Reviewerly/exHarmony huggingface dataset:
CONCERT HALL STAGE ACOUSTICS FROM THE PERSPECTIVE OF THE PERFORMERS AND PHYSICAL REALITY

A three-year study of stage acoustics for symphony orchestras in concert halls has recently been completed. The main goal of the project was to find what acoustic conditions are preferred by orchestral musicians and what objective characteristics best correspond with their views. The study involved both subjective and objective investigations. The subjective data was collected from two questionnaire studies with musicians. The objective study involved three separate types of study: measurements on real concert hall stages, measurements in scale models and investigations using computer simulation models. Though several authors have discussed performing conditions in auditoria, the most significant studies have been by Gade. Gade’s first major publication 1 described experiments in anechoic chambers with musicians performing in simulated room environments. In his second paper 2 he described three different studies: a study of three Danish orchestras playing in nine different halls, a study of one of these orchestras performing on tour in eight British halls and thirdly an experimental study trying to improve acoustic conditions on an existing stage (Danish Radio, Studio 1). These studies led to the proposal of three acoustic parameters for assessing stage acoustics, which were later revised to the following Support parameters 3 : ST early (ST1 ) to measure ensemble conditions, ST late for the impression of reverberation and ST total for measuring support from the room for sound from the musicians’ own instrument. The Support ( ST ) parameters sum the level of sound reflections returning back to a musician on stage from any direction, by use of omnidirectional loudspeaker and microphone. The sum of reflections is taken within different time intervals relative to the emission of sound. The time intervals for ST early , ST late and ST total are 20‐100, 100‐1000 and 20‐1000 ms respectively. From our study, it soon became apparent that the necessary conditions for orchestral performance may well be different to those for small chamber groups. Support measures as proposed by Gade will have some relevance for perceived conditions for the players, but our results suggest that the relevance of Support measures may be limited to assessing the level of acoustic response on stage. Based on the calibration method used for obtaining the Support values, other acoustic parameters appear more reliable for the purpose of assessing the level of acoustic response.
From Fork Decoupling to Star-Topology Decoupling

Fork decoupling is a recent approach to exploiting problem structure in state space search. The problem is assumed to take the form of a fork, where a single (large) center component provides preconditions for several (small) leaf components. The leaves are then conditionally independent in the sense that, given a fixed center path p, the compliant leaf moves - those leaf moves enabled by the preconditions supplied along p - can be scheduled independently for each leaf. Fork-decoupled state space search exploits this through conducting a regular search over center paths, augmented with maintenance of the compliant paths for each leaf individually. We herein show that the same ideas apply to much more general star-topology structures, where leaves may supply preconditions for the center, and actions may affect several leaves simultaneously as long as they also affect the center. Our empirical evaluation in planning, super-imposing star topologies by automatically grouping the state variables into suitable components, shows the merits of the approach.
Using a Sequential SOM to Parse Long-term Dependencies

Simple Recurrent Networks (SRNS) have been widely used in natural language processing tasks. However, their ability to handle long-term dependencies between sentence constituents is somewhat limited. NARX networks have recently been shown to outperform SRNs by preserving past information in explicit delays from the network’s prior output. However, it is unclear how the number of delays should be determined. In this study on a shift-reduce parsing task, we demonstrate that comparable performance can be derived more elegantly by using a SARDNET self-organizing map. The resulting architecture can represent arbitrarily long sequences and is cognitively more plausible.
Ambient Learning Spaces:

This chapter addresses body- and space-related learning of children and adolescents, supported by a wide range of media platforms. These media constitute ambient learning spaces, in which learners use a variety of annotatable multimedia objects (e.g. image, video, sound, 3D models). These multimedia objects can be used for e.g. mobile learning and for learning with tangible interfaces in different environments. Our research started more than a decade ago and relies on a pedagogical background known as critical constructivism. The development of our body- and space-related media is based on innovative scenarios and was done within a participative design process with future users. This media can be assigned to different layers of our Model of Media Layers. Technically, this is made possible by the Network Environment for Multimedia Objects (NEMO), which is currently under development. It offers contextualized, personalized, semantically rich, device-specific access to multimedia objects. The research on the innovative learning environments is founded on Design-Based Research. According to this approach theory and real-world usage are conjoined and of equal importance. Thus theory and applications evolve simultaneously during the design process.
Information Set Generation in Partially Observable Games

We address the problem of making single-point decisions in large partially observable games, where players interleave observation, deliberation, and action. We present information set generation as a key operation needed to reason about games in this way. We show how this operation can be used to implement an existing decision-making algorithm. We develop a constraint satisfaction algorithm for performing information set generation and show that it scales better than the existing depth-first search approach on multiple non-trivial games.
Telling Interactive Player-specific Stories and Planning for It: ASD + PaSSAGE = PAST

