Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests

In this paper, computer vision enables recommending a reduced order model for fast stress prediction according to various possible loading environments. This approach is applied on a macroscopic part by using a digital image of a mechanical test. We propose a hybrid approach that simultaneously exploits a data-driven model and a physics-based model, in mechanics of materials. During a machine learning stage, a classification of possible reduced order models is obtained through a clustering of loading environments by using simulation data. The recognition of the suitable reduced order model is performed via a convolutional neural network (CNN) applied to a digital image of the mechanical test. The CNN recommend a convenient mechanical model available in a dictionary of reduced order models. The output of the convolutional neural network being a model, an error estimator, is proposed to assess the accuracy of this output. This article details simple algorithmic choices that allowed a realistic mechanical modeling via computer vision.
A qualitative survey on frequent subgraph mining

Abstract Data mining is a popular research area that has been studied by many researchers and focuses on finding unforeseen and important information in large databases. One of the popular data structures used to represent large heterogeneous data in the field of data mining is graphs. So, graph mining is one of the most popular subdivisions of data mining. Subgraphs that are more frequently encountered than the user-defined threshold in a database are called frequent subgraphs. Frequent subgraphs in a database can give important information about this database. Using this information, data can be classified, clustered and indexed. The purpose of this survey is to examine frequent subgraph mining algorithms (i) in terms of frequent subgraph discovery process phases such as candidate generation and frequency calculation, (ii) categorize the algorithms according to their general attributes such as input type, dynamicity of graphs, result type, algorithmic approach they are based on, algorithmic design and graph representation as well as (iii) to discuss the performance of algorithms in comparison to each other and the challenges faced by the algorithms recently.
Review of Latent Dirichlet Allocation Methods Usable in Voice of Customer Analysis

The aim of the article is to detect and review existing topic modelling methods of Latent Dirichlet Allocation and their modifications usable in Voice of Customer analysis. Voice of Customer is expressed mainly through textual comments which often focus on the evaluation of products or services the customer consumes. The most studied data source are customer reviews which contain next to the textual comments also ratings in form of scales. The aim of the topic models is to mine the topics and their aspects the customers are evaluating in their reviews and assign to them a particular sentiment or emotion. The author completed a systematic literature review of peer-reviewed published journal articles indexed in leading databases of Scopus and Web of Science and concerning the current use of Latent Dirichlet Allocation model variants in Voice of Customer textual analysis for performing the tasks of aspect detection, emotion detection, personality detection and sentiment assignation. In total, 38 modifications of the LDA model were identified with the reference to their first application in the research of text analytics. The review is intended for researchers in customer analytics the field of sentiment or emotion detection, and moreover as results from the review, for studies in personality recognition based on the textual data. The review offers a basic overview and comparison of LDA modifications which can be considered as a knowledge baseline for selection in a specific application. The scope of the literature examination is limited to the period of years 2003–2018 with the application relevant to the analysis of Voice of Customer subjective textual data only which is closely connected to the area of marketing or customer relationship management.
Encoding Longer-Term Contextual Information with Predictive Coding and Ego-Motion

Studies suggest that, within the hierarchical architecture, the topological higher level possibly represents the scenarios of the current sensory events with slower changing activities. They attempt to predict the neural activities on the lower level by relaying the predicted information after the scenario of the sensorimotor event has been determined. On the other hand, the incoming sensory information corrects such prediction of the events on the higher level by the fast-changing novel or surprising signal. From this point, we propose a predictive hierarchical artificial neural network model that examines this hypothesis on neurorobotic platforms. It integrates the perception and action in the predictive coding framework. Moreover, in this neural network model, there are different temporal scales of predictions existing on different levels of the hierarchical predictive coding architecture, which defines the temporal memories in recording the events occurring. Also, both the fast- and the slow-changing neural activities are modulated by the motor action. Therefore, the slow-changing neurons can be regarded as the representation of the recent scenario which the sensorimotor system has encountered. The neurorobotic experiments based on the architecture were also conducted.
Towards an Automatic 3D Patterns Classification: the GRAVITATE Use Case

When cataloging archaeological fragments, decorative patterns are an indicator of the stylistic canon an object belongs to. In this paper we address a quantitative classiﬁcation of the decorative pattern elements that characterize the models in the GRAVITATE use case, discussing the performance of a recent algorithm for pattern recognition over triangle meshes.
A Review on Personalized Academic Paper Recommendation

