Reviewerly/exHarmony huggingface dataset:
Performance of photovoltaic systems integrated in buildings (BIPV): a case study on campus Itaperuna of the Fluminense Federal Institute Brazil

This article analyzes the energy generation of a photovoltaic system integrated on the rooftop of buildings at the Itaperuna Federal Fluminense Institute (IFF) campus, Rio de Janeiro, Brazil. The simulations were conducted on the PVSyst software (test version). The azimuthal positions of the buildings, the slope of the roof and the geographic location were considered, considering a system of 18.48 kWp. The results revealed that the system installed in scenario III is the one that presented best performance, considering an integrated installation to the architecture.
Can Expert Opinion Improve Effort Predictions When Exploiting Cross-Company Datasets? - A Case Study in a Small/Medium Company

Many studies have shown that the accuracy of the predictions obtained by estimation models built considering data collected by other companies (cross-company models) can be significantly worse than those of estimation models built employing a dataset collected by the single company (within-company models). This is due to the different characteristics among cross-company and within-company datasets. In this paper, we propose an approach based on the opinion of the experts that could help in the context of small/medium company that do not have data available from past developed projects. In particular, experts are in charge of selecting data from public cross-company datasets looking at the information about employed software development process and software technologies. The proposed strategy is based on the use of a Delphi approach to reach consensus among experts. To assess the strategy, we performed an empirical study considering a dataset from the PROMISE repository that includes information on the functional size expressed in terms of COSMIC for building the cross-company estimation model. We selected this dataset since COSMIC is the method used to size the applications by the company that provided the within-company dataset employed as test set to assess the accuracy of the built cross-company model. We compared the accuracy of the obtained predictions with those of the cross-company model built without selecting the observations. The results are promising since the effort predictions obtained with the proposed strategy are significantly better than those obtained with the model built on the whole cross-company dataset.
Pattern similarities of vector matrices

A concept of vector matrix or matrix of vector is introduced. It is a well-known matrix where the elements are vectors. An example of vector matrix is a digital image represented in a matrix Am×n. The elements of A are vectors in R3 represent color in red, green, blue, and m × n is the number of pixels. Similarity of two matrices is defined by the similarity of each corresponding elements. Such definition is very strict, two similar matrices must have the same vector for each corresponding elements. Applying for digital images, two similar matrices are actually the same image. Less similarity concept is introduced, namely pattern similarity. Pattern similarity is a generalization of strict similarity by applying a pattern function. An application of pattern similarity is for object recognition.
Engineering a Distributed Histogram Sort

Sorting is one of the most critical non-numerical algorithms and covers use cases in a wide spectrum of scientific applications. Although we can build upon excellent research over the last decades, scaling to thousands of processing units on modern many-core architectures reveals a gap between theory and practice. We adopt ideas of the well-known quickselect and sample sort algorithms to minimize data movement. Our evaluation demonstrates that we can keep up with recently proposed distribution sort algorithms in large-scale experiments, without any assumptions on the input keys. Additionally, our implementation outperforms an efficient multi-threaded merge sort on a single node. Our implementation is based on a C++ PGAS approach with an STL-like interface and can easily be integrated into many application codes. As part of the presented experiments, we further reveal challenges with multi-threaded MPI and one-sided communication.
Machine learning modeling assistance for non-expert developers

Machine learning is one of crucial components for intelligent service development. This paper is concerned with assisting nonexpert developers to develop machine learning models. It introduces a modelling assistant tool that guides developers to make design choices in the course of machine learning model development process. The tool presents questions along with candidate answers about what to do at each phase and asks the developer to make choice of a candidate with additional associated information editing. For intelligent assistance, we have integrated and organized the knowledge for machine learning model development into the tool. The tool has been designed so as to support incremental integration of machine learning development knowledge. It supports such machine learning tasks as classification, regression, clustering, and so on for some well-known machine learning algorithms. It also partially generates program codes into which developers edit additional code, if needed.
Breast surface variation phase map analysis with digital fringe projection

Breast carcinoma has become one of the most frequently diagnosed life threatening cancer among women. Early detection of breast cancer is highly essential with the aid of non-contact imaging modalities. Recently, non-contact breast imaging methods based on fringe projection has been developed for breast surface change inspection. In this work, a non-contact digital fringe projection imaging modality that utilizes phase shifting fringe patterns for identifying surface changes has been developed for investigating breast surface changes caused by the presence of tumors. A medical grade prosthetic breast was used as the experimental subject and subjugated to breast changes through the enlargement of a round shaped silicon catheter as a tumor. The fringes were projected onto the breast surface based on the three steps phase shift fringe projection. The fringe patterns consisted of a large fringe width to enable the breast image to be confined in fewer fringe patterns. A resulting phase map was obtained where pixel coordinate marking was conducted on the phase map breast image. Each of the pixel's coordinate was compared to identify the location of the surface changes. A range of 2-9 pixel coordinate shifts from the 0.5 – 2 cm tumor growth were obtained from the results which demonstrated the capability of using phase map analysis from digital fringe projection in identifying surface changes of the women's breast.
Pseudo Label Guided Subspace Learning for Multi-view Data