Artificial Intelligence (AI) techniques have been used to improve overall player satisfaction in video games. A thriving area of such research is interactive AI-assisted story-telling. One such line of research explored gains of automatically fitting the story to each individual player during the game via player modeling. Another research line used AI planning techniques to create contingency stories at design time. In this paper we propose a principled way to combine these two lines of research. We describe a system that uses a player model both at the design time and at the play time to generate and select stories fitting a specific player. We implement an early prototype and present it in the paper.
A Methodology for the Automatic Extraction and Generation of Non-Verbal Signals Sequences Conveying Interpersonal Attitudes

In many applications, Embodied Conversational Agents (ECAs) must be able to express various affects such as emotions or social attitudes. Non-verbal signals, such as smiles or gestures, contribute to the expression of attitudes. Social attitudes affect the whole behavior of a person: they are “characteristic of an affective style that colors the entire interaction” [1] . Moreover, recent findings have demonstrated that non-verbal signals are not interpreted in isolation but along with surrounding signals. Non-verbal behavior planning models designed to allow ECAs to express attitudes should thus consider complete sequences of non-verbal signals and not only signals independently of one another. However, existing models do not take this into account, or in a limited manner. The contribution of this paper is a methodology for the automatic extraction of sequences of non-verbal signals characteristic of a social phenomenon from a multimodal corpus, and a non-verbal behavior planning model that takes into account sequences of non-verbal signals rather than signals independently. This methodology is applied to design a virtual recruiter capable of expressing social attitudes, which is then evaluated in and out of an interaction context.
Application quantum renormalization group to optimal dense coding in transverse Ising model

We have merged the object of renormalization group(RG) with quantum dense coding for one-dimensional Ising model in a transverse field. At first, we have shown how the dynamic quantum discord(QD) evolves in present intrinsic decoherence when the size of the system becomes large, i.e., the finite size scaling is obtained. By considering that the generation of highly entangled quantum states is a fundamental requirement for quantum information, the main purpose of this work is to answer the following question: how the valid optimal dense coding can be determined by RG in many body systems? We find that the optimal dense coding capacity depends on the normalization group. It has been found that valid dense coding can exist with the increasing of RG steps. Moreover, the results show that by increasing of RG steps, valid dense coding capacity suddenly occurs near the critical point of the quantum phase transition in present intrinsic decoherence. Using this approach, identifying a critical point of the transverse Ising model in dense coding capacity quality can be very effective.
Measuring the Directional or Non-directional Distance Between Type-1 and Type-2 Fuzzy Sets With Complex Membership Functions

Fuzzy sets (FSs) may have complex, non-normal, or non-convex membership functions that occur, for example, in the output of a fuzzy logic system or when automatically generating FSs from data. Measuring the distance between such non-standard FSs can be challenging as there is no clear correct method of comparison and only limited research currently exists that systematically compares existing distance measures (DMs) for these FSs. It is useful to know the distance between these sets, which can tell us how much the results of a system change when the inputs differ, or the amount of disagreement between individual's perceptions or opinions on different concepts. In addition, understanding the direction of difference between such FSs further enables us to rank them, learning if one represents a higher output or higher ratings than another. This paper builds on previous functions of measuring directional distance and, for the first time, presents methods of measuring the directional distance between any type-1 and type-2 FSs with both normal/non-normal and convex/non-convex membership functions. In real-world applications, where data-driven, non-convex, non-normal FSs are the norm, the proposed approaches for measuring the distance enables us to systematically reason about the real-world objects captured by the FSs.
The effect of training set size in authorship attribution: application on short arabic texts

&lt;span lang="EN-US"&gt;Authorship attribution (AA) is a subfield of linguistics analysis, aiming to identify the original author among a set of candidate authors. Several research papers were published and several methods and models were developed for many languages. However, the number of related works for Arabic is limited. Moreover, investigating the impact of short words length and training set size is not well addressed. To the best of our knowledge, no published works or researches, in this direction or even in other languages, are available. Therefore, we propose to investigate this effect, taking into account different stylomatric combination. The Mahalanobis distance (MD), Linear Regression (LR), and Multilayer Perceptron (MP) are selected as AA classifiers. During the experiment, the training dataset size is increased and the accuracy of the classifiers is recorded. The results are quite interesting and show different classifiers behaviours. Combining word-based stylomatric features with n-grams provides the best accuracy reached in average 93%.&lt;/span&gt;
A robust framework for visibility enhancement of foggy images

