nils-herrmann/clean_scirepeval huggingface dataset:
Feasibility study for the implementation of an automatic system for the detection of social interactions in the waiting area of automatic milking stations by using a video surveillance system
Venue: Comput. Electron. Agric.

Monitoring of social interactions by use of image segmentation and tracking methods.Three cameras with top down view were used for recordings and observations.The social interactions were identified based on collision of geometrical shapes.The overall performance of the detector in its early stage is 85.1% A well planned waiting area is crucial for automatic milking systems. In an enclosed waiting area, cows of different rank compete for entering the milking station and they are exposed for a variety of social interactions. Such interactions could increase standing time and delay milking, which may result in stress, lameness, impaired welfare and reduced performance. The aim was to monitor the waiting area in a free stall dairy by the use of three video cameras to detect occurrence of social interactions by using improved image segmentation and tracking methods. The surveillance system observed 252 cows having free access to any of four milking stations during 24h over a period of two weeks. A two step pattern recognition approach was used. In the first step geometric features (distances) were extracted from every pair of cows in every frame. These features form the input of the second step. It consists of a classifier of the behaviour of the cows. A support vector machine was used to realise this classifier. The social interactions were identified based on collision of geometrical shapes segmented from the image and positively identified as cows by experienced observers. The results showed that the proposed system was capable of a fairly accurate detection of social interactions.
Vaccines for bovine neosporosis: current status and key aspects for development
Venue: Parasite immunology

Bovine neosporosis is a worldwide concern due to its global distribution and great economic impact. Reproductive failure in cattle due to abortion leads to major economic losses associated with the disease. Currently, there is no treatment or vaccine available against abortion or transmission caused by Neospora caninum infection in cattle. However, vaccination is considered the best measure of control against bovine neosporosis. Several host and parasite factors can influence the dynamics of the infection in bovines. Moreover, the availability of well defined infection models is a key factor for the evaluation of vaccine candidates. However, working with cattle is not easy due to difficult handling, facilities and costs, and therefore, 'more affordable' models could be used for screening of promising vaccines to establish proof of concept. So far, live attenuated vaccines have shown good efficacy against exogenous transplacental transmission; however, they have relevant disadvantages and associated risks, which render inactivated or subunit vaccines the best way forward. The identification of novel potential targets and vaccines, and the application of innovative vaccine technologies in harmonized experimental animal models, will accelerate the development of an effective vaccine against bovine neosporosis.
Bounded Solutions to nonlinear problems involving the fractional laplacian depending on parameters

The main goal of this paper is the study of two kinds of nonlinear problems depending on parameters in unbounded domains. Using a nonstandard variational approach, we first prove the existence of bounded solutions for nonlinear eigenvalue problems involving the fractional Laplace operator and nonlinearities that have subcritical growth. In the second part, based on a variational principle of Ricceri [16] we study a fractional nonlinear problem with two parameters and prove the existence of multiple solutions.
Fine Grained Image Classification by Exploring Bipartite Graph Labels
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Given a food image, can a fine grained object recognition engine tell "which restaurant which dish" the food belongs to? Such ultra fine grained image recognition is the key for many applications like search by images, but it is very challenging because it needs to discern subtle difference between classes while dealing with the scarcity of training data. Fortunately, the ultra fine granularity naturally brings rich relationships among object classes. This paper proposes a novel approach to exploit the rich relationships through bipartite graph labels (BGL) We show how to model BGL in an overall convolutional neural networks and the resulting system can be optimized through back propagation. We also show that it is computationally efficient in inference thanks to the bipartite structure. To facilitate the study, we construct a new food benchmark dataset, which consists of 37,885 food images collected from 6 restaurants and totally 975 menus. Experimental results on this new food and three other datasets demonstrate BGL advances previous works in fine grained object recognition. An online demo is available at http:
Plausible Mechanisms Explaining the Association of Periodontitis with Cardiovascular Diseases

The association between periodontitis and cardiovascular diseases is now well established. Cardiovascular diseases include atherosclerosis, coronary heart (artery) disease, cerebrovascular disease, and peripheral artery disease. Atherosclerosis is the underlying pathology of cardiovascular diseases. In this chapter, we describe plausible mechanisms to explain the link between periodontitis, atherosclerosis, and the subsequent cardiovascular diseases. The explanations for the development and exacerbation of atherosclerotic plaques in periodontitis patients include: (1) bacteremia, (2) a pro inflammatory state, (3) a prothrombotic state, (4) an overactive immunity, (5) dyslipidemia, and (6) common genetic risk factors. Most likely, these plausible mechanisms play all simultaneously a role. Obviously, much more fundamental and clinic research is needed to further study the associations between periodontitis and atherosclerotic diseases.
Fairness in Learning: Classic and Contextual Bandits
Venue: NIPS

