Reviewerly/exHarmony huggingface dataset:
Investigation of Reliability of Combinatorial-Metric Algorithm for Recognition of N-Dimensional Group Point Object in Hierarchy Features Space

The scientific research of reliability of combinatorial-metric algorithm for multi-dimensional group point objects recognition in hierarchically organized features space is considered in the paper. The nature of reliability indicator change is examined, as an example, using multilevel descriptions of simulated and real objects under the condition that recognition results obtained at one hierarchy level are used as input data at next level.&#x0D; A priori uncertainty of a view angle, composition incompleteness and coordinate noise of objects determine the combinatorial procedures of quantifiable estimation of proximity of multidimensional GPO, presenting the object of recognition to a particular class.&#x0D; The stability of the recognition algorithm is achieved by the possibility of changing strategy of making a classification decision. For this purpose, we use the representation of a group point object at the lowest level of the hierarchy in the form of: sample, composition of sample elements or a complex a priori indicator. In order to increase the recognition accuracy, it was proposed to use the search of recognition results at low levels of the hierarchy. The experimental dependences of a priori and a posteriori reliability indicators for various conditions for measurements and states of recognition objects are provided in the paper.
An arm motion learning support system using virtual reality

For beginners of sign language, this article proposes the arm motion learning support system to analyze the difference between the example of teacher's motion and user's motion, and to urge the user to improve his/her motion. Using virtual reality (VR) system composed by head-mounted display and motion tracking hardware/software, the proposed system measures how similar between the example motion and user's motion, and gives him/her the result of analysis, followed by the motion replay as visual feedback. For the user's easy understanding, the system scales the character according to the user's physique. Also the system adopts the sight cursor input for hands-free input. Questionary investigation after user test by 21 people shows that the proposed method has the competitive advantage against the conventional learning methods (textbooks and videos) especially in interactivity including visual feedback, depth sense in posing, motivation in learning, and easy user-interface. Detection of fingers' motion should be discussed in the next step. Since heavy and/or bulky devices are unacceptable for the purpose of the self-learning, a suitable device or other methods will be surveyed.
Neural Graph Evolution: Towards Efficient Automatic Robot Design

Despite the recent successes in robotic locomotion control, the design of robot relies heavily on human engineering. Automatic robot design has been a long studied subject, but the recent progress has been slowed due to the large combinatorial search space and the difficulty in evaluating the found candidates. To address the two challenges, we formulate automatic robot design as a graph search problem and perform evolution search in graph space. We propose Neural Graph Evolution (NGE), which performs selection on current candidates and evolves new ones iteratively. Different from previous approaches, NGE uses graph neural networks to parameterize the control policies, which reduces evaluation cost on new candidates with the help of skill transfer from previously evaluated designs. In addition, NGE applies Graph Mutation with Uncertainty (GM-UC) by incorporating model uncertainty, which reduces the search space by balancing exploration and exploitation. We show that NGE significantly outperforms previous methods by an order of magnitude. As shown in experiments, NGE is the first algorithm that can automatically discover kinematically preferred robotic graph structures, such as a fish with two symmetrical flat side-fins and a tail, or a cheetah with athletic front and back legs. Instead of using thousands of cores for weeks, NGE efficiently solves searching problem within a day on a single 64 CPU-core Amazon EC2 machine.
Towards smart teaching and learning: a study in a South African institution of higher learning

This study analyses the responses obtained from a survey conducted among three thousand four hundred and ninety seven students of a South African institution of higher learning. The interviews reflected in the survey questionnaire are sought to have an insight into the usage of smart phones, tablets and other electronic devices by students for teaching and learning purposes. Results obtained showed 64.1% of University students consulted in this survey perceived the use of smart devices to be from very important to absolutely essential, which may compel institutions of higher learning to think of fresh interventions pertaining to the use of smart devices for effective teaching and learning.
Bionic Motion Planning for Robot Arm Catching Based on Tau Guidance Theory

This paper presents a bionic motion planning approach for the catching movement of robot arm based on a novel tau guidance theory, which was established from studying the motion pattern of biology by cognitive scientists. In accordance with the rules of human catching movement, the bionic motion planning method ensures the continuity of the acceleration and makes the acceleration be zero at the start and end of the movement. The approach was implemented on a 3-DOF 3R planar robot arm. The result shows that, within a defined time, the position gap and the attitude gap can be closed at the same time, and the curves of joint velocity, acceleration and driving torque keep continuous and smooth.
Automatic Generation of Leveled Visual Assessments for Young Learners

