Reviewerly/exHarmony huggingface dataset:
Single High-Speed Camera Based 3D Deflection Reconstruction in Frequency Domain

High-speed camera systems are a well-established alternative to traditional vibration measurement techniques, particularly in cases when the region of interest on the observed object is near-planar. With the introduction of 3D digital image correlation some of the traditional limitations of 2D imaging systems are eliminated, but the limited field of view of stereo camera pairs remain problematic in some applications. In this paper the possibility of extending the use of high-speed camera systems to vibration measurement of arbitrarily shaped structures by applying methods, commonly used in multi-view computer vision is explored. A single high speed camera is used to record the vibrating structure from multiple points of view. By utilizing properties of linear, time-invariant mechanical systems, multi-view triangulation is then performed in frequency domain on displacement data, extracted from these image sequences using optical flow or digital image correlation. The acquired 3D spectra are finally used in full-field deflection reconstruction.
Architecture Design and Cultural Deficiency of College English Computer Aided Reading System

Language is one of the important tools for human communication. It carries cultural information and reflects the life of human society. Language and culture are inseparable. The use of information technology to reduce the communication barriers caused by the differences between language and characters is an urgent problem to be solved in the management of English information resources. The system uses the “simplified general semantic code system” as the logical basis for the positioning, association and retrieval of English synonymous morphemes and the logical intermediary of semantic information transformation. Western culture is very different from Chinese culture in many respects. For foreign language learners, it is inevitable to learn from culture. This paper analyzes the lack of Chinese culture in English teaching from the perspective of reading system and culture, English reading architecture design, teaching evaluation and teachers. It discusses the factors and treatment measures of computer-aided reading and cultural loss in college English.
Improving Ambient Assisted Living Through Artificial Intelligence

The longevity of the population is the result of important scientific breakthroughs in recent years. However, living longer with quality, also brings new challenges to governments, and to the society as a whole. One of the most significant consequences will be the increasing pressure on the healthcare services. Ambient Assisted Living (AAL) systems can greatly improve healthcare scalability and reach while keeping the user in their home environment. The work presented in this paper specifies, implements, and validates a smart environment system that aggregates Automation and Artificial Intelligence (AI). The specification includes a reference architecture, composed by three modules, whose tasks are to automate and standardize the collection of data, to relate and give meaning to that data and to learn from it. The system is able to identify daily living activities with different levels of complexity using a temporal logic. It enables a real time response to emergency situations and also a long term analysis of the user daily routine useful to induce healthier lifestyles. The implementation addresses the applications and techniques used in the development of a functional prototype. To demonstrate the system operation three use cases with increasing levels of complexity are proposed and validated. A discussion on related projects is also included, specifically on automation applications, Knowledge Representation (KR) and Machine Learning (ML).
Artefatos Tangíveis e a Avaliação de Estados Afetivos por Crianças

Modern and ubiquitous computational systems increasingly demand more evaluations, which consider aspects beyond ergonomy, usability and accessibility to include means of understanding the affective states of those involved in the interaction. Nevertheless, whenever the involved parties are predominantly children, it becomes necessary to promote ludic and accessible means of involving people in the evaluation activities, because it is expected that the assessment tool used allows all stakeholders to express themselves according to their age and understanding. Existing studies have proposed abstract solutions that difficult the comprehension and participation of those involved in the expression of affective states. In this article, we developed and evaluated the TangiSAM environment, which includes sets of tridimensional concrete manikins that take advantage of tangible technologies, allowing the assessment of affective states in a ludic manner. We conducted an evaluation in a real-world educational setting, including both children and teachers, in order to understand whether the TangiSAM’s tangible artifacts favor a better self-evaluation experience. We found that TangiSAM was more frequently assigned as the most favorite by the participants in the comparison to other affective-state representation proposals.
Towards One Reusable Model for Various Software Defect Mining Tasks

