Reviewerly/exHarmony huggingface dataset:
Multi-modal gesture recognition with voting-based dynamic time warping

Gesture recognition has remained a challenging problem in the fields of human robot interaction. With the development of depth sensors such as Kinect, different modalities become available for gesture recognition while its advantages have not been fully exploited. One of the critical issues for multi-modal gesture recognition is how to fuse features from different modalities. In this article, we present a unified framework for multi-modal gesture recognition based on dynamic time warping. The 3D implicit shape model is applied to characterize the space-time structure of the local features extracted from different modalities. And then, all votes from the local features are incorporated into a common probability space which is then used for building the distance matrix. Meanwhile, an upper-bounding method UB_Pro is proposed to speed up dynamic time warping. The proposed approach is evaluated on the challenging ChaLearn Isolated Gesture Dataset, showing comparable performance in comparison to the state-of-the-art approaches for multi-modal gesture recognition problem.
The Effect of a Macromedia Flash-based Guided Inquiry on Students’ Critical Thinking Skill and Self-Regulated Learning

Abstract This study aims to determine the effect of a macromedia flash-based guided inquiry model on students’ critical thinking skill and self-regulated learning. This study was a quasi-experiment, with a post-test only group design. The subjects were 96 eleventh-graders of divided into two groups. Fifty students in the experimental group used macromedia flash-based guided inquiry model and 46 students in the control group used direct instruction model. Data collection techniques were a test of critical thinking skill consisting seven essay questions to see students’ critical thinking skill and a questionnaire of self-regulated learning consisting 33 statements to see students’ self-regulated learning. The data were analyzed using MANOVA. The results showed that students taught with macromedia flash-based guided inquiry model have a statistically difference on their critical thinking skill and self-regulated learning at significance level 0.05 while students taught with direct instruction model showed critical thinking at lower level. In conclusion, a macromedia flash-based guided inquiry model can improve students’ critical thinking skill and students’ self-regulated learning.
Portuguese as an Additional Language

Additional language is here understood as an overarching term for Portuguese as a foreign language, second language, heritage language, and also for its indigenised varieties. In this chapter, we will attempt to show how these terms are intertwined in different but overlapping contexts and continua, by establishing a link between theory and practice, and by emphasizing that an additional language always originates from language contact, of which the multilingual communities that make up our object of study are representative.
Multi-label Classification Method Based on the Labeling-Importance Degree

The task of multi-label classification is to predict multiple possible labels for the unseen instance. Currently the prevalence of multi-label data in daily life has stirred up a spree of research activities. But most of those established multi-label classification methods mainly focus on such issues as class imbalance, label relations and dimension compression. These methods usually have an untenable hypothesis that each instance is of equal importance for all labels, based on which an identical set of instances is used to establish the learning model for all labels. In this paper we introduce the concept of labeling-importance and work out each instance's labeling-important degrees that are in line with the polynomial distribution. Afterwards, a relatively important set of instances is selected for each label before establishing its classification model. The superior performance of this proposed method is validated by comprehensive experiments.
A Sampling Design for Ordered Populations

Abstract We present a sampling algorithm for an ordered population with elements that have a measure of size. The algorithm enables one to select a sample with specified probabilities and with efficiency for the estimated mean between that of one per stratum and that of two per stratum. The algorithm contains a design parameter for the efficiency of the estimated mean relative to the efficiency of the estimated variance of the estimated mean. For a variable highly correlated with the order, it is possible for both the efficiency of the estimated mean and the efficiency of the estimated variance for an intermediate design to be greater than that for the two-per-stratum design. For most studied populations, the variance of the estimated mean declines and the variance of the estimated variance increases as one moves from the two-per-stratum design toward the one-per-stratum design. We illustrate the trade-off between the variance of the estimated mean and the variance of the estimated variance using an autoregressive process. An estimator of the variance of the estimated mean and a replication form for variance estimation are given.
Software tool for solving inductive modelling tasks based on self-organizing combinatorial-genetic method

The article presents results of application of the software package based on self-organizing combinatorial-genetic algorithm COMBI-GA for inductive construction of optimal models from observation data under incomplete information about an object and investigating the capabilities of the algorithm, in particular, for solving large dimensional problems. Characteristics and capabilities of the software package built on the basis of this algorithm are considered. The analysis of the COMBI-GA effectiveness in the sense of the restoration accuracy of a given test model and the time to find it was carried out in comparison with the LASSO algorithm as well as with the sorting-out GMDH algorithms COMBI and MULTI. It is shown that all algorithms find the correct models but COMBI-GA does it much faster.
Evidential SVM for binary classification

