Reviewerly/exHarmony huggingface dataset:
Olivier Messiaen: Spectralist

Abstract This chapter seeks to understand Messiaen’s music and aesthetics as spectralist through the writings of Hugues Dufourt and Gérard Grisey, and through a Lacanian framework. To understand Messiaen’s music as spectral is to listen to a reciprocal relationship between past and future, and to appreciate the way its religious-modernist fantaisie works on the listener. This chapter examines Messiaen’s output through a number of discourses and follows the trajectory of his compositional development. Initially, it connects Messiaen’s religious-modernist aesthetics with synaesthesia and color in his earliest music, to the organ, and to religious mysticism. It then uses a range of discourses, including symbolism and the occult, the materiality of sound, and the philosophy of listening to examine spectralist thought in works from the 1930s, his ‘experimental’ period (1949–52), to the emancipatory colorist aesthetics of Couleurs de la Cité céleste (1963). The last two sections, on birdsong and meaning in his opera Saint François d’Assise (1975–83), examine liminal concerns of glory, listening, narrative, and meaning in Messiaen’s form of spectralism. The conclusion briefly discusses an arena of mutual critique between Messiaen and the école spectrale.
Scaling the Construction of Wavelet Synopses for Maximum Error Metrics

Modern analytics involve computations over enormous numbers of data records. The volume of data and the stringent response-time requirements place increasing emphasis on the efficiency of approximate query processing. A major challenge over the past years has been the construction of synopses that provide a deterministic quality guarantee, often expressed in terms of a maximum error metric. By approximating sharp discontinuities, wavelet decomposition has proved to be a very effective tool for data reduction. However, existing wavelet thresholding schemes that minimize maximum error metrics are constrained with impractical complexities for large datasets. Furthermore, they cannot efficiently handle the multi-dimensional version of the problem. In order to provide a practical solution, we develop parallel algorithms that take advantage of key-properties of the wavelet decomposition and allocate tasks to multiple workers. To that end, we present (i) a general framework for the parallelization of existing dynamic programming algorithms, (ii) a parallel version of one such DP algorithm, and (iii) two highly efficient distributed greedy algorithms that can deal with data of arbitrary dimensionality. Our extensive experiments on both real and synthetic datasets over Hadoop show that the proposed algorithms achieve linear scalability and superior running-time performance compared to their centralized counterparts.
New School Graduate Image

In recent years, modern, innovative, transnational businesses are actively trying to influence the state of the education system – from participation in projects of specific schools to influence for global trends in the development of education (for example, the idea of 21st century skills). It is important to note that business is not limited to financial support for education. It is important to influence the change in goals, values, content of education, and basic pedagogical technologies. This is done through to the positioning of a new image of the future and another image of the graduate of the school, those professional skills of specialists that will be required for large corporations in the coming years. Analyzing on the materials of online resources, the characteristics of modern startups and educational projects supported by large business, one can understand what changes the business wants from education.
Arctic HARE: A Machine Learning-Based System for Performance Analysis of Cross-Country Skiers

Advances in sensor technology and big data processing enable new and improved performance analysis of sport athletes. With the increase in data variety and volume, both from on-body sensors and cameras, it has become possible to quantify the specific movement patterns that make a good athlete. This paper describes Arctic Human Activity Recognition on the Edge (Arctic HARE): a skiing-technique training system that captures movement of skiers to match those against optimal patterns in well-known cross-country techniques. Arctic HARE uses on-body sensors in combination with stationary cameras to capture movement of the skier, and provides classification of the perceived technique. We explore and compare two approaches for classifying data, and determine optimal representations that embody the movement of the skier. We achieve higher than 96% accuracy for real-time classification of cross-country techniques.
Information Theory Applied to Decision-Making Structures

Decision-making structures, such as control boards, process information on many topics as they select options for the system and project. The decisions are based on the information known to the board members and presenters (subject matter experts) and shared at the board during discussion. This information flow through this process can be modeled using information theory. Information theory provides a mathematical basis to understand the flow of information through the decision-making process and the information needed for a particular decision. Information theory also provides the mathematical relationships on which to base the optimal decision-making structure for a specific system development and organizational structure. Since decision-making bodies provide control for the system or project, control theory can be used to construct a decision-making model. This provides a starting point for adding cognitive science models. Information processing by each individual board participant can be represented through cognitive processes which are integrated across the board participants through information theory relationship. The set theory view of information theory provides a structure in which to look at the relationships between the participants in a decision-making structure.
The Role of Usability Engineering in the Development of an Intelligent Decision Support System

