Reviewerly/exHarmony huggingface dataset:
Lightweight Deep Neural Network for Real-Time Visual Tracking with Mutual Learning

In this work, we develop a real-time tracking algorithm with a lightweight deep neural network. The contributions of this work mainly include two aspects. First, we reformulate the discriminative correlation filter (DCF) based tracker as a fully convolutional neural network and design an effective end-to-end tracking framework. Second, we build our tracker with a pruned convolutional neural network, which is trained by a mutual learning approach to further improve the location accuracy. The proposed tracking algorithm can track objects at 60 FPS. Extensive experiments on OTB2013, OTB2015 and VOT2017 Real-time demonstrate that the proposed tracker performs favorably against state-of-the-art methods.
ARS_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System

In this paper, we present three approaches for Natural Language Inference, Question Entailment Recognition and Question-Answering to improve domain-specific Information Retrieval. For addressing the NLI task, the UMLS Metathesaurus was used to find the synonyms of medical terms in given sentences, on which the InferSent model was trained to predict if the given sentence is an entailment, contradictory or neutral. We also introduce a new Extreme gradient boosting model built on PubMed embeddings to perform RQE. Further, a closed-domain Question Answering technique that uses Bi-directional LSTMs trained on the SquAD dataset to determine relevant ranks of answers for a given question is also discussed.
A Distributed Truthful Auction Mechanism for Task Allocation in Mobile Cloud Computing

In this undertaking, sell off through cell phone application is introduced. It has an expansive potential market of merchants and purchasers. This task, An Online Auction System has two classes as client interface and administrator interface. The venture includes the structure and usage of an online sale framework. This framework introduces an online presentation of classification wised items they need to sell or offer. There is an administrator board by which an administrator can control the entire offering framework. Administrator can support items by the classifications and furthermore can control the enlisted clients. The investigation arrange is performed first for the contextual analysis. Furthermore, the aim to build up what goes on operationally inside the online sales management firm. It likewise characterizes the design in android application.In which many number of bidders offers the bartering and last the closeout won will store the consequence of the triumphant bidder subtleties and send through mail actually lastly the shipment procedure will completed.
Toward Better PV Panel’s Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs

Much work has been carried out for modeling the output power of photovoltaic panels. Using artificial neural networks (ANNS), one could efficiently model the output power of heterogeneous photovoltaic (HPV) panels. However, due to the existing different types of artificial neural network implementations, it has become hard to choose the best approach to use for a specific application. This raises the need for studies that develop models using the different neural networks types and compare the efficiency of these different types for that specific application. In this work, two neural network types, namely, the nonlinear autoregressive network with exogenous inputs (NARX) and the deep feed-forward (DFF) neural network, have been developed and compared for modeling the maximum output power of HPV panels. Both neural networks have four exogenous inputs and two outputs. Matlab/Simulink is used in evaluating the proposed two models under a variety of atmospheric conditions. A comprehensive evaluation, including a Diebold-Mariano (DM) test, is applied to verify the ability of the proposed networks. Moreover, the work further investigates the two developed neural networks using their actual implementation on a low-cost microcontroller. Both neural networks have performed very well; however, the NARX model performance is much better compared with DFF. Using the NARX network, a prediction of PV output power could be obtained, with half the execution time required to obtain the same prediction with the DFF neural network, and with accuracy of ±0.18 W.
Study on Radio-Frequency Signal Gene Characteristics From the Perspective of Fractal Theory

Radio frequency signal identification technology is a non-password authentication method based on the physical layer hardware of the device. This paper explores the feature extraction and recognition method based on the gene characteristics of the radio frequency signal. Since the radio frequency signal has materiality, informationality, transitivity, vulnerability and dividability, these characters are similar to biological gene. The concept of gene characteristics of the radio frequency signal is proposed in the paper, which is inspired by the biological gene. The study on radio frequency signal gene characteristics from the perspective of fractal theory based on the self-similarity of the radio frequency signal is carried out, and the radio frequency signal gene feature extraction is explored by the multifractal dimension characteristics. The experimental results illustrate that the recognition success rate of 500 sets of radio frequency signals from ten identical radio stations can reach 98.2% with the gray relation algorithm as a classifier. It shows that the multifractal dimension characteristics can be used to express the gene features of the radio frequency signals, although the time domain signals from these ten identical radio stations are basically similar. Extending to the cognitive research of radio frequency signals, it can be applied to enhance physical layer security for industrial Internet of Things (IoT) through collaborative identity authentication of industrial IoT wireless devices based on the radio frequency signal gene characteristics.
Assessment of urban-scale potential for solar PV generation and consumption

