Reviewerly/exHarmony huggingface dataset:
Smart Tour Route Planning Algorithm Based on Naïve Bayes Interest Data Mining Machine Learning

A smart tour route planning algorithm based on a Naïve Bayes interest data mining machine learning is brought forward in the paper, according to the problems of current tour route planning methods. A machine learning model of Naïve Bayes interest data mining is set up by learning a mass of training data on tourists’ interests and needs. Through the recommended interest tourist site classifications from the machine learning module, the optimal tourist site mining algorithm based on the membership degree searching propagating tree of a tourist’s temporary accommodation is set up, which mines and outputs the optimal tourist sites. The mined optimal tourist sites are taken as seed points to set up a tour route planning algorithm based on the optimal propagating tree of a closed-loop structure. Through the proposed algorithm, an experiment is designed and performed to output optimal tour routes conforming to tourists’ needs and interests, including the propagating tree closed-loop structures, a minimum heap of propagating tree weight function value, and a weight function value complete binary tree. We prove that the proposed algorithm has the features of intelligence and accuracy, and it can learn tourists’ needs and interests to output optimal tourist sites and tour routes and ensure that tourists can get the best motive benefits and travel experience in the tour process, by analyzing the experiment data and results.
Real-time computer vision system for tracking simultaneously subject-specific rigid head and non-rigid facial mimic movements using a contactless sensor and system of systems approach

Head and facial mimic animations play important roles in various fields such as human-machine interactions, internet communications, multimedia applications, and facial mimic analysis. Numerous studies have been trying to simulate these animations. However, they hardly achieved all requirements of full rigid head and non-rigid facial mimic animations in a subject-specific manner with real-time framerates. Consequently, this present study aimed to develop a real-time computer vision system for tracking simultaneously rigid head and non-rigid facial mimic movements. Our system was developed using the system of systems approach. A data acquisition sub-system was implemented using a contactless Kinect sensor. A subject-specific model generation sub-system was designed to create the geometrical model from the Kinect sensor without texture information. A subject-specific texture generation sub-system was designed for enhancing the reality of the generated model with texture information. A head animation sub-system with graphical user interfaces was also developed. Model accuracy and system performances were analyzed. The comparison with MRI-based model shows a very good accuracy level (distance deviation of ~1 mm in neutral position and an error range of [2–3 mm] for different facial mimic positions) for the generated model from our system. Moreover, the system speed can be optimized to reach a high framerate (up to 60 fps) during different head and facial mimic animations. This study presents a novel computer vision system for tracking simultaneously subject-specific rigid head and non-rigid facial mimic movements in real time. In perspectives, serious game technology will be integrated into this system towards a full computer-aided decision support system for facial rehabilitation.
Bi-Hierarchical Cooperative Coevolution for Large Scale Global Optimization

Taking ''divide-and-conquer'' as a basic idea, cooperative coevolution (CC) has shown a promising prospect in large scale global optimization.However, its high requirement on the decomposition accuracy can hardly be satisfied in practice.Directing against this issue, this study proposes a bi-hierarchical cooperative coevolution (BHCC), which can tolerate a certain degree of decomposition error.Besides the cooperation among sub-problems as in the conventional CC, BHCC introduces a kind of cooperation between sub-problems and the overall problem.By systematically exploiting the excellent sub-solutions obtained during the sub-space optimization process, it initializes the population for the optimization process on the overall problem and thus can conduct search in promising regions of the whole solution space.The newly acquired complete solutions are in turn employed to update the context vector and the population of each sub-problem, where the context vector is used for sub-solution evaluation.Consequently, the search direction misdirected by an improper decomposition can be corrected to a great extent.To keep the balance between the two types of optimization processes, an adaptive triggering mechanism for the overall optimization process is specially designed for BHCC.Experimental results on two widely-used benchmark suites verify the effectiveness of the new strategies in BHCC and also indicate that BHCC is more robust than existing CCs and can achieve competitive performance compared with several state-of-the-art algorithms.
Number-phase entanglement and Einstein-Podolsky-Rosen steering

