Reviewerly/exHarmony huggingface dataset:
Integration of concentration-area fractal model and relative absorption band depth method for mapping hydrothermal alterations using ASTER data

Those image processing methods which use both spatial and spectral properties of remotely sensed images often increase accuracy of image classification. In this study, the concentration-area (C-A) fractal model and Relative absorption Band Depth (RBD) were applied to map hydrothermal alteration zones using ASTER data covering the northern part of Rabor area, Iran. Based on the field observations and spectral analysis of rock samples, three types of RBD were calculated and applied to map hydrothermal altered rocks (RBD6 for phyllic, RBD8 for propylitic, and RBD5 for argillic). A log-log plot of area-digital number value for RBD images showed that there are three populations in each image. Representation of these populations revealed that the third population of each RBD has enhanced separation of alteration zones. To verify the results, a spectral library of ground rock samples was built and used to compare with 10 spectra derived from each image. Spectral Angle Mapping, Spectral Feature Fitting, and Binary Encoding were applied to assess the matching of image spectra with the spectral library spectra. Results confirmed that third population of RBD6 and RBD8 contain pure pixels of phyllic and propylitic alterations, respectively whereas there are some interference between argillic and phyllic alterations in RBD5.
Variational DNN embeddings for text-independent speaker verification

In state-of-the-art text-independent speaker verification systems, a discriminative deep neural network (DNN) model learns speaker-discriminative representations (x-vectors) for utterances using labeled data. For the verification task, a Probabilistic Linear Discriminant Analysis (PLDA) model is used to decide whether two x-vectors come from the same speaker. The PLDA scoring model assumes Gaussian priors and conditional distributions across speakers, which is not the case for x-vectors. This work introduces a variational-based regularization term that encourages the network to generate embeddings that follow a desired prior distribution. The regularization function performs a non-parametric match between the embeddings generated in the mini-batch training and a sample from the desired distribution. Unlike Variational Auto Encoders (VAEs), no distribution parameter is necessary to be learned, and no sampling schema is employed, which makes the proposed method flexible for different desired distributions. Our experiments compared the proposed method with the standard x-vectors system jointly with other approaches recently proposed to generate Gaussianized representations. We assessed the verification performance of the systems using the Fisher English Training Speech - Part II database in eight test conditions based on the gender of the speakers and the duration of the speech segments. Besides using the standard Gaussian distribution as prior, we also applied a less strict distribution (uniform). The proposed method outperformed others in all test conditions for both distributions, with gains of performance between 7.68% and 20.49% when compared to the standard x-vectors. To understand the effect of the regularizations into the embeddings space, we also conducted a 2-dimensional visual comparison, which showed clusters with better quality when the regularizations were applied.
A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention

Graph classification has been a classical problem of interest in machine learning and data mining because of its role in biological and social network analysis. Due to the recent success of graph neural networks for node classification and representation, researchers started extending them for the entire graph classification purpose. The main challenge is to represent the whole graph by a single vector which can be used to classify the graph in an end-to-end fashion. Global pooling, where node representations are directly aggregated to form the graph representation and more recently hierarchical pooling, where the whole graph is converted to a smaller graph through a set of hierarchies, are proposed in the literature. Though hierarchical pooling shows promising results for graph classification, it looses a significant amount of information in the hierarchical architecture. To address this, we propose a novel hybrid graph pooling architecture, which finds the importance of different hierarchies of pooling and aggregates them accordingly. We use a series of graph isomorphism networks, along with a bi-directional LSTM with self attention to implement the proposed hybrid pooling. Experiments show the merit of the proposed architecture with respect to a diverse set of state-of-the-art algorithms on multiple datasets.
DID: A Nested Dense in Dense Structure with Variable Local Dense Blocks for Super-Resolution Image Reconstruction