Abstract The rise of grid electricity price and a growing awareness of climate change is resulting in an increasing number of photovoltaic facilities installed in buildings. Electricity market regulation and climatic conditions, in particular solar radiation, are the main factors that determine the economic viability of a photovoltaic facility. This paper describes a method for evaluating the potential for photovoltaic (PV) production and self-consumption for the building stock of a particular city. A GIS 3D city map is used to calculate solar irradiation. Building-level electricity use is calculated based on building type, geometry and other characteristic inferred from building age, taking the cadastre GIS as main input. The methodology identifies the realistic potential for rooftop photovoltaic installations, as well as the optimum size to be installed from an economic perspective. To represent different regulations that can affect economic viability of PV installations, calculations should adapt for the specific installation conditions and regulatory situation, as for example self-consumption and net metering. The proposed methodology is applied to a case study in Irun (Spain), where results for potential of PV generation and self-consumption for the building stock are presented. The results offer public administration a realistic view of economically viable PV potential for the city and allow to analyse different mechanisms to promote their installations. It also serves for individual electricity consumers to evaluate and optimize new photovoltaic energy facilities. Finally, it serves policy makers to estimate the repercussion of electricity market regulations on the economic viability of PV systems.
Analytical inversion of the focusing of high-numerical-aperture aplanatic systems

We propose a method for an analytical inversion of the electric and magnetic fields at the focus of a high-NA aplanatic system to obtain incident light beam distribution. Our approach is based on an inverse Fourier transform of the Richards–Wolf formalism for targeted longitudinal fields along the radial or axial directions at the non-paraxial focus. Analytical solutions are discussed for both axial and radial focal fields for a radially polarized incident light beam, and a criterion is defined to access a physically valid solution. We also validate the method according to results found in the literature. Finally, we show how the method can be generalized to other incident field distributions.
Real-time image dehazing using genetic programming

A real-time system for restoration of images degraded by haze is presented. First, a transmission function estimator is automatically constructed using genetic programming. Next, the resultant estimator is employed to compute the transmission function of the scene by processing an input hazy image. Finally, the estimated transmission function and the hazy image are used in a restoration model based on atmospheric optics to obtain a haze-free image. The proposed method is implemented in a laboratory prototype for high-rate image processing. The performance of the proposed approach is evaluated in terms of objective metrics using synthetic and real-world images.
Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili

Deep learning has extensively been used in natural language processing with sub-word representation vectors playing a critical role. However, this cannot be said of Swahili, which is a low resource and widely spoken language in East and Central Africa. This study proposed novel word embeddings from syllable embeddings (WEFSE) for Swahili to address the concern of word representation for agglutinative and syllabic-based languages. Inspired by the learning methodology of Swahili in beginner classes, we encoded respective syllables instead of characters, character n-grams or morphemes of words and generated quality word embeddings using a convolutional neural network. The quality of WEFSE was demonstrated by the state-of-art results in the syllable-aware language model on both the small dataset (31.229 perplexity value) and the medium dataset (45.859 perplexity value), outperforming character-aware language models. We further evaluated the word embeddings using word analogy task. To the best of our knowledge, syllabic alphabets have not been used to compose the word representation vectors. Therefore, the main contributions of the study are a syllabic alphabet, WEFSE, a syllabic-aware language model and a word analogy dataset for Swahili.
A Non-Monte-Carlo Parameter-Free Learning Automata Scheme Based on Two Categories of Statistics

Learning automata (LA), which intellectually explores its optimal state by interacting with an external environment continuously, is encountered widely in artificial intelligence. In the evaluation of LA, it has always been a key issue how to tradeoff between “accuracy” and “speed,” which substantially touches on parameter tuning. A latest issue in the design of LA methodology involves bearing a parameter-free property, thus removing the tremendous expenses brought by parameter tuning. Nevertheless, the currently existing parameter-free LA schemes generally maintain a Monte-Carlo technique, which helps avoid the tuning process at the cost of more computations. This paper examines a new measurement of parameter-free LA schemes based on statistics which overcome the difficulties found in other counterparts. Specifically, it has innovatively disengaged from the dependance on Monte-Carlo methods. Of greater significance, the learning mechanisms operating in the common stationary environments are likewise extended to the nonstationary environments. Simulations confirm the effectiveness and efficiency of the proposed algorithm, especially its low computation consumption as well as the strong tracking capability to abrupt environmental changes.
Face tracking with camshift algorithm for detecting student movement in a class

