Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Vision , Tracking , Motion Analysis IMVIP 2019 : Irish Machine Vision and ImageProcessing 2019 Human Action Recognition in Videos Using Transfer Learning

A variety of systems focus on detecting the actions and activities performed by humans, such as video surveillance and health monitoring systems. However, published labelled human action datasets for training supervised machine learning models are limited in number and expensive to produce. The use of transfer learning for the task of action recognition can help to address this issue by transferring or re-using the knowledge of existing trained models, in combination with minimal training data from the new target domain. Our focus in this paper is an investigation of video feature representations and machine learning algorithms for transfer learning for the task of action recognition in videos in a multi-class environment. Using four labelled datasets from the human action domain, we apply two SVM-based transfer-learning algorithms: adaptive support vector machine (A-SVM) and projective model transfer SVM (PMT-SVM). For feature representations, we compare the performance of two widely used video feature representations: space-time interest points (STIP) with Histograms of Oriented Gradients (HOG) and Histograms of Optical Flow (HOF), and improved dense trajectory (iDT) to explore which feature is more suitable for action recognition from videos using transfer learning. Our results show that A-SVM and PMT-SVM can help transfer action knowledge across multiple datasets with limited labelled training data; A-SVM outperforms PMT-SVM when the target dataset is derived from realistic non-lab environments; iDT has a greater ability to perform transfer learning in action recognition.
Estudio sobre automedicación en la Universidad de Antioquia, Medellín, Colombia

An exploratory, cross-section, descriptive research was carried out on self-medication in the University of Antioquia in Medellin, Colombia, in order to better understand this public health problem. Preliminary information was obtained as basis for future work on this subject and as a way to promote an appropriate use of drugs. It was found that 97% of persons in this university self-medicate despite the fact that 88% of them know the risks of such conduct. The role of Pharmaceutical Chemists in educating the community to avoid selfmedication is emphasized.
Healthcare Technology for Social Change: Development of an Innovative Rehabilitation Device

This research aimed to develop an affordable device for the elbow and forearm rehabilitation to be used in Brazilian hospitals, clinics, physician’s offices, and residences. The Brazilian government made investments in technology development for social change, demanding innovative rehabilitation products manufactured with lower cost, compared to the current imported alternatives. Using research through design, we present the development of a module that allows continuous passive motion (CPM), which is a physical therapy resource indicated mainly for post-traumatic and post-surgical rehabilitation. This paper describes all design phases that resulted in this technological device with an important social impact on health care, making CPM treatment accessible to a broader range of patients. The module’s design fully contemplated Stakeholders’ needs, resulting in a working prototype. It was validated with users, producing the intended effects. The product is fully manufactured in Brazil and costs a fraction of the equipment currently used. The design presented in this paper opens a number of future research possibilities in the field of social innovation through health technology.
Nursing Care Actions to Prevent and Control the Zika Virus : A Review

Review Cheila Maria Lins Bentes1, Marcos do Nascimento Bentes2, Kátia Cilene Godinho Bertoncello3, Selma Regina de Andrade3, Vera Lúcia de Azevedo Lima4 and Maria de Lourdes de Souza5 1University of State of Amazonas (DINTER UFSC/UEA), Brazil 2HEMOAM Foundation, Manaus, Amazonas, Brazil 3Department of Nursing, Federal University of Santa Catarina (UFSC), Brazil 4College of Nursing, Federal University of Parả (UFPa), Belẻm, Brazil 5Federal University of Santa Catarina, REPENSUL Institute, Florian polis, Santa Catarina, Brazil
Capital , risk and profitability of WAEMU banks : Do foreign ownership and cross-border banking matter ?

Using hand-collected bank level data from all West African Economic and Monetary Union (WAEMU) countries for 2000-2014, we investigate the simultaneous relationship among bank capital, risk and profitability, taking into account bank ownership and cross-border banking. We split the countries into lower middle-income (LMIs) and low-income (LICs). We find that the sensitivity of bank profitability to an increase in capital ratio is much higher in LMIs (+0.10) than in LICs (+0.05). Moreover, we uncover evidence to support the regulatory hypothesis, i.e. positive relationship between risk and capital. Also, we find that banks’ capital positions tend to be counter-cyclical in LICs, mimicking Basle III. Foreign bank ownership and cross-border banking reduce credit risk and profitability in the banking sector. These findings are generally robust to the use of alternative measures of risk and profitability and alternative estimation techniques.
Separate Loss for Basic and Compound Facial Expression Recognition in the Wild