Software defect mining is playing an important role in software quality assurance. Many deep neural network based models have been proposed for software defect mining tasks, and have pushed forward the state-of-the-art mining performance. These deep models usually require a huge amount of task-specific source code for training to capture the code functionality to mine the defects. But such requirement is often hard to be satisfied in practice. On the other hand, lots of free source code and corresponding textual explanations are publicly available in the open source software repositories, which is potentially useful in modeling code functionality. However, no previous studies ever leverage these resources to help defect mining tasks. In this paper, we propose a novel framework to learn one reusable deep model for code functional representation using the huge amount of publicly available task-free source code as well as their textual explanations. And then reuse it for various software defect mining tasks. Experimental results on three major defect mining tasks with real world datasets indicate that by reusing this model in specific tasks, the mining performance outperforms its counterpart that learns deep models from scratch, especially when the training data is insufficient.
Collaboration Mechanism Between Human Administrators and Agent-Oriented Network Management Systems

Disaster risks are always unpredictable, and it is impossible to prepare with predefined functions of the systems including not only automatic mechanical systems but also human groups of engineers and administrators. In this paper, to tackle the problem of disaster risks reduction, we propose a practical design and demonstration of the mechanism of collaboration between humans and agents of network management systems. As an example function of the collaboration, we develop the collaborative agents mediating the human users and the agents dealing with the network management functions. From the evaluation experiments using prototypical implementations show the possibility of the actual effectiveness of the proposed mechanism.
Social Relation Inference via Label Propagation

Collaboration networks are a ubiquitous way to characterize the interactions between people. In this paper, we consider the problem of inferring social relations in collaboration networks, such as the fields that researchers collaborate in, or the categories of projects that Github users work on together. Social relation inference can be formalized as a multi-label classification problem on graph edges, but many popular algorithms for semi-supervised learning on graphs only operate on the nodes of a graph. To bridge this gap, we propose a principled method which leverages the natural homophily present in collaboration networks. First, observing that the fields of collaboration for two people are usually at the intersection of their interests, we transform an edge labeling into node labels. Second, we use a label propagation algorithm to propagate node labels in the entire graph. Once the label distribution for all nodes has been obtained, we can easily infer the label distribution for all edges. Experiments on two large-scale collaboration networks demonstrate that our method outperforms the state-of-the-art methods for social relation inference by a large margin, in addition to running several orders of magnitude faster.
Informative Value Estimation of the Basic Statements of the School Math Course

The diffi culty of understanding an educational text primarily depends on the degree of the information concentration in its constituent sentences and mathematical statements. The article analyzes the problem of assessing the density of educational information for the theoretical component of mathematics textbooks in various classes of secondary schools. The purpose of research consists in: 1) developing objective methods for measuring the quantity semantic information in educational text; 2) in determining the information density in theoretical reasoning (defi nitions, theorems, conclusions, etc.) presented in school math textbooks. To determine the density of theoretical information in math textbooks the thesaurus approach and the content analysis method, based on automated counting of words (terms) in the text with the account of their complexity with help of computer, are used. More 15 textbooks and manuals on mathematics were subjected to the analysis of content. At this the sentences and formulas, corresponding 1st, 2nd, ..., 11th grades, were chosen. The dictionary of using terms is created; their complexity is determined by the method of the complex concepts (terms) decomposition into simple ones and by the method of pair comparison. With the help of special computer program, the automatic analysis of the corresponding text fi les was carried out, their informative value is determined, the average values of information density in various classes are calculated, and a graph is presented.
Multiple Keyphrase Generation Model with Diversity

Encoder–decoder models have achieved high performance in their application to keyphrase generation. However, keyphrases for a source text generated by these models are similar to each other because each keyphrase is independently generated. To improve the diversity, we propose a model that iteratively generates each keyphrase while considering the formerly generated keyphrase. The experimentally obtained results indicate that our model generates more diverse keyphrases with a performance that is superior or comparable to conventional models.
Innovative Outlier Removal Techniques to Enhance Signature Authentication Accuracy for Smart Society

A smart society is an empowered society, which can improve the lives of its citizens by using the latest innovations and technologies. This improvement can happen in several dimensions out of which security is a major one. Inconsistency and forgery are very common phenomenon where handwritten signatures are often preserved for training a classifier to authenticate a person. The removal of outliers, at the outset, obviously improves the quality of training and the classifier. The present article deals with the mechanized segregation of the poor-quality authentic signatures from reliable ones. Machine learning algorithms for outlier handling utilizing clustering, classification and statistical techniques have been implemented in this context. Subsequent performance evaluation after outlier removal reflects improvement of both true positive and true negative recognition rate accuracy. The performance evaluation presents the significant differences between authentication accuracy and forgery accuracy in the context of building a safe, secure and smart society.
Hierarchical Topic Models for Expanding Category Hierarchies

