Reviewerly/exHarmony huggingface dataset:
Non-decimated Complex Wavelet Spectral Tools with Applications

In this paper we propose spectral tools based on non-decimated complex wavelet transforms implemented by their matrix formulation. This non-decimated complex wavelet spectra utilizes both real and imaginary parts of complex-valued wavelet coefficients via their modulus and phases. A structural redundancy in non-decimated wavelets and a componential redundancy in complex wavelets act in a synergy when extracting wavelet-based informative descriptors. In particular, we suggest an improved way of separating signals and images based on their scaling indices in terms of spectral slopes and information contained in the phase in order to improve performance of classification. We show that performance of the proposed method is significantly improved when compared with procedures based on standard versions of wavelet transforms or on real-valued wavelets. It is also worth mentioning that the matrix-based non-decimated wavelet transform can handle signals of an arbitrary size and in 2-D case, rectangular images of possibly different and non-dyadic dimensions. This is in contrast to the standard wavelet transforms where algorithms for handling objects of non-dyadic dimensions requires either data preprocessing or customized algorithm adjustments. To demonstrate the use of defined spectral methodology we provide two examples of application on real-data problems: classification of visual acuity using scaling in pupil diameter dynamic in time and diagnostic and classification of digital mammogram images using the fractality of digitized images of the background tissue. The proposed tools are contrasted with the traditional wavelet based counterparts.
Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds

Since the introduction of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE), the literature on generative modelling has witnessed an overwhelming resurgence. The impressive, yet elusive empirical performance of GANs has lead to the rise of many GAN-VAE hybrids, with the hopes of GAN level performance and additional benefits of VAE, such as an encoder for feature reduction, which is not offered by GANs. Recently, the Wasserstein Autoencoder (WAE) was proposed, achieving performance similar to that of GANs, yet it is still unclear whether the two are fundamentally different or can be further improved into a unified model. In this work, we study the $f$-GAN and WAE models and make two main discoveries. First, we find that the $f$-GAN and WAE objectives partake in a primal-dual relationship and are equivalent under some assumptions, which then allows us to explicate the success of WAE. Second, the equivalence result allows us to, for the first time, prove generalization bounds for Autoencoder models, which is a pertinent problem when it comes to theoretical analyses of generative models. Furthermore, we show that the WAE objective is related to other statistical quantities such as the $f$-divergence and in particular, upper bounded by the Wasserstein distance, which then allows us to tap into existing efficient (regularized) optimal transport solvers. Our findings thus present the first primal-dual relationship between GANs and Autoencoder models, comment on generalization abilities and make a step towards unifying these models.
On Deriving Probabilistic Models for Adsorption Energy on Transition Metals using Multi-level Ab initio and Experimental Data

In this paper, we apply multi-task Gaussian Process (MT-GP) to show that the adsorption energy of small adsorbates on transition metal surfaces can be modeled to a high level of fidelity using data from multiple sources, taking advantage of the relatively abundant ''low fidelity" data (such as from density functional theory computations) and small amounts of ''high fidelity" computational (e.g. using the random phase approximation) or experimental data. To fully explore the performance of MT-GP, we perform two case studies - one using purely computational datasets and the other using a combination of experimental and computational datasets. In both cases, the performance of MT-GPs is significantly better than single-task models built on a single data source. This method can be used to learn improved models from fused datasets, and thereby build accurate models under tight computational and experimental budget.
LANGUAGE ENDANGERMENT: THE CASE OF THE IGBO LANGUAGE

The present study has the objective of making a case study of Igbo as an endangered language. It sets out to identify the factors leading to the endangerment of the Igbo language, the immediate and long-term effects of this linguistic phenomenon of Igbo language endangerment. Two methods are applied: descriptive and historical. It is established, in this study, that the Igbo language is an endangered language. It is in a dying state and many of the younger generation (Igbo children and families) no longer speak Igbo. Also, they no longer learn it as a mother tongue (MT) informally and imperceptibly at home.  Speaking Igbo is left generally at the hands of the generation of some Igbo parents. Factors of endangerment of Igbo found include undesirable language attitudes and disloyalty to the Igbo language and the marginalization of the Igbo language, a majority language. One long-term negative consequence of the Igbo language endangerment discovered in this study is that the Igbo language runs the risk of becoming extinct forty to fifty years from now if no drastic measures are taken to curb the ugly trend. Another long-term effect is the death of Ndigbo (linguistic and symbolic death).  Some recommendations are proffered, notably: changing undesirable attitudes to the Igbo language; sensitization of Ndigbo both young and old to the display of great loyalty to the Igbo language, socio-linguistic readiness for literacy in Igbo and development of Igbo to become a language of science and technology through more thorough work on its metalanguage. The speedy and effective implementation of the recommendations will, hopefully, avert for the Igbo language and the Igbo nation the impending doom.
Mixture Modelling of High-Dimensional Data

