Reviewerly/exHarmony huggingface dataset:
Robust Identification of an Exponential Autoregressive Model

One of the most common nonlinear time series (random processes with discrete time) models is the exponential autoregressive model. In particular, it describes such nonlinear effects as limit cycles, resonant jumps, and dependence of the oscillation frequency on amplitude. When identifying this model, the problem arises of estimating its parameters --- the coefficients of the corresponding autoregressive equation. The most common methods for estimating the parameters of an exponential model are the least squares method and the least absolute deviation method. Both of these methods have a number of disadvantages, to eliminate which the paper proposes an estimation method based on the robust Huber approach. The obtained estimates occupy an intermediate position between the least squares and least absolute deviation estimates. It is assumed that the stochastic sequence is described by the autoregressive equation of the first order, is stationary and ergodic, and the probability distribution of the innovations process of the model is unknown. Unbiased, consistency and asymptotic normality of the proposed estimate are established by computer simulation. Its asymptotic variance was found, which allows to obtain an explicit expression for the relative efficiency of the proposed estimate with respect to the least squares estimate and the least absolute deviation estimate and to calculate this efficiency for the most widespread probability distributions of the innovations sequence of the equation of the autoregressive model
Interactive Color Theme Editing System for Interior Design

Abstract In this paper, we develop a simple yet effective system for interactive indoor scene color theme editing. Our system permits the user to synthesize material/texture with user-specified colors and attaching the synthesized results to the given 3D indoor scenes. Two main improvements are made in this paper. Firstly, we give a new strategy to decompose texture to color, grey texture image and material parameters thus enable the user editing on certain of these items. Secondly, our system is a plus-in unit that integrated into Unity3D. Thus a quick preview of the color theme edited by the user can be rendered in real time. Experiments show that our system can assist the user to create an interior color theme efficiently. Moreover, our system is simple and easy to use, and has good runtime performance.
Automatic Data Cleaning System for Large-Scale Location Image Databases Using a Multilevel Extractor and Multiresolution Dissimilarity Calculation

In this article, we propose a system for automatically classifying and cleaning location images in large-scale image databases uploaded by arbitrary users. Detecting incorrect scenes uploaded by users and maintaining the correctness of the database through automatic data cleaning are essential because human inspection is not feasible for verifying massive amounts of data. In this study, we compared different feature extractors using deep convolutional neural networks trained using big data. We designed a multilevel extractor to improve feature extraction. Moreover, a detector based on multiresolution dissimilarity calculation was designed to overcome the issue of large intraclass distances and successfully identify incorrect scenes. The proposed system was validated using a highly challenging dataset with 138,000 images collected from Google Places. The experiments show that the multilevel extractor and the detector based on multiresolution dissimilarity calculation can improve the accuracy in identifying incorrect scenes and achieve satisfying data cleaning results.
Novel shrinking residual convolutional neural network for efficient accurate stereo matching

For stereo matching based on patch comparing using convolutional neural networks (CNNs), the matching cost estimation is highly dependent on the network structure, and the patch comparing is time consuming for traditional CNNs. Accordingly, we propose a stereo matching method based on a novel shrinking residual CNN, which consists of convolutional layers and skip-connection layers, and the size of the fully connected layers decreases progressively. Firstly, a layer-by-layer shrinking size model is adopted for the full-connection layers to greatly increase the running speed. Secondly, the convolutional layer and the residual structure are fused to improve patch comparing. Finally, the Loss function is re-designed to give higher weights to hard-classified examples compared with the standard cross entropy loss. Experimental results on KITTI2012 and KITTI2015 demonstrate that the proposed method can improve the operation speed while maintaining high accuracy.
Envisioning the Benefits of Back-Drive in Evolutionary Algorithms

Among the characteristics of traditional evolutionary algorithms governed by models, memory volatility is one of the most frequent. This is commonly due to the limitations of the models used to guide this kind of algorithms, which are generally very efficient when sampling, but tend to struggle when facing large amounts of data to represent. Neural networks are one type of model which conveniently thrives when facing vast amounts of data, and does not see its performance particularly worsened by large dimensionality. Several successful neural generative models, which could perfectly fit as a model for driving an evolutionary process are available in the literature. Whereas the behavior of these generative models in evolutionary algorithms has already been widely tested, other neural models -those intended for supervised learning-have not enjoyed that much attention from the research community. In this paper, we take one step forward in this direction, exploring the capacities and particularities of back-drive, a method that enables a neural model intended for regression to be used as a solution sampling model. In this context, by performing extensive research into the most influential aspects of the algorithm, we study the conditions which favor the performance of the back-drive algorithm as the sole guiding factor in an evolutionary approach.
An Adaptive Water Wave Optimization Algorithm with Enhanced Wave Interaction

