Reviewerly/exHarmony huggingface dataset:
A Reciprocity on Finite Abelian Groups Involving Zero-Sum Sequences

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 6 February 2020Accepted: 15 March 2021Published online: 20 May 2021Keywordszero-sum sequence, finite abelian group, rational Catalan number, rational Dyck path, invariant theoryAMS Subject Headings05A19, 11B13, 15A72Publication DataISSN (print): 0895-4801ISSN (online): 1095-7146Publisher: Society for Industrial and Applied MathematicsCODEN: sjdmec
Confusion and Chinese character learning

This study explores the relationship between the experience of confusion and the outcomes of Chinese character learning by learners of Chinese as a heritage language (CHL). Based on the claim that impasses triggering confusion can lead to deeper learning of conceptually difficult material, the study employed three impasse-driven tasks. The tasks were designed to trigger a state of cognitive disequilibrium in the participants. After first encountering the tasks in Session 1, the 117 CHL learners were given 1 week to resolve their impasse-driven tasks in their own time for 30 min (limited by the computer system). Afterwards, we announced the correct answer for them to look at for 3 days, and then Session 2 was administered 1 week after the feedback. The results of this study showed considerable improvement in differentiating near-homographs and homophones presented in a sentence, and correcting wrong Chinese characters. The study’s results suggest that incorporating well-designed confusing content into Chinese learning may help CHL learners deepen their learning of Chinese characters.
3D Hand Reconstruction from a RGB Image

The research on 3D hand reconstruction by images is a hot topic in computer graphics and computer vision, and it is positively related to virtual reality and augmented reality. There are many methods for 3d hand reconstruction, but most of the existing models only consider the hand pose and geometrical shape, not the delicacy of the surface of the hand. To close this gap, in this paper put forward to add hand texture modeling to the modeling of 3D hand reconstruction, especially the modeling of hand hair. The experiment results express that our method improves the 3d hand fidelity by increasing the modeling of hand hair texture.
Response Probability Distribution Acquisition for Autonomous Dialogue Generation

In this work we present a conversation system that is aimed at encouraging conversation from older adults, where the responses are obtained automatically from existing dialogue corpora. This system is unique as it is designed to respond with questions specifically with the aim of encouraging the older adults to speak more and to prevent the conversation from falling into a dead halt. Human evaluation for naturalness was conducted on the responses of this system and compared to a rule based and a neural based conversation system. Our system was capable to outperform the neural based system while falling slightly short of the rule based system.
Finance, Governance and Inclusive Education in Sub-Saharan Africa

This research assesses the importance of credit access in modulating governance for gender inclusive education in 42 countries in Sub-Saharan Africa with data spanning the period 2004-2014. The Generalized Method of Moments is employed as empirical strategy. The following findings are established. First, credit access modulates government effectiveness and the rule of law to induce positive net effects on inclusive “primary and secondary education”. Second, credit access also moderates political stability and the rule of law for overall net positive effects on inclusive secondary education. Third, credit access complements government effectiveness to engender an overall positive impact on inclusive tertiary education. Policy implications are discussed with emphasis on Sustainable Development Goals.
On the role of distance transformations in Baddeley’s Delta Metric

Comparison and similarity measurement have been a key topic in computer vision for a long time. There is, indeed, an extensive list of algorithms and measures for image or subimage comparison. The superiority or inferiority of different measures is hard to scrutinize, especially considering the dimensionality of their parameter space and their many different configurations. In this work, we focus on the comparison of binary images, and study different variations of Baddeley’s Delta Metric, a popular metric for such images. We study the possible parameterizations of the metric, stressing the numerical and behavioural impact of different settings. Specifically, we consider the parameter settings proposed by the original author, as well as the substitution of distance transformations by regularized distance transformations, as recently presented by Brunet and Sills. We take a qualitative perspective on the effects of the settings, and also perform quantitative experiments on separability of datasets for boundary evaluation.
Earthquake data analysis and data visualization of Maharashtra state, India from 1912 to 2009 using R programming

Abstract This paper presents the seismic data analysis and visualization using R programming for the state of Maharashtra, India. Maharashtra has witnessed a significant number of earthquakes ranging from low to high in magnitude. The past earthquake data of a particular location are useful to the civil and structural engineers for the safe and efficient planning, designing and construction of seismic resistant buildings in that locality. In this paper the past earthquake data of Maharashtra from the year 1912 to 2009 are collected and analyzed. There are 53 earthquake data with magnitudes ranging from 3.6 to 7.2. This analysis shows the relationship between magnitude and depth, intensity and PGA, visualization of classification of earthquakes depends on magnitude and intensity.
Research and Practice of Basic Programming Course

