Reviewerly/exHarmony huggingface dataset:
Virtual Pet: Trends of Development

Information technologies are fundamentally changing modern society. Almost any human activity, including the caring for a pet, is acquiring new formats related to communication in the virtual space. The authors analyzed such a phenomenon as a virtual pet that has been developing since the early 90s of the 20th century on the basis of more than 100 different virtual pet modifications. The most popular among users and purchased more than 1 million times a year around the world are examined in details in this research (video game Petz, Tamagotchi, Furby, Nintendogs, Neopets, My Talking Tom). Thus in this study the evolution of a digital pet is represented. We analyzed how a person models it according to his needs and identified the main trends of the virtual pet development. These basic directions are advanced similarity with a real pet, development of entertaining aspect, virtual pet applying for practical purposes. We proposed four main factors for analyzing changes in the human-virtual pet relationship: touch, game, communication and social interaction concerning the pet.
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data

Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech. Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable. In particular, there is no sufficient evidence that deep learning machinery allows constructing methods that outperform gradient boosting decision trees (GBDT), which are often the top choice for tabular problems. In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data. In a nutshell, the proposed NODE architecture generalizes ensembles of oblivious decision trees, but benefits from both end-to-end gradient-based optimization and the power of multi-layer hierarchical representation learning. With an extensive experimental comparison to the leading GBDT packages on a large number of tabular datasets, we demonstrate the advantage of the proposed NODE architecture, which outperforms the competitors on most of the tasks. We open-source the PyTorch implementation of NODE and believe that it will become a universal framework for machine learning on tabular data.
An efficiency‐improved genetic algorithm and its application on multimodal functions and a 2D common reflection surface stacking problem

ABSTRACT Although Genetic Algorithms have found many successful applications in the field of exploration geophysics, the convergence speed remains a big challenge as Genetic Algorithms usually require a huge amount of fitness function evaluations. In this paper, we propose an efficiency‐improved Genetic Algorithm, which has both a good global search capability and a good local search capability, and is also capable of robustly handling the premature convergence challenge commonly seen in linear and directed non‐linear optimization methods. In our new genetic algorithm, the global search capability is performed via a modified island model, while the local search capability is provided by a novel self‐adaptive differential evolution fine tuning scheme. Premature convergence is dealt with via a local exhaustive search method. We first demonstrate the much improved convergence speed of this efficiency‐improved Genetic Algorithm over that of our previously proposed advanced Genetic Algorithm on several multimodal functions. We further demonstrate the effectiveness of our efficiency‐improved Genetic Algorithm by applying it to a two‐dimensional common reflection surface stacking problem, which is a highly nonlinear geophysical optimization problem, to obtain very encouraging results.
Innovations in Tourism Industry &amp; Development Using Augmented Reality (AR), Virtual Reality (VR)

Over recent times, the evolution of digital technology has become a necessity to stay competitive in tourism and to attract the modern tourist. A new form of digital technology that is used in the public space is Virtual and Augmented Reality. This paper emphasizes on the analysis of scientific & technical aspects of developing mobile AR applications in smart tourism, which provides the necessary information about the destinations and their attractions. We address how the requirements of a user are essential to developing an AR application to attract tourists. We aim to present the technologies for tourism concerning AR and VR. This paper also acknowledges the technological limitations concerning end-user adaption.
Optimization-based Path Planning for an Autonomous Vehicle in a Racing Track

Path planning is discussed in this article for an autonomous vehicle given a route to follow. Route data is considered to be available for a distance ahead of the vehicle in a receding horizon manner. Linear approximation of the nonlinear equations for a vehicle following a path is obtained. Based on these equations, the optimization problem is formed in a convex optimization format and solved to find the optimal path. Optimality is a trade-off between comfort and travel time. Results are provided for some cases considering that the vehicle is traveling in the Suzuka circuit and the observable horizon ahead of the vehicle is a part of this track. Results are discussed for a few trade-off values and analyzed from the practical point of view, which shows that the method is capable of producing an optimal path to follow in an insignificant amount of time. Finally, an alternative approach for improving model accuracy is proposed and discussed. Finally, it has been concluded that the proposed method has a significant potential for motion planning/controlling applications for an autonomous vehicle using model predictive control.
Multi-frame super-resolution reconstruction based on mixed Poisson–Gaussian noise