The success of single image super-resolution reconstruction (SR) relies on a refined mapping from low-resolution (LR) examples to high-resolution (HR) signals. However, the relation is sometimes chaos, especially in a deep SR network. We try to improve the mapping interpretability in two folds: i) The variable local dense blocks (VLDB) are suggested to match receptive fields in different depths of a residual dense network (RDN), with each VLDB a dyadic increment of layer numbers than its predecessor. ii) Based on VLDBs, a dense in dense (DID) network is created. It substitutes nodes in a regular RDN with super nodes, i.e. VLDBs; and formulates a joint learning by flexible hierarchical feature scaling, reusing and long-short term aggregating. VLDBs deal with feature underfitting occurred when a big receptive field meets a fixed-depth dense block, and the DID network provides a relative complete feature dictionary to preserve details for feature shift, dilating and grouping in high dimension image reconstruction. To demonstrate the validness of DID structure, detail experiments are performed on the benchmark datasets Set5, Set14, B100 and Urban100, where the accuracy PSNR and the visual perceptive SSIM are superior to most state-of-the-art methods. Besides, due to the depth adaption of VLDBs and its nesting in generalized RDN, DID network is converged easily and gradient explosion or disappearance are alleviated even when the network deepens.
Program Comprehension and Code Complexity Metrics: A Replication Package of an fMRI Study

In this artifact, we document our publicly shared data set of our functional magnetic resonance imaging (fMRI) study on programmers. We have conducted an fMRI study with 19 participants observing program comprehension of short code snippets at varying complexity levels. We dissected four classes of code complexity metrics and their relationship to neuronal, behavioral, and subjective correlates of program comprehension. Our data corroborate that complexity metrics can-to a limited degree-explain programmers' cognition in program comprehension. In the paper on the fMRI study, we outline several follow-up experiments investigating fine-grained effects of code complexity and describe possible refinements to code complexity metrics. We view our conducted experiment as a starting point to link code complexity metrics to neural and behavioral correlates. To enable future research to continue this line of work, we aim to provide as much support as possible to conduct similar studies with this artifact.
Non-Recursive Graph Convolutional Networks

Graph Convolutional Networks (GCNs) are powerful models for node representation learning tasks. However, the node representation in existing GCN models is usually generated by performing recursive neighborhood aggregation across multiple graph convolutional layers with certain sampling methods, which may lead to redundant feature mixing, needless information loss, and extensive computations. Therefore, in this paper, we propose a novel architecture named Non-Recursive Graph Convolutional Network (NRGCN) to improve both the training efficiency and the learning performance of GCNs in the context of node classification. Specifically, NRGCN proposes to represent different hops of neighbors for each node based on inner-layer aggregation and layer-independent sampling. In this way, each node can be directly represented by concatenating the information extracted independently from each hop of its neighbors thereby avoiding the recursive neighborhood expansion across layers. Moreover, the layer-independent sampling and aggregation can be precomputed before the model training, thus the training process can be accelerated considerably. Extensive experiments on benchmark datasets verify that our NRGCN outperforms the state-of-the-art GCN models, in terms of the node classification performance and reliability.
VRtangibles: Assisting Children in Creating Virtual Scenes using Tangible Objects and Touch Input

Children are increasingly exposed to virtual reality (VR) technology as end-users. However, they miss an opportunity to become active creators due to the barrier of insufficient technical background. Creating scenes in VR requires considerable programming knowledge and excludes non-tech-savvy users, e.g., school children. In this paper, we showcase a system called VRtangibles, which combines tangible objects and touch input to create virtual scenes without programming. With VRtangibles, we aim to engage children in the active creation of virtual scenes via playful hands-on activities. From the lab study with six school children, we discovered that the majority of children were successful in creating virtual scenes using VRtangibles and found it engaging and fun to use.
Network simulation of the sewage treatment system in machine-building enterprises

Abstract The article examines the technological complex of wastewater treatment (WWS) of machine-building production. The wastewater treatment complex (WWS) of machine-building production has a complex multi-level structure; therefore, modern methods of system analysis, computer modeling, and the theory of Petri nets are used to study, model, and analyze such systems. The apparatus of the theory of Petri nets (SP) is selected from the main methods for modeling discrete-continuous chemical-technological systems (CTS) for modeling the process of SAL of engineering production. Petri nets are used, focused on modeling and analysis of discrete-continuous CTS, by including priority transitions, delay time of labels in positions and transitions. A model of a typical apparatus that implements the OSV technological process was synthesized in the form of a modified Petri net (SME). The software package for the OSV technological process control system was created using the TRACE MODE SCADA technology.
THE ROLE OF KNOWLEDGE ACQUISITION IN ENHANCING KNOWLEDGE MANAGEMENT PROCESSES IN HIGHER EDUCATION INSTITUTIONS