Images are an essential tool for communicating with children, particularly at younger ages when they are still developing their emergent literacy skills. Hence, assessments that use images to assess their conceptual knowledge and visual literacy, are an important component of their learning process. Creating assessments at scale is a challenging task, which has led to several techniques being proposed for automatic generation of textual assessments. However, none of them focuses on generating image-based assessments. To understand the manual process of creating visual assessments, we interviewed primary school teachers. Based on the findings from the preliminary study, we present a novel approach which uses image semantics to generate visual multiple choice questions (VMCQs) for young learners, wherein options are presented in the form of images. We propose a metric to measure the semantic similarity between two images, which we use to identify the four options – one answer and three distractor images – for a given question. We also use this metric for generating VMCQs at two difficulty levels – easy and hard. Through a quantitative evaluation, we show that the system-generated VMCQs are comparable to VMCQs created by experts, hence establishing the effectiveness of our approach.
Data Mining Optimization Using Sample Bootstrapping and Particle Swarm Optimization in the Credit Approval Classification

Credit approval is a process carried out by the bank or credit provider company. Where the process is carried out based on credit requests and credit proposals from the borrower. Credit approval is often difficult for banks or credit providers. Where the number of requests and classifications must be made on various data submitted. This study aims to enable banks or credit card issuing companies to carry out credit approval processes effectively and accurately in determining the status of the submissions that have been made. This research uses data mining techniques. This study uses a Credit Approval dataset from UCI Machine Learning, where there is a class imbalance in the dataset. 14 attributes are used as system inputs. This study uses the C4.5 and Naive Bayes algorithms where optimization is needed using Sample Bootstrapping and Particle Swarm Optimization (PSO) in the algorithm so that the results of the research produce good accuracy and are included in the good classification. After using the optimization, it produces an accuracy rate of C4.5 which is initially 85.99% and the AUC value of 0.904 becomes 94.44% with the AUC value of 0.969 and Naive Bayes which initially has an accuracy value of 83.09% with an AUC value of 0.916 to 90 , 10% with an AUC value of 0.944.
Comprehensive Survey on Detection of Living or Dead Humans and Animals Using Different Approaches

The main idea behind this paper is to recognize living or dead humans and animals using different approaches. Object detection and tracking are the most challenging task faced by different software or computer applications. Especially in dynamic environment for recognizing the living beings is the greatest difficulty faced by applications, as it involves efficient object detection system in environments that are very complex. In image processing, object detection is considered to be a very difficult process. Many solutions for this problem involve the usage of deep learning techniques where a set of data is given to the machine which saves the key features of the objects and help in giving the desired output. This paper describes comprehensive survey on various methods and techniques used for object detection during natural disasters and finally comparative study of different methods are discussed.
A Mathematical Model for Linguistic Universals.

We characterize the meaning of words with language-independent numerical fingerprints, through a mathematical analysis of recurring patterns in texts. Approximating texts by Markov processes on a long-range time scale, we are able to extract topics, discover synonyms, and sketch semantic fields from a particular document of moderate length, without consulting external knowledge-base or thesaurus. Our Markov semantic model allows us to represent each topical concept by a low-dimensional vector, interpretable as algebraic invariants in succinct statistical operations on the document, targeting local environments of individual words. These language-independent semantic representations enable a robot reader to both understand short texts in a given language (automated question-answering) and match medium-length texts across different languages (automated word translation). Our semantic fingerprints quantify local meaning of words in 14 representative languages across 5 major language families, suggesting a universal and cost-effective mechanism by which human languages are processed at the semantic level. Our protocols and source codes are publicly available on this https URL
MCMC with delayed acceptance using a surrogate model with an application to cardiovascular fluid dynamics

Parameter estimation and uncertainty quantification in physiological modelling is a vital step towards personalised medicine. Current state-of-the-art in this research area performs parameter optimisation, with very limited uncertainty quantification. This paper demonstrates the advantage of novel sampling methods, applied on a complex biological PDE system of the pulmonary circulation. The aim is to find an efficient and accurate method for the inference and uncertainty quantification of unknown parameters, relevant for disease diagnosis (pulmonary hypertension) from limited and noisy blood pressure data. The data likelihood is expensive to evaluate as it requires solving numerically a system of PDEs. Therefore, having a model that best trades off accuracy and computational efficiency is of uppermost importance. In this study, we employ fast Bayesian methods, namely MCMC algorithms coupled with emulation using Gaussian Processes, to achieve a computational speed-up. We compare the Delayed Rejection Adaptive Metropolis algorithm in a History Matching framework to the delayed acceptance Adaptive Metropolis algorithm. The first algorithm draws samples from the approximate posterior distribution, while the latter is guaranteed to generate samples from the exact posterior distribution asymptotically. In this paper we propose and derive the n-steps ahead delayed acceptance Metropolis-Hastings algorithm, which is a generalisation of the classical 1-step ahead delayed acceptance Metropolis-Hastings. We show the superiority of the n-steps ahead algorithm over the 1-step ahead method.
Skills, Standards, and Sapp Nelson's Matrix: Evaluating Research Data Management Workshop Offerings

