Reviewerly/exHarmony huggingface dataset:
Solar Resource Evaluation with Numerical Weather Prediction Models

The use of numerical weather prediction (NWP) models for solar resource evaluation is examined. The theory behind NWP models is described highlighting relevant components for solar energy applications as well as how to use NWP models for mapping the solar resource at the regional scale. Future perspectives are briefly outlined.
A virtual test-bed for building Model Predictive Control developments

This paper presents the result of the work performed to develop a virtual test-bed for development and testing of Model Predictive Controllers within the district cooling networks field.These controllers are used for its application in improving the cooling energy efficiency in the network's building.The article explains the use of the different tools to develop and simulate the models with an emphasis on the advantages and challenges of co-simulation and model exchange using the Functional Mockup Interface.
Learning (Can Be) S.W.E.E.T.

The National Teaching & Learning ForumVolume 28, Issue 2 p. 1-3 Learning (Can Be) S.W.E.E.T. Learning (Can Be) S.W.E.E.T. James Rhem, James Rhem Executive EditorSearch for more papers by this author James Rhem, James Rhem Executive EditorSearch for more papers by this author First published: 13 February 2019 https://doi.org/10.1002/ntlf.30183Citations: 4Read 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 Volume28, Issue2February 2019Pages 1-3 RelatedInformation
Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps

Saliency Map, the gradient of the score function with respect to the input, is the most basic technique for interpreting deep neural network decisions. However, saliency maps are often visually noisy. Although several hypotheses were proposed to account for this phenomenon, there are few works that provide rigorous analyses of noisy saliency maps. In this paper, we firstly propose a new hypothesis that noise may occur in saliency maps when irrelevant features pass through ReLU activation functions. Then, we propose Rectified Gradient, a method that alleviates this problem through layer-wise thresholding during backpropagation. Experiments with neural networks trained on CIFAR-10 and ImageNet showed effectiveness of our method and its superiority to other attribution methods.
Parcel Tracking by Detection in Large Camera Networks

Inside parcel distribution hubs, several tenth of up 100 000 parcels processed each day get lost. Human operators have to tediously recover these parcels by searching through large amounts of video footage from the installed large-scale camera network. We want to assist these operators and work towards an automatic solution. The challenge lies both in the size of the hub with a high number of cameras and in the adverse conditions. We describe and evaluate an industry scale tracking framework based on state-of-the-art methods such as Mask R-CNN. Moreover, we adapt a siamese network inspired feature vector matching with a novel feature improver network, which increases tracking performance. Our calibration method exploits a calibration parcel and is suitable for both overlapping and non-overlapping camera views. It requires little manual effort and needs only a single drive-by of the calibration parcel for each conveyor belt. With these methods, most parcels can be tracked start-to-end.
The usage of social network services in school management and their effects

This study, which aims to find out the positive and negative aspects of the usage of social networking services in the management of schools, was designed as a qualitative research. Open-ended questionnaire and semi-structured interview forms were used together with multiple data collection techniques in this study. It was found that some of the schools used the Facebook account as social network service in the past, and then they deactivated their accounts. On the other hand, it was noted that all school principals used WhatsApp application for communication with other administrators and teachers of their schools. The findings of this study revealed that the school principalsâ€™ following positive views such as eliminating the time and space constraints in communication, being fast and safe, being useful in sharing school resources and materials, being easy to manage and being effective in moving the school culture out of the school.Keywords: School management, school culture, technology aided communication, social network services, interaction
Predicting human lexical richness ratings of short French, German, and Portuguese texts using text-based indices

We investigated how well readers’ perceptions of the lexical richness of short texts can be predicted on the basis of automatically computable indices of the texts’ lexical properties. 3,060 French, German and Portuguese texts (between 9 and 284 words long) written by 8- to 10-year-olds were rated for their lexical richness by between 3 and 18 uninstructed raters, and over 150 indices were derived from these texts. We found that the ratings could to a substantial degree be predicted on the basis of these indices and that the accuracy with which the ratings of shorter texts could be predicted was comparable to that of longer texts. For French and German, the greatest predictive power was attained by opaque models with scores of predictors, but models with fewer predictors based on a 6-dimensional framework of lexical richness perception or even with a single, easily computed predictor, Guiraud’s index, fared only slightly worse.
Development of a Software for the Semantic Analysis of Social Media Content