Support Vector Machine (SVM) is one of the most popular supervised learning methods for its better performances over diversified applications. SVM constructs a maximum-margin hyperplane and predicts the class of a new incoming data point based on that hyperplane. However, the hyperplane may not give a reliable decision in some cases specially when the training data is imprecise and noisy. To improve this situation, we propose an evidence based binary SVM classifier (EbSVM) where we first identify the sources of information (SoI) and propose a method to generate mass value from these SoIs. Finally, we combine these mass values using DS theory of evidence. An experiment over six benchmark datasets illustrates that EbSVM significantly performs better than the existing state-of-the-art methods.
AutoML for DenseNet Compression

DenseNet, which connects each convolutional layer to all preceding layers, is a classic model of utilizing skip connections to improve the performance and learning efficiency of deep convolutional neural networks. However, many of the skip connections in DenseNet are redundant, which may lead to huge consumption of computational resources and computing time. In this paper, we propose an automatic model compression method based on reinforcement learning to prune redundant skip connections in DenseNet and improve its performance. We call the proposed method automatic DenseNet sparsification (ADS). ADS can be implemented with remarkable efficiency, for a 40-layer DenseNet, only running on 1 single GPU and taking less than 1 day. Experimental results on image classification tasks show that, the sparsified DenseNet outperforms not only the original DenseNet, but also related state-of-the-art deep architectures. Moreover, the sparsified DenseNet has strong transferability to new image classification tasks.
Using the Research Tutorial as a Training Strategy for Tutor Professional Development in an Undergraduate Course

This chapter is part of a larger research project that seeks to investigate sustainable ways of improving group-based tutoring in higher education courses.A growing body of research into teaching and learning in higher education acknowledges that higher education institutions are regarded as bastions of active teaching and learning that encourage students' deep learning and critical engagement.However, existing research also suggests that there is a lack of active participation by students during learning activities in tutorials; one of the reasons is the poor quality of the interactions between tutors and students during tutorials.Postgraduate students, who make up the majority of tutors, receive little formal training and lack sophisticated instructional skills on how to facilitate tutorials.By using an example, this chapter argues for the use of a research tutorial as a training strategy for tutor professional development (TPD) in an undergraduate Quantitative Literacy (QL) intervention course.The research methodology employed in this study is the lesson study.A research tutorial is a tutorial designed by both tutors and researchers that is used for TPD purposes.Suggestions for future research include focussing on how tutors notice, and attend to, the students' productive struggles during an undergraduate QL tutorial.
Automatic Segmentation of Prostate from Multiparametric MR Images Using Hidden Features and Deformable Model

This paper proposes a hidden feature learning technique to segment prostate from Multiparametric Magnetic Resonance Imaging. In image guided intervention an automatic segmentation of prostate is an essential task. The automated segmentation of prostate is challenging process due to blurred prostate boundaries and extensive variations in prostate shape among the subject population. To defeat these challenges, we used image patches to construct probabilistic map. Atlas based segmentation is used to minimize an energy function to label the patches as the prostate or background. The latent features are determined from prostate MR image by using sparse auto encoder. Deformable model segmentation is deployed to perform final segmentation by integrating sparse patch matching method. The performance of presented method is enormously tested on the dataset that contains T2-weighted prostate MR images of 184 subjects. The performance indices like dice similarity coefficient (DSC), and mean absolute surface distance (MASD) are used to evaluate the performance of our method by considering manual groundtruth delineated by experienced radiologist. DSC obtained by our algorithm is 87.4 % ±4.6%, and MASD of 1.80 mm. The preliminary results demonstrate that the learned features are more competent in MR prostate segmentation.
Time and Space Efficient Optimal Pairwise Sequence Alignment using GPU

Sequence alignments are currently gaining close attention due to their great impact on the quality aspects of life such as facilitating early disease diagnosis, identifying the characteristics of a newly discovered sequence, etc.. With the rapid growth of genomic data, searching for a sequence homology over huge databases is unable to produce results within a realistic time. Hence, it demands an efficient sequence alignment accelerator to improve the performance of the system. Though some popular acceleration platforms, like supercomputers, Very Large Scale Integration (VLSI) chip, Graphics Processing Unit (GPUs) and Field Programmable Gate Arrays (FPGAs) based devices are available, but they are unable to meet the intended requirement to the current growth of genome database. This paper presents a parallel dynamic approach for global pairwise alignment algorithm using GPU to handle such large database. The approach performs the alignment procedure in CUDA-enabled GPU platform by creating dynamic threads to perform parallel execution. This GPU-based implementation is tested using pseudorandom database and various length of sequences. The execution result shows reasonably better performance in terms of time and space requirement with respect to existing GPU-based optimal pairwise sequence alignment approaches.
Disaster mitigation solutions with Bayesian network

