Reviewerly/exHarmony huggingface dataset:
Sharp Images Detection for Microscope Pollen Slides Observation

In this paper, a new preprocessing algorithm to qualify images of different pollen grains for further processing is proposed. This algorithm provides a score related to the sharpness of the image and will be used to automatically adjust the focal length of a microscope that magnifies the image. The obtained score has been compared to four quality metrics generally used to estimate the clarity of an image and to a reference made by a human. The results of the simulations show that the proposed algorithm combines better performance with low complexity on the set of images.
Applications of Machine Learning in Building Energy Prediction and Savings

In a constantly advancing world with growing needs, buildings play in important role in the daily functioning of the society.Developing more and more advanced techniques to optimize the working of these buildings is highly important for a constant growth.Modern computational techniques have opened doors to create advanced models that can utilize efficient techniques to produce highly accurate results.This paper introduces a model that utilizes machine learning algorithms to predict energy consumption in buildings.Energy data were used from two actual and two simulated buildings to fine tune the models.The model is also compared to a baseline regression model as well as a model based on Artificial Neural Network.The results show that the proposed model performs much better than the other two compared models.The proposed model can be used for many intelligent applications such as measurement and savings verification, optimization, building-energy assessment and fault detection and diagnosis.The models were tested to predict the savings calculations for a simulated building and the results proved the proposed model to be the closest predictor to actual savings.
BRACELET: Edge-Cloud Microservice Infrastructure for Aging Scientific Instruments

Recent advances in cyber-infrastructure have enabled digital data sharing and ubiquitous network connectivity between scientific instruments and cloud-based storage infrastructure for uploading, storing, curating, and correlating of large amounts of materials and semiconductor fabrication data and metadata. However, there is still a significant number of scientific instruments running on old operating systems that are taken offline and cannot connect to the cloud infrastructure, due to security and network performance concerns. In this paper, we propose BRACELET - an edge-cloud infrastructure that augments the existing cloud-based infrastructure with edge devices and helps to tackle the unique performance & security challenges that scientific instruments face when they are connected to the cloud through public network. With BRACELET, we put a networked edge device, called cloudlet, in between the scientific instruments and the cloud as the middle tier of a three-tier hierarchy. The cloudlet will shape and protect the data traffic from scientific instruments to the cloud, and will play a foundational role in keeping the instruments connected throughout its lifetime, and continuously providing the otherwise missing performance and security features for the instrument as its operating system ages.
Score-based Learning for Relevance Prediction in Image Similarity Search

Predicting the performance of queries when labels are not present has been a recurring problem faced in information retrieval systems. Beyond its clear importance, it can also be applied to aid post-retrieval optimization approaches such as re-ranking or rank-aggregation. However, most post-retrieval performance prediction approaches to retrieval systems rely on generating a single effectiveness value of performance for queries. We propose an alternative method to assess the performance of systems reliant on similarity search, which consists of predicting the individual relevance of ranked results according to the distribution of similarity scores of a given query compared to instances in a collection. The idea is that relationships between the ith ranked score and other scores of the rank can be leveraged to generate features which, in turn, are used to classify ranked objects according to their relevance to the query. We propose a positional classification scheme, in conjunction with simple and fast score-based features to predict the relevance of the top-10 results of a similarity search rank. Our results in nine scenarios, comprising three different large image datasets, show good prediction accuracy for the top-10 results, with the advantage of being amenable suitable to deploy at query time.
Neural Networks Sequential Training Using Variational Gaussian Particle Filter

In this paper, we propose a sequential training algorithm for feed-forward neural networks based on particle filtering. The proposed algorithm uses variational learning to tailor a proposal density by minimizing the variational energy. This density is then incorporated into the Gaussian particle filter framework. The proposed algorithm and an extension to it using evolutionary resampling are compared to training a neural network using a random walk-based particle filter, an extended Kalman filter, the use of variational learning only, and the backpropagation algorithm, using a synthetic dataset generated by a time-varying random process and a real dataset, where the proposed approach resulted in a moderately lower training and testing errors and a better convergence behavior, rendering the algorithm attractive for uses such as neural networks pre-training.
Multi-Flash Light Field Photography