With the advent of the era of big data, it has become extremely easy for scientific users to have to access academic papers, which has enhanced their efficiency and capacity to search or browse papers. However, it also faces some problems such as the explosion of the literature or information overwhelming. Many researchers focus on academic paper recommendation service, hoping to help scientific users to find documents more efficiently and recommend interested or potentially interested papers which could assist academic users doing research. Through literature review, this paper make a comprehensive summary of the research on personalized academic papers recommendation, presenting the state-of-art of academic paper recommendation methodologies, pointing out its pros and cons and indicating primary evaluation metrics and popular datasets. Finnaly, we outlook the research trend of personalized academic paper recommendation as a reference for interested researchers.
Stochastic Block-Coordinate Gradient Projection Algorithms for Submodular Maximization

We consider a stochastic continuous submodular huge-scale optimization problem, which arises naturally in many applications such as machine learning. Due to high-dimensional data, the computation of the whole gradient vector can become prohibitively expensive. To reduce the complexity and memory requirements, we propose a stochastic block-coordinate gradient projection algorithm for maximizing continuous submodular functions, which chooses a random subset of gradient vector and updates the estimates along the positive gradient direction. We prove that the estimates of all nodes generated by the algorithm converge to some stationary points with probability 1. Moreover, we show that the proposed algorithm achieves the tight (pmin/2F⁎-ϵ) approximation guarantee after O(1/ϵ2) iterations for DR-submodular functions by choosing appropriate step sizes. Furthermore, we also show that the algorithm achieves the tight (γ2/1+γ2pminF⁎-ϵ) approximation guarantee after O(1/ϵ2) iterations for weakly DR-submodular functions with parameter γ by choosing diminishing step sizes.
Safe Walking Zones: Visual Guidance for Redirected Walking in Confined Real-World Spaces

Walking is usually considered the most natural form of self-motion in a virtual environment (VE). However, the conﬁned physical workspace of typical virtual reality (VR) labs often prevents natural exploration of larger VEs. Redirected walking (RDW) has been introduced as a potential solution to this restriction, but corresponding techniques often induce enormous manipulations if the workspace is considerably small and lack natural experiences therefore. In this paper we propose a user interface approach that supports natural walking in a potentially inﬁnite virtual scene while conﬁned to a considerably restricted physical workspace. This virtual locomotion technique relies on a safety volume, which is displayed as a semi-transparent half-capsule, inside which the user can walk without manipulations caused by RDW. We designed a circular redirection approach when the user leaves this safety volume that is complemented by a deterrent approach for user guidance outside the safety volume. We discuss in detail the process of transferring user movements inside these regions to the virtual camera in order to enable walking between points of interest in VEs, and we present the results of a usability study in which we evaluate the approach.
Higgs structures of dyonic instantons

We study Higgs field configurations of dyonic instantons in spontaneously broken (4 + 1)-dimensional Yang-Mills theory. The adjoint scalar field solutions to the covariant Laplace equation in the ADHM instanton background are constructed in general noncanonical basis, and they are used to study explicitly the Higgs field configurations of dyonic instantons when the gauge fields are taken by Jackiw-Nohl-Rebbi instanton solutions. For these solutions corresponding to small instanton number we then consider in some detail the zero locus of the Higgs field, which describes the cross section of supertubes connecting parallel D4-branes in string theory. Also the information on the Higgs zeroes is used to discuss the residual gauge freedom concerning the Jackiw-Nohl-Rebbi solutions.
Studying Levels of Presence in a Virtual Environment Simulating Drug Use in Schools: Effect on Different Character Perspectives

This paper studies the aspect of presence in a Virtual Reality (VR) environment that can be used for training purposes in the education sector and more speciﬁcally for teacher training and professional development. During the VR experience trainees had the chance to view the world from different perspectives through the eyes of different characters appearing in the scene. The experimental evaluation conducted aims to examine the effect of viewing the experience from different perspectives and viewpoints in relation to the overall user experience and the level of presence achieved. To accomplish these objectives an experiment was performed investigating presence and the correlation between presence and different viewpoints/perspectives. To measure presence a combination of methods were used including two different questionnaires, the use of an eeg device, EMOTIV EPOC+ and the analysis of heart rates. The results indicate that high levels of presence were recorded and that increased levels of presence are associated with viewing the VE from a student rather than a teacher perspective
An RGB-D-Based Cross-Field of View Pose Estimation System for a Free Flight Target in a Wind Tunnel