In this paper, a non-blind multi-frame super-resolution (SR) model based on mixed Poisson–Gaussian noise (MPGSR) is proposed. Poisson noise arises from the stochastic nature of the photon-counting process. Readout noise and reset noise inherent to the readout circuitry can be modeled by an additive Gaussian noise. Therefore, a mixed Poisson–Gaussian noise model is more appropriate for real imaging system. Instead of deriving the data fidelity term from the perspective of error norms and the corresponding influence functions, we address the multi-frame SR problem based on a statistical noise model. The derived objective function is decomposed into sub-functions and solved by the alternating direction method of multipliers (ADMM) algorithm which allows using techniques of constrained optimization. The validation of the proposed MPGSR was performed quantitatively and qualitatively on natural and X-ray images. In comparison to the optimization-based and learning-based state-of-the-art methods, we have demonstrated the feasibility of MPGSR and the significance of applying a more appropriate noise model on the SR image reconstruction.
Existence Computation: Revelation on Entity vs. Relationship for Relationship Defined Everything of Semantics

Towards tackling the challenge of clarification of the conceptual difference of DIKW concepts of data, information, knowledge and wisdom, we proposed the debut of existence computation (EC) which reveals the ultimate semantics argumentation on concepts in areas including software engineering, service computing, requirement specification, philosophy, etc, from an existence semantic based foundation and organized upwards from this foundation. The description model of the target concepts and their composition is built on our conceptual formalization of the semantic expression space in terms of relationship defined everything of semantics (RDEX) where all semantics of concepts are mapped to relationships. We proposed the axioms and theorems of transitive existence value scopes locating and evaluation for implementation of reasoning and computation of existence value of concepts in relative and absolute manner covering both top down and bottom up tracing. We show the effectiveness of EC in RDE through conceptual reasoning for the revelation of the semantic of key concepts of “Entity vs. Relationship” on the existence level difference in terms of existence value of False, denoted with E=0, and existence value of True, denoted with E=1.
Benefit-Cost Analysis for Earthquake Early Warning in Washington State

This benefit-cost analysis (BCA) assesses the economic value of the implementation and operation of an earthquake early warning system (EEWS) over a 50-year period in the state of Washington. Based on a cost avoidance approach to assess benefits, this analysis compares the expected economic losses under earthquake scenarios with and without an operating EEWS. The FEMA’s earthquake economic loss software program, Hazards US Multi-Hazard (HAZUS-MH), is used to make these comparisons. This analysis demonstrates the net benefits of an EEWS for Washington residents. A Monte Carlo simulation predicts a positive net present value for Washington residents at a 98% probability. A mean savings of $289 million is predicted from the implementation and operation of the proposed EEWS, an amount that considers only some of the potential benefits.
Image Segmentation and Denoising Algorithm Based on Partial Differential Equations

The image denoising algorithm based on partial differential equation can selectively solve the problem of noise and image smoothing, which has become the focus of current image denoising algorithms. In the research of image segmentation, this paper proposes a new image segmentation model, which not only can segment multi-phase images, but also achieves simple implementation, which greatly saves the time required for segmentation. The total variation denoising model is analyzed to improve the fidelity of the model: (1) The total variation and the image denoising model, which constructs a trend fidelity term that can effectively suppress the “staircase effect”; 2) Wavelet transform image denoising model based on curvature variation, which uses a horizontal set of enhanced images to establish a curvature drive function based on the horizontal set curvature, and then introduces the curvature drive function as a correction factor into the variational model to establish the curvature change. The experimental results show that the two improved Partial differential equation image segmentation models (PDEISM) have obvious denoising effects, good visual effects and strong comprehensive performance. The simulation experiment was carried out with MATLAB software to verify the superiority of the improved algorithm. The conclusions drawn from the final simulation experiment are consistent with the conclusions of the theoretical analysis, which indicates that the improved algorithm is superior to the traditional algorithm in overall performance and has better practical application value.
Analysis of CT Scan Images to Predict Lung Cancer Stages Using Image Processing Techniques

Lung cancer is one of the most dangerous and common cancer diseases in the world. Early detection of lung cancer can increase survival time of a patient. It is difficult for doctors to identify the cancer stages from Computed Tomography (CT) scan images. In this era of technology computer-aided system can help us to predict lung cancer stages more accurately. Inspired by the recent success of image processing and machine learning techniques in medical field we have developed models using Gray level co-occurrence matrix (GLCM) based texture image analysis and Statistical parametric approach for helping doctors to detect lung cancer stages. Our approach involves image acquisition, preprocessing, feature extraction and finally classification. For feature extraction purpose two approaches are used: Gray level co-occurrence matrix (GLCM) based texture image analysis and Statistical parametric approach. For detecting lung cancer stages four different classifiers are used and obtained the highest accuracy 78.95% with 0.77 precision and 0.83 recall using Support Vector Machine(SVM) in the Statistical parametric approach of feature selection.
Conceiving an application ontology to model patient human papillomavirus vaccine counseling for dialogue management