Category hierarchies often help users efficiently find information they need. When newly arrived documents convey new concepts that are not well covered in a hierarchy, it may be appropriate to split an existing category or insert a new category, to which related documents are relocated, in the hierarchy. However, it is often difficult to decide whether to create a new category or not (which we call the category-expansion problem). To address this problem, we propose a novel hierarchical topic model, which we call Generalized SSHLDA (G-SSHLDA). This model can insert a latent subtree at any level in an observed hierarchy. In the latent subtree, its root node can be an arbitrary observed node, and the other nodes are latent nodes. One of the nodes in the latent subtree is linked up with a deeper-level observed node. On the basis of these ideas, G-SSHLDA can automatically expand a category hierarchy that is associated with the target data collection. We demonstrate through experiments with two real-world datasets that G-SSHLDA effectively addresses the category-expansion problem.
Mobile Learning Experience

As the world is moving towards experience economy, consumers are paying more and more attention to memorable and fun experience beyond a product or service. Learners are the same, especially when learning goes mobile. Mobile learning has been examined in different areas ranging from forms and formats to features and functions. Mobile experience in learning, however, has not yet fully examined. After identifying mechanisms to measure and evaluate mobile learning experience, this chapter reviewed what mobile learning resources could be leveraged to enhance mobile learning experience, followed by recommendations for further studies.
Single Image Super-Resolution with Vision Loss Function

Super-resolution is the use of low-resolution images to reconstruct corresponding high-resolution images. This technology is used in many places such as medical fields and monitor systems. The traditional method is to interpolate to fill in the information lost when the image is enlarged. The initial use of deep learning is SRCNN, which is divided into three steps, extracting image block features, feature nonlinear mapping and reconstruction. Both PSNR and SSIM have significant progress compared with traditional methods, but there are still some details in detail restoration. defect. SRGAN will generate anti-network applications to SR problems. The method is to improve the image magnification by more than 4 times, which is easy to produce too smooth. In this study, we hope to improve the EnhanceNet by training with different loss functions and different types of images to achieve better reconstruction results.
Clustering Oriented Scale Invariant Dictionaries for Single Image Super-Resolution

This paper presents a novel methodology based on single image super-resolution problem by a unique scale invariant image feature. This unique feature is used to classify the image patches into separate unique classes. For that aspect, a pair of joint dictionaries are trained for every class. For each class a separate mapping matrix is also learned. By doing so, we are able to enrich the demonstration capacity for super-resolution. The joint dictionary training and a separate mapping matrix learning helps in making the approximate invariant high resolution and low resolution sparse coefficients further similar. The proposed algorithm is a patch based sparse representation algorithm. Instead of a single dictionary pair for whole image, this paper proposed unique scale invariant class dependent dictionaries in the training. In the testing, each patch is first confirmed to a specific class by utilizing the proposed scale invariant feature, afterward, the dictionary pair and the mapping matrix of that class are selected and utilized for its rebuilding. By using the above proposed methodology, the worth of demonstration is enhanced by getting the scale invariant features properly. The testing results display that the proposed algorithm is more efficient in accomplishing the task of super-resolution.
Gender Identification Via Voice Analysis

Human voice is basically sound which is made by humans from their vocal tracts. Voice is made of different constituents and has various characteristics such as frequency, amplitude etc. These characteristics are produced by combination of vocal folds and articulations. This paper reflects development of a system using these characteristics which altogether are called acoustic parameters to detect the gender of the speaker. We have used four models to classify the genders namely CART, XGBoost, SVM and Random Forest. An ensemble of all the models is also used to make the entire system more accurate. This system can be used as a building block for many other softwares where it will take the first step to extract the acoustic parameters and detect the gender of the speaker.
Wide Research on Software Defect Model With Overgeneralization Problems

