Reviewerly/exHarmony huggingface dataset:
Evaluating the Voice Type Component Distributions of Excised Larynx Phonations at Three Subglottal Pressures

Purpose The excised canine larynx provides an advantageous experimental framework in the study of voice physiology. In recent years, signal processing methods have been applied to analyze phonations in excised canine larynx experiments. However, phonations have a highly complex and nonstationary nature corresponding to different proportions of regular and chaotic signal elements. Current nonlinear dynamic methods that are used to assess the degree of irregularity in the voice fail to recognize the distribution of voice type components (VTCs). Method Based on measures of intrinsic dimension, this article presents a method to analyze the VTC distribution of phonations in excised canine larynx experiments. Thirty-nine phonation samples from 13 excised canine larynges at three different subglottal pressures were analyzed. Results Phonation produced with subglottal pressures above phonation instability pressure (PIP) and below phonation threshold pressure (PTP) resulted in high proportions of Voice Types 3 and 4, characterized by chaotic and noisy signals. Phonation produced with pressure between PTP and PIP contained mostly Type 1 voice, characterized by a regular and nearly periodic signal. Mean proportions of all VTCs varied significantly in comparisons of phonations produced with Sub-PTP and PTP as well as in comparisons of phonations produced with PTP and PIP. Conclusions Across all VTCs, the VTC profiles of normal and abnormal phonation differ significantly. Normal phonation is strongly associated with VTC1 (Voice Type Component 1), whereas abnormal phonation exhibits increased VTC4 (Voice Type Component 4). The study further demonstrates the ability of intrinsic dimension to successfully detect multiple voice types in an acoustic signal and highlights the need for expanded use of intrinsic dimension in human voice. Supplemental Material https://doi.org/10.23641/asha.14417585.
User Profiling in Travel Recommender System using Hybridization and Collaborative Method

The recommender system is improving with the increase in the information obtained from numerous application domains. Recommendation or the prediction of an item depends on the rating and review given by an individual or a group of customers. New user information can also be predicted using the searching history or the profile information of the customer. In Travel Recommender System, the locations of interest are figured out based on the activities carried out by the user or the preference of that particular user. It also helps in exploring the diverse geographical areas of interest of the user. The increasing demands of this system enhances the scope in development of user behaviour that is based on recommendation approaches. It also effectively deals with the sparsity problem by searching through a large amount of data to provide users with individual contents and services. This article explores the various aspects and potentiality of existing approaches in the Travel Recommender System based on user profiles for future research directions and recommendation framework.
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission

Low-bandwidth communication, such as underwater acoustic communication, is limited by best-case data rates of 30-50 kbit/s. This renders such channels unusable or inefficient at best for single image, video, or other bandwidth-demanding sensor-data transmission. To combat data-transmission bottlenecks, we consider practical use-cases within the maritime domain and investigate the prospect of Single Image Super-Resolution methodologies. This is investigated on a large, diverse dataset obtained during years of trawl fishing where cameras have been placed in the fishing nets. We propose down-sampling images to a low-resolution low-size version of about 1 kB that satisfies underwater acoustic bandwidth requirements for even several frames per second. A neural network is then trained to perform up-sampling, trying to reconstruct the original image. We aim to investigate the quality of reconstructed images and prospects for such methods in practical use-cases in general. Our focus in this work is solely on learning to reconstruct the high-resolution images on “real-world” data. We show that our method achieves better perceptual quality and superior reconstruction than generic bicubic up-sampling and motivates further work in this area for underwater applications.
Storage Optimization using Adaptive Thresholding Motion Detection

Data storage is always an issue, especially for video data from CCTV cameras that require huge amounts of storage. Moreover, monitoring past events is a laborious task. This paper proposes a motion detection method that requires fewer calculations and reduces the required data storage up to 70%, as it stores only the informative frames, enabling the security personnel to retrieve the required information more quickly. The proposed method utilized a histogram-based adaptive threshold for motion detection, and therefore it can work in variable luminance conditions. The proposed method can be applied to streamed frames of any CCTV camera to efficiently store and retrieve informative frames.
Geodesically Parameterized Covariance Estimation