In the United States and parts of the world, the human papillomavirus vaccine uptake is below the prescribed coverage rate for the population. Some research have noted that dialogue that communicates the risks and benefits, as well as patient concerns, can improve the uptake levels. In this paper, we introduce an application ontology for health information dialogue called Patient Health Information Dialogue Ontology for patient-level human papillomavirus vaccine counseling and potentially for any health-related counseling.The ontology's class level hierarchy is segmented into 4 basic levels - Discussion, Goal, Utterance, and Speech Task. The ontology also defines core low-level utterance interaction for communicating human papillomavirus health information. We discuss the design of the ontology and the execution of the utterance interaction.With an ontology that represents patient-centric dialogue to communicate health information, we have an application-driven model that formalizes the structure for the communication of health information, and a reusable scaffold that can be integrated for software agents. Our next step will to be develop the software engine that will utilize the ontology and automate the dialogue interaction of a software agent.
Arabic Light Stemming: A Comparative Study between P-Stemmer, Khoja Stemmer, and Light10 Stemmer

Arabic is a derived language that has a deep structure and words meaning, one of the Arabic challenges is its morphology dependency. Arabic Natural Language Processing (ANLP) tools are required to achieve many tasks, such as Machine learning. For the text classification task, the ANLP is considered as preprocessing steps. These preprocessing steps include but not limited to Stemming, Normalization, and Stop-words Removal. In this work, we collected 2,000 news articles from Arabic online newspapers, the data were classified using Support Vector Machine (SVM) and Nave Base (NB) classifiers. The classification task was conducted for the purpose of comparing three different Arabic light stemmers; P-Stemmer, Khoja Stemmer, and Light10 Stemmer. The P-Stemmer results was dominating the other two stemmers in both SVM and NB classifiers with accuracy of 0.92 for F1-measure in SVM classifier and 0.90 for F1-Measure in NB classifier.
A Survey on Smooth Path Generation Techniques for Nonholonomic Autonomous Vehicle Systems

Autonomous vehicles (AVs) are at the heart of academic and industry research because of various advantages such as safety improvement, lower energy and fuel consumption, exploitation of the road network, reduced congestion and greater mobility. Obstacle avoidance, searching the safest path to follow, generation of suitable manoeuver, optimization based most comfortable trajectory along with road boundaries and traffic rules are important concerns in motion planning. The purpose of this paper is to review the existing approaches for trajectory planning in terms of their feasibility, dynamic constraints, handling obstacles, and optimality for comfort.
UID-GAN: Unsupervised Image Deblurring via Disentangled Representations

Recent advances in deep convolutional neural networks (DCNNs) and generative adversarial networks (GANs) have significantly improved the performance of single image blind deblurring algorithms. However, most of the existing algorithms require paired training data. In this paper, we present an unsupervised method for single-image deblurring without paired training images. We introduce a disentangled framework to split the content and blur features of a blurred image, which yields improved deblurring performance. To handle the unpaired training data, a blurring branch and the cycle-consistency loss are added to guarantee that the content structures of the restored results match the original images. We also add a perceptual loss to further mitigate the artifacts. For natural image deblurring, we introduce a color loss to reduce color distortions in outputs. Extensive experiments on both domain-specific and natural image deblurring show the proposed method achieves competitive results compared to recent state-of-the-art deblurring approaches.
Image/Video Restoration via Multiplanar Autoregressive Model and Low-Rank Optimization

In this article, we introduce an image/video restoration approach by utilizing the high-dimensional similarity in images/videos. After grouping similar patches from neighboring frames, we propose to build a multiplanar autoregressive (AR) model to exploit the correlation in cross-dimensional planes of the patch group, which has long been neglected by previous AR models. To further utilize the nonlocal self-similarity in images/videos, a joint multiplanar AR and low-rank based approach is proposed (MARLow) to reconstruct patch groups more effectively. Moreover, for video restoration, the temporal smoothness of the restored video is constrained by the Markov random field (MRF), where MRF encodes a priori knowledge about consistency of patches from neighboring frames. Specifically, we treat different restoration results (from different patch groups) of a certain patch as labels of an MRF, and temporal consistency among these restored patches is imposed. The proposed method is also suitable for other restoration applications such as interpolation and text removal. Extensive experimental results demonstrate that the proposed approach obtains encouraging performance comparing with state-of-the-art methods.
Robust Few-Shot Pose Estimation of Articulated Robots using Monocular Cameras and Deep-Learning-based Keypoint Detection