In the past few years, facial expression recognition has made great progress because of the development of convolutional neural networks. However, the features learned only using the softmax loss are not discriminative enough for highly accurate facial expression recognition in the wild, especially for the compound facial expression recognition. To enhance the discriminative power of the learned features, we propose the separate loss for both basic and compound facial expression recognition in the wild in this paper. Such loss maximizes intra-class similarity while minimizing the similarity between different classes. The qualitative and quantitative analysis shows that the features learned using such loss function are characterized by intra-class compactness and inter-class separation. Experiments are performed on two databases in the wild and the proposed method achieves state-of-theart results on both basic and compound expressions. Furthermore, another two databases are used to perform cross database experiments to show the generalization ability of our method.
Energy Management with Battery Storage for a Hybrid Residential PV-Wind System – A Case Study for Denmark

District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
Clinical application of quantitative spect in patient specific dosimetry and beta cell quantification

This thesis demonstrates how, despite different challenges, quantitative analysis of single photon emission computed tomography (SPECT) images, facilitates in answering specific clinical questions that cannot (yet) be answered with another method. First, different technical and practical challenges are discussed. Currently, most technical challenges can be counteracted by using advanced reconstruction protocols enabling attenuation, scatter and collimator correction. Then, different methods for calculation of bone marrow radiation doses (including one SPECT-based method) were used, and results were compared to the bone marrow toxicity in order to select a method for prediction of bone marrow toxicity. This showed that the SPECT-based dosimetry seemed to be most predictive. Quantitative analysis of SPECT images was also applied for characterization of the amount of pancreatic beta cells. Beta cells are the insulin producing cells in the pancreas and play a key role in diabetes. To facilitate future research on the pathophysiology and treatment of diabetes, radiolabeled exendin was developed. Exendin specifically targets the glucacon-like-1 (GLP-1) receptor, expressed on beta cells, and the radiolabel can be visualized with a SPECT camera. Therefore this can be used for in vivo beta cell quantification. This was applied in a preclinical study, in ten type 1 diabetes patients, and in ten healthy controls. To optimize SPECT acquisition and reconstruction protocols, a phantom was developed for mimicking the human images. From these studies we conclude that from a biological perspective the uptake can be used as a measure for the beta cell mass and we demonstrated that uptake in the healthy individuals and patients with diabetes is in line with the expected variation in beta cell mass, and the expected differences between these groups. Finally, different methods for dosimetry and quantification of radioactivity distribution were applied to calculate the radiation dose to the beta cells as a result of imaging with radiolabeled exendin. This showed that doses remain below the levels at which toxicity is expected. Altogether, this work demonstrates that although we face numerous challenges in quantitative use of SPECT images, the applications are so valuable for answering different clinical questions, that it is worthwhile to tackle them.
L G ] 1 5 O ct 2 01 9 Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis

In this paper, we introduce a physics-driven regularization method for training of deep neural networks (DNNs) for use in engineering design and analysis problems. In particular, we focus on prediction of a physical system, for which in addition to training data, partial or complete information on a set of governing laws is also available. These laws often appear in the form of differential equations, derived from first principles, empirically-validated laws, or domain expertise, and are usually neglected in data-driven prediction of engineering systems. We propose a training approach that utilizes the known governing laws and regularizes data-driven DNN models by penalizing divergence from those laws. The first two numerical examples are synthetic examples, where we show that in constructing a DNN model that best fits the measurements from a physical system, the use of our proposed regularization results in DNNs that are more interpretable with smaller generalization errors, compared to other common regularization methods. The last two examples concern metamodeling for a random Burgers’ system and for aerodynamic analysis of passenger vehicles, where we demonstrate that the proposed regularization provides superior generalization accuracy compared to other common alternatives.
Mining Landmark Papers