Estimating the real-time pose of a free flight aircraft in a complex wind tunnel environment is extremely difficult. Due to the high dynamic testing environment, complicated illumination condition, and the unpredictable motion of target, most general pose estimating methods will fail. In this paper, we introduce a cross-field of view (FOV) real-time pose estimation system, which provides high precision pose estimation of the free flight aircraft in the wind tunnel environment. Multiview live RGB-D streams are used in the system as input to ensure the measurement area can be fully covered. First, a multimodal initialization method is developed to measure the spatial relationship between the RGB-D camera and the aircraft. Based on all the input multimodal information, a so-called cross-FOV model is proposed to recognize the dominating sensor and accurately extract the foreground region in an automatic manner. Second, we develop an RGB-D-based pose estimation method for a single target, by which the 3D sparse points and the pose of the target can be simultaneously obtained in real time. Many experiments have been conducted, and an RGB-D image simulation based on 3D modeling is implemented to verify the effectiveness of our algorithm. Both the real scene’s and simulation scene’s experimental results demonstrate the effectiveness of our method.
Fully Flexible Parallel Merge Sort for Multicore Architectures

The development in multicore architectures gives a new line of processors that can flexibly distribute tasks between their logical cores. These need flexible models of efficient algorithms, both fast and stable. A new line of efficient sorting algorithms can support these systems to efficiently use all available resources. Processes and calculations shall be flexibly distributed between cores to make the performance as high as possible. In this article we present a fully flexible sorting method designed for parallel processing. The idea we describe in this article is based on modified merge sort, which in parallel form is designed for multicore architectures. The novelty of this idea is in particular way of processing. We have developed a fully flexible method that can be implemented for a number of processors. The tasks are flexibly distributed between logical cores to increase the efficiency of sorting. The method preserves separation of concerns; therefore, each of the processors works separately without any cross actions and interruptions. The proposed method was described in theoretical way, examined in tests, and compared to other methods. The results confirm high efficiency and show that with each newly added processor sorting becomes faster and more efficient.
An Investigation of Optimal Light Source Setups for Photometric Stereo Reconstruction of Historical Coins

In this paper, we address the 3D reconstruction of historical coins by means of Photometric Stereo. We investigate the inﬂuence of the number and arrangement of lights to the reconstruction quality by comparing mean angular errors on 22 historical coin models. Our results demonstrate that 6 lights circularly placed at an optimal elevation angle do not show a signiﬁcant loss of reconstruction quality compared to a full semispherical dome setup with 54 lights. This represents a considerable saving of acquisition time and system complexity when it comes to the mass digitization of historical coins
Consensus Path-following Control of Multiple Underactuated Unmanned Underwater Vehicles

This work mainly studies the problem of how to steer a group of underactuated unmanned underwater vehicles UUVs to specified paths coordinately. The algorithm proposed consists of a single path-following strategy and a path parameter consensus tracking strategy. In the context of single path following, we describe the path to be followed by an arbitrary scalar, then by using Lyapunov and backstepping theories, a single path-following strategy was derived to drive each UUV move to the predefined path asymptotically. In the coordinated level, we focus on the coordination for the scalar parameters. In particular, we show that all the path parameters can track with a virtual reference leader who is a neighbor of only a subset of following UUVs with local interactions. The stability of the closed system was proved and analyzed theoretically. The validity of the algorithm proposed is supported by simulation results.
Aggression Identification Using Deep Learning and Data Augmentation

Social media platforms allow users to share and discuss their opinions online. However, a minority of user posts is aggressive, thereby hinders respectful discussion, and — at an extreme level — is liable to prosecution. The automatic identification of such harmful posts is important, because it can support the costly manual moderation of online discussions. Further, the automation allows unprecedented analyses of discussion datasets that contain millions of posts. This system description paper presents our submission to the First Shared Task on Aggression Identification. We propose to augment the provided dataset to increase the number of labeled comments from 15,000 to 60,000. Thereby, we introduce linguistic variety into the dataset. As a consequence of the larger amount of training data, we are able to train a special deep neural net, which generalizes especially well to unseen data. To further boost the performance, we combine this neural net with three logistic regression classifiers trained on character and word n-grams, and hand-picked syntactic features. This ensemble is more robust than the individual single models. Our team named “Julian” achieves an F1-score of 60% on both English datasets, 63% on the Hindi Facebook dataset, and 38% on the Hindi Twitter dataset.
Analyzing Policymaking for Tuberculosis Control in Nigeria

