Reviewerly/exHarmony huggingface dataset:
Online Meta-Forest for Regression Data Streams

Stream learning is essential when there is limited memory, time and computational power. However, existing streaming methods are mostly designed for classification with only a few exceptions for regression problems. Although being fast, the performance of these online regression methods is inadequate due to their dependence on merely linear models. Besides, only a few stream methods are based on meta-learning that aims at facilitating the dynamic choice of the right model. Nevertheless, these approaches are restricted to recommend learners on a window and not on the instance level. In this paper, we present a novel approach, named Online Meta-Forest, that incrementally induces an ensemble of meta-learners that selects the best set of predictors for each test example. Each meta-learner has the ability to find a non-linear mapping of the input space to the set of induced models. We conduct a series of experiments demonstrating that Online Meta-Forest outperforms related methods on 16 out of 25 evaluated benchmark and domain datasets in transportation.
Design and evaluation of an educational virtual reality application for learning how to perform on a stage

This work is focused on the design of a VR application for helping actors during the training phase of a play. The project starts from the analysis of a previous work and from additional research, involving real actors, for evaluating the parameters of interest in an actor's training. These results have been used as the starting point for the design of an immersive VR application, where different interaction techniques have been designed and experimented for supporting the actors. These techniques have been applied to a case study, a play written by the well-known Italian playwright Carlo Goldoni, and tested with a group of high-school students, considering for the evaluation a set of variables which are meaningful for VR experiences and for learning.
An Automatic Type-Inferential General Latent Feature Model

With ever more data becoming available, there has been a recent drive to develop modelling tools for heterogeneous data sets such as electronic healthcare records. Therein appears both Bayesian non-parametric latent feature models, as well as methods for automatically determining the statistical data type (e.g. ordinal or categorical) of the attributes present in the data. We present a model which combines both of these attractive features in an end-to-end framework. By jointly learning the model complexity and statistical type of the data, we demonstrate that redundant information can be discarded while higher accuracy statistical type estimates are produced on real experimental data.
Tool Support for Code Change Inspection with Deep Learning in Evolving Software

Similar changes are common during the development of a project. Many of the changes are similar but appears different based on the local context. During code review the code changes are inspected per each source file. The process of identifying similar code change is time-taking and error-prone. To overcome this problem, we propose Similar Changes Inspection with Deep Learning (SIL) which (1) creates a generalized edit script based on the data and control dependence to (2) identify and summarize similar code changes by (3) modelling a deep learning classifier. In order to train a classifier, we have identified clones of four types from a clone database mined from 25,000 programs. SIL summarizes the changes and identifies the change anomalies. To obtain feedback on the SIL approach, we have conducted an user study with seven Computer Science students. The study revealed that SIL helped these developers to conduct peer code reviews more effectively. SIL is available as an Eclipse plug-in and its demonstration video is available at https://sites.google.com/unomaha.edu/codereview-deeplearning.
Schema-agnostic SPARQL-driven faceted search benchmark generation

In this work, we present a schema-agnostic faceted browsing benchmark generation framework for RDF data and SPARQL engines. Faceted search is a technique that allows narrowing down sets of information items by applying constraints over their properties, whereas facets correspond to properties of these items. While our work can be used to realise real-world faceted search user interfaces, our focus lies on the construction and benchmarking of faceted search queries over knowledge graphs. The RDF model exhibits several traits that seemingly make it a natural foundation for faceted search: all information items are represented as RDF resources, property values typically already correspond to meaningful semantic classifications, and with SPARQL there is a standard language for uniformly querying instance and schema information. However, although faceted search is ubiquitous today, it is typically not performed on the RDF model directly. Two major sources of concern are the complexity of query generation and the query performance. To overcome the former, our framework comes with an intermediate domain-specific language. Thereby our approach is SPARQL-driven which means that every faceted search information need is intensionally expressed as a single SPARQL query. In regard to the latter, we investigate the possibilities and limits of real-time SPARQL-driven faceted search on contemporary triple stores. We report on our findings by evaluating systems performance and correctness characteristics when executing a benchmark generated using our generation framework. All components, namely the benchmark generator, the benchmark runners and the underlying faceted search framework, are published freely available as open source.
Lexicon-Enhanced Transformer with Pointing for Domains Specific Generative Question Answering

Aiming at the problem of inaccurate generation caused by the lack of external knowledge in the generative automatic question answering system, we propose a new answer generation model (LEP-Transformer) that integrates domain lexicon and copy mechanism, which can enable the Transformer to effectively deal with the long-distance dependence of different text granularity and have the ability to reproduce the details of the facts when generating answers. And the experimental results on two different datasets show that the model can alleviate this problem and has ability to model short text and long text sequences simultaneously.
A system for pose analysis and selection in virtual reality environments