Statistical modeling of spatiotemporal phenomena often requires selecting a covariance matrix from a covariance class.Yet standard parametric covariance families can be insufficiently flexible for practical applications, while nonparametric approaches may not easily allow certain kinds of prior knowledge to be incorporated.We propose instead to build covariance families out of geodesic curves.These covariances offer more flexibility for problem-specific tailoring than classical parametric families and are preferable to simple convex combinations.Once the covariance family has been chosen, one typically needs to select a representative member by solving an optimization problem, e.g., by maximizing the likelihood of a data set.We consider instead a differential geometric interpretation of this problem: minimizing the geodesic distance to a sample covariance matrix (``natural projection"").Our approach is consistent with the notion of distance employed to build the covariance family and does not require assuming a particular probability distribution for the data.We show that natural projection and maximum likelihood estimation within the covariance family are locally equivalent up to second order.We also demonstrate that natural projection may yield more accurate estimates with noise-corrupted data.
Comic Based Learning for Students with Visual Impairments

Visual impairment can affect a student’s ability to learn since their concept development when interacting with educational material is being limited. Learning activities based on images and visually rich content are mainstream learning methods, where facilitating students with visual impairments for engaged learning can be challenging. For comic books, which have shown promising results in engaged student learning, this problem is more severe. To overcome this challenge, this research presents a novel voice synthesised learning method to reduce the gap between the learning experience of a student with visual impairments compared to a mainstream learning activity. Utilising comic books, the proposed technique and the tool developed extracts semantic content, stores them in a database, and generates an audio stream in multiple languages on the user’s demand. To assess the usability of the system, a survey for a selected set of students with visual impairments was carried out. The results showed a mean rating of 5.76 out of 7 for the Informative Interest. Furthermore, a concept-mapping approach was used to analyse the feedback given through the open-ended questions. From the analysis, key concepts with an emphasis on positive emotions, willingness to try again, and features to be improved, were identified.
A Fuzzy Approach to Sentiment Analysis at the Sentence Level

The objective of this chapter is to present a hybrid approach to the Sentiment Analysis problem focused on sentences or snippets. This new method is centred around a sentiment lexicon enhanced with the assistance of SentiWordNet and fuzzy sets to estimate the semantic orientation polarity and intensity for sentences. This provides a foundation for computing with sentiments. The proposed hybrid method is applied to three different datasets and the results achieved are compared to those obtained using Naïve Bayes (NB) and Maximum Entropy (ME) techniques. It is demonstrated through experimentation that this hybrid approach is more accurate and precise than both NB and ME techniques. Furthermore, it is shown that when applied to datasets containing snippets, the proposed method performs similar to state-of-the-art techniques.
Sentiment Analysis for Electoral Prediction Using Twitter Data

Since the appearance of the World Wide Web, there has been a significant change in the way we share information. People can express themselves on the web through many mediums. Examples of such mediums are Blogs, wikis, forums and social networks, where in these platforms being mentioned, people are able to exercise posting and sharing of opinions, thereby leading to the need for research in sentiment analysis. This paper contributes to the field of Sentiment Analysis of electoral prediction using Twitter, which aims to extract opinions from text using machine learning classifiers such as Naive Bayes, Max Entropy and SVM. The problem statement for this research is the classification of textual information as whether it bears positive sentiment or negative sentiment. Our research focuses on a novel data pre-processing method which enhances the performance of different supervised algorithms. With our method, the accuracy of Naive Bayes was enhanced to 89.78% (hybrid), Maximum Entropy was enhanced to 87% (hybrid), SVM was enhanced to 91% (unigram), and ensembling of Naive Bayes and SVM was enhanced to 95.3% (bigram).
Label or Message: A Large-Scale Experimental Survey of Texts and Objects Co-Occurrence

Our daily life is surrounded by textual information. Nowadays, the automatic collection of textual information becomes possible owing to the drastic improvement of scene text detectors and recognizer. The purpose of this paper is to conduct a large-scale survey of co-occurrence between visual objects (such as book and car) and scene texts with a large image dataset and a state-of-the-art scene text detector and recognizer. Especially, we focus on the function of “label” texts, which are attached to objects for detailing the objects. By analyzing co-occurrence between objects and scene texts, it is possible to observe the statistics about the label texts and understand how the scene texts will be useful for recognizing the objects and vice versa.
biDeepFM: A multi-objective deep factorization machine for reciprocal recommendation

