Miracle-dz/UniFAR-FTUs huggingface dataset:
Wind Speed Forecasting by Using Time Series

As a result of the rapid development of technology and consequently increasing energy demand, the world's underground resources have started to run out. Global warming, along with the damage to the environment, has become a bigger problem. This has increased the interest in renewable energy sources, called clean energy. However, some problems have arisen in the integration of renewable energy sources into the power system. The biggest problem encountered in this regard is that the power outputs of these systems cannot be known with absolute accuracy. This is because renewable energy generation systems are directly connected to the environmental parameters (pressure, temperature, humidity, wind direction, ghosting effect, etc.). There are many methods and many researches that have been developed in order to estimate how much renewable energy sources will produce within the desired time. In this study, information about time series methods, AR, MA, ARMA and ARIMA methods were examined. In order to estimate the wind speed, 3 separate regions, 3 different estimation time scales and 3 different time zones were selected. The wind speed was analyzed in detail, and the time series were evaluated to be efficient and not efficient. Keyword: ARIMA; Time Series Analysis; Wind Energy; Wind Speed Forecast
Evaluation of AVMs in 4 DCTA

The aim of this bachelor thesis is to improve the evaluation of arteriovenous malformations (AVM) in 4D computed tomography angiography (4D-CTA) using Matlab. For this evaluation anonymised AVM datasets from patients from the UMC St Radboud are loaded into Matlab. With this loaded data an automatic evaluation of the AVM will be developed. The AVM datasets have been made Matlab compatible and afterwards they were cropped. The cropping reduces the size of the data and makes Matlab perform better. Following the blood vessels are segmented using a mask image to filter the surrounding irrelevant tissue. These images of solely blood vessels are aligned and played in a movie where the vascular filling is shown. To continue placing a mark near the nidus to indicate and visualize the AVM can be a valuable progress. Next to this visualization it would be useful to look at navigation for the treatment of an AVM. keywords: AVM, arteriovenous malformation, 4D CTA, 4D computed tomography, Matlab, navigation, DSA, digital subtraction angiography
Positive Negative Positive Negative Positive Negative Meaningful Subspace 2 D 3 Boxy 3 D 3 Boxy

Many automatic attribute discovery methods have been developed to extract a set of visual attributes from images for various tasks. However, despite good performance in some image classification tasks, it is difficult to evaluate whether these methods discover meaningful attributes and which one is the best to find the attributes for image descriptions. An intuitive way to evaluate this is to manually verify whether consistent identifiable visual concepts exist to distinguish between positive and negative images of an attribute. This manual checking is tedious, labor intensive and expensive and it is very hard to get quantitative comparisons between different methods. In this work, we tackle this problem by proposing an attribute meaningfulness metric, that can perform automatic evaluation on the meaningfulness of attribute sets as well as achieving quantitative comparisons. We apply our proposed metric to recent automatic attribute discovery methods and popular hashing methods on three attribute datasets. A user study is also conducted to validate the effectiveness of the metric. In our evaluation, we gleaned some insights that could be beneficial in developing automatic attribute discovery methods to generate meaningful attributes. To the best of our knowledge, this is the first work to quantitatively measure the semantic content of automatically discovered attributes.
Optimal Energy Management of Unbalanced Three-Phase Grid-Connected Microgrids

This paper presents a non-linear optimization problem based on nodal current injections to solve the optimal energy management of unbalanced, three-phase, grid-connected microgrids. The focus is on the modelling of small synchronous machines under unbalanced operation. Inverter-interfaced distributed energy resources, i.e., renewable sources and energy storage systems, are also considered and described. The proposed formulation minimizes the cost of the energy consumed by the microgrid while satisfying active and reactive power balances, the devices operational constraints, as well as current and voltage operational limits. The proposed model is tested using the IEEE 123 node test feeder and compared to commonly used formulations. Simulation results show the impact of unbalanced models on the energy management of three-phase microgrids.
Methodology for Deploying a Security Framework in Mission Critical Infrastructure Based Wireless Sensor Networks

This paper presents a methodology on design of a security framework for Wireless Sensor Network (WSN). This framework is proposed for an infrastructure based deployment of the sensor nodes for mission critical application like static military deployment. Major security prerequisite, like confidentiality, integrity, freshness and authentication are being addressed. In addition a key management and distribution methodology is also been proposed in the solution. The paper also outlines the factors that have to be considered while designing a framework. It also gives an insight in the available platform that are available for the design of the framework and considerations that have to be kept in mind while setting up an experimental setup for testing the security framework. The major focus of this proposal is to achieve the desired level of security within the resource restriction imposed by WSN.
A Conditional Generative Adversarial Network for Rendering Point Clouds