A renowned topic of interest in software engineering research is software defect prediction. The standard of the software is enhanced through the effectiveness of the bug prediction. The bug indicators are significant in the development of defect prediction breakthroughs and also help in attaining software dependability. The quality of software is improved through the efficiency of the bug prediction. Bug indicators play an important role towards the establishment of defect prediction advances and are of assistance in obtaining software dependability. In software defect prediction, the class of accomplishment is defect modules; any how the number of defect modules is much few and far between than that of non-defect modules. That is, software defect prediction faces the class-imbalance problem. Since the class-imbalance affects the accuracy of defect prediction, it has to do with to give a snappy comeback this problem. There are sprinkling methods to respond class-imbalance such as over-sampling and under-sampling. We study close but no cigar the gat a handle on something using SMOTE which is a pretty over-sampling algorithm in term to re-return the class-imbalance. For overfitting or overgeneralization problems in datasets has been solved through the SMOTE, where number of minority class have been improved this technique. A propose one technique used as Multiple Trials/Feedback, In order to avoid SMOTE's lack of flexibility.
On ‘Finance and economic growth in the aftermath of the crisis’

Exchange-traded funds (ETFs) are one of the most rapidly-expanding categories of innovative financial products that have been introduced on many financial markets, in both emerging and developed economies. Our research contributes to the present state of knowledge by examining factors, including information and communication technologies (ICTs), influencing the diffusion of ETFs. In our research, we consider also the impact of other segments of the financial system, such as the banking sector, and changes in the real side of the economy. The main aim of the paper is to provide empirical evidence on relationships between penetration of ICTs and diffusion of ETFs. Using a sample of 32 emerging and developed economies; we analyze all countries for which data on the turnover of ETFs on the local stock exchanges are available. The time span of our analysis is 2004–2014. The methodological framework combines innovation diffusion models, which are applied to characterize the key features of the process of diffusion of ETFs and ICT, with dynamic panel models, panel VAR models with exogenous variables and VAR models with exogenous variables (for country-specific analysis), which are used to examine the relationships between ETFs, ICTs, and other selected factors. Our major findings confirm that adoption of ICTs constitutes an important prerequisite for the diffusion of ETFs due to potential demand- and supply-side linkages. Among the other factors that potentially influence the diffusion of ETFs, we found three variables that demonstrate positive and statistically significant impacts: stock market turnover, financial development, and financial markets. Country-wise VAR models with selected exogenous variables confirm the influence of ICTs in most of the countries analyzed.
Practically surreal: Surreal arithmetic in Julia

This paper presents an implementation of arithmetic on Conway’s surreal numbers. It also provides tools for visualising complicated surreals in the form of graph visualisations, and illustrates their use through several examples, and a small contribution to the theory of surreals.
A Categorical Model of an $$\mathbf {i/o}$$ -typed $$\pi $$ -calculus

This paper introduces a new categorical structure that is a model of a variant of the $$ \mathbf {i/o} $$ -typed $$ \pi $$ -calculus, in the same way that a cartesian closed category is a model of the $$ \lambda $$ -calculus. To the best of our knowledge, no categorical model has been given for the $$ \mathbf {i/o} $$ -typed $$ \pi $$ -calculus, in contrast to session-typed calculi, to which corresponding logic and categorical structure were given. The categorical structure introduced in this paper has a simple definition, combining two well-known structures, namely, closed Freyd category and compact closed category. The former is a model of effectful computation in a general setting, and the latter describes connections via channels, which cause the effect we focus on in this paper. To demonstrate the relevance of the categorical model, we show by a semantic consideration that the $$ \pi $$ -calculus is equivalent to a core calculus of Concurrent ML.
Implementation of Human Cognitive Bias on Neural Network and Its Application to Breast Cancer Diagnosis

AbstractThe neural network is one of the most successful machine learning models. However, the neural network often requires large amounts of well-balanced training data to ensure prediction accuracy. Meanwhile, human learners can generalize a new concept from even a small quantity of biased examples, simultaneously enlarging knowledge with an increase in experience. As a possible key factor in the ability to generalize, human beings have cognitive biases that effectively support concept acquisition. In this study, to narrow the gap between human and machine learning, we have implemented human cognitive biases into a neural network in an attempt to imitate human learning to enhance performance. Our model, named loosely symmetric neural network, has shown superior performance in a breast cancer classification task in comparison with other representative machine learning methods.neural networkcognitive biasesimbalanced sample data