We use the uncertainty relation between the operators associated to the total number of particles and to the relative phase of two bosonic modes to construct entanglement and Einstein-Podolsky-Rosen steering criteria. These can be tested experimentally in a variety of systems, such as optical fields, Bose-Einstein condensates or mechanical oscillators. While known entanglement criteria involving the phase observable typically require to perform interference measurements by recombining the two systems, our criteria can be tested through local measurements at two spatially distinct positions, to investigate the nonlocal nature of quantum correlations. We present simple examples where our criteria are violated, and show their robustness to noise. Apart from being useful for state characterization, they might find application in quantum information protocols, for example based on number-phase teleportation.
Design of multi-view graph embedding using multiple kernel learning

The graph embedding is the process of representing the graph in a vector space using properties of the graphs and this technique has now being widely used for analyzing the graph data using machine learning algorithms. The existing graph embeddings rely mostly on a single property of graphs for data representation which is found to be inappropriate to capture all the characteristics of the data. Hence we designed graph embedding using multi-view approach, where each view is an embedding of the graph using a graph property. The input space of multi-view learning is then taken as the direct sum of the subspaces in which the graph embedding lie. We did analysis on real world data by incorporating the proposed model on support vector machines (SVM). The reproducing kernel used in SVM is represented as the linear combination of the kernels defined on the individual embeddings. The optimization technique used in simple multiple kernel learning (simpleMKL) is used to find the parameters of the optimal kernel. To analyze the individual representation capability of the embeddings, an R-convolution graph kernel is designed over each of the views. In our experimental analysis, the multi-view graph embedding showed a superior performance in comparison with that of the state-of-the-art graph embeddings as well as graph kernels.
Faculty Views on the Goals of an Undergraduate CS Education and the Academia-Industry Gap

Previous work has found that recent computer science graduates often experience difficulty transitioning into their new roles in industry due to a significant gap between their academic experiences and industry's expectations. Although multiple studies have identified the views of students and members of industry on the value of a CS degree as preparation for industry, the faculty perspective on this topic remains unclear. Understanding these views could shed light on why the academia-industry gap has persisted despite the attention. This study identified faculty views on the goals of an undergraduate education and a CS major, focusing on preparation for careers in industry. In order to identify a spectrum of faculty views, we interviewed 14 faculty from a variety of backgrounds across three institutions. A phenomenographic analysis of the transcripts reveals that many faculty believe that industry preparation is an important programmatic goal, yet they encounter significant resource obstacles to achieving that goal.
Structural Analysis of Factual, Conceptual, Procedural, and Metacognitive Knowledge in a Multidimensional Knowledge Network

Discovering the most suitable network structure of the learning domain represents one of the main challenges of knowledge delivery and acquisition. We propose a multidimensional knowledge network (MKN) consisting of three components: multilayer network and its two projections. Each network layer constitutes factual, conceptual, procedural, or metacognitive knowledge within the domain of databases as a standard course of computer science study. In the MKN layer, nodes are concepts or knowledge units and the edges are weighted with regard to Bloom's cognitive learning level. The projected network layers are contrasted with a monolayer network by comparing characterizations of the centrality measures: degree centrality, closeness centrality, betweenness centrality, and eccentricity. The study revealed indications of how concepts, supported with the higher number of previously introduced concepts, have a dominant role in knowledge acquisition, from a view of knowledge structure and content. The analysis of communities, assortativity coefficient, and overlap between MKN layers contributes to better structuring of knowledge. MKN enables systematic insights into the efficiency of knowledge integration across metacognitive layers, as well as the detection of crucial cognitive concepts that reduce/increase the cognitive load during learning.
Kernels and Density Estimation

au> Depends R (>= 3.5.0),spatstat.data(>= 3.0-1), spatstat.geom(>= 3.2-1), spatstat.random(>= 3.1-5), spatstat.explore(>= 3.2-1), spatstat.model(>= 3.2-4), spatstat.linnet(>= 3.1-1), utilsImports spatstat.utils(>= 3.0-3) SuggestsDescription Comprehensive open-source toolbox for analysing Spatial Point Patterns.Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region.Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects.Supports spatial covariate data such as pixel images.Contains over 2000 functions for plotting spatial data, exploratory data analysis, modelfitting, simulation, spatial sampling, model diagnostics, and formal inference.Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks.Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects.Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, twostage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm().Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes.Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks.Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. spatstat-packageA model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm().The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above.Fitted point process models can be simulated, automatically.Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr).Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Deep Neural Attention-Based Model for the Evaluation of Italian Sentences Complexity