Depth cameras provide a natural and intuitive user interaction mechanism in virtual reality environments by using hand gestures as the primary user input. However, building robust VR systems that use depth cameras are challenging. Gesture recognition accuracy is affected by occlusion, variation in hand orientation and misclassification of similar hand gestures. This research explores the limits of the Leap Motion depth camera for static hand pose recognition in virtual reality applications. We propose a system for analysing static hand poses and for systematically identifying a pose set that can achieve a near-perfect recognition accuracy. The system consists of a hand pose taxonomy, a pose notation, a machine learning classifier and an algorithm to identify a reliable pose set that can achieve near perfect accuracy levels. We used this system to construct a benchmark hand pose data set containing 2550 static hand pose instances, and show how the algorithm can be used to systematically derive a set of poses that can produce an accuracy of 99% using a Support Vector Machine classifier.
DR-TiST: Disentangled Representation for Time Series Translation Across Application Domains

In the last few years, a huge progress has been made to achieve image-to-image translation by mapping images from a source domain to a target domain. We exploit the recent progress made in this field to tackle another issue, namely time series translation. This work targets time series translation i.e. maps time series data from a source domain to a target domain. We present our new algorithm DR-TiST, a modified version of DRIT [1], that enables time series translation. We apply DR-TiST to a real word use case where we transfer the time series behavior of a ventilation system to the environmental conditions of a different ventilation system and introduce new evaluation metrics to evaluate its performance. The performance of DR-TiST is compared to CycleGAN-VC [14], a special form of an image-to-image translation algorithm used for voice conversion. We demonstrate that the time series generated by DR-TiST are more realistic than the ones generated by CycleGAN-VC.
Learning-based absolute 3D shape measurement based on single fringe phase retrieval and speckle correlation

Speckle projection profilometry (SPP), which is highly suited for dynamic 3D acquisition, can build the global correspondences between stereo images by projecting a single random speckle pattern. But SPP suffers from the low matching accuracy of traditional stereo matching algorithms which limits its 3D measurement quality and precludes the recovery of the fine details of complex surfaces. For enhancing the matching precision of SPP, in this paper, we propose an end-to-end speckle matching network for 3D imaging. The proposed network first leveraged a multi-scale residual subnetwork to synchronously extract feature maps of stereo speckle images from two perspectives. Considering that the cost filtering based on 3D convolution is computationally costly, the 4D cost volume with a quarter of the original resolution is established and implemented cost filtering to achieve higher stereo matching performance with lower computational overhead. In addition, for the dataset of SPP built for supervised deep learning, the label of the sample data only has valid values in the foreground. Therefore, in our work, a simple and fast saliency detection network is integrated into our end-to-end network, which takes as input the features computed from the shared feature extraction subnetwork of the stereo matching network and produces first a low-resolution invalidation mask. The mask is then upsampled and refined with multi-scale multi-level residual layers to generate the final full-resolution mask. This allows our stereo matching network to avoid predicting the invalid pixels in the disparity maps, such as occlusions, backgrounds, thereby implicitly improving the disparity accuracy for valid pixels. The experiment results demonstrated that the proposed method can achieve fast and absolute 3D shape measurement with an accuracy of about 100um through a single speckle pattern.
Assosiation Rules for Product Sales Data Analysis Using The Apriori Algorithm

To increase sales transactions, the company must be able to compete with other competitors so that it requires an appropriate strategy in carrying out the sales process carried out. In addition to the marketing strategy, the company must be able to analyze the products sold based on the number of sales that have occurred so that the company can see which products are more dominant in consumer demand so that the company can determine a more effective sales strategy. PT. Surya Indah City is a company engaged in the sale of various clothing and accessories. In an effort to increase sales of its products, an analysis is needed to be able to increase company revenue by utilizing sales transaction data it has. To analyze the relationship between clothing products and accessories which are more predominantly sold and other available clothing and accessories products, a data mining algorithm is used, namely the a priori algorithm. With the help of the tanagra application to carry out the calculation process, the dominant product that consumers are interested in can be determined. By using two variables that meet support and minimum confidence, it can be concluded that the most sold products are from the type of clothing, namely clothes and pants. It was concluded that the results of the known final association rules, if you buy a shirt, you will buy pants with 50% support and 75% confidence. If you buy pants, you will buy clothes with 50% support and 85% confidence.
Outliers Detection in Multi-label Datasets