This paper presents a robust framework for visibility enhancement of images degraded by foggy weather conditions. The proposed defogged algorithm is developed by the combining of modified visibility restoration approach and S-shaped transfer function for foggy weather degraded images. The proposed defogged algorithm works in two steps: in first step trilateral filter based visibility restoration algorithm is used for visibility and smoothness of the degraded images. Further in the second step S-shaped transfer function is used for contrast enhancement of the foggy images. The image quality metrics of proposed defogged and other existing visibility restoration algorithms are evaluated in terms of Fog Reduction Factor (FRF), Measure of Enhancement Factor (EMF) and blind parameter (η) on different foggy image databases. We demonstrate the strength of proposed defogged algorithm by estimation the thickness of fog in the input image as well as the output image. Finally, the simulation results and visualisation of defogged images indicate that the proposed defogged algorithm is highly effective and efficient for visibility enhancement of foggy images.
Towards an Automatic Determination of Grasping Points through a Machine Vision Approach

A system is presented which coordinates a parallel-jaw gripper and a vision system with the aim of choosing grasping points taking into account stability conditions. The choice of grasping points is done by a new heuristic approach that tries to manage all kind of planar parts with or without internal centre of gravity, possibly containing holes. It needs only two moment-based features, centroid and the direction of the main axis, easily obtained from a Freeman chain-code representation of the boundary. The results are exclusively visual parameters, expressed in visual coordinates (pixels). These algorithms have been implemented in a 4D0F robot arm with one camera fixed over the hand. Visual feedback was used in the control system to improve precision up to the limits of the available visual system.
Developing an institutional repository using Digital Commons

Purpose The purpose of this paper is to share the knowledge and lessons learned about the process of developing an institutional repository (IR) using a hosted solution, Digital Commons from bepress, and to make the case that Digital Commons is still the best IR solution for smaller university libraries. Design/methodology/approach This is a case study based on Rowan University Libraries developing an IR using the Digital Commons platform. Findings To implement a hosted solution successfully, key actions include defining the repository’s scope and mission early in the process, including a variety of stakeholders in promoting the repository, integrating the repository with a faculty profile system and being able to effectively work collaboratively with both internal and external professionals in developing the system. Originality/value This paper will be valuable to academic librarians considering implementing an IR on, or migrating an existing repository to, the Digital Commons platform.
Bayesian non-crossing quantile regression for regularly varying distributions

Quantile regression is a very important statistical tool for predictive modelling and risk assessment. For many applications, conditional quantile at different levels are estimated separately. Consequently the monotonicity of conditional quantiles can be violated when quantile regression curves cross each other. In this paper, we propose a new Bayesian multiple quantile regression based on heavy tailed distribution for non-crossing. We consider a linear quantile regression model for simultaneous Bayesian estimation of multiple quantiles based on a regularly varying assumptions. The numerical and competitive performance of the proposed method is illustrated by simulation.
Development of a Stand-Alone Independent Graphical User Interface for Neurological Disease Prediction with Automated Extraction and Segmentation of Gray and White Matter in Brain MRI Images

This research presents an independent stand-alone graphical computational tool which functions as a neurological disease prediction framework for diagnosis of neurological disorders to assist neurologists or researchers in the field to perform automatic segmentation of gray and white matter regions in brain MRI images. The tool was built in collaboration with neurologists and neurosurgeons and many of the features are based on their feedback. This tool provides the user automatized functionality to perform automatic segmentation and extract the gray and white matter regions of patient brain image data using an algorithm called adapted fuzzy c -means (FCM) membership-based clustering with preprocessing using the elliptical Hough transform and postprocessing using connected region analysis. Dice coefficients for several patient brain MRI images were calculated to measure the similarity between the manual tracings by experts and automatic segmentations obtained in this research. The average Dice coefficients are 0.86 for gray matter, 0.88 for white matter, and 0.87 for total cortical matter. Dice coefficients of the proposed algorithm were also the highest when compared with previously published standard state-of-the-art brain MRI segmentation algorithms in terms of accuracy in segmenting the gray matter, white matter, and total cortical matter.
Separating value functions across time-scales

In many finite horizon episodic reinforcement learning (RL) settings, it is desirable to optimize for the undiscounted return - in settings like Atari, for instance, the goal is to collect the most points while staying alive in the long run. Yet, it may be difficult (or even intractable) mathematically to learn with this target. As such, temporal discounting is often applied to optimize over a shorter effective planning horizon. This comes at the risk of potentially biasing the optimization target away from the undiscounted goal. In settings where this bias is unacceptable - where the system must optimize for longer horizons at higher discounts - the target of the value function approximator may increase in variance leading to difficulties in learning. We present an extension of temporal difference (TD) learning, which we call TD($\Delta$), that breaks down a value function into a series of components based on the differences between value functions with smaller discount factors. The separation of a longer horizon value function into these components has useful properties in scalability and performance. We discuss these properties and show theoretic and empirical improvements over standard TD learning in certain settings.
Technical Writer in the Framework of Modern Natural Language Processing Tasks