Natural disasters are events that threaten and disrupt the lives and livelihoods of people caused by natural factors. Indonesia is a country prone to natural disasters. This is triggered by its geographical position flanked by two large oceans and geologically at the confluence of the three main plates. One way to reduce the impact of natural disasters is to mitigate hazards. Mitigation will reduce the negative impact caused by disasters, especially for residents. It can also be a guideline for development planning. The way of mitigation efforts can be done by introducing and monitoring disaster risk. For example, to observe what factors affect the level of building damage caused by a disaster. We can do this with the Bayesian Network approach because this approach provides flexibility in seeing relationships between variables and adding new variables based on expert analysis. These advantages are very supportive related to cases of natural disasters; sometimes, there are often developments in variables that affect the level of damage in the field. The first step in the approach is to form a structure. In this study, we conducted two types of structure formation, namely using the Naive Bayes algorithm and expert opinion. From these two methods, the creation of a structure based on expert opinion is more accurate.
Outliers Detection on Fisheries Commodity Transaction from Local Market in Tual City based on The x-means Clustering

Abstract Correctly estimating factors that affect the income and profitability of coastal communities specifically catching fishermen is one way to increase the level of sustainable coastal community welfare. The purpose of this study is to detect outliers from fisheries commodity transactions in the fish market in Tual City, to have full view on which cases that may have certain traits that can improve the profitability of fishermen in the region. The research method used in this study is a survey method in which used as many as 1420 datasets obtained from interviews conducted starting from June 2017 to June 2018. The data mining approach is used in this study, in order to obtain the appropriate outliers then x-means clustering is used to cluster profitable transaction data, then data collected further being process with outlier detection. We found 25 high profitability transactions of 1420 datasets. The outlier analysis was carried out and found 1 outliers. We have figured that the species of fish and the location of the seller are the factors that influence the level of profit from the fishermen. This result is an input for policy makers and catch fishermen in conducting capture fisheries.
Using the Cumulative Function Method to Transform non-Gaussian Random Processes, Signals and Noise in the Differentiating Systems

It is shown that one of commonly used approximate methods is the description of non-Gaussian processes, signals and noise as a finite sequence of elements or cumulant functions. In this case, if a large number of terms of the sequence is used, an acceptable error of the description can be obtained. The analysis of characteristics of the non-Gaussian random process by the method of cumulant functions for a linear system is carried out. Here, the random process is given by a set of cumulant functions, whereas the linear system is described by a certain differentiation operator. It is shown that the stationarity of the input process determines the stationarity of the output process. The values of a cumulant function of the second order for a given process at the output of a linear system are determined. We also determined cumulant functions of derivatives of random processes. It is shown that cumulant functions at the output of the linear system can be determined by using a transition function of the linear system. The characteristics of the non-Gaussian random process for an ideal linear filter are analyzed by the method of cumulant functions. Expressions for cumulant functions of two-moment probability density functions at the output of the filter and expressions describing the spectra of the above-mentioned cumulant functions are obtained. The characteristics of the nonGaussian random process for the nonlinear differentiating system are analyzed by the method of cumulant functions. The expressions for determining the spectra of cumulant functions of the first four orders are given. The dependence graphs of normalized spectra of cumulant functions for the analyzed nonlinear system are obtained. Each of the spectra contains both low-frequency and high-frequency components.
Using machine learning to generate engaging behaviours in immersive virtual environments

Our work aims at implementing autonomous agents for Immersive Virtual Reality (IVR). With the advances in IVR environments, users can be more engaged and respond realistically to the events delivered in IVR, a state described in literature as presence. Agents with engaging verbal and nonverbal behaviour help preserve the sense of presence in IVR. For instance, gaze behaviour plays an important role, having monitoring and communicative functions. The initial step is to look at a machine learning model that generates flexible and contextual gaze behaviour and takes into account the rapport between the user and the agent. In this paper, we present our progress to date on the problem of creating realistic nonverbal behaviour. This includes analysing a multimodal dyad data, creating a data-processing pipeline, implementing a Hidden Markov Model and linking the Python scripts with the VR game engine (Unity3D). Future work consists of using richer data for more complex machine learning models, with a final aim of integrating the gaze model (plus future nonverbal behaviour models) into an autonomous virtual character framework.
Arabic-English Corpus for Cross-Language Textual Similarity Detection