Multi-view spectral clustering methods could utilize the complementary information from different views to increase the robustness of clustering performances. Graph structures are usually revealed as affinity matrices. A pseudo label guided spectral embedding algorithm (PLGS) is proposed in this paper to enhance the consistence between graph matrices and spectral clustering results. Through iteratively estimating the pseudo labels of all samples and similarity matrices, the cluster assignment vector could be calculated with more confidence. Extensive experimental results on several benchmark datasets show promising performance and verify the effectiveness of our method.
Application of Computational Linguistics Techniques for Improving Software Quality

Progress in Artificial Intelligence, Big Data and Computational Linguistics domains offered new way to perform n-depth analysis and evidence-based quality assessments of open source software components. In this paper we will see how this can be integrated into industrial development to improve the quality of developed software.
Hardware-Based Fast Hybrid Morphological Reconstruction

The optimized hybrid (FH) morphological reconstruction algorithm used in image analysis with FPGA and ASIC implementation is discussed in this article. FPGA enables a fast prototyping and, today, even the introduction to the market dementedly on the market volume. The complex algorithm is analyzed according to its efficient realization in terms of performance and power consumption.
Monocular Piecewise Depth Estimation in Dynamic Scenes by Exploiting Superpixel Relations

In this paper, we propose a novel and specially designed method for piecewise dense monocular depth estimation in dynamic scenes. We utilize spatial relations between neighboring superpixels to solve the inherent relative scale ambiguity (RSA) problem and smooth the depth map. However, directly estimating spatial relations is an ill-posed problem. Our core idea is to predict spatial relations based on the corresponding motion relations. Given two or more consecutive frames, we first compute semi-dense (CPM) or dense (optical flow) point matches between temporally neighboring images. Then we develop our method in four main stages: superpixel relations analysis, motion selection, reconstruction, and refinement. The final refinement process helps to improve the quality of the reconstruction at pixel level. Our method does not require per-object segmentation, template priors or training sets, which ensures flexibility in various applications. Extensive experiments on both synthetic and real datasets demonstrate that our method robustly handles different dynamic situations and presents competitive results to the state-of-the-art methods while running much faster than them.
Validating the Enlarged Kyd Canon: a New Approach

The canon of plays traditionally attributed to the Elizabethan playwright Thomas Kyd consists of The Spanish Tragedy (1587), Soliman and Perseda (1588), and Cornelia (1594).1 In 2008, Brian Vickers...
Good Practices in Aligning Software Engineering Research and Industry Practice

There is a long-standing challenge to narrow the gap between software engineering research and industry practice, to align their interests and realize true synergies between the two communities. Some difficulties to this challenge include mismatched agendas, priorities and expectations from the research collaboration on both sides. To overcome these difficulties, an initial step is to gain a clearer understanding of collaboration challenges from both perspectives. With this goal in mind, we organized the 5th International Workshop on Software Engineering Research and Industrial Practice, collocated with the International Conference on Software Engineering 2018. The workshop featured two keynote talks, one from industry and one from academia, followed by paper presentations and a round-table discussion session. Here we summarize experiences shared by the keynotes from industry and academia, along with findings from paper presentations and overall discussions by workshop participants on the ways of aligning software engineering research and industry practice.
A Study on Creating a Successful Classroom Learning Community via Action Research

The studies of students learning together in learning community (LC) programmes have been widely explored.Implementing and evaluating LC programmes challenge teachers considerably.This paper explains how action research was used to design, trial and refine a same-year LC programme to motivate students.The LC programme was successfully integrated into a course of English in 2017 and 2018 about 8 months in span.The paper demonstrates the value of action research for designing the LC programme to increase student motivation.In the meanwhile, the role of transformational teacher leadership and peer leaders played in a successful learning community is elaborated as well.Key elements influencing the success of the programme were: task-based learning activities; appointment of peer leaders; the provision of structured and unstructured student support; teacher assessment and peer assessment; and the use of interactive tutorials.Achieving the appropriate balance of these factors increased students' motivation in their English learning.
Phonetic Normalization for Machine Translation of User Generated Content