The paper presents a developed intelligent tool for Opinion Mining of social media. In addition, the article presents new algorithms to the hybridization of ontological analysis and methods of knowledge engineering with methods of nature language processing (NLP) for extracting the semantic and emotional component of semi-structured and unstructured text resources. These approaches will improve the efficiency of the analysis of social media content-specific data and fuzziness of natural language. Also the original algorithm for translating the RDF/OWL-ontology into a graphical knowledge base is proposed. In addition, the article presents an approach to the inference on the ontology repository. The approach based on translating the SWRL constructs into the elements of the Cypher language.
Evolutionary Revision Model for Improvement of Computer Science Curriculum

The onrush of information technology raises the task of revising curricula of specialties in computer science. The objectives of the study are to define the requirements and develop a model of the curriculum revision system in computer science. Subject of the research: curriculum revision system in computer science. Research methods: review and analysis of scientific publications, modeling of complex systems, questionnaires, expert method of evaluation. The model of the revision system of the curriculum was created in accordance with the modern requirements of employers to the competence of university graduates in computer sciences. The architecture of this system is based on the research on the functionality of such elements as the Top Competences (required in the labor market), Employers’ Requirements, Learning outcomes (expected results of teachers) and the formation of the ICT curriculum. The expert method is used to assess the quality of the revision model of the curriculum on the example of a specialty in the field of computer science. Results of the research: the requirements of the labor market to the competence of university graduates were investigated, systematized and formulated in the field of computer science. These requirements are the basis for the formation of a new or improved university curriculum.
Teachers’ Reflection: Does The Instructional Technology Implementation Transform Learning?

The development of the digital era requires teachers to insert technology into English instruction. Teachers must be able not only to use the internet and computer software but also to implement technology appropriately in the teaching and learning process in order to help students achieve learning objectives. Many teachers have done inserting technology into English instruction. In addition, research on technology integration in English class has been mushrooming since years ago. However, the evaluation dealing with the implementation of technology in English instruction is limited. This research aims at evaluating how teachers use technology in the teaching and learning process. The evaluation focuses on whether the use of technology has enhanced students’ learning experiences or transformed students’ learning experiences. Four levels of the SAMR model (Substitution, Augmentation, Modification, and Redefinition) are used to assess the implementation. Here, the researchers gauged information from six English teachers of the senior high school in Banyuwangi regency concerning how they use technology in the teaching and learning process.
Quantum dialogue protocol with four-mode continuous variable GHZ state

Quantum dialogue can realize the mutual transmission of secret information between two legal users. In most of the existing quantum dialogue protocols, the information carriers applied in quantum dialogue are discrete variable (DV) quantum states. However, there are certain limitations on the preparation and detection of DV quantum states with current techniques. Continuous variable (CV) quantum states can overcome these problems effectively while improving the quantum channel capacity. In this paper, we propose a quantum dialogue protocol with four-mode continuous variable GHZ state. Compared with the existing CV-based quantum dialogue protocols, the protocol allows two users to transmit two groups of secret information with different lengths to each other simultaneously. The channel capacity of the protocol has been improved as each traveling mode carries two- or four-bits of information. In addition, the protocol has been proved to be secure against information leakage problem and some common attacks, such as beam splitter attack and intercept-and-resend attack.
Database of speech and facial expressions recorded with optimized face motion capture settings

The broad objective of the present research is the analysis of spoken English employing a multiplicity of modalities. An important stage of this process, discussed in the paper, is creating a database of speech accompanied with facial expressions. Recordings of speakers were made using an advanced system for capturing facial muscle motion. A brief historical outline, current applications, limitations and the ways of capturing face muscle motion as well as the problems with recording facial expressions are discussed. In particular, the scope of the present analysis concerns the registration of facial expressions related to emotions of speakers which accompany articulation. The camera system, instrumentation and software used for registration and for post-production are outlined. An analysis of the registration procedure and the results of the registration process was performed. The obtained results demonstrate how muscle movements can be registered employing reflective markers and point at the advantages and limitations of applying FMC (Face Motion Capture) technology in compiling a multimodal speech database. A short discussion pertaining to the usage of FMC as ground truth data source in facial expression databases concludes the paper.
An interview with Indrajit Roy