In computer graphics, point clouds from laser scanning devices are difﬁcult to render into photo-realistic images due to lack of information they carry about color, normal, lighting, and connection between points. Rendering a point cloud after surface mesh reconstruction generally results into poor image quality with many noticeable artifacts. In this paper, we propose a conditional generative adversarial network that directly renders a point cloud given the azimuth and elevation angles of camera viewpoint. The proposed method, called pc2pix, renders point clouds into objects with higher class similarity with the ground truth as compared to images from surface reconstruction. pc2pix is also signiﬁcantly faster, more robust to noise and can operate on a lower number of points. The code is available at: https://github.com/roatienza/pc2pix.
Divide and Conquer the Embedding Space for Metric Learning-Supplementary Material-Artsiom Sanakoyeu

Re-clustering every T epochs: As pointed out in Sec. 3.3 of the main submission, we update the data partitioning by re-clustering every T epochs using the full embedding space, composed by concatenating the embeddings produced by the individual learners. To maintain consistency, each learner is associated to the cluster, which is most similar to the cluster assigned to this learner in the previous iteration (i.e. in epoch t−T ). This amounts to solving a linear assignment problem where similarity between clusters is measured in terms of IoU of points belonging to the clusters. The source code is available at https://bit.ly/ dcesml.
Detecting Textured Contact Lens in Uncontrolled Environment Using DensePAD

The ubiquitous use of smartphones has spurred the research in mobile iris devices. Due to their convenience, these mobile devices are also utilized in unconstrained outdoor conditions. This scenario has necessitated the development of reliable iris recognition algorithms for such an uncontrolled environment. Additionally, iris presentation attacks such as textured contact lens pose a major challenge to current iris recognition systems. Motivated by these, this paper presents two key contributions. First, a new Unconstrained Multi-sensor Iris Presentation Attack (UnMIPA) database is created. It consists of more than 18,000 iris images of subjects wearing textured contact lens and without wearing contact lenses captured in both indoor and outdoor environment using multiple iris sensors. The second contribution of this paper is a novel algorithm, DensePAD, which utilizes DenseNet based convolutional neural network architecture for iris presentation attack detection. In-depth experimental evaluation of this algorithm reveals its superior performance in detecting iris presentation attack images on multiple databases. The performance of the proposed DensePAD algorithm is also evaluated in real-world scenarios of open-set iris presentation attacks which highlights the challenging nature of detecting iris presentation attack images from unseen distributions.
Modelling hyponymy for DisCoCat

In natural language processing words are commonly represented as vectors. However, vector representations do not intrinsically incorporate the hierarchical relationships that obtain between many words. We model words as positive operators. These have an ordering which we interpret as modelling hierarchical information. We describe a simple way of building positive operators for words, and give methods for composing these words representations to form phrases and sentences. We test the methods on simple sentencelevel entailment datasets.
BIOMECHANICAL ANALYSIS OF THE MOMENT ABOUT THE CENTER OF MASS DURING THE DOWNSWING PHASE IN WOMEN’S DRIVER SHOT

The purpose of this study was to investigate the moment about the center of mass (CM) acting on the golfer-club system during the downswing phase in women’s driver shots. Fourteen female golfers volunteered as subjects. The maximum clubhead speed, angular momentum ot the thorax and moment about the CM were calculated. There was a strong significant correlation between the frontal-plane moment about the CM and the maximum clubhead speed (p<0.01). At the same time, there was a significant correlation between the frontal-plane moment about the CM and angular momentum of the thorax about the Z-axis (p<0.05). In conclusion, in order to get higher clubhead speed it is important to generate the larger frontal-plane moment about the CM and to transfer the momentum generated from the body to the clubhead.
Argoverse: 3D Tracking and Forecasting With Rich Maps

We present Argoverse, a dataset designed to support autonomous vehicle perception tasks including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a fleet of autonomous vehicles in Pittsburgh and Miami as well as 3D tracking annotations, 300k extracted interesting vehicle trajectories, and rich semantic maps. The sensor data consists of 360 degree images from 7 cameras with overlapping fields of view, forward-facing stereo imagery, 3D point clouds from long range LiDAR, and 6-DOF pose. Our 290km of mapped lanes contain rich geometric and semantic metadata which are not currently available in any public dataset. All data is released under a Creative Commons license at Argoverse.org. In baseline experiments, we use map information such as lane direction, driveable area, and ground height to improve the accuracy of 3D object tracking. We use 3D object tracking to mine for more than 300k interesting vehicle trajectories to create a trajectory forecasting benchmark. Motion forecasting experiments ranging in complexity from classical methods (k-NN) to LSTMs demonstrate that using detailed vector maps with lane-level information substantially reduces prediction error. Our tracking and forecasting experiments represent only a superficial exploration of the potential of rich maps in robotic perception. We hope that Argoverse will enable the research community to explore these problems in greater depth.
Programmatic Task Network Planning