This paper presents an overview of the usability engineering process for the development of a personalised clinical decision support system for the management of type 1 diabetes. The tool uses artificial intelligence (AI) techniques to provide insulin bolus dose advice and carbohydrate recommendations that adapt to the individual. We describe the role of human factors and user-centred design in the creation of medical systems that must adhere to international standards. We focus specifically on the formative evaluation stage of this process. The preliminary analysis of data shows promising results.
Education and Business

The chapter is devoted to the research of interaction between business structures and educational institutions. The current state of the problem is described through the analysis of the sociocultural situation, features of communication, and behavioral stereotypes. The reasons for impeding and facilitating their successful interaction are considered. The work is based on a series of interviews with representatives of the business community, education, and university students. The results obtained suggest possible forms of effective cooperation and formulate a number of recommendations.
Building a supertagger for Spanish HPSG

We created a supertagger for the Spanish language aimed at disambiguating the HPSG lexical frames for the verbs, nouns and adjectives in a sentence. The supertagger uses a maximum entropy model and achieves an accuracy of 84.16% over the verb classes, 86.60% over the noun classes and 91.30% over the adjective classes on the test set. The tagset contains 92 verb classes, 27 noun classes and 13 adjective classes extracted from a Spanish HPSG-compatible annotated corpus that was created by automatically transforming the AnCora Spanish corpus. The tags include information about the arguments structure, their syntactic categories and semantic roles. These are important pieces of HPSG style feature structures.
Introduction to R

R is a software environment for statistical computing and graphical programming languages. This chapter is an introductory chapter to equip beginners with the essential concept, definitions, and terms used in R. This chapter will guide the reader in installing R and RStudio using a step-by-step procedure to enable novice readers to grasp and understand the steps. Many examples are given to clarify programming in R, and an easy description for every command, including the name and the function of each symbol, is given. Readers will be able to use built-in functions to carry out analysis, become familiar with the working directory and be able to set a new working directory for the work, write simple scripts, and define a vector or matrix to prepare the data for the next step. Furthermore, beautiful and colorful graphs can be created by using the function plot with a simple command.
A Framework for Robust Assimilation of Potentially Malign Third-Party Data, and Its Statistical Meaning

This article presents a model-based method for fusing data from multiple sensors with a hypothesis-test-based component for rejecting potentially faulty or otherwise malign data. Our framework is based on an extension of the classic particle filter algorithm for real-time state estimation of uncertain systems with nonlinear dynamics with partial and noisy observations. This extension, based on classical statistical theories, utilizes statistical tests against the system's observation model. We discuss the application of the two major statistical testing frameworks, Fisher significance testing and Neyman?Pearson hypothesis testing, to the Monte Carlo and sensor fusion settings. The Monte Carlo Neyman?Pearson test we develop is useful when one has a reliable model of faulty data, while the Fisher method is applicable when one does not have a model of faults, which may occur when dealing with third-party data, such as the Global Navigation Satellite System (GNSS) data of transportation system users. These statistical tests can be combined with a particle filter to obtain a Monte Carlo state estimation scheme that is robust to faulty or outlier data. We present a synthetic freeway-traffic state estimation problem where the filters are able to reject simulated faulty GNSS measurements. The fault-model-free Fisher filter underperforms the Neyman?Pearson filter when the latter has an accurate fault model but outperforms it when the assumed fault model is incorrect.
Policy Option Simulation in Socio-ecological Systems

Social-ecological systems (SESs) i.e., systems linking human behavior and environmental dynamics, are characterized as complex, dynamic, and heterogeneous systems. A comprehensive policy simulation in SESs has to consider all these properties. In this paper, we present a conceptual analysis on key aspects of policy simulation in SESs and introduce a combined use of fuzzy cognitive mapping (FCM) and agent-based modeling (ABM) methods as an approach to cover those aspects. We illustrate the applicability of this combined method for policy simulation in the case of a farming community facing water scarcity.
The Improved Light Field Depth Image and 3D Reconstruction