The purpose of cross-language plagiarism detection is to detect similar fragments of text among different languages. There are a limited number of free Arabic-English parallel corpora. This paper reports on the creation of Arabic-English parallel corpora for the estimation of Arabic-English cross-language document similarity detection. The paper discusses available Arabic-English corpora and their limitations. The paper then explains the building of our dataset. The proposed dataset is parallel Arabic-English and involves alignment for different granularities. It is constructed on comparable and parallel corpora and includes human and machine translated text. In addition, the collected texts were written by authors of varying ability. Cross-language plagiarism detection methods are used on the dataset. These methods are then assessed.
The Semiotics of Emoji:<i>Infinite Jest</i>and the Yellow Smiley Face

“Infinite Jest” is an appropriate caption to the yellow smiley face: fixed, static, and permanently happy, it is a jest that never ends. The cultural history of the yellow smiley face indicates a semantic shift since its invention in 1963, from sincere positivity to an ironic association with false pleasure and consumerism. David Foster Wallace takes the latter as his starting point, making the face a sign of the counter-cultural terrorist organization A.F.R.. Yet the role of the smiley face in Infinite Jest is not only semantic but structural; its appearance in unrelated points of the narrative reveals the extradiegetic force plotting the patterns of the text, connecting together various parts of the novel. The privileging of form of over meaning is associated with Avril Incandenza, who embodies the smiley face in this capacity: she connects rather than refers, facilitates rather than means. Like contemporary emoji, Avril’s smiley face performs a linguistically “phatic” function, based on facilitation rather than semantics. Nevertheless, the yellow smiley face taps into Wallace’s wider project of writing sincere fiction, and the solution to the formalistic smiley face of Avril is the sincere smiley face of Mario: a sign which means what it proposes to mean.
Grammar of living systems: allometric scaling laws: Power Laws of Nature: Random, Chance, Limits and Limitations of Emergence

The structures and functions of living systems are linked by scaling independent characteristics [1] and quantitative laws [2]. Systems merging into `Associations for the Reciprocal and Mutual Sharing of Advantages and Dis-Advantages' (ARMSADA) allows the emergence of new blueprints. Actors become together more and more mutually integrated into their new Whole and more and more independent from the previous situations [3]. Reversely the Whole is more and more integrating actors. According to the gauge invariance paradigm, not the Kleiber's law but another scaling invariance relation between actors is evidenced: a 2/3 exponent power-law. At every level of organisation, every living system is described with 4 dimensions: VA, the Adult system Volume, and tg, the time of generation, the duration that is necessary to acquire the reproduction capacity [4]. Considering the gauge invariance paradigm as a system, an analysis of a database of interactions allows to quantify 45×18 relations, from the quantum of Planck to the whole Universe, and a `grammar' of processes was evidenced: invariant processes (power laws with exponent € = 0), simultaneous limiting processes (€ = +1), feedbacks (€ = -1), competitions between actors (€ = 1/2) and optimal exchanges (€ = 2/3). The true process governing all functions is Brownian motion [5]. The regulation increase allows more and more autonomy. Living systems optimise input and output of matter and energy at the interfaces [6]. A greater diversity of processes occurs for throughput flows. Whatever the level, living systems optimise the survival by adjusting `the capacity to be hosted' of the endophysiotope (HOSTED) to the changes of `the hosting capacity' of the ecoexotope (HOSTING) [7] (Fig. 10).
Research on Identity Certification Framework Model in core business of Power Grid

Abstract Based on provable security theory for security protocols, a provable security identification framework model suitable for the core business of power grid is proposed to enhance the management level of user identification and improve security of user identification schemes. To verify the model, a new identification scheme for identification scenarios is given according to the model. Security analysis shows that the scheme is proven to be secure in the model. In addition, performance analysis shows that the scheme can timely response to hundreds of thousands of user requests, thus it can be used as an effective scheme for identification in the core business of grid.
Tag Information Recognition Approaches and Algorithms for Cross-Border Products Checking

The images with fixed layouts, such as images from ID cards, driving licenses, and invoices can be recognized from prior knowledge[1]-[7]. However, The non-immobilized images, such as product labels at ports, is very difficult to be extracted structured data information from tag images because the formats and contents of tags in different countries and different product vary widely[8]. The process is complex and the error rate is high.
Novel Multi-Dimensional Spatially-Adaptive Image Recovery Using Spline Interpolation

A method to increase the resolution of single image sensor digital cameras to the equivalent of three image sensors digital cameras is investigated in this paper. The novelty in the proposed method lies in the development of a mosaic color filter array (CFA) whose structure is capable of receiving full resolution color component signals. This is made possible by using multi-dimensional spatially-adaptive image recovering and exploiting the spatial periodic micro displacement of the light flux against the mosaic CFA itself. Test results obtained with popular images are presented to illustrate the effectiveness of the proposed method.