Camera-based pose estimation is a necessity for flexible applications in robotics, especially interaction between robots and mobile entities. Inspired by recent advancements in human pose estimation based on Convolutional Neural Networks, it is aspired to substitute the usage of artificial marker by automatically detecting inherent keypoints of the robot representing its 2D skeleton model. In addition, current encoder readings of the robot are utilized establishing the corresponding 3D skeleton model through forward kinematics. With the help of these 2D - 3D point correspondences, an estimation of the translation and orientation deviation between robot and camera is derived solving the perspective-n-point problem. An adequate approach for markerless keypoint detection of an UR5 robot is presented and evaluated in terms of precision and pose dispersion considering a dynamically moving robot. The promising results show that the novel method works robustly and reliably as a few-shot approach and copes with false positives as well as with partly occlusions and non-detected keypoints. Further potential is identified regarding enhancing the accuracy through the use of synthetic data.
Economic Union, Finance and Growth: A Prognosis in West Africa

Abstract This paper compares the impact of financial development on economic growth in the Union Économique et Monetaire Ouest Africaine (UEMOA) and non‐UEMOA countries. It also examines the finance‐growth nexus before and after the formation of UEMOA, as well as the indirect effect of economic union on economic growth through the financial sector. The study reveals a significant difference in the finance‐growth nexus between UEMOA and non‐UEMOA countries. Specifically, financial development has a robust positive impact on growth in UEMOA countries, while the impact is tenuous in non‐UEMOA countries. However, we find no remarkable difference in the finance‐growth nexus before and after the formation of UEMOA. We also reveal that economic union has no robust indirect effect on economic growth via the financial sector. The economic implication is that UEMOA could have economic benefits, but financial development is not a channel through which it influences growth. Therefore, it is necessary to reposition the union so that it can enhance the impact of finance on growth.
Collaborative or individual identification of code smells? On the effectiveness of novice and professional developers

The code smell identification aims to reveal code structures that harm the software maintainability. Such identification usually requires a deep understanding of multiple parts of a system. Unfortunately, developers in charge of identifying code smells individually can struggle to identify, confirm, and refute code smell suspects. Developers may reduce their struggle by identifying code smells in pairs through the collaborative smell identification. The current knowledge on the effectiveness of collaborative smell identification remains limited. Some scenarios were not explored by previous work on effectiveness of collaborative versus individual smell identification. In this paper, we address a particular scenario that reflects various organizations worldwide. We also compare our study results with recent studies. We have carefully designed and conducted a controlled experiment with 34 developers. We exploited a particular scenario that reflects various organizations: novices and professionals inspecting systems they are unfamiliar with. We expect to minimize some critical threats to validity of previous work. Additionally, we interviewed 5 project leaders aimed to understand the potential adoption of the collaborative smell identification in practice. Statistical testing suggests 27% more precision and 36% more recall through the collaborative smell identification for both novices and professionals. These results partially confirm previous work in a not previously exploited scenario. Additionally, the interviews showed that leaders would strongly adopt the collaborative smell identification. However, some organization and tool constraints may limit such adoption. We derived recommendations to organizations concerned about adopting the collaborative smell identification in practice. We recommend that organizations allocate novice developers for identifying code smells in collaboration. Thus, these organizations can promote the knowledge sharing and the correct smell identification. We also recommend the allocation of developers that are unfamiliar with the system for identifying smells. Thus, organizations can allocate more experience developers in more critical tasks.
A smart Geo-Location Job Recommender System Based on Social Media Posts

Social networks in real life is growing quickly, Social media have become a day base use for most of Internet users, because of the huge amount of provided services. Social media shares users' thoughts and data through a virtual networks. The proposed work focuses on implementing a smart job seeking system based on client's geo-location. This work proposes a smart system that mines social media networks, such as Twitter and Facebook to match the best vacancy for the exact job seeker. The matches have been presented based on client's geo-locations, which are located by mining their social media posts history. This system overcomes recent such systems as it employs modern recommender methods, Geolocation concepts, and Natural Language Processing (NLP) together in social media for real life problem solution.