Reviewerly/exHarmony huggingface dataset:
Real-Time Multi-cue Object Tracking: Benchmark

Object tracking is imperative research domain of computer vision. In the last decade, a lot of progress has been made in terms of tracking approach and the datasets. However, to estimate the performance of trackers in real time it is essential to have a dataset that can have videos of adequate length with various attributed challenges. In order to bridge the gap, we have proposed a fully annotated datasets with video sequences of adequate length for object tracking. Also, the existing tracking of the state-of-the-art is reviewed on the proposed dataset and the performance is evaluated in real-time.
Variation of Solar Photovoltaic Power Estimation due to Solar Irradiance Decomposition Models

Long-term solar irradiance data are required for reliable performance evaluation and feasibility analysis of solar photovoltaic systems. However, measurement data of the global horizontal irradiance (GHI) are only available for major cities in Korea. Neither the direct normal irradiance (DNI) nor the diffuse horizontal irradiance (DHI) are available, which are also needed to calculate the irradiance on the tilted surface of PV array. It is a simple approach to take advantage of the decomposition model that extracts DNI and DHI from GHI. In this study, we investigate variations of solar PV power estimation due to the choice of decomposition model. The GHI data from Korea Meteorological Administration (KMA) were used and different sets of typical meteorological year (TMY) data using some well-known decomposition models were generated. Then, power outputs with the different TMY data were calculated, and a variation of 3.7% was estimated due to the choice of decomposition model.
Hardware and Software Co-Design for Object Detection with Modified ViBe Algorithm and Particle Filtering Based Object Tracking

This paper describes a hardware and software codesign approach for object detection and tracking algorithm which is based on modified ViBe background subtraction method and scale adaptable particle filtering algorithm. The modified ViBe performs with a close accuracy to the original work of ViBe but with less complexity and it outperforms the Mixture of Gaussian(MoG) method. Since the modified algorithm has a low off chip memory access requirement, the real time object detection in embedded level with the help of hardware acceleration techniques could be achieved. The implemented particle filtering algorithm is capable of adapting to the scale variation of the tracking objects. After careful investigation of the time consumption profile of each function in our algorithm, the complete algorithm was implemented in IoT edge devices targeted Avnet Ultra 96 development board which is based on Xilinx Zynq MPSoC. We could achieve an average frame rate of 40 fps for a $854\times 480$ resolution data set.
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution

Super-resolution (SR) is by definition ill-posed. There are infinitely many plausible high-resolution variants for a given low-resolution natural image. Most of the current literature aims at a single deterministic solution of either high reconstruction fidelity or photo-realistic perceptual quality. In this work, we propose an explorative facial super-resolution framework, DeepSEE, for Deep disentangled Semantic Explorative Extreme super-resolution. To the best of our knowledge, DeepSEE is the first method to leverage semantic maps for explorative super-resolution. In particular, it provides control of the semantic regions, their disentangled appearance and it allows a broad range of image manipulations. We validate DeepSEE on faces, for up to 32x magnification and exploration of the space of super-resolution. Our code and models are available at: this https URL
Validating the ontolex-lemon lexicography module with K dictionaries'' multilingual data

The OntoLex-lemon model has gradually acquired the status of de-facto standard for the representation of lexical information according to the principles of Linked Data (LD). Exposing the content of lexicographic resources as LD brings both benefits for their easier sharing, discovery, reusability and enrichment at a Web scale, as well as for their internal linking and better reuse of their components. However, with lemon being originally devised for the lexicalization of ontologies, a 1:1 mapping between its elements and those of a lexicographic resource is not always attainable. In this paper we report our experience of validating the new lexicog module of OntoLex-lemon, which aims at paving the way to bridge those gaps. To that end, we have applied the module to represent lexicographic data coming from the Global multilingual series of K Dictionaries (KD) as a real use case scenario of this module. Attention is drawn to the structures and annotations that lead to modelling challenges, the ways the lexicog module tackles them, and where this modelling phase stands as regards the conversion process and design decisions for KD’s Global series.
Atmospheric effect study of continuous-variable quantum key distribution

We proposed a data acquisition method to monitor local oscillator (LO) intensity in real time in unidimensional continuous-variable quantum key distribution protocol. Based on this proposed method, the influence of weather conditions on the average transmittance and excess noise is studied, such as relative wetness, visibility, and beam jitter.
Investigating Network Coherence to Assess Students’ Conceptual Understanding of Energy