Face detection (face tracking) has been widely applied for various purposes, including in the fields of entertainment, education and security. Face detection can certainly be done with the camera in real time. For example on a camera on a laptop or camera in a room in real time will detect facial movements. Face detection is implemented using the camshift algorithm. The camshift algorithm works on a search window that can find facial movements in each frame. The camshift algorithm that has been applied can calculate the size and location of the search window that will be used for the next frame. The camshift algorithm can be used for detection such as face detection. The distribution used is hue in the HSV color dimension (Hue, Saturation, Value). The use of this hue distribution is done to overcome differences in human skin color and the background used when taking frames.
TCL - a Lexicon of Turkish Discourse Connectives

It is known that discourse connectives are the most salient indicators of discourse relations. State-of-the-art parsers being developed to predict explicit discourse connectives exploit annotated discourse corpora but a lexicon of discourse connectives is also needed to enable further research in discourse structure and support the development of language technologies that use these structures for text understanding. This paper presents a lexicon of Turkish discourse connectives built by automatic means. The lexicon has the format of the German connective lexicon, DiMLex, where for each discourse connective, information about the connective‘s orthographic variants, syntactic category and senses are provided along with sample relations. In this paper, we describe the data sources we used and the development steps of the lexicon.
Hospital Queue Control System using Quick Response Code (QR Code) as Verification of Patient’s Arrival

Hospital is an organization that primarily provides services in the form of examination, treatment, medical treatment and other diagnostic measures required by each patient in the limits of the technology and the means provided by the hospital.So that patients get the maximum service, hospitals have to provide the best services, one of the services that are being highlighted is the queue system.This study proposed a queue control system at the hospital using a quick response code (QR Code) as verification of the patient's arrival that serves to speed up the administrative process and provide effective services, to facilitate the patient in terms of the queuing line (registration) at the hospital.The queue control system uses the website as a registration and database repository for patient data.This website has two kinds of display form, as a patient or a doctor.This kind of display is useful to speed up the administrative process, so that registration can be done at any time by the patient, with patient entering/filling data themselves and their complaints or medical records that are automatically available on the website, so that patients do not need to come to the hospital with a medical record.Furthermore, the website will filter the data entered by the patient as the patient's complaints, so that patients can choose the hospital that provides services for patients' illness complaints which are also showing hospital addresses to facilitate patients in finding the location of the hospital.After the administration process on the website, the system will provide a QR Code as a form of verification for patients who have registered, with their data already exists in the database.When the patient arrives at the hospital, the patient only needs to scan the QR code that already exists, without taking care of the administration back because at the time the system gives a QR Code, indicating that patients already had a hospital destination along with the queue number.It is expected that the queue control system implemented at the hospital using a quick response code (QR Code) as verification of the patient's arrival, could speed up the administrative process to be more effective and efficient.
Attention-Based Word Vector Prediction with LSTMs and its Application to the OOV Problem in ASR

We propose three architectures for a word vector prediction system (WVPS) built with LSTMs that consider both past and future contexts of a word for predicting a vector in an embedded space where its surrounding area is semantically related to the considered word.We introduce an attention mechanism in one of the architectures so the system is able to assess the specific contribution of each context word to the prediction.All the architectures are trained under the same conditions and the same training material, following a curricular-learning fashion in the presentation of the data.For the inputs, we employ pretrained word embeddings.We evaluate the systems after the same number of training steps, over two different corpora composed of ground-truth speech transcriptions in Spanish language from TCSTAR and TV recordings used in the Search on Speech Challenge of IberSPEECH 2018.The results show that we are able to reach significant differences between the architectures, consistently across both corpora.The attention-based architecture achieves the best results, suggesting its adequacy for the task.Also, we illustrate the usefulness of the systems for resolving out-of-vocabulary (OOV) regions marked by an ASR system capable of detecting OOV occurrences.
Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1