Water Wave Optimization (WWO), as a nature-inspired optimization algorithm, has received much attention. In this paper, we tend to improve the algorithm by proposing an adaptive WWO with enhanced wave interaction (AWWOEI). In the proposed method, two operators (namely, Gaussian-based propagation and refraction learning) with an adaptive mechanism are introduced to enhance the wave interaction in the algorithm. The first operator, Gaussian-based propagation operation, is designed to strengthen the exploration ability of the algorithm by encouraging each individual to learn from different exemplars. While, the second operator, refraction learning, aims to improve the exploitation capability of WWO. Further, rather than using a fixed breaking coefficient, an adaptive mechanism has been employed to dynamically adjust its values during evolution. Experiments have been carried out to evaluate the performance of the proposed method and compare it with related methods. The results have demonstrated the superiority of the proposed method.
Competitive-Adaptive Algorithm-Tuning of Metaheuristics inspired by the Equilibrium Theory: A Case Study

This paper proposes a competitive-adaptive algorithm tuning framework for meta-heuristic algorithms. Our proposed method, called CAT, is inspired by the Equilibrium Theory in economics, which explains competitors eventually converge to an equilibrium status, e.g. in terms of the price of products. In detail, our proposal runs multiple optimizers with different algorithmic configurations, e.g. mutation variants. Then, the configurations of inferior optimizers are adaptively tuned so that they can derive good solutions that superior ones have derived. This intends to boost the performance even with a limited number of fitness evaluations, by the following technical advantage. The CAT preliminarily validates a search capacity of tuned algorithmic configurations and then constructs an ensemble optimizer by utilizing multiple optimizers. As a case study, this paper applies the CAT to tune the differential evolution algorithms (DEs). Experimental results show that our proposal outperforms the standard DE and an alternative approach i.e. jDE, which adapts hyper-parameters of genetic operators.
Research on Multiple Digital Watermarking Algorithm Based on Double Scrambling

Multiple digital watermarks were embedded into the color carrier image based on the YIQ color space. In the color space, the Y component can independently display the brightness information of the color image, and embedding watermarks in the Y component has little effect on the other two color components. So this paper used haar wavelet to perform 3-level discrete wavelet decomposition on the Y component of the color carrier image, and two robust watermarks were embedded in LL3 and HL3 layers of Y component respectively for copyright protection and authentication of digital products. The authentication watermark was embedded in HL2 layer of the Y component to locate the areas where the color carrier image has been tampered with maliciously. Then through a series of simulation experiments to test, it can be seen that the algorithm in this paper has strong robustness from the analysis of experimental results. It not only can achieve copyright protection, but also can achieve the function of tampering positioning.
Path Planning of Mobile Robot Based on Improved Fuzzy Control

Aiming at the problem of local path planning of mobile robot under unknown environment, an improved fuzzy control method of local path planning is proposed, and the strategies to solve the problems existed in the planning process are also researched. Firstly, the behavior of avoiding obstacles and walking along the wall are integrated into a fuzzy controller, and the behavior alternation is realized by internal rules. Secondly, aiming at the deadlock problem of multi-U-shaped obstacles in fuzzy control method, a judgment method of cumulative angle sum is proposed to help the robot escape the deadlock state. Then, by adding the trap prediction mechanism, the robot can overcome the limitations of the sensor measurement range to a certain extent, and predict whether the front is feasible and make decisions, then reduce redundant road sections. In addition, the steering strategy based on artificial potential field method is designed to solve the path redundancy caused by the symmetry of obstacles. Finally, the simulation tests are carried out on the MATLAB platform to verify the feasibility and validity of the designed methods.
A Study on Digital Literacy Perspectives when Designing Library Learning for Rural Community Colleges in the Connected Age