A depth map and a full focus image can be obtained by using the image sequence and the image evaluation function. The depth map obtained by gradient operator as the evaluation function has good resolution but is also affected by the deviation value. This kind of noise is different from common salt-and-pepper noise and Gaussian noise, so the median filter or mean filter cannot play a very good role. In order to improve the effect of 3D modeling, it is necessary to eliminate the deviation and retain and highlight the depth information. In this paper, an adaptive image enhancement method based on mode cooperative filtering is proposed. This method uses the general level of mode data to express the data, and uses the method of cooperative filtering to form the centralized balance and isolated deviation around the mode. Compared with other filters, the results show that it can achieve better results.
Temporal stochastic linear encoding networks

Convolutional Neural Networks (CNNs) have achieved great success for action recognition. Technically, extracting effective long-range temporal dynamics is critical for such temporal tasks. This paper proposes a temporal stochastic linear encoding (TSLE) to construct the global video representations for action recognition, which can be embedded inside of CNN as a layer. The advantages of temporal stochastic linear encoding networks (TSLEN) are: (a) Compared with algorithms focusing on the short-term motions it can implement an easy yet robust manipulation of long range temporal clues. (b) We propose an arbitrarily directional motion boundary (ADMB) unit, which can save the training time and hard disk space. (c) The proposed TSLE unit maps the highly-dimensional videos to the compact spatio-temporal representations. On the efficiency and recognition accuracy experimental results demonstrate that the proposed TSLENs achieve competitive performance among the effective algorithms.
USER CENTERE DESIGN (UCD) UNTUK RANCANG BANGUN SISTEM INFORMASI PENGOLAHAN DATA SIMPAN PINJAM PADA KOPERASI

The use of technology in support of corporate or organizational activities is currently growing, one of which is in the operational data processing company or organization. Technology that is currently widely used for data processing is a computerized system, with computerized data processing system can be done quickly and accurately. Women Empowerment Cooperative Prima Resources Jakarta is a cooperative savings and loan, currently processing transaction data is still done manually, both for savings transactions, loans, and reporting, so that allows errors in the recording, paper easily lost and torn because it is still in the form of archives, the limitations in search of necessary data, and less accurate reports made. with the design of computerized saving and loan system is expected to be a solution in solving the existing problems in this cooperative, in this design the author uses the user center design (UCD) as a method to analyze how the design of savings and loan information systems viewed from the user or user, so that system designed in accordance with the needs of users, the results of this study is the application of savings and loan information system accompanied by a database that has been tailored to the needs of users, then tested against the system that has been created using the method blackbox
Experimental Validation of Minimax Entropy Principle in Ultrasound Images

Ultrasound imaging is one of the non-invasive imaging, that diagnoses the disease inside a human body and there are numerous ultrasonic devices being used frequently. Entropy as a well known statistical measure of uncertainty has a considerable impact on the medical images. A procedure for minimizing the entropy with respect to the region of interest is demonstrated. This new approach has shown the experiments using Extracted Region Of Interest Based Sharpened image, called as (EROIS) image based on Minimax entropy principle and various filters. In this turn, the approach also validates the versatility of the entropy concept. Experiments have been performed practically on the real-time ultrasound images collected from ultrasound centers and have shown a significant performance. The present approach has been validated with showing results over ultrasound images of the Human Gallbladder.
Post-quantum Secure Cryptographic Algorithms

This chapter gives an overview of current developments in cryptographic algorithmic solutions against upcoming threats posed by quantum computers as well as unsolved problems in the classical IT landscape. It is an update of our 2018 publication in Computeralgebra-Rundbrief (Bogomolec und Gerhard 2018) and contains some new developments. However, the mathematics described are of course still the same. We thank the Fachgruppe Computeralgebra for their support.
Live interactive queries to a software application's memory profile

Memory operations are critical to an application's reliability and performance. To reason about their correctness and track opportunities for optimisations, sophisticated instrumentation frameworks, such as Valgrind and Pin, have been developed. Both provide only limited facilities for analysing the collected data. This work presents a Valgrind's extension for examining a software applications’ dynamic memory profile through live interactive analysis with SQL. The Pico COllections Query Library (pico ql) module maps Valgrind's data structures that contain the instrumented application's memory operations metadata to a relational interface. Queries are type-safe and the module imposes only a trivial overhead when idle. The authors evaluate the proposed approach on ten applications and through a qualitative study. They find 900 kb of undefined bytes in bzip2 that account for 12% of its total memory use and a performance-critical code execution path in the Unix commands sort and uniq. The referenced functions are part of glibc and have been independently modified to boost the library's performance. The qualitative study has users rate the usefulness, usability, effort, correctness, and expressiveness of pico ql queries compared to Python scripts. The findings indicate that querying with pico ql incurs lower user effort.
An Approach to Improvement of Master's and PhD Studies in Data Processing and Management Systems