In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems.
Forum Duplicate Question Detection by Domain Adaptive Semantic Matching

Community Question Answering (CQA) forums, such as Stack Overflow, Stack Exchange and Massive Open Online Course (MOOC) forums, spend a lot of manpower and time to manage duplicate questions on the forum. Mismatch of duplicate questions makes users keep asking “new” questions, and the continuous accumulation of duplicate questions may interfere with their information searching again, affecting user satisfaction. Neural Networks (NN) models for parsing semantics provide the possibility of end-to-end duplicate question detection. Whereas, due to lack of domain data and expertise, NN models for semantic parsing are rarely directly applied to CQA duplicate question detection. This paper proposes a Semantic Matching Model (SMM) integrated with the multi-task transfer learning framework for multi-domain forum duplicate question detection. By designing the word-to-sentence interaction mechanism based on the word-to-word interaction, SMM can automatically choose to ignore or pay attention to potential similar words according to the semantics at the sentence level. The experiments on the benchmark data set and MOOC forum data set state that SMM outperforms baselines, its interaction mechanism is effective and it has an advantage in cross-domain duplicate question detection.
Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models

The paper presents a method of computational complexity reduction in Extended Kalman Filters dedicated for systems with non-linear measurement models. Extended Kalman filters are commonly used in radio-location and radio-navigation for estimating an object’s position and other parameters of motion, based on measurements, which are non-linearly related to the object’s position. This non-linearity forces designers to use non-linear filters, such as the Extended Kalman Filter mentioned, where linearization of the system’s model is performed in every run of the filter’s loop. The linearization, consisting of calculating Jacobian matrices for non-linear functions in the dynamics and/or observation models, significantly increases the number of operations in comparison to the linear Kalman filter. The method proposed in this paper consists of analyzing a variability of Jacobians and performing the model linearization only when expected changes of those Jacobians exceed a preset threshold. With a properly chosen threshold value, the proposed filter modification leads to a significant reduction of its computational burden and does not noticeably increase its estimation errors. The paper describes a practical simulation-based method of determining the threshold. The accuracy of the filter for various threshold values was tested for simplified models of radar systems.
Group Activity Recognition by Using Effective Multiple Modality Relation Representation With Temporal-Spatial Attention

Group activity recognition has received a great deal of interest because of its broader applications in sports analysis, autonomous vehicles, CCTV surveillance systems and video summarization systems.Most existing methods typically use appearance features and they seldom consider underlying interaction information.In this work, a technology of novel group activity recognition is proposed based on multi-modal relation representation with temporal-spatial attention.First, we introduce an object relation module, which processes all objects in a scene simultaneously through an interaction between their appearance feature and geometry, thus allowing the modeling of their relations.Second, to extract effective motion features, an optical flow network is fine-tuned by using the action loss as the supervised signal.Then, we propose two types of inference models, opt-GRU and relation-GRU, which are used to encode the object relationship and motion representation effectively, and form the discriminative frame-level feature representation.Finally, an attention-based temporal aggregation layer is proposed to integrate frame-level features with different weights and form effective video-level representations.We have performed extensive experiments on two popular datasets, and both have achieved state-of-the-art performance.The datasets are the Volleyball dataset and the Collective Activity dataset, respectively.
A Probabilistic Niching Evolutionary Computation Framework Based on Binary Space Partitioning