Purpose – this article investigates the impact of knowledge acquisition on the leading knowledge management processes such as knowledge storage, application, and creation in higher education institutions. Research methodology – scientific literature review and hypotheses were used to develop the research model. The re-search data was collected from 202 academics from Lebanese higher education institutions. The structural questionnaire and structural equation modeling techniques were employed to test the research hypotheses. Findings – the findings confirmed the knowledge acquisition process’s leading role with the highest impact on knowl-edge creation and provided valuable insights for scientists in the knowledge acquisition field. Research limitations – this study’s results are primarily applicable in Lebanon and can’t be generalized to other coun-tries. Therefore, a further researcher should encompass diverse countries to generalize the findings on a global level. Practical implications – the results emphasize more attention for higher education institutions’ administration to focus on higher education institutions’ knowledge management cycle, particularly on the knowledge acquisition process. Originality/Value – in this study, the significant influence of knowledge acquisition on other knowledge management processes in the Lebanese higher education institutions has been revealed, as no prior research in Lebanon is found.
Magnification Vision – a Novel Gaze-Directed User Interface

We present a novel magnifying tool for virtual environments, where users are given a view of the world through a handheld window controlled by their real-time eye gaze data. The system builds on the optics of real magnifying glasses and prior work in gaze-directed interfaces. A pilot study is run to evaluate these techniques against a baseline, that reveals no significant improvement in performance, though users appear to prefer the new technique.
People Counting System Using Python

Nowadays population growth increased. The linear growth of population has resulted in a large number of people comes to public places. This system will thus provide the count of the person in particular area malls, supermarket, etc. some business is only depending upon on the customer schedule and its timing. Hence, our work satisfies the problem and provides a solution by designing a people counting system. So, the single shot detector (SSD) mobile net is proposed along with a centroid tracker. This model replaces the VGG16 base network with a Mobile Net network for better extracting features, and after the base network, it connects with six convolutional layers for classification. The centroid tracking algorithm obtains bounding box coordinates from an object detector SSD and applies them to calculate the center of a bounding box. After the centroid is calculated, it will assign an id to each person and this model operates the dataset with training and testing the data which is shown in fig. 4. The maximum true positive rate (TPR) is 95.03% and FPR (false positive rate) is 0.08%. A total of 2416 positive images samples and 1218 negative samples. After that, the accuracy of the model is obtained and also 96.64% accuracy rate is obtained by testing the given data set. Sometimes method do not produce high accuracy due to the high traffic counting, clothes of person, shadow issue etc.
Facetwise Mesh Refinement for Multi- View Stereo

Mesh refinement is a fundamental step for accurate Multi- View Stereo. It modifies the geometry of an initial manifold mesh to minimize the photometric error induced in a set of camera pairs. This initial mesh is usually the output of volumetric 3D reconstruction based on min-cut over Delaunay Triangulations. Such methods produce a significant amount of non-manifold vertices, therefore they require a vertex split step to explicitly repair them. In this paper, we extend this method to preemptively fix the non-manifold vertices by reasoning directly on the Delaunay Triangulation and avoid most vertex splits. The main contribution of this paper addresses the problem of choosing the camera pairs adopted by the refinement process. We treat the problem as a mesh labeling process, where each label corresponds to a camera pair. Differently from the state-of-the-art methods, which use each camera pair to refine all the visible parts of the mesh, we choose, for each facet, the best pair that enforces both the overall visibility and coverage. The refinement step is applied for each facet using only the camera pair selected. This facetwise refinement helps the process to be applied in the most evenly way possible.
Enhanced context-aware recommendation using topic modeling and particle swarm optimization

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.
Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System Feasibility

The meteorological environment is a determining factor in photovoltaic (PV) system feasibility (PVSF). To evaluate this impact more accurately, a quantitative analysis model based on multimeteorological factors and the Random Forest Regression model is proposed in this work. Firstly, an evaluation system is established to assess the impact. Then, to predict the indicators of the evaluation system, a parameter, i.e., performance ratio in sampling period, is defined. Secondly, a set of essential influences on the performance ratio in the sampling period is established through analyzing and reducing the discovered influences on the PV system performance. Finally, data from the Desert Knowledge Australia Solar Centre (DKASC) website are used to conduct the experiment. During the experiment, the sample set is cleaned using the model based on the cosine of the zenith angle. The functional relationship between the performance ratio in the sampling period and its essential influences is established through training a Random Forest Regression model with the data of the modeling system. The data of the test system are used to verify the forecast performance of the proposed model. Compared with the reference model, which is based on the traditional physical experiment, the results of the proposed model accord better with the measured values.
Bishift-Net for Image Inpainting