This chapter focuses on the finite mixture models that have been used in high-dimensional settings and on the approaches that have been used for fitting and inference for these models. It describes motivating data examples are introduced as examples of high-dimensional continuous, categorical and mixed data. The chapter provides some issues involved in modeling high-dimensional data and discusses models for high-dimensional continuous data, categorical data and mixed data. It reviews some variable selection approaches for mixture modeling. The chapter explores the motivating examples are investigated using mixture models and shows that mixture models are able to appropriately model substructures in these data. Modeling the data using a mixture model will help establish if the clinical indicators suggest the existence of subgroups within the population, whether the subgroups correspond to the established pain types. One approach to modeling high-dimensional data using mixture models is to incorporate data reduction to reduce the data dimension.
Novel Activities Detection Algorithm in Extended Videos

Due to participation in TRECVID ActEV[1] competition, we conduct research on temporal activity recognition. In this paper, we propose a system for activity detection and localize detected activities temporally in extended videos. Our system firstly detects objects in video frames. Secondly, we use position information of detected object, as input to the object tracking model, which can obtain motion information of multiple objects in consecutive frames. Lastly, we input consecutive video frames containing only detected objects into 3D Convolutional Neural Network to achieve features, and 3D CNN is followed by a recurrent neural network for accurately localizing the detected activity.
Penerapan Data Mining Untuk Pengelompokan Hasil Diagnosa Penyakit Pasien Pengguna BPJS Kesehatan (Studi Kasus Pada Rsud Undata Palu)

This research was conducted to process medical record data in RSUD Undata of central Sulawesi province, for some BPJS Kesehatan insurance member. Medical record contain information about identity and medical history by patient in Hospital or community health center (Puskesmas). Medical records have disease information by patients encoded according to WHO standart. The code is called ICD (International Classification of Disease) and this research use C4.5 Algorithm as Classification method to process patient medical record which then uses address attributes, gender, age and disease diagnosis (ICD-10). Of the five attributes are groupings then processed into group of age, regional and icd. The result of this studi can find patterns of disease tendency that most suffered by people in a region.
NIF: A Framework for Quantifying Neural Information Flow in Deep Networks.

In this paper, we present a new approach to interpreting deep learning models. More precisely, by coupling mutual information with network science, we explore how information flows through feed forward networks. We show that efficiently approximating mutual information via the dual representation of Kullback-Leibler divergence allows us to create an information measure that quantifies how much information flows between any two neurons of a deep learning model. To that end, we propose NIF, Neural Information Flow, a new metric for codifying information flow which exposes the internals of a deep learning model while providing feature attributions.
Shock Wave Characteristics in the Initial Transient of an Organ Pipe

A new approach to investigate the role of pressure waves in the initial transient of an organ pipe is presented. By numerical simulations solving the compressible Navier-Stokes equations with suitable boundary and initial conditions, it is possible to retrace the generation, propagation, reflection, damping and radiation of sound waves. The focus is on the contribution of occurring pressure wave fronts in the initial transient that show shock wave characteristics, in particular their role at the formation process of the sound field inside the organ pipe’s resonator. Utilizing spectral analysis as well as extended visualization methods, a wide range of aspects of the dynamics of the initial transient of an organ pipe is discovered. In particular the damping processes in the resonator which are nonlinear are analyzed and discussed in detail. The numerical approach presented in this case study, allows to study the initial transient of an organ pipe with an extraordinary level of precision, thereby helping to understand the underlying first principles of the sound field formation and the mutual interaction of the jet’s flow field and the sound field inside organ pipes and similar wind instruments that produce complex sounds listeners find both interesting and joyful. Animations of the temporal and spatial development of relevant physical quantities like pressure, turbulent kinetic energy, vorticity and velocity magnitude calculated in the numerical simulations are provided as supplementary material.
The design of an open, secure and scalable blockchain-based architecture to exchange trade documents in trade lanes