Conceptual knowledge is a crucial tool for students to understand scientific phenomena. Knowledge about the structure and function of mental concepts potentially helps science educators to foster the acquisition of this tool. Specifically, the coherence of students’ mental concepts is an intensely discussed issue within the related conceptual change discourse. While former discussions focused on the question of whether these conceptions are coherent or not, recent approaches describe them as dynamic systems behaving more or less coherently in different situations. In this contribution, we captured this dynamic behavior of individual concepts by means of network analysis. Transcribed video data of 16 pairs of students working on four subsequent experiments on energy were transformed into weighted networks, which in turn were characterized by standardized coherence parameters. These coherence parameters and more basic network parameters were correlated with students’ pre-post scores of a multiple-choice test on the energy concept. We found that the coherence parameter is significantly related to the students’ test scores. Even more intense relations are indicated if networks are calculated solely based on conceptual key terms. Implications as well as methodological constraints of this approach are discussed.
TinyMBERT: Multi-Stage Distillation Framework for Massive Multi-lingual NER.

Deep and large pre-trained language models are the state-of-the-art for various natural language processing tasks. However, the huge size of these models could be a deterrent to use them in practice. Some recent and concurrent works use knowledge distillation to compress these huge models into shallow ones. In this work we study knowledge distillation with a focus on multi-lingual Named Entity Recognition (NER). In particular, we study several distillation strategies and propose a stage-wise optimization scheme leveraging teacher internal representations that is agnostic of teacher architecture and show that it outperforms strategies employed in prior works. Additionally, we investigate the role of several factors like the amount of unlabeled data, annotation resources, model architecture and inference latency to name a few. We show that our approach leads to massive compression of MBERT-like teacher models by upto 35x in terms of parameters and 51x in terms of latency for batch inference while retaining 95% of its F1-score for NER over 41 languages.
TypeNet: Scaling up Keystroke Biometrics

We study the suitability of keystroke dynamics to authenticate 100K users typing free-text. For this, we first analyze to what extent our method based on a Siamese Recurrent Neural Network (RNN) is able to authenticate users when the amount of data per user is scarce, a common scenario in free-text keystroke authentication. With 1K users for testing the network, a population size comparable to previous works, TypeNet obtains an equal error rate of 4.8% using only 5 enrollment sequences and 1 test sequence per user with 50 keystrokes per sequence. Using the same amount of data per user, as the number of test users is scaled up to 100K, the performance in comparison to 1K decays relatively by less than 5%, demonstrating the potential of TypeNet to scale well at large scale number of users. Our experiments are conducted with the Aalto University keystroke database. To the best of our knowledge, this is the largest free-text keystroke database captured with more than 136M keystrokes from 168K users.
Application research of mixed teaching mode based on Wechat applet

Abstract in the era of rapid development of mobile Internet, Wechat applet because it does not need to install and uninstall characteristics, become a very popular application. Wechat applet applied to teaching is a new teaching auxiliary means. Firstly, the feasibility of WeChat is analyzed from the perspective of function and students’ willingness to use it. Then it discusses the application strategy of Wechat applet in the course of database application technology teaching. Finally, through the analysis of the effect of Wechat applet auxiliary course teaching, it is shown that Wechat applet is an effective auxiliary means of classroom teaching, which can be promoted and applied in other courses.
Comparison on Hyper ledger Fabric and Hyper ledger Composer of Block Chain Technology

The innovation that has had the best effect on our ways of life in the course of the most recent decade is Blockchain. The key feature of blockchain is the secure storage of data in encrypted block chains that have a distributed database that processes a growing list of transactions. This makes the blockchain incredibly impervious to unapproved changes to put away information. In this paper, the study to get a brief idea about the platforms of blockchain and explore what exactly is Hyperledger Fabric and also understand the steps of working with Hyperledger fabric and Hyperledger Composer. While hyperledger composer is a set of tools, APIs for modeling, building and distributing a blockchain corporate network. The Hyperledger fabric is an implementation of the blockchain framework for developing solutions with a modular architecture. The composer runs on hyperledger fabric. It is a framework to accelerate the development of applications based on Hyperledger Fabric.To create a Simple Commodity Blockchain with Hyperledger Composer.
InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics.