We introduce the study of fairness in multi armed bandit problems. Our fairness definition can be interpreted as demanding that given a pool of applicants (say, for college admission or mortgages) a worse applicant is never favored over a better one, despite a learning algorithm's uncertainty over the true payoffs. We prove results of two types. First, in the important special case of the classic stochastic bandits problem (i.e. in which there are no contexts) we provide a provably fair algorithm based on "chained" confidence intervals, and provide a cumulative regret bound with a cubic dependence on the number of arms. We further show that any fair algorithm must have such a dependence. When combined with regret bounds for standard non fair algorithms such as UCB, this proves a strong separation between fair and unfair learning, which extends to the general contextual case. In the general contextual case, we prove a tight connection between fairness and the KWIK (Knows What It Knows) learning model: a KWIK algorithm for a class of functions can be transformed into a provably fair contextual bandit algorithm, and conversely any fair contextual bandit algorithm can be transformed into a KWIK learning algorithm. This tight connection allows us to provide a provably fair algorithm for the linear contextual bandit problem with a polynomial dependence on the dimension, and to show (for a different class of functions) a worst case exponential gap in regret between fair and non fair learning algorithms
The State of the Art of Set Visualization
Venue: Comput. Graph. Forum

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non trivial problem due to the large number of possible relations between them. We provide a systematic overview of state of the art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http:/www.setviz.net.
Banach Algebra Techniques In Operator Theory

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A new fuzzy logic based query expansion model for efficient information retrieval using relevance feedback approach
Venue: Neural Computing and Applications

Efficient query expansion (QE) terms selection methods are really very important for improving the accuracy and efficiency of the system by removing the irrelevant and redundant terms from the top retrieved feedback documents corpus with respect to a user query. Each individual QE term selection method has its weaknesses and strengths. To overcome the weaknesses and to utilize the strengths of the individual method, we used multiple terms selection methods together. In this paper, we present a new method for QE based on fuzzy logic considering the top retrieved document as relevance feedback documents for mining additional QE terms. Different QE terms selection methods calculate the degrees of importance of all unique terms of top retrieved documents collection for mining additional expansion terms. These methods give different relevance scores for each term. The proposed method combines different weights of each term by using fuzzy rules to infer the weights of the additional query terms. Then, the weights of the additional query terms and the weights of the original query terms are used to form the new query vector, and we use this new query vector to retrieve documents. All the experiments are performed on TREC and FIRE benchmark datasets. The proposed QE method increases the precision rates and the recall rates of information retrieval systems for dealing with document retrieval. It gets a significant higher average recall rate, average precision rate and F measure on both datasets.
Prototyping bodies: a post phenomenology of wearable simulations

This article explores wearable simulations, which are analogue and low tech tools that designers deploy to gain first hand insight into the experiences of different users, including elderly and disabled people. Ranging from gloves and goggles to elaborate whole body suits, wearable simulations are said to facilitate an inclusive and empathic design process by encouraging practitioners to seek affective and sensory connection with others. The article builds on post phenomenological thinking to propose an alternative understanding, where wearable simulations are seen as prototypical compositions that generate unexpected bodily variations, which have the potential to sensitise designers to the limits of knowing bodies and invite experimentation with experience.
Data driven three dimensional reconstruction of human bodies using a mobile phone app

The advances and availability of technologies for the acquisition, registration and analysis of the three dimensional (3D) shape of human bodies (or body parts) are resulting in the formation of large databases of parameterised meshes from which digital human body models can be derived. Such models can be used for the data driven reconstruction of parameterised human body shapes from partial information such as one dimensional (1D) measurements or 2D images. In this paper, we propose a new method for the reconstruction of 3D bodies from images gathered with a smartphone or tablet. Moreover, the method is implemented into a prototype app and tested at different levels through three experimental studies including synthetic models, 1:10 scale figurines and real children. The results demonstrate the feasibility of acquiring reliable anthropometric information easily at home by non experts. This method and implementation have great potential for their application to the personalisation, size recommendation and virtual try on simulation of wearable products.
A novel framework for cross spectral iris matching
Venue: IPSJ Transactions on Computer Vision and Applications