Customs authorities are responsible for secure cross-border activities and collection of duties. To that extent, customs authorities perform risk assessment. The customs declaration on which risk assessment is based often lacks detailed data or is incorrect. Therefore, customs authorities are in need of new ways to improve risk assessment. One way to improve risk assessment is exchange of other trade documents (e.g., Bill of Lading or pro forma invoice) the customs declaration is based on. The documents can be used for piggybacking that involves the cross-validation of documents to determine correctness of the declaration. Blockchain technology can in potential support exchange of trade documents. However, challenges related to openness, security and scalability need to be addressed to come up with effective solutions. Therefore, this research develops an open, secure and scalable blockchain-based architecture that supports exchange of trade documents. Using a Design Science Research approach, several use cases to exchange documents are developed, requirements based on previous research on the exchange of trade documents (e.g., data pipeline concept) are derived, core blockchain architecture components are derived and a design that support the use cases is developed. The research finds that a open, secure and scalable blockchain-based architecture can support exchange of trade documents. However, slight differences in the design of each use case show that blockchain technology is not a black box. Generalization is therefore not straightforward. In-depth analysis of use cases is needed to come up with effective solutions. Future research should focus on the development of a Proof-of-Concept of the blockchain-based architecture design to test whether actual exchange of trade documents is supported.
Blockchain Examining the Technical Architecture

Patrick McConnell, Honorary Fellow at MAFC (Macquarie University Applied Finance Centre) aims to demystify some of the hype and describe the technical architecture underlying blockchain, using common IT terminology.
An efficient sorting algorithm - Ultimate Heapsort(UHS)

Motivated by the development of computer theory, the sorting algorithm is emerging in an endless stream. Inspired by decrease and conquer method, we propose a brand new sorting algorithmUltimately Heapsort. The algorithm consists of two parts: building a heap and adjusting a heap. Through the asymptotic analysis and experimental analysis of the algorithm, the time complexity of our algorithm can reach O(nlogn) under any condition. Moreover, its space complexity is only O(1). It can be seen that our algorithm is superior to all previous algorithms.
Identifiability of Gaussian Structural Equation Models with Homogeneous and Heterogeneous Error Variances

In this work, we consider the identifiability assumption of Gaussian linear structural equation models (SEMs) in which each variable is determined by a linear function of its parents plus normally distributed error. It has been shown that linear Gaussian structural equation models are fully identifiable if all error variances are the same or known. Hence, this work proves the identifiability of Gaussian SEMs with both homogeneous and heterogeneous unknown error variances. Our new identifiability assumption exploits not only error variances, but edge weights; hence, it is strictly milder than prior work on the identifiability result. We further provide a structure learning algorithm that is statistically consistent and computationally feasible, based on our new assumption. The proposed algorithm assumes that all relevant variables are observed, while it does not assume causal minimality and faithfulness. We verify our theoretical findings through simulations and real multivariate data, and compare our algorithm to state-of-the-art PC, GES and GDS algorithms.
Efficient production of large-scale optical Schrodinger cat states

We present novel theory of effective realization of large-scale optical Schrodinger cat states.
Stick-breaking processes, clumping, and Markov chain occupation laws.

We consider the connections among `clumped' residual allocation models (RAMs), a general class of stick-breaking processes including Dirichlet processes, and the occupation laws of certain discrete space time-inhomogeneous Markov chains related to simulated annealing and other applications. An intermediate structure is introduced in a given RAM, where proportions between successive indices in a list are added or clumped together to form another RAM. In particular, when the initial RAM is a Griffiths-Engen-McCloskey (GEM) sequence and the indices are given by the random times that an auxiliary Markov chain jumps away from its current state, the joint law of the intermediate RAM and the locations visited in the sojourns is given in terms of a `disordered' GEM sequence, and an induced Markov chain. Through this joint law, we identify a large class of `stick breaking' processes as the limits of empirical occupation measures for associated time-inhomogeneous Markov chains.
Design and Implementation of Data Resource Management Platform Based on SSM

In order to improve the efficiency of resource management of existing e-book reading websites, optimize the quality of e-book resources and reduce the management threshold of operation and maintenance staff. This paper focuses on the design and implementation of data resource management. Through the integration of the current commonly used development framework Spring, SpringMVC, Mybatis (SSM framework) to achieve the establishment of the background management system. The third party library epublib and JCE encryption and decryption tools are introduced to realize data library resource production encryption and export and other management functions. The test proves that the implementation meets the design requirements and can be used in the actual operation process.
Towards Self-Supervised High Level Sensor Fusion