Many planning problems benefit from extensions of classical planning formalisms and modeling techniques, or even require such extensions. Alternatives such as functional STRIPS or planning modulo theories have therefore been proposed in the past. Somewhat surprisingly, corresponding extensions are not available for hierarchical planning, despite their potential usefulness in applications like automated service composition. In this paper, we present programmatic task networks (PTN), a formalism that extends classical HTN planning in three ways. First, we allow both operations and methods to have outputs instead of only inputs. Second, formulas may contain interpreted terms, in particular interpreted predicates, which are evaluated by a theory realized in an external library. Third, PTN planning allows for a second type of tasks, called oracle tasks, which are not resolved by the planner itself but by external libraries. For the purpose of illustration and evaluation, the approach is applied to a realworld use case in the field of automated service composition.
Multi-Factor Congressional Vote Prediction

In recent times we have seen a trend of having the ideologies of the two dominant political parties in the U.S. growing further and further apart. Simultaneously we have entered the age of big data raising enormous interest in computational approaches to solve problems in many domains such as political elections. However, an overlooked problem lies in predicting what happens once our elected officials take office, more specifically, predicting the congressional votes, which are perhaps the most influential decisions being made in the U.S. This, nevertheless, is far from a trivial task, since the congressional system is highly complex and heavily influenced by both ideological and social factors. Thus, dedicated efforts are required to first effectively identify and represent these factors, then furthermore capture the interactions between them. To this end, we proposed a robust end-to-end framework Multi-Factor Congressional Vote Prediction (MFCVP) that defines and encodes features from indicative ideological factors while also extracting novel social features. This allows for a principled expressive representation of the complex system, which ultimately leads to MFCVP making accurate vote predictions. Experimental results on a dataset from the U.S. House of Representatives shows the superiority of MFCVP to several representatives approaches when predicting votes for individual representatives and also the overall outcome of the bill voted on. Finally, we perform a factor analysis to understand the effectiveness and interplay between the different factors.
Economic Growth and Inequality in the Mass Market Economy

The mass market economy is characterized by the combination of technological innovation and absorption and generates ine¢ cient and unequal growth. Innovation-for-pro ts originates monopolistic competition in some sectors, while absorption occurs in competitive sectors for which innovation is inviable. Innovative sectors produce pro ts that concentrate on a small number of owners. Yet the economy-wide wage level is de ned by a lower, average technological level. It follows that there are policies that can promote equality, e¢ ciency and growth. An empirical study for US states from 1997 to 2011 con rms that the proportional size the large scale sector drives aggregate employment, payroll and wages, top 1 and top 10% income participation, and poverty.
Drawing Out Information from WebPages with Visual Unit

: The Document Object Model is a convention to represent and interact with objects in HTML, XHTML and XML documents. It uses a tree structure to represent objects in HTML. To extract information from web pages, leaf nodes of DOM tree can be considered as basic objects. Web pages aren’t made up of individual DOM tree nodes, but are more of information blocks having a visual consistent format. These blocks may not be linked to the DOM tree nodes. This paper proposes to extract the news content by visual unit which will be a visual oriented extraction method. According to visual features and text features, the visual units are identified by a top down approach. The page content is extracted according to the domain characteristic. The method is an encouraging approach for news content extraction consisting of visual units and domain characteristic. The weight parameters can also be trained by machine learning technique. Also, in the content extraction phase we can analyse that which visual unit was selected as the main content unit so that precision can be obtained.
A Virtual Promotional Tool for Student Enrolment

Virtual Reality (VR) has become more of a commodity in the past years due to the emergence of mobile VR head-mounts, which present a more affordable provider of VR by combining smartphone sensory data, and head-mount with applications made for the platform using the infrastructure of the smartphones. The VR phone application designed for the purpose of this study enables a realistic 360° virtual environment. The application is designed with the Aalborg University Esbjerg public relations department staff as stakeholders. The content displayed within is designed to attract potential students of Aalborg University Esbjerg, thus the scenes depict facilities provided by the university and its surrounding areas. Achieving this is possible using a Samsung Gear VR head-mount and one of its compatible smartphone attachments (Samsung Galaxy S7). The content displayed within the app is live-action and shot with a Samsung Gear 360 camera. A presence questionnaire is used to measure the level of involvement the user experienced. This facilitates a comparison study between the VR application and a more traditional tablet application. A secondary study was conducted in order to further analyse the possible impact that the addition of sound to the VR application might have over the level of presence. Results indicate that the users wearing VR head-mount experienced a higher level of presence compared to the ones using the tablet version. However, results show that the addition of sound has not statistically contributed to the increase in the level of presence as expected.
Semantic Relation’s Weight Determination on a Graph Based WordNet