Technology is integral to contemporary life; where the digital transformation to virtual information accessibility impacts instruction, it alters the skills of learning and comprehension (Gonzalez-Patino & Esteban-Guitart, 2014; Lloyd, 2010). Although librarians/media specialists provide orientation, instruction, and research methods face-to-face and electronically, they recognize that digital learning instruction is not a linear process, and digital literacy (DL) is multi-disciplinary (Belshaw, 2012). Policy and public research findings indicate that higher education must be prepared to adapt to rapid changes in digital technology (Maybee, Bruce, Lupton, & Rebmann, 2017). Digital learning undergoes frequent transformations, with new disruptive innovation and research attempts at redefinition (Palfrey, 2015). Research often overlooks junior/community colleges. We are all learners and we need to understand the digital learning challenges that incorporating DL includes in the new digital ecology (Adams Becker et al., 2017). This study provides real faculty/librarian commentaries regarding the understanding needed to develop digital learning and contemporary digital library resources. The authors investigate faculties’ and librarians’ degree of DL perceptions with instruction at junior/community colleges. Survey data analysis uses the mean of digital self-efficacy of variables collected, revealing that participants surpassed Rogers’s (2003) chasm of 20% inclusion. Findings provided data to develop the Dimensions of Digital Learning rubric, a new evaluation tool that encourages faculty DL cross-training, librarians’ digital learning collaboration, and effective digital learning spaces.
Model-Free Sliding Mode Iterative Learning Control for Cz Silicon Single Crystal Diameter

Aiming at the complex nonlinear dynamic time-varying characteristics for Czochralski (Cz) silicon single crystal growth process and the difficulty in modeling and controlling the crystal diameter by conventional mechanisms, based on the idea of data-driven modeling and control, this paper proposes an improved model-free sliding mode iterative learning control (MFA-SMILC) method. First, a data-driven model of crystal diameter is established using an extreme learning machine (ELM) with actual process data; Then, based on the compact-format dynamic linear (CFDL) data model, a discrete sliding mode control algorithm is used to design a data-driven controller structure for crystal diameters, and the stability of the iterative tracking error is verified by the stability analysis; Finally, the proposed MFA-SMILC controller is applied to silicon single crystal diameter control, and compared with the conventional model-free adaptive iterative learning control (MFA-ILC), it is found that MFA-SMILC has faster response speed and convergence speed, which verifies the effectiveness of the proposed control method.
InferPy: Probabilistic modeling with deep neural networks made easy

InferPy is a Python package for probabilistic modeling with deep neural networks. It defines a user-friendly API that trades-off model complexity with ease of use, unlike other libraries whose focus is on dealing with very general probabilistic models at the cost of having a more complex API. In particular, this package allows to define, learn and evaluate general hierarchical probabilistic models containing deep neural networks in a compact and simple way. InferPy is built on top of Tensorflow Probability and Keras.
A Novel Person Re-ID Method based on Multi-Scale Feature Fusion

Person re-identification (Re-ID) is a challenging but significant research topic. Previous methods based on global features are not pay much attention to the fine-grained feature information. While the methods based on local features strictly rely on the accurate pedestrian detection bounding-boxes to align the image pairs. In this paper, the Multi-Scale Feature Fusion Network is proposed which can extract global and local features simultaneously and to learn jointly. The network structure consists of two branches. One is a global branch for global feature learning, in this branch, we extract the whole body feature information of human body to express the pedestrian image as a whole. The other is a local branch for local feature learning. In the local branch, according to the characteristics of pedestrian’s human structure, we segment the image into 6 horizontal strips to bring the local feature representation of the pedestrian image. In addition, we use a novel integration of multiple loss functions to further improve the recognition accuracy of the network. Experiments on Market-1501 and DukeMTMC-reID datasets show that our proposed method can achieve state-of-the-art results.
Noise Removal With Filtering Techniques

An overview of the image noise models and the de-noising techniques available are presented here. Basically, filtering is one of the de-noising approaches that is normally performed in both spatial and frequency domains. Thus, this chapter focuses on these two approaches. Few filters like mean, median, sharpening, and adaptive median filter are discussed under spatial domain. In the frequency domain, as Butterworth filter suits better for images, Butterworth low pass, high pass, and band pass filters along with homomorphic filters are also analyzed. It also provides a comparative analysis of these approaches for both synthetic and medical images with some performance measures.
A Sentiment Analysis Method for Analyzing Users Opinions About Drugs for Chronic Diseases