This study focuses on technical writer competences and necessary specialized language resources, supporting any language worker in the framework of modern natural language processing domain (terminologist, translator, lexicographer, technical writer, grammarian, teaching language specialist etc
Enhancing single-shot fringe pattern phase demodulation using advanced variational image decomposition

In many full-field optical metrology techniques, i.e. interferometry, moiré, and structured light, the information about the measurand, e.g. displacement, strain, or 3D shape, is stored in the phase distribution of a recorded two-dimensional intensity pattern—the fringe pattern. Its analysis (phase demodulation) therefore plays a crucial role in the measurement procedure, significantly affecting the total accuracy of the optical system. Phase demodulation methods based on just a single fringe pattern are especially interesting, due to their robustness to environmental disturbances and ability to examine dynamic events. However, the calculated phase map is easily spoiled by errors, which appear mainly because of fringe pattern imperfections, i.e. random noise, parasitic interferences, a nonsinusoidal fringe pattern profile or a non-uniform image background. In this contribution, an advanced variational image decomposition scheme is proposed to reduce these phase demodulation errors. The reported purely phase domain method can be easily adopted to aid virtually any fringe analysis method, including single-frame and multi-frame phase-shifting, possibly enhancing retrieved phase distribution without the need for hardware manipulation. We employed it to improve single-frame Hilbert–Huang transform-based fringe analysis. The remarkable efficiency and versatility of the developed algorithm are verified by processing synthetic and experimental fringe patterns and phase maps. The demonstrated approach compares favorably with the very capable 2D empirical mode decomposition reference method.
T-thinker

Many computationally expensive problems are solved by a divide-and-conquer algorithm: a problem over a big dataset can be recursively divided into independent tasks over smaller subsets of the dataset. We present a distributed general-purpose framework called T-thinker which effectively utilizes the CPU cores in a cluster by properly decomposing an expensive problem into smaller independent tasks for parallel computation. T-thinker well overlaps CPU processing with network communication, and its superior performance is verified over a re-engineered graph mining system G-thinker available at http://cs.uab.edu/yanda/gthinker/.
MARIO Project: Experimentation in the Hospital Setting

In the EU funded MARIO project, specific technological tools are adopted for the patient with dementia (PWD). At this stage of the project, the experimentation phase is under way, and the first two trials were completed as shown below: the first trial was performed in November 2016, and second trial was performed in April 2017. The current implemented and assessed applications (apps) are My Music app, My News app, My Games app, My Calendar app, My Family and Friends app, and Comprehensive Geriatric Assessment (CGA) app. The aim of the present study was to provide a preliminary analysis of the acceptability and efficacy of MARIO companion robot on clinical, cognitive, neuropsychiatric, affective and social aspects, resilience capacity, quality of life in PWD, and burden level of the caregivers. Thirteen patients [5 patients (M = 3; F = 2) in first trial, and 8 patients (M = 6; F = 2) in second trial] were screened for eligibility and all were included. At admission and at discharge, the following tests were administered: Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Clock Drawing Test (CDT), Frontal Assessment Battery (FAB), Hachinski Ischemic Scale (HIS), Neuropsychiatric Inventory (NPI), Geriatric Depression Scale (GDS), Hamilton Rating Scale for Depression (HDRS-21), Multidimensional Scale of Perceived Social Support (MSPSS), Social Dysfunction Rating Scale (SDRS), Brief Resilience Scale (BRS), Quality of Life in Alzheimer’s Disease (QOL-AD), Caregiver Burden Inventory (CBI), Tinetti Balance Assessment (TBA), and Comprehensive Geriatric Assessment (CGA) was carried out. A questionnaire based on the Al-mere Acceptance model was used to evaluate the acceptance of the MARIO robot. During the first trial, My Music, My Games and My News apps were used. At discharge, no significant improvement was shown through the above questionnaires. During the second trial, My Music, My Games, My News, My Calendar, My Family and Friends, and CGA apps were used. At discharge, significant improvements were observed in the following parameters: NPI (p = 0.027), GDS-15 (p = 0.042), and BRS (p = 0.041), CBI (p = 0.046). Instead, the number of medications is increased at discharge (p = 0.038). The mean of hospitalization days is 5.6 ± 3.9 (range = 3–13 days). The Almere Model Questionnaire suggested, a higher acceptance level was shown in first and second trial.