A new researching and teaching project of basic programming was proposed and carried out for freshmen of computer science and software engineering including the refinement of training objective, the adjustment of course schedule, the argumentation of course content, the extension of practice training, the establishment of the evaluation system and the assessment index and the aggrandizement of the usage of online practice platform and online judge platform. Finally, the effectiveness of the project proposed in this paper is validated through the case study.
Intelligent monitoring system for danger sources of infrastructure construction site based on deep learning

To master the real-time security status of the infrastructure construction site, an intelligent monitoring system for the recognition of the danger sources at infrastructure construction site, which is based on the theory of depth learning, is proposed. The monitoring system mainly includes signal acquisition equipment, internal optical network, server control center and display terminal. The intelligent identification algorithm of the danger sources is the core of the entire system. The characteristics from the image signal are extracted by the sparse self-coding, which is used to train the neural network. Then, the convolution is proposed to reduce the dimensionality of the features. The experiments confirm that the identification algorithm based on deep learning illustrates high accuracy in hazard identification. In terms of feedback, the identification results will be transmitted to the display terminal, and the safety status of the entire infrastructure construction site can be fully controlled to ensure the safety of both the infrastructure site and the power system.
Development of Compression Algorithms for Computed Tomography and Magnetic Resonance Imaging

Medical image compression finds extensive applications in the fields of healthcare, teleradiology, teleconsultation, telemedicine, and telematics. The development of picture archiving and communication systems (PACS) is implemented by efficient compression algorithms. The medical imagery also needs to be compressed to obtain optimum compression with high diagnostic quality. In order to achieve reduction of transmission time and storage costs, efficient image compression methods without degradation of images are needed. In medical image compression techniques, the lossy and lossless methods do not produce an optimum compression with no loss of information. The high compression and without loss of diagnosis ability for the medical image should only be aimed at developing an optimum image compression techniques. Medical image compression algorithms developed so far focused toward only on space reduction and did not concentrate much on the characterization of the images and the effects of compression on the image quality.
Blind Image Quality Evaluator with Scale Robustness

Image quality assessment (IQA) algorithms usually need define some visual-related features and build their relationship with image visual quality. Regardless of whether the features are extracted in spatial or frequency domain, these features are often sensitive to image resolution, resulting in that existing no-reference (NR) IQA algorithms are only efficient for images with similar resolution. In this paper, we propose a more generalized blind image quality evaluator with scale robustness (BIQESR) to assess image quality by locating the robust feature points in a multi-scale space. Compared with the state-of-the-art NR IQA methods, BIQESR has higher performance and stability for images with significantly different resolutions.
An Optimal Stochastic Compositional Optimization Method with Applications to Meta Learning

Stochastic compositional optimization generalizes classic (non-compositional) stochastic optimization to the minimization of com-positions of functions. Each composition may introduce an additional expectation. The series of expectations may be nested. Stochastic compositional optimization is gaining popularity in applications such as meta learning. This paper presents a new Stochastically Corrected Stochastic Compositional gradient method (SCSC). SCSC runs in a single-time scale with a single loop, uses a fixed batch size, and guarantees to converge at the same rate as the stochastic gradient descent (SGD) method for non-compositional stochastic optimization. It is easy to apply SGD-improvement techniques to accelerate SCSC. This helps SCSC achieve state-of-the-art performance for stochastic compositional optimization. In particular, we apply Adam to SCSC, and the exhibited rate of convergence matches that of the original Adam on non-compositional optimization. We test SCSC using the model-agnostic meta-learning tasks.
Heuristic and interactive technologies in teaching informatics at the pedagogical university as a means of developing the flexibility of thinking of students

The aim of the study was to study heuristic and interactive technologies in teaching informatics at a pedagogical university as a means of developing students' thinking flexibility. The main method used in this research work is the analysis of scientific and pedagogical literature. It was concluded that the integration of heuristic and interactive technology in teaching informatics is a pedagogical condition for the formation of flexibility of thinking of students of a pedagogical university.
Manifold Approximation and Projection by Maximizing Graph Information