In this paper, we propose a multi-objective learning approach for online recruiting. Online recruiting and online dating are the most known reciprocal recommendation problems. However, the reciprocal recommendation has gained little attention in the literature due to the lack of public datasets consisting of reciprocal preferences of users in a network. We aim to resolve this shortage in our study. Since the satisfaction of both candidates and companies is indispensable for successful hiring as opposed to traditional recommenders, online recruiting should respect to expectations of all parties and meet their common interests as much as possible. For this purpose, we integrated our multi-objective learning approach into various state-of-the-art methods, whose success has been proven on similar prediction problems, and we achieved encouraging results. We named and proposed one of the prominent architectures that we’ve tested on the problem as a prototype of our multi-objective learning approach however our approach is applicable to any recommender system employing neural networks as its final decision-maker.
Vehicle Flow Statistics System in Video Surveillance based on Camshift and Kalman Filter

With the rapid development of the modern economy, transportation safety and security are becoming critical. The standards for the management of transportation systems are also becoming higher and higher. The modern real-time control technology is integrated into the real-time control of the intelligent traffic system. This paper establishes a smart transportation system that uses traffic pavement cameras to detect traffic flow in a specific period. The system used the background difference method based on the Gaussian model and the Camshift algorithm based on the Kalman filter. It is mainly divided into background modeling, foreground detection, vehicle tracking, and vehicle flow calculation. After background detection, remove the background parts and then use foreground detection to accurately extract different vehicle types of various models that need to be processed. Through tracking and scientific statistics of the detected targets, real-time traffic flow data is calculated. The traffic flow changes are intuitively fed back to the system users in a line chart. Thus significantly reduce the manual part's workload, effectively improve the efficiency and effectiveness of traffic management.
A Backend-agnostic, Quantum-classical Framework for Simulations of Chemistry in C <tt>++</tt>

As quantum computing hardware systems continue to advance, the research and development of performant, scalable, and extensible software architectures, languages, models, and compilers is equally as important to bring this novel coprocessing capability to a diverse group of domain computational scientists. For the field of quantum chemistry, applications and frameworks exist for modeling and simulation tasks that scale on heterogeneous classical architectures, and we envision the need for similar frameworks on heterogeneous quantum-classical platforms. Here, we present the XACC system-level quantum computing framework as a platform for prototyping, developing, and deploying quantum-classical software that specifically targets chemistry applications. We review the fundamental design features in XACC, with special attention to its extensibility and modularity for key quantum programming workflow interfaces and provide an overview of the interfaces most relevant to simulations of chemistry. A series of examples demonstrating some of the state-of-the-art chemistry algorithms currently implemented in XACC are presented, while also illustrating the various APIs that would enable the community to extend, modify, and devise new algorithms and applications in the realm of chemistry.
Avoiding biases in binned fits

Binned maximum likelihood fits are an attractive option when analysing large datasets, but require care when computing likelihoods of continuous PDFs in bins. For many years the widely used statistical modelling package RooFit evaluated probabilities at the bin centre, leading to significant biases for strongly curved probability density functions. We demonstrate the biases with real-world examples, and introduce a PDF class to RooFit that removes these biases. The physics and computation performance of this new class are discussed.
An algorithmic approach to entanglement-assisted quantum error-correcting codes from the Hermitian curve

We study entanglement-assisted quantum error-correcting codes (EAQECCs) arising from classical one-point algebraic geometry codes from the Hermitian curve with respect to the Hermitian inner product. Their only unknown parameter is $ c $, the number of required maximally entangled quantum states since the Hermitian dual of an AG code is unknown. In this article, we present an efficient algorithmic approach for computing $ c $ for this family of EAQECCs. As a result, this algorithm allows us to provide EAQECCs with excellent parameters over any field size.
The transition from the classroom to non-classroom teaching at the UPC during the COVID-19 pandemic

Faced with the unexpected state of alarm decreed due to the pandemic produced by Covid-19, Spanish universities have been forced to adapt teaching to this situation, using Information and Communication Technologies as the primary means. Most teachers have had to make an abrupt transition to do our classes online. This experience has probably been hectic and yet perhaps an educational and revealing experience for many of us. Of all the changes, the most substantial is to move from face-to-face teaching to online teaching-learning, this change requires a series of adjustments in education that must be assumed from the re-planning of the subject to the delivery of classes in virtual mode. They have also had to adapt the spaces of the university since the exams that were presential to virtual have been replanned.
[DC] The Effect of Modulating The Step Length of an Embodied Self-Avatars on Gait Symmetry During Treadmill Walking