Image inpainting remains a challenging task in computer vision which aims to fill in the missing area of a corrupted image with proper contents and generate photorealistic images by using the information from the existing area. Most existing methods always generate contents with blurry texture caused by propogating the convolutional feature through a fully connected layer. To address this problem, Shift-Net[1] is proposed to shift the encoder feature from existing area to serve as an estimation of the missing parts. However, the decoder feature with new encoding information is ingnored. Inspired by this, we propose a new inpainting model, which is called BiShift-Net. BiShift-Net adopts the structure of U-Net, and we introduce a BiShift layer to it. We use the BiShift layer to capture the information from both encoder and decoder features, rearranging the features to generate sharp texture. Experiments show that Bishift-Net outperforms the other state-of-the-art CNN-based methods, while produce more faithful results at the same time.
Deformation Techniques for Shape Changing Interfaces

Since the introduction of shape changing interfaces, research in the field has contributed a number of taxonomies to classify shape changing interfaces according to different characteristics, including shape, interaction mapping, material, actuation, and information affordances, in an attempt to grasp the diversity of these interfaces in terms of design and information. However, to our knowledge there exists no classifications of input techniques that are used to deform shape changing interfaces through physical interaction. The interaction affordances provided by shape changing interfaces are important for interaction design and interaction mapping. The work presented here aims to analyse how deformable properties in shape changing interfaces are related to deformation techniques, in order to provide a first step towards the development of design guidelines for physical interaction with shape changing interfaces. The results of the study contribute an overview of interaction techniques based on the analysis of a set of 7 models with different deformable properties, each presented in 2D and 3D form.
A Novel Feature Selection Method on Mutual Information and Improved Gravitational Search Algorithm for High Dimensional Biomedical Data

In the past few decades, the field of bioinformatics has accumulated a large amount of gene expression data which provided important support fur the diagnosis of disease. However, high dimensionality, small sample sizes, and redundant features often adversely affect the accuracy and the speed of prediction. Existing feature selection models cannot obtain the information of these datasets accurately. Filter and wrapper are two commonly used feature selection methods. Combining the advantages of the fast calculation speed of the filter and the high accuracy of the wrapper, a new hybrid algorithm called MIIBGSA, is proposed, which hybridizes mutual information and improved Gravitational Search Algorithm (GSA). First, mutual information is used to rank and select important features, these features are further chosen into the population of the wrapper method. Then, due to the effectiveness of the GSA algorithm, GSA is adopted to further seek an optimal feature subset. However, GSA also has the disadvantages of slow search speed and premature convergence, which limit its optimization ability. In our work, a scale function is added to the speed update to enhance its search ability, and an adaptive kre,t particle update formula is proposed to improve its convergence accuracy and propose a fitness sharing strategy to enhance the randomness of particle populations and searchability through the niche algorithm of fitness sharing. We used 10fold-CV method with the K? N classifier to evaluate the classification accuracy. Experimental results on five publicly available high-dimensional biomedical data sets show that the proposed NH-LBGSA has superior performance than other algorithms.
Similarity Forests Revisited: A Swiss Army Knife for Machine Learning

Random Forests are one of the most reliable and robust general-purpose machine learning algorithms. They provide very competitive baselines for more complex algorithms. Recently, a new algorithm has been introduced into the family of decision tree learners – Similarity Forests, aiming at mitigating some of the well-known deficiencies of Random Forests. In this paper we extend the originally proposed Similarity Forests algorithm to one-class classification, multi-class classification, regression and metric learning tasks. We also introduce two new criteria for split evaluation in regression learning. The results of conducted experiments show that Similarity Forests can be a competitive alternative to Random Forests, in particular, when high quality data representation is difficult to obtain.
CDVVAR: VR/AR Collaborative Data Visualization Tool

The emergence of immersive platforms has opened room for designing more effective data visualization tools. Therefore, we developed CDVVAR, a VR/AR collaborative tool that can visualize any dataset using three different techniques. Our prototype enables users to share graphs and highlight (ping) specific data point across VR or mobile AR platforms. We conducted a within-subject study (12 pairs) to evaluate the effectiveness of our prototype. Each pair was shown graphs and asked to point specific data values in both VR and mobile AR setup. The time to collaboratively answer the questions was recorded along with users&#x2019; general feedback.