In recent years, the number of electronic journal articles is growing faster than ever before; information is generated faster than people can deal with it. In order to handle this problem, many electronic periodical databases have proposed keyword search methods to decrease the effort and time spent by users in searching the journal's archives. However, the users still have to deal with a huge number of search results. In this paper, we present the problem of mining landmark papers. We treat papers that introduce important keyphrases for the first time as landmark papers. Our approach combines simple ideas from text mining, information extraction and information retrieval to identify landmark papers. We show that existing related techniques such as first story detection, mining hot topics and theme mining do not effectively handle the landmark paper mining problem. Our approach is simpler and more direct for this task. We experimentally evaluate our approach on a large dataset of papers in the database or data mining areas downloaded using DBLP. Keywords-Landmark papers, Text Mining, Information Extraction and Retrieval, First-Story Detection.
Aalborg Universitet Organizing for interorganizational learning in service networks

Purpose – The purpose of this study is to consolidate existing research on ‘service networks’ and to frame this literature as a new ‘context for learning’. Research from inter-organizational learning is used to qualify this consolidation and advances from inter-organizational learning are used to operationalize how service network actors in this new context can organize for inter-organizational learning to create more value for themselves and their customers. Design/methodology/approach – By conceptualizing the learning context of a service network and the interrelated dimensions, an overview of the learning challenges for improved service performance is provided. Findings – Inspired by the service triangle, the proposed framework highlights the learning challenges among two or more actors and the knowledge and skills needed for them to organize the service network. To build a collaboration characterized by trust, behaviors associated with transparency and receptivity are argued to be imperative. Practical implications – The framework can increase the opportunities for inter-organizational learning in a service network. Knowing the learning context and the challenges associated with this learning allows for a more accurate intervention and allocation of resources to improve service network performance. Originality/value – The novelty lies in the consolidation of the literature of service networks and the extension of the literature on inter-organizational learning hereto.
Adding Proximity Sensing Capability to Tactile Array Based on Off-the-Shelf FSR and PSoC

Tactile sensors have been incorporated into robots to help in many tasks, such as preventing damage to humans in collaborative works or assistance or in dexterous manipulation. Proximity sensors are also common in robotics and are proposed to implement pretouch in multimodal realizations. Many tactile sensors are based on off-the-shelf force-sensing resistors (FSRs). The parasitic capacitance associated with the electrodes of the FSR can be exploited to measure proximity. Moreover, it is possible to implement both force and proximity signal acquisition with a single chip. This article presents a multimodal proximity and tactile patch based on commercial FSR plus a programmable system on chip (PSoC). The ability to measure proximity is achieved without adding any extra element. Discussions about key design issues as well as results that show the performance of the sensor patch are presented. As proximity sensing array, the range is in the order of the size of the electrodes, and it depends on the properties of the object nearby, as expected. The best performance is observed for conductive objects. Therefore, this approach is especially interesting in human–robot interaction tasks, such as shaping robotic hands or grippers around the human body. Furthermore, dielectric objects are also detected, so it can also be used to implement pretouch in manipulation tasks.
A Study on the Quasi-Static Lightpath Configuration Method in Large-Scaled WDM Networks

Recently, progress has been made in the Generalized Multi–Protocol Label Switching (GMPLS) standardization lets WDM (Wavelength Division Multiplexing) networks be interconnected. As known that the Autonomous System (AS)–level topologies of the current Internet are found to exhibit the power–law, large–scale WDM networks constructed by interconnecting local WDM networks will also exhibit the power–law attribute. In this paper, we propose a quasi–static lightpath configuration method to utilize the wavelength resources more effectively and to reduce blocking probability in such networks. We compared our method with one not using a pre–determined lightpath by computer simulation. The results show that our method reduces the blocking probabilities by more than 80%.
Estimating daily rainfall from NDVI using the wavelet transform

Quantifying rainfall at spatial and temporal scales is a challenge posed to scientists in different disciplines given its importance in agriculture, natural resource management and land-atmosphere interactions. This paper describes a new approach to assess rainfall combining rain gauge data with the normalized difference vegetation index (NDVI) based on the fact that both events are periodic and proportional. The procedure developed to reconstruct the rainfall signal combining the Fourier Transform (FT) and the Wavelet Transform (WT) is described. FT was used to estimate the lag time between rainfall and the vegetation response. Third level decompositions of both signals with WT were used for the reconstruction process, determined by the entropy difference between levels and R. The low frequency signal from the NDVI data was used as the base signal for the reconstruction to which the high frequency signal (noise) extracted from the rainfall data was added. The reconstructed daily rainfall was compared to the measured one obtaining determination coefficients > 0.81. This finding is quite an improvement over the estimates reported in the literature where this level of precision is only found for comparisons at the seasonal levels. This methodology has clear scope to improve spatial interpolation of rainfall based on high-resolution NDVI fields and a limited number of meteorological stations.
GLCM Algorithm and SVM Classification Method for Orange Fruit Quality Assessment