Indrajit Roy is a staff engineer at Google. He is currently working on peta-scale distributed databases. Previously, he was a principal researcher at HP Labs where he led the development of Distributed R, an open source HP product that brings the benefits of parallelism to data scientists. Roy received his Ph.D. in computer science from UT Austin. He is also an inaugural member of the ACM Future of Computing Academy.
PVGeo: an open-source Python package for geoscientific visualization in VTK and ParaView

PVGeo is an open-source Python package for geoscientific visualization and analysis, harnessing an already powerful software platform: the Visualization Toolkit (VTK) and its front-end application, ParaView.The VTK software platform is well-maintained, contains an expansive set of native functionality, and provides a robust foundation for scientific visualization, yet the development of tools compatible with geoscience data and models has been limited.As a software extension package to VTK and ParaView, PVGeo addresses the lack of geoscientific compatibility by creating a framework for geovisualization.PVGeo aims to make the process of importing geoscience data into VTK-based software fluid and straightforward for users while providing a framework for new features that avoids the typical, ambitious programming endeavor of building VTK software plugins.We have developed this code library, PVGeo, to link geoscientific data and models with VTK-based 3D rendering environments like ParaView: an open-source platform built on top of VTK (Ayachit, 2015).Since VTK is an established and robust visualization platform, it provides a rich toolbox of features common for visualization and spatial analysis across disciplines (Schroeder, Lorensen, & Martin, 2006).Examples of standard features include volume rendering, glyphing, subsetting, K-Means clustering, volume interpolation, iso-contouring, and Virtual Reality (Ayachit, 2015;Schroeder et al., 2006).By linking geoscience to VTK and ParaView, geoscientists can harness all of the native tools within ParaView, and other VTK powered libraries like ParaViewWeb (Jourdain, Ayachit, & Geveci, 2010), VTK.js (Kitware, Inc., 2019), and PyVista (Sullivan & Kaszynski, 2019) or extend that data into new domains like Virtual Reality, as outlined in Figure 1.PVGeo couples geoscientific information to software libraries at the forefront of scientific visualization, which enables scientists to cost-effectively and reproducibly communicate their findings.
Multi-objective cellular particle swarm optimization and RBF for drilling parameters optimization

Wellbore drilling parameters optimization is one of the most important issue in drilling engineering. Rate of penetration or mechanical specific energy was usually utilized as the optimization objective. The rate of penetration directly relates to the drilling cycle, while mechanical specific energy reflects the drilling efficiency. In this paper, except for rate of penetration and mechanical specific energy, the drilling life of bit is also summarized as a comprehensive assessment indicator in wellbore drilling parameters optimization problem. The drilling life of bit is taken into consideration for the design and manufacturing cost of bit compose a significant part of the drilling cost and the bit drilling life greatly influences the drilling efficiency. However, those objectives are usually related in a highly nonlinear relationship and in conﬂict with each other. Thus, a multi-objective cellular particle swarm optimization (MOCPSO) is developed to solve the three-objective drilling parameters optimization problem. Moreover, the radius basis function (RBF) method is employed into the formation parameters identification for rate of penetration model. Performance of MOCPSO is investigated by taken a comparison with multi-objective PSO and non-dominated sorting genetic algorithm-II (NSGA-II). Effect of the four commonly used neighborhood function is also investigated by making contrasts with each other. It can be inferred that MOCPSO is statistically superior to both multi-objective PSO, NSGA-II at the 0.05 level of significance on the wellbore drilling parameters optimization problem. And the four commonly used neighborhood templates perform comparable with each other, and are not statistically different for the drilling parameters optimization problem.
An evolving museum metaphor applied to cultural heritage for personalized content delivery

The aim of this article concerns adaptive and personalized navigation in a cultural heritage database. The theoretical grounding of the proposition relies on cognitive science, particularly constructivism and enaction. The navigation is conducted via an intelligent interface through a 3D “living” museum metaphor. The purpose of this interface is to recommend dynamic cultural heritage objects according to a user profile that is computed online from the interactions that a user has with these objects. To this end, objects are linked to semantic structures that represent relations between cultural heritage concepts. The user profile is described in terms of cultural heritage interests. A prototype of this principle is used to evaluate some of the basic hypotheses of this proposition.
Constructing Odd‐Variable Rotation Symmetric Boolean Functions with Optimal Algebraic Immunity and High Nonlinearity