Graph representation learning is an effective method to represent graph data in a low dimensional space, which facilitates graph analytic tasks. The existing graph representation learning algorithms suffer from certain constraints. Random walk based methods and graph convolutional neural networks, tend to capture graph local information and fail to preserve global structural properties of graphs. We present MAPPING (Manifold APproximation and Projection by maximizINg Graph information), an unsupervised deep efficient method for learning node representations, which is capable of synchronously capturing both local and global structural information of graphs. In line with applying graph convolutional networks to construct initial representation, the proposed approach employs an information maximization process to attain representations to capture global graph structures. Furthermore, in order to preserve graph local information, we extend a novel manifold learning technique to the field of graph learning. The output of MAPPING can be easily exploited by downstream machine learning models on graphs. We demonstrate our competitive performance on three citation benchmarks. Our approach outperforms the baseline methods significantly.
Vehicle Trajectory Extraction in Traffic Scene Based on Monocular Vision

Abstract Accurately extracting the trajectory information of multiple vehicles in urban traffic videos is of great significance in applications. It can help analyze and predict drivers’ behavior, count road flow, and restore accident scenarios. However, due to the complexity of the interaction between vehicles and the environment, it is still a challenging problem to accurately extract the trajectory of multi-target vehicles in urban traffic scenes, which often requires the assistance of a variety of different devices, which increases the practicality and difficulty of application. A method is proposed based on the combination of detection and tracking. This method only needs to use a video stream captured by a fixed panoramic camera and the coordinate information of several feature points in the scene to extract the speed and trajectory information of the vehicle in the scene. Different optimization methods are used in each link to reduce errors, and build a simulation environment for the test scenario, reconstruct and verify the extracted trajectory information in the simulation environment. Experiments have proved that the effect of the method is basically satisfactory. It has the advantages of fewer constraints, convenient deployment, and accurate restoration while ensuring a certain real-time performance, which has practical engineering significance.
Analysis of students' database design skills in capstone projects

In this paper, we use Association rule learning to analyze the relationship between students' performance in database courses and their performance of database design in the capstone projects. Students need to design and implement databases to store related data in their capstone projects. Some of these databases are well designed, however, some of the designs have common problems such as redundant information, incorrect relationships between tables, etc. Therefore, we want to use Association rules to determine if database related courses affect the quality of students' database design performance in the capstone projects. The interesting strong rules found will be shared with instructors for these courses as teaching references.
Transversal Competence: Importance in Educational System

The importance of developing students’ transversal competences, as well as the level of develop- ment of transversal competences in higher education institutions are discussed in the article. The com- ponents of transversal competences and methods are analyzed and aimed at the formation of students as citizens in accordance with the educational system suitable for a developing society. The following set of transversal competences such as critical and innovative thinking, creativity, digital literacy, informative literacy, problem solving, teamwork, foreign language proficiency, etc. that are at the center of attention of the global educational system and are recognized in the universities of the European Union and which should be developed are revealed in the article and the connection between 21st century skills or “life skills” with transversal competence are shown as well. These competences are widely used in scientific literature, in the learning process under the follow- ing names: general competences, strategic competences and key competences. These competences are characterized by the ability to quickly adapt and move from one field of activity to another, and also play a key role in achieving success in the labor market. In addition, the research was carried out on the basis of TRANSMOD project questionnaire (2014). 50 respondents participated in the current study. The level of formation and development of transversal competence in higher educational institutions of the country was identified and analyzed as a result of the research. Key words: transversal competence, transversal skills, digital literacy, 21st century skills, labor market.
Improving Short Text Classification Using Context-Sensitive Representations and Content-Aware Extended Topic Knowledge

Most existing short text classification models suffer from poor performance because of the information sparsity of short texts and the polysemous class-bearing words. To alleviate these issues, we propose a context-sensitive topic memory network (cs-TMN) by learning context-sensitive text representations and content-aware extended topic knowledge. Different from TMN that utilizes context-independent word embedding and extended topic knowledge, we further employ context-sensitive word embedding, comprised of local context representation and global context representation to alleviate the polysemous issue. Besides, extended topic knowledge matched by context-sensitive word embedding is proven content-aware in comparison with previous works. Empirical results demonstrate the effectiveness of our cs-TMN, outperforming state-of-the-art models on short text classification on four public datasets.
Triangle Search Optimization Algorithm for Single-Objective Bound-Constrained Numerical Optimization