:- The image processing is the approach which is applied to process information which is stored in the form of pixels. This research work is related to orange fruit quality assessment. The orange fruit quality assessment has the various phases like pre-processing, feature extraction and classification. In the previous method Naïve Bayes classifier is applied for the quality assessment. The Naïve Bayes classification method gives low accuracy for the quality assessment. In this research work, the Naïve Bayes classification method is replaced with SVM classification for the quality assessment. The GLCM algorithm is applied for the feature extraction along with the threshold based segmentation. The proposed method is compared with the existing method in terms of accuracy, execution time, sensitivity, specificity. It is analyzed that accuracy, sensitivity, specificity of proposed method is high and execution time is low as compared to existing method for orange fruit quality assessment .
R-CNN Based Object Detection and Classification Methods for Complex Sceneries

The computer vision system based application growths are very many needs to gather the information in the unusual environment. Surveillance systems are using in many applications. To get better surveillance system, object detection and recognition techniques need to discuss. In this field, object recognition has gained lots of attraction for real-time application. Various techniques have proposed for recognizing and detecting the object from the picture. In the proposed article, R-CNN techniques are presenting for object recognition in the unusual environment. This technique gets reduce the complexity to detect the object from complex scenarios.
Quantifying CH4 emissions from hard coal mines using mobile sun-viewing Fourier transform spectrometry

Abstract. Methane (CH4) emissions from coal production amount to roughly one-third of European anthropogenic CH4 emissions in the atmosphere. Poland is the largest hard coal producer in the European Union with the Polish side of the Upper Silesian Coal Basin (USCB) as the main part of it. Emission estimates for CH4 from the USCB for individual coal mine ventilation shafts range between 0.03 and 20 kt a−1, amounting to a basin total of roughly 440 kt a−1 according to the European Pollutant Release and Transfer Register (E-PRTR, http://prtr.ec.europa.eu/, 2014). We mounted a ground-based, portable, sun-viewing FTS (Fourier transform spectrometer) on a truck for sampling coal mine ventilation plumes by driving cross-sectional stop-and-go patterns at 1 to 3 km from the exhaust shafts. Several of these transects allowed for estimation of CH4 emissions based on the observed enhancements of the column-averaged dry-air mole fractions of methane (XCH4) using a mass balance approach. Our resulting emission estimates range from 6±1 kt a−1 for a single shaft up to 109±33 kt a−1 for a subregion of the USCB, which is in broad agreement with the E-PRTR reports. Three wind lidars were deployed in the larger USCB region providing ancillary information about spatial and temporal variability of wind and turbulence in the atmospheric boundary layer. Sensitivity studies show that, despite drawing from the three wind lidars, the uncertainty of the local wind dominates the uncertainty of the emission estimates, by far exceeding errors related to the XCH4 measurements themselves. Wind-related relative errors on the emission estimates typically amount to 20 %.
Online learning af fi nity measure with CovBoost for multi-target tracking

In this paper, we propose a new online learning method for measuring affinity between tracklets in multi-target tracking. As targets and background usually keep changing in the video, fixed affinity measurement could not adapt to their variations. Most existing affinity learning methods construct labeled samples based on the obtained tracklets, and then minimize a predefined loss function to get an optimal affinity measurement. However, those methods simply assume that the training error equals to testing error which is not true in many of real time tracking scenarios. Differently, we propose to learn affinity measurement through CovBoosting, which considers the evolution of the tracklets and could obtain affinity measurement with more discriminative ability. To deal with targets' disappearance and new targets' appearance, we combine tracklet affinity with contextual information to do an optimal inference. Moreover, an online updating algorithm is developed to guarantee that the learned tracklet affinity is always optimal for tracking targets in current sliding window. Experimental results on benchmark datasets demonstrate that tracklet affinity learned with our method is more discriminative and could greatly improve the performance of the multi-target tracker. & 2015 Elsevier B.V. All rights reserved.