Multimodal optimization problems have multiple satisfactory solutions to identify. Most of the existing works conduct the search based on the information of the current population, which can be inefficient. This article proposes a probabilistic niching evolutionary computation framework that guides the future search based on more sufficient historical information, in order to locate diverse and high-quality solutions. A binary space partition tree is built to structurally organize the space visiting information. Based on the tree, a probabilistic niching strategy is defined to reinforce exploration and exploitation by making full use of the structural historical information. The proposed framework is universal for incorporating various baseline niching algorithms. In this article, we integrate the proposed framework with two niching algorithms: 1) a distance-based differential evolution algorithm and 2) a topology-based particle swarm optimization algorithm. The two new algorithms are evaluated on 20 multimodal optimization test functions. The experimental results show that the proposed framework helps the algorithms obtain competitive performance. They outperform a number of state-of-the-art niching algorithms on most of the test functions.
Tone-Mapped Image Quality Assessment for Electronics Displays by Combining Luminance Partition and Colorfulness Index

Tone mapping operators (TMOs) reproduce the high dynamic range (HDR) images on low dynamic range (LDR) consumer electronics devices such as monitors or printers. To accurately measure and compare the performance of different TMOs, this article proposes a tone-mapped images (TMIs) luminance partition model and corresponding quality measure. First, each tone-mapped (TM) image is segmented into highlight region (HR), dark region (DR) and midtone region (MR) based on luminance partition. Second, local entropies and contrast features are extracted in the HR and DR, and color-based features are captured in the MR. Meanwhile, the gray-level co-occurrence matrix (GLCM) and Canny operator are utilized to measure the microstructural distortions and halo effects, respectively. Finally, all extracted features are combined and trained together with subjective ratings to form a regression model using support vector regression (SVR). Experimental results show that the proposed method outperforms the state-of-the-art no-reference (NR) methods. Specifically, the spearman correlation coefficients (SRCC) values of our method reach 0.83 and 0.76 on the tone-mapped image database (TMID) and the ESPL-LIVE HDR database, respectively.
High-frequency color-encoded fringe-projection profilometry based on geometry constraint for large depth range

In multi-view fringe projection profilometry (FPP), a limitation of geometry-constraint based approaches is the reduced measurement depth range often used to reduce the number of candidate points and increase the corresponding point selection reliability, when high-frequency fringe patterns are used. To extend the depth range, a new method of high-frequency fringe projection profilometry was developed by color encoding the projected fringe patterns to allow reliable candidate point selection even when six candidate points are in the measurement volume. The wrapped phase is directly retrieved using the intensity component of the hue-saturation-intensity (HSI) color space and complementary-hue is introduced to identify color codes for correct corresponding point selection. Mathematical analyses of the effect of color crosstalk on phase calculation and color code identification show that the phase calculation is independent of color crosstalk and that color crosstalk has little effect on color code identification. Experiments demonstrated that the new method can achieve high accuracy in 3D measurement over a large depth range and for isolated objects, using only two high-frequency color-encoded fringe patterns.
An Approach to Semantic and Structural Features Learning for Software Defect Prediction

Research on software defect prediction has achieved great success at modeling predictors. To build more accurate predictors, a number of hand-crafted features are proposed, such as static code features, process features, and social network features. Few models, however, consider the semantic and structural features of programs. Understanding the context information of source code files could explain a lot about the cause of defects in software. In this paper, we leverage representation learning for semantic and structural features generation. Specifically, we first extract token vectors of code files based on the Abstract Syntax Trees (ASTs) and then feed the token vectors into Convolutional Neural Network (CNN) to automatically learn semantic features. Meanwhile, we also construct a complex network model based on the dependencies between code files, namely, software network (SN). After that, to learn the structural features, we apply the network embedding method to the resulting SN. Finally, we build a novel software defect prediction model based on the learned semantic and structural features (SDP-S2S). We evaluated our method on 6 projects collected from public PROMISE repositories. The results suggest that the contribution of structural features extracted from software network is prominent, and when combined with semantic features, the results seem to be better. In addition, compared with the traditional hand-crafted features, the F -measure values of SDP-S2S are generally increased, with a maximum growth rate of 99.5%. We also explore the parameter sensitivity in the learning process of semantic and structural features and provide guidance for the optimization of predictors.