Today, tuberculosis (TB) is still one of the major threats to humankind, being the first cause of death by an infectious disease worldwide. TB is a communicable chronic disease that every year affects 10 million people and kills almost 2 million people in the world. The main key factors fueling the disease are the progressive urbanization of the population and poverty-related socioeconomic factors. Moreover, the lack of effective tools for TB diagnosis, prevention, and treatment has decisively contributed to the lack of an effective model to predict TB spread. In Nigeria, the rapid urbanization along with unprecedented population growth is causing TB to be endemic. This paper proposes a mathematical model to evaluate TB burden in Nigeria by using data obtained from the local TB control program in the community. This research aims to point out effective strategies that could be used to effectively reduce TB burden and death due to TB in this country at different levels. The study shows that efforts should be oriented to more active case finding rather than increasing the treatment effectiveness only. It also reveals that the persistence of the disease is related to a large number of latently infected individuals and quantifies the lives that could be saved by increasing the notification rate using active case finding strategy. We conclude that undiagnosis is the bottleneck that needs to be overcome in addition to the incorporation, improvement, and/or strengthening of treatment management and other essential TB control measures in Nigeria.
Robust Exponential Stability of Switched Complex-Valued Neural Networks with Interval Parameter Uncertainties and Impulses

In this paper, dynamic behavior analysis has been discussed for a class of switched complex-valued neural networks with interval parameter uncertainties and impulse disturbance. Sufficient conditions for guaranteeing the existence, uniqueness, and global robust exponential stability of the equilibrium point have been obtained by using the homomorphism mapping theorem, the scalar Lyapunov function method, the average dwell time method, and M-matrix theory. Since there is no result concerning the stability problem of switched neural networks defined in complex number domain, the stability results we describe in this paper generalize the existing ones. The effectiveness of the proposed results is illustrated by a numerical example.
Managing the complexity of new product development project from the perspectives of customer needs and entropy

To successfully develop a complex product, a firm must answer two critical questions: how to “develop the right product” and how to “develop the product right. ” Motivated by the real practice of Xiaomi’s new product development (NPD) projects, this article responds to these calls in the following ways. To design the right product, using an enhanced PageRank algorithm to investigate customer needs, NPD managers can select appropriate function modules of the new product to meet customers’ demand. To develop the new product in the right way, NPD managers should optimize the NPD organization. This article applies the multi-domain matrix (MDM) to identify the technical coordination dependency strength among different teams and then to measure the NPD organization’s complexity according to its entropy. By proposing the External Entropy of Cluster (EEC) and Internal Entropy of Cluster (IEC), we develop an entropy-based two-stage clustering criterion of design structure matrix (DSM) to optimize the NPD organization. The first-stage clustering criterion maximizes the added average dependency strength of DSM, and the second-stage clustering criterion minimizes the Weighted Total Entropy, including the IEC and EEC. An industrial example is provided to illustrate the proposed model. The results indicate that the clustered DSM can reduce the organization’s complexity.
Characterization of Complex Image Spatial Structures Based on Symmetrical Weibull Distribution Model for Texture Pattern Classification

Texture pattern classification has long been an essential issue in computer vision (CV). However, texture is a kind of perceptual concept of human beings in scene observation or content understanding, which cannot be defined or described clearly in CV. Visually, the visual appearance of the complex spatial structure (CSS) of texture pattern (TP) generally depends on the random organization (or layout) of local homogeneous fragments (LHFs) in the imaged surface. Hence, it is essential to investigate the latent statistical distribution (LSD) behavior of LHFs for distinctive CSS feature characterization to achieve good classification performance. This work presents an image statistical modeling-based TP identification (ISM-TPI) method. It firstly makes a theoretical explanation of the Weibull distribution (WD) behavior of the LHFs of the imaged surface in the imaging process based on the sequential fragmentation theory (SFT), which consequently derives a symmetrical WD model (SWDM) to characterize the LSD of the TP’s SS. Multidirectional and multiscale TP features are then characterized by the SWDM parameters based on the oriented differential operators; in other words, texture images are convolved with multiscale and multidirectional Gaussian derivative filters (GDFs), including the steerable isotropic GDFs (SIGDFs) and the oriented anisotropic GDFs (OAGDFs), for the omnidirectional and multiscale SS detail exhibition with low computational complexity. Finally, SWDM-based TP feature parameters, demonstrated to be directly related to the human vision perception system with significant physical perception meaning, are extracted and used to TP classification with a partial least squares-discriminant analysis- (PLS-DA-) based classifier. The effectiveness of the proposed ISM-TPI method is verified by extensive experiments on three texture image databases. The classification results demonstrate the superiority of the proposed methods over several state-of-the-art TP classification methods.