<strong>Objective</strong>: To evaluate library workshops on their coverage of data management topics. <strong>Methods:</strong> We used a modified version of Sapp Nelson’s Competency Matrix for Data Management Skills, a matrix of learning goals organized by data management competency and complexity level, against which we compared our educational materials: slide decks and worksheets. We examined each of the educational materials against the 333 learning objectives in our modified version of the Matrix to determine which of the learning objectives applied. <strong>Conclusions</strong>: We found it necessary to change certain elements of the Matrix’s structure to increase its clarity and functionality: reinterpreting the “behaviors,” shifting the organization from the three domains of Bloom’s taxonomy to increasing complexity solely within the cognitive domain, as well as creating a comprehensive identifier schema. We appreciated the Matrix for its specificity of learning objectives, its organizational structure, the comprehensive range of competencies included, and its ease of use. On the whole, the Matrix is a useful instrument for the assessment of data management programming.
Hard to get? Tracking Knowledge Heritage From Invention to Product Market Innovation Using NLP

Patents and patent citations are a window into the innovation process. Yet even established firms have difficulty commercializing inventions. A conundrum for scholars is: does the inventive activity captured in patents and patent citations translate into innovation in the product market? In other words, how reliably can we understand the link between patents and introduction of novel products to market? Thus, unpacking the link between knowledge and innovation is crucial for innovation, entrepreneurship, and strategy scholars. This paper brings to the innovation literature an important methodological approach from computer science: application of natural language processing and the vector space model in information retrieval to the study of innovation. Specifically, this paper details a novel methodology to algorithmically compare the similarity of patent texts to the description of innovations in two distinct contexts: 1) novel products brought to market by corporate venture capital investors in the medical device industry , and 2) IPO prospectus of startups in the communications equipment industry.
AN ANALYSIS OF DIURNAL AIR TEMPERATURE RANGE CHANGE AND ITS COMPONENTS IN SHAHEED BENAZIR ABAD SINDH

A study has been carried out to analyse the temporal and seasonal patterns in the trends of diurnal air temperature range (DTR) and its components in Shaheed Benazir Abad for the time period 1996–2014.The magnitude, the slope and the significance of trends were investigated by using the linear regression method, the trend magnitude, the Mann-Kendall test and the Sen’s estimator of slope. The Mann-Kendall test and Sen's estimator of slope were calculated by using Addinsoft's XLSTAT 2015 software. The hypothesis of Mann-Kendall test was investigated at 95% confidence level for all variables. The result shows that minimum temperature of Shaheed Benazir Abad has increased at the rate of 0.063°C per year during study period while the maximum temperature for all months exhibits no change. This increase of minimum temperature contributed to the decreasing trend of diurnal temperature range. The DTR decreased at the rate of 0.057°C /year in Shaheed Benazir Abad. The investigation of seasonal DTR trends revealed that Summer and Spring seasons also witnessed a decreasing trends at the rate of 0.26 °C/year and 0.047°C/year respectively. Winter and aseasons, on the other hand, have experienced the increasing trends of DTR at the rate of 0.136 °C/year and 0.115 °C/year respectively. It is found by MK test that Tmax (winter), Tmax (Spring) and Tmin (Spring) exhibited the significant positive trends at the rate of 0.21°C/year, 0.368 °C/year and 0.421°C/year respectively. The increasing trends of Tmax of winter and spring indicate that winter and spring are warmer now.
MAi: An Intelligent Model Acquisition Interface for Interactive Specification of Dialogue Agents

The state of the art in automated conversational agents for enterprise (e.g. for customer support) require a lengthy design process with experts in the loop who have to figure out and specify complex conversation patterns. This demonstration looks at a prototype interface that aims to bring down the expertise required to design such agents as well as the time taken to do so. Specifically, we will focus on how a metawriter can assist the domain-writer during the design process and how complex conversation patterns can be derived from simplifying abstractions at the interface level.
Remote spatial programming and collaboration using a real-time volumetric capture space