We present a novel multi-flash light field photography (MFLF) technique that couples a light field camera with a ring of four flash lights to combine the benefits of both imaging systems.The multi-flash photography technique enables reliable detection of occlusion edges at individual subaperture images (light field views) in the light field image.We then separately treat occlusion vs. non-occlusion edges in light field stereo matching.For non-occlusion edge pixels, we conduct correspondence matching and depth-from-focus.For pixels on or near the occlusion boundary, we estimate the shape of the occlusion boundary using a novel Support Vector Machine (SVM) approach.We collect an MFLF dataset of synthetic and real scenes that exhibit strong occlusions.The extensive experiments show that our method significantly outperforms the state-of-the-art light field stereo matching methods in accuracy and robustness.
TubeExtractor: A Crawler and Converter for Generating Research DataSet from YouTube Videos

With the advent of the internet and e-resources, there has been an exponential growth of data available to the users.Amongst many content providers, YouTube succeeds in securing the second most popular website in the world.The data from YouTube is easily available to the users, due to which many researchers gather YouTube videos as their dataset for research.Searching the required video for data analysis from YouTube is a cumbersome task as YouTube is overloaded with trillions of videos.Researchers thus need to spend a huge amount of time to get required dataset.To save the time taken by researchers for accumulating dataset, an open source application "TubeExtractor" is proposed in this paper.The TubeExtractor application will allow researchers to download the videos and its metadata from YouTube based on the desired parameters provided by the researcher.The TubeExtractor will also provide as an output a plain text file of the downloaded video.This file can be used by the researchers to do additional processing of their choice if required.The keywords to download the videos are provided to the crawler in the form of a document, generated using a keyphrase extractor algorithm.If the vtt (Video Text Tracks) file of the video to be downloaded is available then a plain text file is created using a two-step parser.This TubeExtractor can save enough time of researchers.
AR/VR-TRAININGS IN THE ARSUL-PROJECT: RESULTS FROM A PROTOTYPE EVALUATION

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 4353-4362ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1085Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Representing anything from scholar papers

Many scholar knowledge mining tasks depend on the representations of different scholar entities and their compositions, including author, publication venue and paper. However, the existing methods always tried to learn different kinds of scholar representations individually. The citation relationships among different types of entities are sometimes ignored. Moreover, the separated learned representations are in separate vector space and hence cannot be operated together. Indeed, representations for different kinds of entities are constructed based on the papers and the citation relationships, which provide the opportunity for developing an unified representation method. In this paper, we propose a novel method for mapping scholar entities and their compositions to a unified distributed vector space. Each paper is firstly defined as a full composition of entities, including title, author, publication venue and so on. Then, with the citation relationships among papers, we train a model that tries to maximize the likelihood of references of papers. After training, the model can map all kinds of entities and their compositions to a unified vector space. We quantitatively evaluate our vectors on two tasks of collaborate prediction and related paper recommendation. The results show our method can learn good representations for scholar entities and their compositions. Since our method allows different types of representations to be operated together, we also show some interesting applications, such as publication venue recommendation and reviewer recommendation.
Bag of Embedding Words for Sentiment Analysis of Tweets

This paper presents an alternative of solution based in artificial intelligence to simplify the human effort that implies the analysis of the impact for businesses of their publications in social networks services.This analysis is very important because the audience manifest its opinion mostly in texts that must be processed one by one to know their content and use it in benefit of the business, this implies the use of resources to read each comment and extract characteristics that make possible to determine whether the comments are, positive reactions or negative.Our solution can obtain most effective reports than the ones generated by manual procedures, it means that demands less resources and leads to the save of time and money during the extraction of the answers to a Twitter's publication.We use BOEW and Word2vec to generate the characteristic vector for each of the answers.Finally, to make the sentiment analysis we use statistic classification models to polarize comments.
EMPAN PAPAN ‘AGREE WITH THE SETTING AND SPEECH EVENT’ AS REFLECTED IN THE POLITENESS STRATEGIES USED IN THE PARLIAMENT OF YOGYAKARTA

Purpose: The aim of this study is to investigate the reflection of one of the Javanese values called empanpapan ‘agree with the setting and speech event’ in the dialogues used by the MPs of Yogyakarta Provincial Parliament.&#x0D; Methodology: The subjects of this study are all the MPs attending the RapatKerja ‘Working Meetings’ in a certain period. To see the reflection of this Javanese value, the writer applies to play back interviews. Passive participatory, video recordings and field notes are also conducted so as to support the findings.&#x0D; Results: The results show the reflection of empanpapan “agree with the setting and speech event’ in the politeness strategies used in the meetings of Yogyakarta Provincial Parliaments.&#x0D; Implications: Thus, the analysis shows that the reflection of empanpapan ‘agree with the setting and speech event’ can be seen in the use of Ngoko‘low Javanese’, indirect strategies, and pronoun kita ‘we (incl)’ instead of saya ‘I’ and kami ‘we (excl).
Classification of Pitch Disguise Level with Artificial Neural Networks