This work explores the biases in learning processes based on deep neural network architectures. We analyze how bias affects deep learning processes through a toy example using the MNIST database and a case study in gender detection from face images. We employ two gender detection models based on popular deep neural networks. We present a comprehensive analysis of bias effects when using an unbalanced training dataset on the features learned by the models. We show how bias impacts in the activations of gender detection models based on face images. We finally propose InsideBias, a novel method to detect biased models. InsideBias is based on how the models represent the information instead of how they perform, which is the normal practice in other existing methods for bias detection. Our strategy with InsideBias allows to detect biased models with very few samples (only 15 images in our case study). Our experiments include 72K face images from 24K identities and 3 ethnic groups.
Sonic to knuckles: Evaluations on transfer reinforcement learning

Reinforcement Learning holds the potential to enable many systems with rapid, intelligent automated decision- making. However, reinforcement learning on embodied systems is a much greater challenge than the simulated environments and tasks which have been solved to date. A learner in an embodied system cannot run millions of trials or easily tolerate fatal trajectories. Therefore, the ability to train agents in simulated environments and effectively transfer their knowledge to real-world environments will be crucial, and likely an integral part of constructing future robotic systems. We perform experiments in an original transfer reinforcement learning task we constructed using the game “Sonic 3 and Knuckles," evaluating two transfer learning techniques from the literature.
Model‐based clustering and classification for data science: With applications in RCharlesBouveyronGillesCeleuxT. BrendanMurphyAdrian E.Raftery (2019). New York, NY: Cambridge University Press. 446 pages. CDN$91.95 (hardback). ISBN: 9781108494205.

Biometrical JournalVolume 62, Issue 4 p. 1120-1121 BOOK REVIEW Model-based clustering and classification for data science: With applications in R Charles Bouveyron , Gilles Celeux , T. Brendan Murphy , Adrian E. Raftery (2019). New York, NY: Cambridge University Press. 446 pages. CDN$91.95 (hardback). ISBN: 9781108494205. Li-Pang Chen, Li-Pang Chen lchen723@uwo.ca orcid.org/0000-0001-5440-5036 Department of Statistical and Actuarial Sciences, University of Western Ontario, Ontario, CanadaSearch for more papers by this author Li-Pang Chen, Li-Pang Chen lchen723@uwo.ca orcid.org/0000-0001-5440-5036 Department of Statistical and Actuarial Sciences, University of Western Ontario, Ontario, CanadaSearch for more papers by this author First published: 27 April 2020 https://doi.org/10.1002/bimj.201900390Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume62, Issue4July 2020Pages 1120-1121 RelatedInformation
Temporal Relationship Extraction for Natural Language Texts by Using Deep Bidirectional Language Model

In general, documents contain temporal information and recognizing that information is crucial in understanding the overall content of documents written in natural language. To find the temporal information, there are three tasks that capturing the time representation itself, finding out the event associated with the time representation, and extracting the temporal relationship between times or events. As inherent linguistic characteristics of the multiple languages, it is hard to capture every time information from a given sentence without considering the context of temporal relationships. In this paper, we design an artificial neural network model that extracts temporal relations, one of the tasks that extract temporal information from natural language sentences. Our proposed model is based on a deep bidirectional architecture to design temporal relationships learning from given sentences. The model separates an input single sentence into individual word tokens and converts them into embedding vectors, and then learns whether each token is a subject or an object of temporal relationship information in the given sentence. Before using models and datasets that target multiple languages, we first conduct our research on English and Korean.
Bubbles on Bitcoin Price: The Bitcoin Rush

The following sections are included:IntroductionBitcoin: Is Bitcoin a New Currency?Testing for BubblesThe Bitcoin RushConclusionsReferences
A Deinterleaving Method for Mixed Pulse Signals in Complex Electromagnetic Environment

In complex electromagnetic environment, pulse signals from different emitters are highly overlapped in time, spatial and frequency domains. Traditional methods perform poor in deinterleaving mixed signals having similar pulse parameters, arrival directions and frequency sets. In this paper, a deinterleaving method employing the clustering and the machine leaning is proposed to solve this problem. The proposed method first clusters pulse signals based on multiple parameters, and trains a supervised learning model using features representing pulse sequences' variation trend. The model is used to predict whether different clustered signal groups belong to the same emitter. As a result, mixed pulse signals are deinterleaved into different emitter clusters. A verification test is presented to measure the performance of the proposed method.
Using word embeddings for ontology-driven aspect-based sentiment analysis

