual characters that interact with humans on a physical and emotional level is mutual robotic touch. Novel touch sensing and actuation systems need to be developed in order to endow such digital partners with this emotional sense. This chapter presents an early work of a series of projects on digital touch we have been working on in our lab over the last ten years or so. Huggy Pajama is a remote hugging communication system that allows family members to hug each other over the Internet. The input interface is a small, mobile device that measures human touch force, whereas the output interface is a wearable jacket with embedded pneumatic actuators for force actuation. The results of this work made it possible to substantiate the development of subsequent robotic touch communication systems.