Bircok dogal dil isleme uygulamasinda metinsel iki ogenin anlamsal iliskisinin tespit edilmesi cok onemli bir asamadir. Bu calismada WordNet 3.0 ve Men’s veri seti kullanarak sozluk tabanli anlamsal iliski belirleme metodlari icin yeni bir yaklasim sunulmaktadir. Anlamsal agirliklarin hesaplanmasinda Men’s veriseti kullanilmis ve bulunan degerler anlamsal agirlik hesaplanmasinda kullanilmistir. Onerilen metodun dogrulugunu olcmek icin RG65 benzerlik veriseti kullanilmis, kayislama sonucunda 0.81 Spearman korelasyon degeri elde edilmistir. Buyuk boyutlu bir verisetinin gelistirme ve test icin kullanilip, diger onemli bir verisetinin de kiyaslama amacli olarak anlamsal iliski tiplerinin agirliklarinin belirlenmesi ve anlamsal iliskinin hesaplanmasinda kullanilmasi anlamsal benzerlik ve anlamsal iliski hesaplanmasina farkli bir bakis acisi getirmektedir.
Amendment history : Corrigendum ( June 2015 ) MHCII-independent CD 4 + T cells protect injured CNS neurons via IL-4

A body of experimental evidence suggests that T cells mediate neuroprotection following CNS injury; however, the antigen specificity of these T cells and how they mediate neuroprotection are unknown. Here, we have provided evidence that T cell–mediated neuroprotection after CNS injury can occur independently of major histocompatibility class II (MHCII) signaling to T cell receptors (TCRs). Using two murine models of CNS injury, we determined that damage-associated molecular mediators that originate from injured CNS tissue induce a population of neuroprotective, IL-4–producing T cells in an antigenindependent fashion. Compared with wild-type mice, IL-4–deficient animals had decreased functional recovery following CNS injury; however, transfer of CD4+ T cells from wild-type mice, but not from IL-4–deficient mice, enhanced neuronal survival. Using a culture-based system, we determined that T cell–derived IL-4 protects and induces recovery of injured neurons by activation of neuronal IL-4 receptors, which potentiated neurotrophin signaling via the AKT and MAPK pathways. Together, these findings demonstrate that damageassociated molecules from the injured CNS induce a neuroprotective T cell response that is independent of MHCII/TCR interactions and is MyD88 dependent. Moreover, our results indicate that IL-4 mediates neuroprotection and recovery of the injured [. ] Research Article
Nutrient Physiology , Metabolism , and Nutrient-Nutrient Interactions Skeletal Muscle Protein Anabolic Response to Increased Energy and Insulin Is Preserved in Poorly Controlled Type 2 Diabetes 1 , 2

Type 2 diabetes (T2DM) subjects failing diet treatment are characterized by hyperinsulinemia and insulin resistance leading to fasting and postprandial hyperglycemia and hyperlipidemia. Energy is essential for allowing the process of protein synthesis to proceed. Additionally, insulin can stimulate protein synthesis in human muscle. The aims of this study were to determine if poorly controlled T2DM affects postabsorptive muscle protein anabolism, and if the muscle anabolic response to hyperinsulinemia with high energy availability is maintained. Control (n 1⁄4 6) and T2DM subjects (n 1⁄4 6) were studied in the postabsorptive state and during an isoenergetic high nutritional energy clamp (relative to postabsorptive state). Muscle protein synthesis and breakdown (nmol min 1 100 g leg muscle ) were assessed using stable isotope methodology, femoral arterio-venous sampling, muscle biopsies, and a three-pool model to calculate protein turnover. Postabsorptive phenylalanine net balance and whole body rate of appearance (Ra) were not different between groups; however, basal muscle protein breakdown was higher in T2DM (94 6 9) than in controls (58 6 12) (P , 0.05) and muscle protein synthesis tended (P 1⁄4 0.07) to be elevated in T2DM (66 6 14) compared with controls (39 6 6). During the clamp, net balance increased, whole body Ra and muscle protein breakdown decreased (P , 0.05), and muscle protein synthesis tended to decrease (P 1⁄4 0.08) to a similar extent in both groups. We conclude that postabsorptive muscle protein turnover is elevated in poorly controlled T2DM, however, there is no excessive loss of muscle protein because net balance is not different from controls. Moreover, the anabolic response to increased insulin and energy availability is maintained in T2DM. J. Nutr. 136: 1249–1255, 2006.