Nowadays, the Web is the main platform to gather information. The growing amount of freely available unstructured data has increased the interest in sentiment analysis, where the goal is to extract opinions from text. In this paper we focus on review-level aspect-based sentiment analysis, where we predict the sentiment of a certain aspect in a review. We propose a two-stage sentiment analysis algorithm. In the first stage a domain ontology is utilized to predict the sentiment. If the domain ontology stage is inconclusive, a back-up stage based on an SVM bag-of-words model is employed. Furthermore, the use of word embeddings to improve the domain ontology coverage in the first stage by finding semantically similar words is investigated. We find that the two-stage approach significantly outperforms two baseline methods and achieves competitive results for the SemEval-2016 data. Furthermore, by not employing the back-up stage, we still perform significantly better than the baselines. Lastly, we find that employing word embeddings improves the accuracy when the domain ontology size is relatively small.
Text-Mining Maintenance Records to Automate the Identification and Grouping of Failure Modes

Abstract The investigation of failure data typically involves manual interpretation of free text maintenance system records. Even if failure classification codes exist within an organization's system, they often do not include enough detail or accuracy to group or identify trends. The objective of this study is to develop an automated method to identify functional failures from maintenance record data. The result is a reduction in workload for manual analysis, as well as improved identification of failures and trends. This study's methodology involves the sequential arrangement of multiple text-mining techniques. The techniques include: term frequency-inverse document frequency (TF-IDF), clustering, association rules, term matrix creation, and lexicon development for pre-processing text. In isolation these techniques have shown to be effective in non-industrial pursuits, such as marketing and retail sales. This study serves to apply them in the domain of equipment reliability. They are iteratively implemented and refined on maintenance system records, including work orders (which may or may not represent failures), as well as failure report records. The ability to identify failure modes, failed components, and trends is then evaluated. The techniques were successfully implemented, and the effectiveness was evaluated for each when applied to science of equipment reliability. Text mining was shown to be partially effective in the pursuit of identifying failure modes from maintenance record free text. Certain sub-techniques were shown to be quite effective, in particular the clustering technique's ability to group failed components and failure modes. Hierarchical clustering is a promising technique for technical and industrial themed free text. It was also shown that the outputs of clustering can achieve different and valuable insights based on the types of text records implemented, and the types of pre-processing available to the organization. The association rules method was somewhat effective relative to clustering, as it was able to identify certain failure modes; however, this method still requires a degree of manual intervention and interpretation at this time. The overall results are promising. There is great opportunity for continued study along multiple fronts, including additional techniques such a sentiment analysis and topic modelling.
Enriched Pre-trained Transformers for Joint Slot Filling and Intent Detection

Detecting the user's intent and finding the corresponding slots among the utterance's words are important tasks in natural language understanding. Their interconnected nature makes their joint modeling a standard part of training such models. Moreover, data scarceness and specialized vocabularies pose additional challenges. Recently, the advances in pre-trained language models, namely contextualized models such as ELMo and BERT have revolutionized the field by tapping the potential of training very large models with just a few steps of fine-tuning on a task-specific dataset. Here, we leverage such model, namely BERT, and we design a novel architecture on top it. Moreover, we propose an intent pooling attention mechanism, and we reinforce the slot filling task by fusing intent distributions, word features, and token representations. The experimental results on standard datasets show that our model outperforms both the current non-BERT state of the art as well as some stronger BERT-based baselines.
Triangulation: Why Optimize?

For decades, it has been widely accepted that the gold standard for two-view triangulation is to minimize the cost based on reprojection errors. In this work, we challenge this idea. We propose a novel alternative to the classic midpoint method that leads to significantly lower 2D errors and parallax errors. It provides a numerically stable closed-form solution based solely on a pair of backprojected rays. Since our solution is rotationally invariant, it can also be applied for fisheye and omnidirectional cameras. We show that for small parallax angles, our method outperforms the state-of-the-art in terms of combined 2D, 3D and parallax accuracy, while achieving comparable speed.