In audio forensics, voice disguise raises many serious challenges in finding the criminal suspects. Most of the criminals usually disguise their voice just before and/or after committing a crime. Hence, to perform forensic speaker verification, original voice is to be recovered from the disguised voice. Pitch disguise is one type of voice disguise which results in very low speaker recognition rate compared to other types of disguises. Original voice can be easily recovered from the pitch disguised voice if the level of disguise is known. Hence, this paper proposes a novel technique for finding the level of pitch disguise using Artificial Neural Networks (ANN) modelling. The performance of the system is measured in terms of accuracy and the proposed technique gives an accuracy of 96.23%
Illocution and attitude

This paper aims at investigating the prosodic relations between the category of illocution and that of attitude, the latter defined as the way the illocution (verbal action) is performed (Modis on Actum). We set three experiments and relative perception tests seeking to understand: (i) how different attitudes of the same illocution (Order) are perceived in different contexts; (ii) whether the illocutions of Order and Instruction are conveyed by the same prosodic form; (iii) how pragmatic/cognitive parameters work to accommodate a different prosodic form, using the illocutions of Offer and Question of Confirmation. We conclude that the methodology for the study of the illocutionary prosodic forms must pay close attention to the prosodic aspects of attitude, since they are always present when an illocution is performed, superposing their features over those of the illocution. We also claim that the identification of a specific illocution must consider some pragmatic and cognitive parameters, and not only prosody, since different illocutions can be prosodically performed with the same form. This becomes clear if we look for data in spontaneous speech corpora, where the pragmatic conditions can be at least partially reconstructed.
A Bag-of-concepts Model Improves Relation Extraction in a Narrow Knowledge Domain with Limited Data

This paper focuses on a traditional relation extraction task in the context of limited annotated data and a narrow knowledge domain. We explore this task with a clinical corpus consisting of 200 breast cancer follow-up treatment letters in which 16 distinct types of relations are annotated. We experiment with an approach to extracting typed relations called window-bounded co-occurrence (WBC), which uses an adjustable context window around entity mentions of a relevant type, and compare its performance with a more typical intra-sentential co-occurrence baseline. We further introduce a new bag-of-concepts (BoC) approach to feature engineering based on the state-of-the-art word embeddings and word synonyms. We demonstrate the competitiveness of BoC by comparing with methods of higher complexity, and explore its effectiveness on this small dataset.
When Green Screen Meets Panoramic Videos: An Interesting Video Combination Framework

In this paper, an interesting video combination framework was established based on Unreal Engine 4. By substituting the green screen image with panorama videos, our framework could produce lifelike synthetic videos in which the foreground is the portrait captured by real-time TV cameras whereas the background is a pseudo scenario generated by previously captured panorama videos. The pseudo background updates itself from the panorama video according to the pose information of the TV camera so that the portrait looks as if immersed into the pseudo scenario rather than the green screen.
Cellular Self-Organising Maps - CSOM

This paper presents CSOM, a Cellular Self-Organising Map which performs weight update in a cellular manner. Instead of updating weights towards new input vectors, it uses a signal propagation originated from the best matching unit to every other neuron in the network. Interactions between neurons are thus local and distributed. In this paper we present performance results showing than CSOM can obtain faster and better quantisation than classical SOM when used on high-dimensional vectors. We also present an application on video compression based on vector quantisation, in which CSOM outperforms SOM.
Spotlight on the Multimedia Signal Processing Technical Committee [In the Spotlight]

Presents a listing of the SPS society Multimedia Signal Processing Technical Committee.
The real-time dynamics of child-directed speech: Using pupillometry to evaluate children’s processing of natural pitch contours