To make Master's and PhD theses more influential on the evolution of the software industry, and to make them even more effective and successful, the authors propose that they be directed towards developing cognitive systems and expanding the functionality of integrated big data platforms. Within this field, the authors propose that their themes be organized in the form of the following dual model: for each thesis that develops an emerging new cognitive component inside a core big data platform, there is another thesis that develops a corresponding cognitive component/module within the application layer that uses that component of the core big data platform.
Hierarchical Video Understanding

We introduce a hierarchical architecture for video understanding that exploits the structure of real world actions by capturing targets at different levels of granularity. We design the model such that it first learns simpler coarse-grained tasks, and then moves on to learn more fine-grained targets. The model is trained with a joint loss on different granularity levels. We demonstrate empirical results on the recent release of Something-Something (Second release of Something-Something is used throughout this paper) dataset, which provides a hierarchy of targets, namely coarse-grained action groups, fine-grained action categories, and captions. Experiments suggest that models that exploit targets at different levels of granularity achieve better performance on all levels.
Tail Redundancy and its Characterization of Compression of Memoryless Sources

We formalize the tail redundancy of a collection of distributions over a countably infinite alphabet, and show that this fundamental quantity characterizes the asymptotic per-symbol minimax redundancy of universally compressing sequences generated i.i.d. from a collection P of distributions over a countably infinite alphabet. Contrary to the worst case formulations of universal compression, finite single letter minimax (average case) redundancy of P does not automatically imply that the expected minimax redundancy of describing length-n strings sampled i.i.d. from P grows sublinearly with n. Instead, we prove that universal compression of length-n i.i.d. sequences from P is characterized by how well the tails of distributions in P can be universally described, showing that the asymptotic per-symbol redundancy of i.i.d. strings is equal to the tail redundancy.
Recognizing social relationships from an egocentric vision perspective

In this chapter we address the problem of partitioning social gatherings into interacting groups in egocentric scenarios. People in the scene are tracked, and their head pose and 3D location are estimated. Following the formalism of the f-formation, we define as regards the orientation and distance inherently social pairwise features capable of describing how two people stand in relation to one another. We present a structural SVM-based approach to learn how to weight each component of the feature vector depending on the social situation being applied to. To better understand the social dynamics, we also estimate what we call the social relevance of each subject in a group using a saliency attentive model. Extensive tests on two publicly available datasets show that our solution achieves encouraging results when detecting social groups and their relevant subjects in challenging egocentric scenarios.
Correlation‐guided multi‐object tracking with correlation feature transfer

Here, the authors propose a correlation‐guided Monte Carlo Markov chain (MCMC) solver to promote the efficiency for tracking multiple objects under recursive Bayesian filtering framework. Instead of randomly proposing the target location according to certain distribution, the authors’ method guides the MCMC solver to sample among locations that the targets are more likely to appear. The high possible locations for each target are obtained using its corresponding response map by evaluating the correlation between the target appearance and its online model. Furthermore, the proposed tracking framework is natural to transfer the rich domain‐specific offline correlation features into the target online model. With the calculation in the Fourier domain and the reversible jump strategy for MCMC, the correlation‐guided method is able to track variable multiple objects with high efficiency. At the same time, the correlation feature transfer strategy is capable of improving tracking precision with easy offline training. The proposed method is evaluated by both the synthetic and real scenario videos. The experimental results demonstrate the effectiveness of the proposed method and its superior performance against its counterparts.
Reuse, recycle, and shine: using everyday objects to teach optics

It is not always possible to have access to commercial experimental kits in order to impart knowledge to a class or perform an outreach presentation, and this often happens because of the lack of funding in schools and universities. We aimed to create an outreach demonstration using only recyclable materials and reusing old CDs or DVDs, so that everyone can use it. In this work, we present the outreach activity "Homemade spectrometer using a CD/DVD", which won the first place in the Optics Outreach games 2017 in San Diego. This spectrometer can be built with a CD or DVD and a template that can be acquired for free by emailing the University of Exeter Optics and Photonics Society (EUOPS), or can be collected in our outreach activities. We have tested this outreach demonstration in several events during 2017 and 2018 with a very positive feedback. Teachers and students within UK, USA, Mexico and Chile have required the activity worksheet in order to replicate the demonstration at their institutions.