Collaboratively program a machine through a spatial drag-and-drop interface while being captured real-time in volumetric-video. We explore the future of industrial programming by using AR to build a drag-and-drop spatial interface. One person programs a machine using their phone while another can collaborate remotely through a volumetric-video
Automatic Identification of Bond Information Based on OCR and NLP

With the rapid development of the financial industry, the realization of automatic recognition of bond information is a hot issue in the current financial science and technology research.This paper realizes the automatic recognition of bond information through the combination of OCR and NLP technology.The text information of bond is extracted from the picture by OCR technology.In view of various kinds of interference in the image, the difference subtraction operation based on HSV model of color image is adopted to get better image information for image-based sequence model.The image-based sequence model is composed of convolution neural network and recursive neural network.The bond information can be well recognized by the model.Finally, the text information is processed by the algorithm based on Levenshtein distance, and the key-value is extracted, which has a certain practical value.
Multiplicative operations of Pythagorean fuzzy matrices

In this paper, we define two new multiplicative operations ⊗ 1P and ⊗ 2P on Pythagorean fuzzy matrix and investigate their algebraic properties.The properties of PFMs are also obtained in the case where these new operations are combined with the well known operations of Pythagorean fuzzy matrices.
Dimensions Grading Optimization of Orthogonal Volterra DPD Based on Signal-Projection

An efficient grading method for dimensions-reduced digital pre-distorter based on orthogonal Volterra series for band-limited nonlinear components is proposed. The method is based on a combination of dimension variance and signal-projection.
Efficient Barcode Localization Method for Low-Quality Images

Barcode technology is widely applied to industrial automatic identification field for its low cost and high reliability as well as the fast real-time performance. Due to the complexity of low-light, rotation and blur in the industrial field, several barcode localization approaches which are superior in speed or accuracy fail to accurately locate and even detect the barcode. This paper proposes a real-time barcode approach that can effectively cope with the above problems when dealing with low-quality images. First, we rely on the gradient information of the pixels to obtain both orientation map and magnitude map. Then, we use the Shannon entropy theorem to get a salient map for the sake of segmenting salient patches of the high score. Later, we utilize the smoothing filter to remove the noise and connect the salient patches to form the barcode candidate BLOBs (Binary Large OBject). Finally, the correct barcode is selected from the above candidate BLOBs with a covariance matrix. We obtained 500 experimental images covering the conditions of reflection, rotation, and low illumination from the industrial site. The experimental results based on the dataset show that our method exceeds significantly the other three advanced methods in accuracy.
Asymptotic Analysis of the kth Subword Complexity

Patterns within strings enable us to extract vital information regarding a string’s randomness. Understanding whether a string is random (Showing no to little repetition in patterns) or periodic (showing repetitions in patterns) are described by a value that is called the kth Subword Complexity of the character string. By definition, the kth Subword Complexity is the number of distinct substrings of length k that appear in a given string. In this paper, we evaluate the expected value and the second factorial moment (followed by a corollary on the second moment) of the kth Subword Complexity for the binary strings over memory-less sources. We first take a combinatorial approach to derive a probability generating function for the number of occurrences of patterns in strings of finite length. This enables us to have an exact expression for the two moments in terms of patterns’ auto-correlation and correlation polynomials. We then investigate the asymptotic behavior for values of k = Θ ( log n ) . In the proof, we compare the distribution of the kth Subword Complexity of binary strings to the distribution of distinct prefixes of independent strings stored in a trie. The methodology that we use involves complex analysis, analytical poissonization and depoissonization, the Mellin transform, and saddle point analysis.
National Features Of Infographics

Traditionally considered the territory of the verbal, newspapers and magazines are starting to give more space for photos and infographics, television and cinema, cultivating image content in non-stop mode, are becoming more spectacular. Visual elements of content stopped being just an addition or illustration to textual information; verbal content becomes something like a commentary to a visual series, and a visual image turns out to be the semantic center of a journalists message. We observe infographics in the modern mass media on the daily basis. It acts as an addition to textual information in some materials, in others it can almost completely replace the journalistic text and be the semantic center, the “core” of the journalistic message (when a solid infographics and short text annotation is presented in the article). Moreover, infographics can be one of the segments of a large multimedia article, including photo galleries, video and/or audio files, etc. This kind of visualization has came to us from the English-language media, and it has taken a strong position in the Russian media space. That is why it seemed to us important to consider the use of infographics in the Russian and English editions. In our opinion, there is not enough research on the differences and similarities between English-speaking and Russian-speaking infographics that complement / accompany journalistic materials today, and it doesn’t provide a complete understanding of the specifics of using this method of information visualization in the context of online media. This is the novelty of our research.