Nowadays, more and more people use the Web as the primary medium for answering their queries and making decisions in multiple domains, not being the exception the healthcare domain. In this sense, blogs, forums, wikis, social networks, and review websites about chronic diseases management must be exploited to derive actionable knowledge that support and improve the health self-management of this kind of diseases, which are among the most prevalent medical conditions in low and middle. This work proposes a sentiment analysis method that combines machine learning with psycholinguistic features to analyze opinions of users about drugs and determine their polarity. The effectiveness of this method was measured by using a corpus of opinions written in Spanish. These opinions were provided by people with diabetes and/or hypertension that have lived with such diseases for many years. We obtained hopeful results with an F-measure of 0.725–0.793 for the SMO, and BayesNet classification techniques.
Framework for rare event detection using Artificial Neural Network based context free grammar

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.
Students’ Response Towards the Improvement of Reading Comprehension Through Herringbone Technique

This research aimed to find out how students’ response after they learned reading to improve their comprehension skill through herringbone technique. Descriptive quantitave method was used in this research. It aimed at finding out and describing students’ response towards the improvement of reading comprehension through herringbone technique.The subject of the research was the fourth semester students in Academic year 2019/2020. The researchers applied purposive sampling technique and took class C from three classes. The total sample of this research was 38 students. In collecting the data, the researchers used a questionnaire to gain students’ response. Total number of questionnaires were 10 numbers with positive statements. The result shows there were 35 students (92.11%) who responded strongly agree and 3 students (7.89%) who responded agree. It means that all students gave good response for this technique. Based on the result of this research, the researchers can conclude that herringbone technique is a good technique to be applied in comprehending a reading text because this method used 5W+1H questions as guidance for the reader to comprehend the content of a reading text.
<scp>De‐dollarization</scp> in Zimbabwe: What lessons can be learned from other <scp>sub‐Saharan</scp> countries?

Abstract Zimbabwe has had a tumultuous currency history. In 2019, it introduced a new currency. To make the new currency widely acceptable, International experience suggests that the introduction of a new currency, following a period of official dollarization, requires the introduction of policies that go beyond just changing the law. This paper, aims to draw lessons from other African countries on how to de‐dollarize effectively. Using various econometric techniques and a new database, this paper documents the pattern of dollarization in African economies over the last decade and assesses the determinants of dollarization compared to the rest of the world. It illustrates that despite an improved macroeconomic environment, dollarization remains elevated, likely due to the region's volatile history. This indicates that dollarization should decline going forward provided that macroeconomic stability is achieved and preserved. The lessons for Zimbabwe are also that it's important to use legal, regulatory and market based incentives to nudge de‐dollarization, and that it will take time.
Deep Neural Network for Evaluating Web Content Credibility Using Keras Sequential Model

Web content credibility determines the measure of acceptable and reliable of the web content that is observed. Content will prove to be unreliable if it is not updated, and it is not controlled for remarkable, and therefore, web content credibility is considerably essential for the people to assess the content. The analysis of content credibility is a vital and challenging task as the content credibility is outlined on crucial factors. This paper focuses on building deep neural network (DNN)-based predictive model using sequential model API to evaluate credibility of a webpage content. Deep neural network (DNN) is considered as an extremely promising decision-making architecture, and it performs feature extraction and transformation with the use of refined statistical modeling. A corpus of 400 webpage contents has been developed, and the factors like readability, freshness, and duplicate content are defined and captured from the webpage content. These features are redefined, and a new set of features is self-learned through the deep layers of neural network. Numeric labeling is used for defining credibility, wherein five-point Likert scale rating is used to denote the content credibility. By using sequential model, KerasRegressor with ADAM optimizer and a multilayer network is generated for building DNN-based predictive model and discovered that deep neural network outperforms other general regression algorithms in prediction scores.
3D Human Motion Capture Based on Neural Network and Triangular Gaussian Point Cloud

In this paper, an optical unmarked motion capture method based on convolutional neural network and triangular gaussian point cloud is proposed to achieve accurate 3D human pose estimation. Firstly, the Direct Linear Transformation(DLT) method is used to calibrate the actual multi camera system and obtain the internal and external parameters of all cameras. Then the depth neural network Cascaded Pyramid Network(CPN) is used to extract the 2D human key points in the images collected by each camera in the system. Next the triangle positioning and the least square method are used to preliminarily obtain the 3D human key point coordinates, and then the 3D key points of human body are optimized by gauss point cloud method to get the accurate 3D results of human body.