In many knowledge discovery applications, finding outliers, i.e. objects that behave in an unexpected way or have abnormal properties, is more interesting than finding inliers in a dataset. Outlier detection is important for many applications, including those related to intrusion detection, credit card fraud, and criminal activity in e-commerce. Several methods of outlier detection have been proposed, and even many of them from the perspective of Rough Set Theory, but at the moment none of them is specifically intended for multi-label datasets. In this paper, we propose a method that measures the degree of anomaly of an object in a multi-label dataset. This score or measure quantifies the degree of irregularity of an object with respect to the dataset. In addition, a method for generating anomalies in this type of datasets is proposed. From these synthetic datasets, the efficacy of the proposed method is proved. The results show the superiority of our proposal over other methods in the literature adapted to multi-label problems.
Structured light fields 3d imaging with temporal phase unwrapping

The passive depth map recovery based on light field data always suffers from low accuracy and high computational burden due to the noise in sub-aperture images and the narrow disparity between sub-aperture views. To overcome this problem, in this paper, temporal digital fringe projections are used to guide the accurate depth map recovery with light field camera. Since the temporal structured light field casts a set of high signal-to-noise-ratio projections onto the object, the depth map of sub-aperture image can be recovered pixel-by-pixel independently. Therefore, the accuracy of the depth map recovery could be significantly improved. In our propose method, the sub-aperture images with temporal fringe projections are extracted from structured light field information, wrapped phase maps of sub-aperture images are obtained from phaseshifting fringe analysis, the absolute phase map and depth map are recovered from a group of wrapped phase maps with temporal phase unwrapping. In our experiment, when the distance between the light field camera and reference plane is 36 cm, the measurement error of the proposed method is within 0.2 mm. The experimental results validate the effectiveness of the proposed method.
An evolutionary approach to black-box optimization on matrix manifolds

Optimization on matrix manifolds is a class of methods for solving matrix optimization problems, subject to constraints which admit the structure of a Riemannian manifold. These problems are intractable for traditional evolutionary algorithms due to the non-Euclidean nature. This paper generalizes the classical technique of covariance matrix adaptation to matrix manifolds, and proposes a manifold evolution strategy named ManES. By exploiting the manifold structure, we turn an originally constrained problem into a sequence of unconstrained ones in the Euclidean subspace. The proposed algorithm is coordinate-free, in the sense that it is independent of the choice of basis and requires no global coordinate system. All genetic operators take the form of matrix transformations and thus are computationally efficient. The algorithm exhibits state-of-the-art performance on four benchmark problems and one real-world application posed on three different kinds of matrix manifolds.
An Optimized Clustered Based Video Synopsis by Using Artificial Intelligence

The proposed paper is about a static video rundown strategy based on the development of Artificial Bee Colony, which refers to the outline of a video by the most indispensable edges present in that particular video. First the video outlines pixel bunches or regions of interest that capture the most important varieties of substances are differentiated. A tale set of highlights estimated as far as the normal tone estimations of every one of these areas is then used to describe the edges. In view of these highlights, the grouping of casings the Artificial Bee Colony Advancement Measurement divides the video into parts. The serving lengths are increased to the point that all the edges of a specific fragment have comparative highlights, while, the center edges of various sections are essentially not the same as one another. These center edges are viewed as the key-edges of the concerned video. Any excess present in the main outlines chosen is dispelled by looking at their histograms of shades. The proposed work is accepted on this freely accessible SumMe dataset and also, on other hazardously selected web video recordings.
A Noise Adaptive Model for Distantly Supervised Relation Extraction

Relation extraction is an important task in natural language processing. To obtain a large amount of annotated data, distant supervision is introduced by using large-scale knowledge graphs as external resources. The disadvantage is that distant supervision brings a new issue: noise label, which means the labels obtained by distant supervision may be unreliable and the performance of the models decreases significantly on these datasets. To address the problem, we propose a new framework where noise labels are modeled directly by context-dependent rectification strategy. Intuitively, we adjust the labels that might otherwise be wrong in the right direction. In addition, considering the lack of effective guidance in training with noise, we propose a new curriculum learning-based adaptive mechanism. It learns simple relation extraction task first, then takes the reliability of labels into consideration, so that the model can learn more from the data. The experimental results on a widely used dataset show a significant improvement in our approach and outperform current state-of-the-art.
Multi-domain Sentiment Classification on Self-constructed Indonesian Dataset