Young children prefer child-directed speech (CDS) to adult-directed speech (ADS) (Cooper and Aslin 1990), and its structural and prosodic features are known to facilitate learning (Thiessen et al., 2005; Graf Estes and Hurley, 2013). However, little is known about how the real-time dynamics of CDS prosody affect young children’s engagement and learning. In Experiment 1, we evaluated moment-to-moment processing of pitch variation using measures of pupil size synchrony across children (Kang and Wheatley, 2017). 24-to-30-month-old children listened to a story in CDS and ADS, and we found that pupil synchrony was higher for CDS than ADS. Next, using hierarchical clustering, we uncovered 4 main word-level pitch contours from a natural CDS corpus and identified contours (specifically, U- and inverted-U-shaped contours), which elicited lower vs. higher synchrony, respectively. In Experiment 2, we found that children learned novel words better when they were presented in higher- vs. lower-synchrony contours. Importantly, synchrony for a novel word during training significantly predicted learning for the same word at test. By revealing a physiological response that is sensitive to the real-time dynamics of prosody, this investigation yields a new subsecond framework for understanding children’s engagement with a signal known to support early language learning.
Graph Theory Teaches Us Something About Grammaticality

Abstract Graph theory, which quantitatively measures the precise structure and complexity of any network, uncovers an optimal force balance in sentential graphs generated by the computational procedures of human natural language (C HL ). It provides an alternative way to evaluate grammaticality by calculating ‘feature potential’ of nodes and ‘feature current’ along edges. An optimal force balance becomes visible by expressing ‘feature current’ through different point sizes of lines. Graph theory provides insights into syntax and contradicts Chomsky’s current proposal to discard tree notations. We propose an error minimization hypothesis for C HL : a good sentential network possesses an error-free self-organized force balance. C HL minimizes errors by (a) converting bottom-up flow (structure building) to top-down flow (parsing), (b) removing head projection edges, (c) preserving edges related to feature checking, (d) deleting DPmovement trajectories headed by an intermediate copy, (e) ensuring that covert wh-movement trajectories have infinitesimally small currents and conserving flow directions, and (f) robustly remedying a gap in wh-loop by using infinitesimally inexpensive wh-internally-merged (wh- IM) edge with the original flow direction. The C HL compels the sensorimotor (SM) interface to ground nodes so that Kirchhoff’s current law (a fundamental balance law) is satisfied. Internal merges are built-in grounding operations at the C HL –SM interface that generate loops and optimal force balance in sentential networks.
Graf Sardinskii: Dmitrii Khvostov i russkaia kul΄tura. By Il΄ia Vinitskii. Moscow: Novoe Literaturnoe Obozrenie, 2017. 352 pp. Bibliography. Index. ₽420, hard bound.

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Effect of Emotional Intelligence and Achievement Motivation on Self-directed Learning in University Freshmen

The purpose of this study was to investigate the effect of self - directed learning on emotional intelligence and achievement motivation, The results of this study were as follows: First, there was no significant difference in the self - directed learning according to gender of freshman college students. Second, emotional intelligence and achievement motivation showed a positive correlation. Third, emotional intelligence and achievement motivation of college students had a positive (+) positive effect. As the emotional intelligence and achievement motivation of college students increased, self - directed learning was found to be better. These results are expected to be used as basic data for career guidance and counseling of university students.
HANDWRITTEN AMHARIC CHARACTER RECOGNITION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS

Amharic language is an official language of the federal government of the Federal Democratic Republic of Ethiopia. Accordingly, there is a bulk of handwritten Amharic documents available in libraries, information centres, museums, and offices. Digitization of these documents enables to harness already available language technologies to local information needs and developments. Converting these documents will have a lot of advantages including (i) to preserve and transfer history of the country (ii) to save storage space (ii) proper handling of documents (iv) enhance retrieval of information through internet and other applications. Handwritten Amharic character recognition system becomes a challenging task due to inconsistency of a writer, variability in writing styles of different writers, relatively large number of characters of the script, high interclass similarity, structural complexity and degradation of documents due to different reasons. In order to recognize handwritten Amharic character a novel method based on deep neural networks is used which has recently shown exceptional performance in various pattern recognition and machine learning applications, but has not been endeavoured for Ethiopic script. The Convolutional neural network model is evaluated for its performance using our database that contains 132,500 datasets of handwritten Amharic characters. Common handwritten recognition systems using machine learning use a combination of both feature extractors and classifiers. Currently the use of deep learning techniques shows promising improvements for machine learning based classification tasks. Our proposed CNN model gives an accuracy of 91.83% on training data and 90.47% on validation data.
Exceeding 95% system efficiency within the telecom C-band in superconducting nanowire single photon detectors

We present a single-photon detector with system detection efficiencies exceeding 95% over the wavelength range 1520–1550 nm, with polarization sensitivities in the range of 1.02-1.08. The wavelength range is tunable over 200 nm via variation of the ton-two dielectric laver thicknesses. © 2019 The Author(s)