In this paper, we present a framework to control a self-driving car by fusing raw information from RGB images and depth maps. A deep neural network architecture is used for mapping the vision and depth information, respectively, to steering commands. This fusion of information from two sensor sources allows to provide redundancy and fault tolerance in the presence of sensor failures. Even if one of the input sensors fails to produce the correct output, the other functioning sensor would still be able to maneuver the car. Such redundancy is crucial in the critical application of self-driving cars. The experimental results have showed that our method is capable of learning to use the relevant sensor information even when one of the sensors fail without any explicit signal.
How do Mixture Density RNNs Predict the Future?

Gaining a better understanding of how and what machine learning systems learn is important to increase confidence in their decisions and catalyze further research. In this paper, we analyze the predictions made by a specific type of recurrent neural network, mixture density RNNs (MD-RNNs). These networks learn to model predictions as a combination of multiple Gaussian distributions, making them particularly interesting for problems where a sequence of inputs may lead to several distinct future possibilities. An example is learning internal models of an environment, where different events may or may not occur, but where the average over different events is not meaningful. By analyzing the predictions made by trained MD-RNNs, we find that their different Gaussian components have two complementary roles: 1) Separately modeling different stochastic events and 2) Separately modeling scenarios governed by different rules. These findings increase our understanding of what is learned by predictive MD-RNNs, and open up new research directions for further understanding how we can benefit from their self-organizing model decomposition.
Effects of cueing and signalling on change blindness in multimedia learning environment

In this study, it was aimed to investigate the change blindness that may occur in multimedia learning environments used cueing and signaling effects. For this purpose, a multimedia animation which had some changes about computer parts was designed. Twenty-one undergraduate students participated in the experiment voluntarily. Eye and mouse movements were recorded and analysed with eye-tracking method, and participants were asked to say when they noticed the changes; their voices were simultaneously recorded. The results show that the participants were more focused on the objects being signaled, and less detected the changes in other areas. It is seen that the percentage of detecting the change occurring on the most focused object is low, except for the object using the signaling and visual cueing. The change in the object that was used the signaling and the visual cueing is the most detected change.Keywords: Multimedia learning, change blindness, eye tracking.
ALMA FITS header keywords: a study from the archive User perspective

ALMA products are stored in the Science Archive in the form of FITS images. It is a common idea that the FITS image headers should collect in their keywords all the information that an archive User might want to search for in order to quickly select, compare, or discard datasets. With this perspective in mind, we first present a short description of the current status of the ALMA FITS archive and images. We realized that at the moment most of the parameters that could be useful for a general User are still missing in the archived data. We then provide a CASA task generating the image header keywords that we suggest to be relevant for the scientific exploitation of the ALMA archival data. The proposed tool could be also applied to several types of interferometer data and part of it is implemented in a web interface. An example of the scientific application of the keywords is also discussed.
CyberMaster: An Expert System to Guide the Development of Cybersecurity Curricula

The growing number of reported cyber-attacks pose a difficult challenge to individuals, governments and organizations. Adequate protection of information systems urgently requires a cybersecurity-educated workforce trained using a curriculum that covers the essential skills required for different cybersecurity work roles. The goal of the CyberMaster expert system is to assist inexperienced instructors with cybersecurity course design. It is an intelligent system that uses visual feedback to guide the user through the design process. Initial test executions show the promise of such a system in addressing the enormous shortage of cybersecurity experts currently available for designing courses and training programs.
An Implementation of Malaria Detection Using Regional Descriptor and PSO-SVM Classifier

Malaria is a serious worldwide health issue which causes an expected 13,444 individuals in danger of malaria in 2017. The estimated cost of detection of malaria in India is 11,640 crores per year. So there is an urgent need for a new tool to diagnose malaria. Malaria is completely preventable and treatable disease. In this project, we make a new tool to diagnose malaria using regional descriptor and PSO-SVM classifier. The proposed work used various image processing techniques like image acquisition, image pre-processing, image segmentation, feature extraction and classification. The implementation work is mainly focusing on detection accuracy, computational time, less estimation time for parasite detection. In this way, the new tool for the detection of malaria parasites gives faster and accurate results, and by using this proposed methods pathologists can easily detect malaria parasites, and they can achieve 98% accuracy. This new tool is useful to reduce deaths.