In virtual reality, it is possible to simulate visual self-representation by mapping one's body movements to those of an avatar. The acceptance of the virtual body as part of one's own body creates an ownership illusion. This paper discusses different protocols to study the effects of modulating a self-avatar's step length on the step length of embodied healthy participants and post-stroke patients.
Introducing a data availability policy for journals at <scp>IOP</scp> Publishing: Measuring the impact on authors and editorial teams

Abstract As the open research movement continues to gather pace, a number of publishers, funders, and institutions are mandating the sharing of underlying research data. At the same time, concerns about introducing extra quality control steps around data availability statements (DAS) are driving a discussion about the best way to make data more open without slowing down publication. This article describes a pilot project to introduce a new Open Data policy to three IOP Publishing (IOPP) journals as part of IOPP's commitment to increasing transparency and support for open science. An investigation was undertaken using an automated workflow monitoring tool to understand the impact of this change on authors and the editorial staff. Changes in revised submission processing times and how often manuscripts were returned to the author were measured. An overall increase in the time editorial staff spent processing manuscripts was found as well as an increase in the number of times manuscripts were returned to authors. Detailed analysis shows that manuscripts in which authors claim in the DAS to have included data within the manuscript were the most strongly affected. Steps to mitigate the effects through improved author communication were found to be effective.
Role Aware Multi-Party Dialogue Question Answering

Multi-party dialogue question answering (MPDQA) is an emerging topic in speech and language processing where the goal is to answer the questions according to the multiparty conversations. Different from conventional QA, which assumes a single speaker (writer) and general listeners (readers), MPDQA involves multiple speakers and specific listeners. Prior works simply treat the dialogues as plain passages, which neglect the importance of role awareness, such as speakers’ perspectives and co-references. In a novel aspect, this paper proposes the Role Aware Multi-Party Network (RAMPNet), a model utilizing the information of speaker and role to present "who is speaking" and "who is mentioned", making role awareness an available message for our model. Experiments show that our RAMPNet outperforms the BERT baseline model on a large-scale MPDQA dataset, FriendsQA, especially in the "Who" and "How" questions, which strongly need the ability of conversation relations understanding to answer these questions. In addition, our further analysis demonstrates RAMPNet’s effectiveness in those questions contain verbs such as "reply" or "talk", related to interactions between speakers and roles. All of the results show the capability and utility of RAMPNet.
A High Performance Blockchain Platform for Indian Banking System

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Biometric face classification with the hybridised rough neural network

Biometric face classification is an important indexing scheme to reduce face matching time for large volumes of a database. In this paper, a hybridised approach based on rough set theory (RST) and back propagation neural network (BPN) to classify human face is proposed. Local binary pattern (LBP) method is exploited to extract the features from pre-processed face images. The evolutionary optimisation algorithms such as genetic algorithm (GA), particle swarm optimisation (PSO), ant colony optimisation (ACO), hybridisation of ACO and GA (ACO-GA) and hybridisation of PSO and GA (PSO-GA) are investigated for feature selection. Finally, the hybridised rough neural network (RNN) is employed for classification. The experimental results of the proposed RNN is compared in terms of precision, recall, f-measure, accuracy and error rate with Naive Bayes, support vector machine (SVM), radial basis function network (RBFN), conventional BPN, and convolutional neural network (CNN) to conclude the efficacy of the proposed approach.
Effect of acoustic excitation on fiber-reinforced polypropylene and the influence on melt viscosity

Abstract The paper describes a novel technological approach to influencing the rheological properties of thermoplastic materials exposed to acoustic energy. The flow behavior of polypropylene with different mass percentages of glass fibers is investigated in a parallel plate rheometer under high-frequency longitudinal excitation. The influence of oscillation frequency on the melt viscosity is explained by means of shear thinning criteria. The dependence of the oscillation shape using sinusoidal excitation on shear thinning as a function of different fiber reinforcement percentages is also investigated. A phenomenological view describes the mutually influencing parameters with regard to different material compositions and different excitation frequencies over the time course of the rheometric measurement. Interacting relationships are analyzed and discussed and the potential of the actuator system to influence the plastic melt is worked out. Based on this, a technological approach follows which describes the transfer of an oscillating mold surface to plastics processing methods, which, especially in the case of energy-intensive injection molding technology, leads to the expectation of possible resource efficiency in energy and material.