We present an approach to correct noisy User Generated Content (UGC) in French aiming to produce a pre-processing pipeline to improve Machine Translation for this kind of noncanonical corpora.Our approach leverages the fact that some errors are due to confusion induced by words with similar pronunciation which can be corrected using a phonetic lookup table to produce normalization candidates.We rely on a character-based neural model phonetizer to produce IPA pronunciations of words and a similarity metric based on the IPA representation of words that allow us to identify words with similar pronunciation.These potential corrections are then encoded in a lattice and ranked using a language model to output the most probable corrected phrase.Compared to other phonetizers, our method boosts a Transformer-based machine translation system on UGC.
Earthquake Hazard Perception Of The Education Stakeholders In Sukabumi Regency, West Java, Indonesia

Abstract The western part of Java is adjacent to the intersection of major tectonic plates between Eurasia and Indo-Australia. This region is often stricken by natural disasters due to its tectonic and geological setting, such as earthquakes, tsunamis, volcanic eruptions, floods, and landslides. West Java Province with more than 40 million inhabitants is the most populous region in Indonesia, where Sukabumi Regency in the southwestern part of this province is known for the frequent location of earthquake. In the southwestern part of Sukabumi Regency, the Ciletuh-Pelabuhan Ratu Geopark has been recognized as one of the newest UNESCO Global Geopark. This study concerns about assignment of Geopark area, which will increase the visit of tourists despite its vulnerable condition to earthquake event. Schools as part of education sector in the study area are functioned as central location for people-based education as well as shelter when disaster happens. This paper is aimed to reveal the perception of earthquake hazard and knowledge among school community as disaster stakeholders. This study is carried out on schools near the vulnerable areas to earthquake events. Several schools had been visited and examined the curriculum, disaster preparedness for earthquake, and mitigation activities. The earthquake brochures and GIS-based vulnerability maps were distributed to students and school’s library. Discussion about the maps with the teachers and students are conducted to examine the understanding of contents as well as to obtain their perception about earthquake. Results showed that earthquake vulnerability map increased the knowledge and gained positive perception on how to respond during earthquake event because initially most of participants were unaware what to do when earthquake happen. This result suggests that dissemination and improvement on perception to respond the earthquake are deeply needed. The school community as disaster stakeholder can play an important role on community-based disaster risk mitigation.
Logistic regression for potential modeling

Abstract Regression or regression‐like models are often employed in potential modeling, i.e., for the targeting of resources, either based on 2D map images or 3D geomodels both in raster mode or based on spatial point processes. Recently, machine learning techniques such as artificial neural networks have gained popularity also in potential modeling. Using artificial neural networks, decent results in the prediction of the target event are obtained. However, insight into the problem, e.g., about importance of specific covariables, is difficult to obtain. On the other hand, logistic regression has a well understood statistical foundation and works with an explicit model from which knowledge can be gained about the underlying problem. However, establishing such an explicit model is rather difficult for real world problems. We propose a model selection strategy for logistic regression, which includes nonlinearities for improved classification results. At the same time, interpretability of the results is preserved.
Surface parameters measurement for braided composite preform based on gray projection

In this article, a method based on gray projection is proposed for automatic measurement of surface braiding angle and pitch length of braided composite preforms. The surface braiding angles are measured by rotated gray projection. First, the original image is preprocessed using Lab transform and block-matching and three-dimensional filter. Then, edge map of gray scale is acquired based on phase congruency and non-maximum suppression. Third, edge direction angles are computed by image rotation and gray projection. Finally, the average surface braiding angles are measured from the edge direction angles. The pitch lengths are measured by cropped gray projection. First, the original image is filtered by an artistic edge and corner enhancing filter. Second, block images are cropped based on gray projection. Finally, pitch lengths are measured by block images gray projection. Experimental results show that the proposed method can achieve the automatic measurement of average surface braiding angle and average pitch length of two-dimensional braided composite preform with high accuracy.
A Novel Watermarking Algorithm for Color Point-Cloud Models Based on 2D-DCT

As a result of the development of 3D color printers, three-dimensional color point-cloud models tends to be digitization. At the same time, the copyright protection is becoming more prominent. Current researches on digital watermarking for color point-cloud models have not been found in the literature libraries. A new digital watermarking scheme for three-dimensional color point-cloud models is proposed based on two-dimensional DCT (2D-DCT) transform to achieve an appropriate trade-off between transparency and robustness. Some effective points selected is firstly projected for the color three-dimensional point-cloud model and then a 2D color image is obtained. In the color image, the watermarking information is inserted by modifying the coefficient of 2D-DCT and then the image with watermarking information is mapped back to the color three-dimensional point-cloud model. Experimental results show the proposed method can successfully embed and extract the watermark information in color three-dimensional point-cloud model. The algorithm outperforms other three-dimensional point-cloud model watermarking methods in terms of imperceptibility and robustness, especially for against geometric attack.
Automated Classification of Attacker Privileges Based on Deep Neural Network