Domain-dependence limits the application of a well-trained sentiment classifier based on one domain data in other different domains. To solve this problem, multi-domain sentiment classification has received great attention recently. It aims to construct a domain-specific sentiment classifier at once from datasets of multi-domains. However, research on multi-domain sentiment classification mainly focuses on high-resource languages, and there is no research on Indonesian multi-domain sentiment classification. To fill the gap, we constructed an Indonesian multi-domain dataset, including 489,000 reviews from four domains with three sentiment polarities (positive, neutral, and negative), and proposed an integrated model for Indonesian multi-domain sentiment classification. This model is consisted of lemmatization layer, domain-general module, domain-specific module, and domain classifier module. Based on the Indonesian multi-domain dataset, the model was evaluated and compared with baseline methods commonly used in the sentiment analysis of high-resource languages. The effectiveness of some essential components in the model was also verified. The model achieved an average weighted F1 over four domains with 87.24%, outperforming the baseline methods and demonstrating its effectiveness.
Question Classification in a Question Answering System on Cooking

Question Answering is a very actively developing filed of Natural Language Processing. In this field, the most active research work is currently going on with relation to the Question Classification module of a Question Answering System. It plays a very important role in determining the expectations of the user. The aim of question classification is to identify the type of questions and based on question type the expected answer will be extracted from the data. In the past years, question classification was done by using only rule-based approach. This approach was too specific for the users and it was difficult to achieve the purpose. In this paper, we will classify the cooking questions into different classes; the overall system is able to identify the new question type that will able to give a perfect answer of the question by using machine learning approach.
Design and performance of cylindrical-parabolic solar collectors with different control systems

In this study, the advantages and disadvantages of GPS based and fuzzy logic controller-based solar tracking systems were determined and compared. For comparison, the four-cell cylindrical-parabolic solar collector was designed and tested. Then, half of the cells followed the sun with GPS-based solar tracking system, the other half followed the sun with fuzzy logic-based solar tracking system. In the GPS-based solar tracking system, the global coordinates and date-time information obtained from GPS were calculated using the mathematical formulas of the solar motion axis geometry with the “Arduino UNO” controller, and the cells followed the sun. Fuzzy logic-based solar tracking system was designed by using “Waijung Blocksets” and cells followed the sun with “STM32F4 Discovery” controller. As a result of the experiments, it was observed that solar tracking system using fuzzy logic controller is advantageous compared to GPS-based solar tracking system. Since the system using the fuzzy logic controller moves according to the data it receives from the sensors, it shows less angle error, whereas this error is higher in GPS-based system. According to the experimental results, the error averages of the fuzzy system are 0.659 and 0.415, and the error average of the gps system is 1.278 and 0.876.
Reversible Data Hiding Based on Prediction-Error-Ordering

In this paper, we propose a new reversible data hiding scheme called prediction-error-ordering (PEO). Instead of sorting the pixels directly as that in pixel-value-ordering (PVO) series, the obtained prediction errors in each block are sorted in ascending (or descending) order. After sorting, the maximum/minimum value in each block is predicted with the second largest/smallest value. Since the prediction errors are around zero in general, a steeper histogram with a peak value of 0 can be easily obtained, which means that more elements can be expanded for data hiding in the next embedding process. Combined with a new adaptive embedding strategy, the marked images with better visual quality can be obtained compared with those popular PVO series.
Computer vision methods for fragmented skull prototyping: Bio-CAD application

In biomedical domain, human skull fractures caused by accidents or any other reasons are required to reassemble. The process of reassembling the fragmented skulls is called skull prototyping or skull completion, followed by the skull repairing process. In the human body, skulls are the regions that are fragmented along a wide range of geometric information that may be fragile. Resolved skull has been supposed to possess higher conformity, particularly under the area of frontal facial along the subtle substrate. With the development of the computer-aided design (CAD) tool, it is possible to complete the fragmented skulls and/or repair damaged skulls using the various computer vision techniques. The bio-CAD methods are introduced for fragmented skull completion and/or damaged skull repairing. This paper has presented a study of various skull prototyping methods. The main focus of this study is to review the computer vision techniques used for human skull prototyping. A systematic study of the state of methods and their comparative analysis is presented in this paper. The automatic skull completion and/or repairing identified as the critical challenge and requirement according to the reviewed literature.
Tutorial: Distributed Knowledge Graphs for the Web of Things

Knowledge Graph technologies from the Semantic Web and Linked Data stack can help to address the interoperability problem on the Internet of Things and to realise the vision of an interlinked Web of Things. The tutorial covers methods and approaches that allow for data integration on the Internet of Things with a focus on dynamics, distribution, and control.