V.G.Vinod Vydiswaran, Grace Ganzel, Bryan Romas, Deahan Yu, Amy Austin, Neha Bhomia, Socheatha Chan, Stephanie Hall, Van Le, Aaron Miller, Olawunmi Oduyebo, Aulia Song, Radhika Sondhi, Danny Teng, Hao Tseng, Kim Vuong, Stephanie Zimmerman. Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task. 2019.
Real-Time Object Tracking with Template Tracking and Foreground Detection Network

In this paper, we propose a fast and accurate deep network-based object tracking method, which combines feature representation, template tracking and foreground detection into a single framework for robust tracking. The proposed framework consists of a backbone network, which feeds into two parallel networks, TmpNet for template tracking and FgNet for foreground detection. The backbone network is a pre-trained modified VGG network, in which a few parameters need to be fine-tuned for adapting to the tracked object. FgNet is a fully convolutional network to distinguish the foreground from background in a pixel-to-pixel manner. The parameter in TmpNet is the learned channel-wise target template, which initializes in the first frame and performs fast template tracking in the test frames. To enable each component to work closely with each other, we use a multi-task loss to end-to-end train the proposed framework. In online tracking, we combine the score maps from TmpNet and FgNet to find the optimal tracking results. Experimental results on object tracking benchmarks demonstrate that our approach achieves favorable tracking accuracy against the state-of-the-art trackers while running at a real-time speed of 38 fps.
A Survey on Various Machine Learning and Deep Learning Algorithms used for Classification of Spam and Non-Spam Emails

An email client receives emails from different websites, portals and domains, which can be an advertisement. Receiving a bulk amount of emails can cause serious damages like suspension of a particular email id.Mostly an email client gets exposed to the number of malicious receipts by registering an email account to a web portal, which in turn sends a bulk amount of emails. The email client wants to be decisive about differentiating the useful emails and spam emails. One of the solutions to escape from spam emails is to develop a decision based system which can classify the spam and non-spam emails. This survey gives an overview about different machine learning and deep learning algorithms to classify the spam and non-spam emails by accessing the received emails of an email client. The machine learning approaches and mechanisms like support vector machine, naive Bayesian classifier, artificial neural networks and logistic regression can be of important help to determine spam emails. These approaches use decision trees to run tests on a given sets of data (emails). After classifying a spam email source a user can navigate, block and report the source of the spam email generator. Most of the times the spam emails are generated by the autonomous sources which are called spam-bots.
A Novel Method for Temporal Action Localization and Recognition in Untrimmed Video Based on Time Series Segmentation

Positioning of each action in a long complicated video is a challenging task in computer vision. To address this issue we propose a method with temporal boundary regression based on time series segmentation, which can generate proposals with flexible temporal duration. Firstly, we use a clustering algorithm to generate proposals, which is more efficient than sliding window method. It generates proposals by aggregating areas of high-probability behavior in time domain, and uses non-maximum suppression to remove redundancy. Then a multi-layer perceptron is used to refine boundary regression of behavior proposals, the process makes boundary coordinates closer to the real boundaries. Secondly, each behavioral proposal is represented by concatenating a three-subsegment feature description, which includes the proposal segment, its starting subsegment and its ending subsegment. Finally, the behavior proposal including a target action is identified by multi-layer perceptron. Our method is evaluated in two large data sets THUMOS14 and ActivityNet, which are commonly used in time series behavior detection task. The recognition rates can reach 30.1% and 33.19% respectively, which proves that the method can effectively improve the classification accuracy.
Blockchain Based Network for Tuberculosis: A Data Sharing Initiative in Brazil.

Data sharing, information exchange, knowledge acquisition and health intelligence are the basis of an efficient and effective evidence-based decision-making tool. A decentralized blockchain architecture is a flexible solution that can be adapted to institutional and managerial culture of organizations and services. Blockchain can play a fundamental role in enabling data sharing within a network and, to achieve that, this work defines the high-level resources necessary to apply this technology to Tuberculosis related issues. Thus, relying in open-source tools and in a collaborative development approach, we present a proposal of a blockchain based network, the TB Network, to underpin an initiative of sharing of Tuberculosis scientific, operational and epidemiologic data between several stakeholders across Brazilian cities.