Rotation symmetric Boolean functions (RSBFs) have attracted widespread attention due to their good cryptographic properties. We present a new construction of RSBFs with optimal algebraic immunity on odd number of variables. The nonlinearity of the new function is much higher than other best known RSBFs with optimal algebraic immunity. The algebraic degree of the constructed n-variable RSBF can achieve the upper bound n-1 when n/2 is odd or when n/2 is a power of 2 for n-11. In addition, the constructed function can possess almost perfect immunity to fast algebraic attacks for n=11, 13, 15
ML Health: Fitness Tracking for Production Models

Deployment of machine learning (ML) algorithms in production for extended periods of time has uncovered new challenges such as monitoring and management of real-time prediction quality of a model in the absence of labels. However, such tracking is imperative to prevent catastrophic business outcomes resulting from incorrect predictions. The scale of these deployments makes manual monitoring prohibitive, making automated techniques to track and raise alerts imperative. We present a framework, ML Health, for tracking potential drops in the predictive performance of ML models in the absence of labels. The framework employs diagnostic methods to generate alerts for further investigation. We develop one such method to monitor potential problems when production data patterns do not match training data distributions. We demonstrate that our method performs better than standard "distance metrics", such as RMSE, KL-Divergence, and Wasserstein at detecting issues with mismatched data sets. Finally, we present a working system that incorporates the ML Health approach to monitor and manage ML deployments within a realistic full production ML lifecycle.
THE NO FREE LUNCH THEOREM: BAD NEWS FOR (WHITE'S ACCOUNT OF) THE PROBLEM OF INDUCTION

Abstract White (2015) proposes an a priori justification of the reliability of inductive prediction methods based on his thesis of induction-friendliness. It asserts that there are by far more induction-friendly event sequences than induction-unfriendly event sequences. In this paper I contrast White's thesis with the famous no free lunch (NFL) theorem. I explain two versions of this theorem, the strong NFL theorem applying to binary and the weak NFL theorem applying to real-valued predictions. I show that both versions refute the thesis of induction-friendliness. In the conclusion I argue that an a priori justification of the reliability of induction based on a uniform probability distribution over possible event sequences is impossible. In the outlook I consider two alternative approaches: (i) justification externalism and (ii) optimality justifications.
A Vocoder-free WaveNet Voice Conversion with Non-Parallel Data

In a typical voice conversion system, vocoder is commonly used for speech-to-features analysis and features-to-speech synthesis. However, vocoder can be a source of speech quality degradation. This paper presents a vocoder-free voice conversion approach using WaveNet for non-parallel training data. Instead of dealing with the intermediate features, the proposed approach utilizes the WaveNet to map the Phonetic PosteriorGrams (PPGs) to the waveform samples directly. In this way, we avoid the estimation errors caused by vocoder and feature conversion. Additionally, as PPG is assumed to be speaker independent, the proposed method also reduces the feature mismatch problem in WaveNet vocoder based approaches. Experimental results conducted on the CMU-ARCTIC database show that the proposed approach significantly outperforms the baseline approaches in terms of speech quality.
Optical attenuation without absorption

When dealing with statistics, postselection and heralding are known to produce counterintuitive results. By monitoring the state of an ensemble of two-level atoms and considering only the events where the atoms did not get excited, it is theoretically shown that one could induce abnormally large attenuation, whereas the opposite would be expected.
Robust ConvNet Landmark-Based Visual Place Recognition by Optimizing Landmark Matching

Visual place recognition (VPR) is a fundamental but challenging problem that has not been solved completely for a long time, especially in a kaleidoscopic environment. Recent advanced works which exploit ConvNet landmarks as a representation of an image for the VPR have demonstrated promising performance under condition and viewpoint changes. In this paper, we propose an improved ConvNet landmark-based VPR with better robustness and higher matching efficiency by extending this method from two aspects. First, we introduce hashing to find global optimal landmark matches for each landmark in the query image to boost the quality of matched landmark pairs. Second, we apply the sequence search for finding the best matches basing on the temporal information attached in both query and reference images. The experiments which conducted on four challengeable benchmark datasets show that our approach significantly enhances the robustness of traditional ConvNet landmark-based VPR and outperforms the state-of-the-art ConvNet feature-based VPR named SeqCNNSLAM. Moreover, our method has higher computing efficiency than previous ConvNet landmark-based VPR.