Attack graphs generated from the detected vulnerabilities in a network depict all possible attack paths that an intruder can take. Conventional approaches to generating attack graphs require well-categorized data of prerequisites and postconditions for the identified vulnerabilities. However, generating them in an automated way is an open issue. Hence automatic classification methods are desirable to effectively generate attacker privilege fields as prerequisites and postconditions, improve the generation of the attack graph, and reduce the security risks of the system. In this paper, we propose a new automatic attacker privilege model (IG-DNN). The information gain (IG) is used for obtaining an optimal set of feature words from vulnerability description, and the deep neural network model is served as an automatic attacker privilege classifier. We use the National Vulnerability Database (NVD) to validate the effectiveness of the IG-DNN model. We observe that prerequisite and postcondition privileges can be generated with overall average F-measure of 99.53% and 98.90% with the IG-DNN models, respectively. Moreover, compared with Naïve Bayes, KNN, and SVM, the IG-DNN model has achieved the best performance in precision, recall, and F-measure.
Multispectral demosaicing via non-local low-rank regularization

Demosaicing is an essential technique in filter array (FA) based color and multispectral imaging. It aimes to recover missing pixels at different spectrum bands. The existing methods are limited to specific FAs and local regularization. To enhance generalization on different FA structures and improve reconstruction quality, here we present a non-local low-rank regularized demosaicing method, based on the non-local grouped sparsity of natural images. Specifically, the optimization model consists of two parts, including the regularization term of image formation model, and the low-rank term of non-local grouped image patches. The two terms ensure to remove noise and distortion while preserving image details. The model is solved by the weighted nuclear norm minimization and the alternating direction multiplier method framework. Experiments validate that the proposed algorithm has good generalization performance on both different FA patterns and channel numbers. The reconstruction accuracy is improved compared with the existing demosaicing algorithms.
Developing Robust Nonlinear Models through Bootstrap Aggregated Deep Belief Networks

The development of data-driven process models based on bootstrap aggregated deep belief networks (BAGDBN) is presented in this paper. In developing a BAGDBN model, the original data are replicated by using bootstrap resampling with replacement technique. The replications of original processes data are utilized for the developments of individual DBNs. These DBN models are combined to form BAGDBN. A BAGDBN model can give more robust and accurate estimations and predictions of chemical process variables compared with conventional deep belief networks (DBN). The effectiveness of this novel modelling approach is demonstrated using two application examples, inferential estimation of polymer melt index in an industrial polypropylene polymerization process and modelling a conic water tank.
An Efficient Algorithm for Enumerating Maximal Bicliques from a Dynamically Growing Graph

Enumerating maximal biclique subgraphs from a graph is substantially important in many fields such as web mining, business intelligence, bioinformatics, social network analysis and so on. Although many previous studies have contributed much to efficient enumerating maximal biclique subgraphs from a graph, all of them proposed their algorithms based on a large static graph which remained unchanged in their application scenarios. In the real world, the social networks and other models have often dynamically changed thus either vertices or edges will be added to or removed from the corresponding graph. Therefore, these traditional approaches are not effective because they will repeat a complete enumeration process which has much repetitive work done in the previous enumeration. To reduce repetitive work and achieve high efficiency, we proposed a novel algorithm for enumerating maximal bicliques in a dynamically changed graph. Rather than to search the whole graph from first to last, we only search for newly created or reduced maximal bicliques based on the results enumerated before the change of the graph. In this paper, we proved the rightness of our methods and performed many simulations compared with other algorithms only for static graphs. The results demonstrated that our methods achieved high efficiency for dynamically changed graphs.
Look for Saliency In Hyperspectral Images

Image saliency detection plays an important role in many vision tasks such as object detection, visualization, and classification. Many existing methods are applicable to trichromatic or grayscale images, but most of them are failure to remote sensing hyperspectral images with hundreds of bands. In this paper, a novel purity-aware saliency method is specifically proposed for hyperspectral images. Firstly, an autoencoder model for unmixing is designed to extract the abundance. Then, an abundance fraction vector norm is calculated as pixel spectral purity. On real-world hyperspectral images, our saliency detection method achieves good performance in holding structure information and purity distribution.