Real-parameter optimization has been a focus of the last decade. An inspired algorithm, triangle search optimization (TSO), is proposed for the Congress on Evolutionary Computation (CEC) 2020 competition. In this paper, the TSO algorithm is divided into two phases: the triangle vertex searching (TVS) and triangle edge searching (TES) phases. In the TVS phase, the population is divided into two subpopulations, which can be enhanced by vertex searching operators and the covariance matrix adaptation evolution strategy (CMA-ES) for exploitation. In the TES phase, the differential evolution vector between superior and inferior solutions is employed to improve the exploration ability. The TSO algorithm is evaluated on the CEC 2020 benchmark problems and compared with the winners of the CEC competition and novel competitors. The experimental results show that TSO outperforms the compared algorithms.
Augmented Reality Game for Brand Recognition and Recall

Augmented Reality (AR) technology offers the opportunity to change the customer experience in the retail context. For example, AR applications can be used as a bridge to close the gap between physical and online shops, mixing the best characteristics of both. Different researchers have measured the quality of the user's experience through AR. However, here we focused not only on user experience but also on the impact of AR on brand recall and recognition. We analyzed the impact on brand recall and recognition of an AR application by comparing three contexts: a traditional context where participants look at three products, a focused context where participants have only one product to look at, and a technology-enhanced context where participants interacted with the product using the designed AR experience. Experimental results revealed that AR improved brand recall and recognition by creating an engaging environment. Moreover, participants in the AR context recalled additional information about the product. Also, the AR experience motivated them to participate in an omnichannel activity based on a QR interaction. These results show the influence of well-designed AR experiences on brand recall and recognition opening the possibility for new technology in retail environments.
Detection of Patterns in Smart Home User Data using Color Coded Images

The expansion of the Internet of Things (IoT) has added new dimension to modern living by enabling communication with and between smart objects. In the smart living domain, current living habitudes are dictating new requirements. Since generous amount of data is being generated in smart home, it offers a possibility for customizing the user experience, by creating an autonomous system. In order to automate certain routines, the pattern of user behavior should be detected. In this paper, we propose a method for pattern detection in user behavior and present an implementation of data representation in an existing smart home system.
A Novel Framework for Discovering Cognitive Models of Learning

A cognitive model is a descriptive account or computational representation of human thinking about a given concept, skill, or domain. A cognitive model of learning, includes both a way of organizing knowledge within a subject area and an account of how humans develop accurate and complete knowledge of that subject area. Learning designers engage in a variety of practices to unpack knowledge from subject matter experts and novices to develop cognitive models of learning and use those models to guide the design of instruction or instructional technologies. Traditional approaches to eliciting and organizing knowledge, such as conducting a cognitive task analysis (CTA) [10] with experts and novices, are labor-intensive and require specific expertise that many learning designers do not have. However, learning data generated from learners' interaction with the courses, reveal how humans think about and develop knowledge. We propose a novel framework that uses learning data to discover and refine cognitive models of learning. The framework includes a Variational Autoencoder (VAE) module and a Gaussian Mixture Model (GMM) module. We provide one case study in a corporate setting to demonstrate the effectiveness of the proposed framework compared to other approaches.
Projection pursuit regression and principal component regression on statistical downscaling using artificial neural network for rainfall prediction in Jember

Abstract Rainfall forecasting is essential for Indonesia, which is an agricultural country. Forecasting to see the rainfall needed to anticipate the danger of drought that will harm farmers. However, due to the complexity of topography and the interactions between the oceans, land, and atmosphere in Indonesia, it is difficult to predict rainfall. Therefore, Statistical Downscaling (SD) is needed to provide accurate rainfall predictions by considering the information about global atmospheric circulation obtained from the General Circulation Model (GCM). Statistics Downscaling (SD) modeling is a basic regression model based on the functional relationship between local scales, which is the response variable with the Global Circulation Model (GCM) global scale as a predictor variable. The Statistics Downscaling (SD) method used is Principal Component Regression (PCR) and Projection Pursuit Regression (PPR). The prediction of both methods was conducted by an Artificial Neural Network (ANN). The results showed that the prediction of rainfall in Jember using the PPR + ANN method (with the RMSE value of 79.58723) had better accuracy than the PPR, PCR, and PCR + ANN methods, which had RMSE values of 103.7539, 112.337 and 83.62029, respectively.
The Language L

Chapter 4 introduced the symbols of L, explained their meaning, and illustrated how they can be used to formalize sentences of a natural language. Now it is time to define L in a rigorous way by making fully explicit its syntax and its semantics.