Previous work on iris recognition focused on either visible light (VL) near infrared (NIR) imaging, or their fusion. However, limited numbers of works have investigated cross spectral matching or compared the iris biometric performance under both VL and NIR spectrum using unregistered iris images taken from the same subject. To the best of our knowledge, this is the first work that proposes a framework for cross spectral iris matching using unregistered iris images. To this end, three descriptors are proposed namely, Gabor difference of Gaussian (G DoG) Gabor binarized statistical image feature (G BSIF) and Gabor multi scale Weberface (G MSW) to achieve robust cross spectral iris matching. In addition, we explore the differences in iris recognition performance across the VL and NIR spectra. The experiments are carried out on the UTIRIS database which contains iris images acquired with both VL and NIR spectra for the same subject. Experimental and comparison results demonstrate that the proposed framework achieves state of the art cross spectral matching. In addition, the results indicate that the VL and NIR images provide complementary features for the iris pattern and their fusion improves notably the recognition performance.
Simultaneous adsorption of heavy metal ions and anions from aqueous solutions on chitosan Investigated by spectrophotometry and SEM EDX analysis

Chitosan (flakes with a degree of deacetylation of 90% was used as adsorbent for heavy metal ions in solution (copper, iron, nickel) The adsorption capacities were determined in dependence on the adsorption time and the initial metal salt concentration. With increasing adsorption time as well as the initial metal salt concentration the adsorption capacities increased. Highest adsorption capacity was achieved for copper(II)ions with 110 mg/L. Iron(II) and nickel(II)ions adsorbed with an adsorption capacity of 80 mg/L. The surface of chitosan flakes were investigated before and after the adsorption process by SEM and SEM EDX, respectively. The formation of crystal like structures was observed by SEM analysis for the investigation of copper(II)sulfate and iron(II)sulfate. It has been noticed that iron(II)ions oxidized before the adsorption on chitosan occurs. In comparison, the adsorption of nickel salt resulted in a smooth layer on the chitosan surface. SEM EDX analysis revealed that sulfate adsorbs on the chitosan surface besides the metal cations used.
STEM EDX tomography of bimetallic nanoparticles: A methodological investigation.
Venue: Ultramicroscopy

This paper presents an investigation of the limitations and optimisation of energy dispersive X ray (EDX) tomography within the scanning transmission electron microscope, focussing on application of the technique to characterising the 3D elemental distribution of bimetallic AgAu nanoparticles. The detector collection efficiency when using a standard tomography holder is characterised using a tomographic data set from a single nanoparticle and compared to a standard low background double tilt holder. Optical depth profiling is used to investigate the angles and origin of detector shadowing as a function of specimen field of view. A novel time varied acquisition scheme is described to compensate for variations in the intensity of spectrum images at each sample tilt. Finally, the ability of EDX spectrum images to satisfy the projection requirement for nanoparticle samples is discussed, with consideration of the effect of absorption and shadowing variations.
Nanochemo mechanical signature of organic rich shales: a coupled indentation EDX analysis

The organic inorganic nature of organic rich source rocks poses several challenges for the development of functional relations that link mechanical properties with geochemical composition. With this focus in mind, we herein propose a method that enables chemo mechanical characterization of this highly heterogeneous source rock at the micron and submicron length scale through a statistical analysis of a large array of energy dispersive X ray spectroscopy (EDX) data coupled with nanoindentation data. The ability to include elemental composition to the indentation probe via EDX is shown to provide a means to identify pure material phases, mixture phases, and interfaces between different phases. Employed over a large array, the statistical clustering of this set of chemo mechanical data provides access to the properties of the fundamental building blocks of clay dominated organic rich source rocks. The versatility of the approach is illustrated through the application to a large number of source rocks of different origin, chemical composition, and organic content. We find that the identified properties exhibit a unique scaling relation between stiffness and hardness. This suggests that organic rich shale properties can be reduced to their elementary constituents, with several implications for the development of predictive functional relations between chemical composition and mechanical properties of organic rich source rocks such as the intimate interplay between clay packing, organic maturity, and mechanical properties of porous clay/organic phase.
Big Data Application in Education: Dropout Prediction in Edx MOOCs
Venue: 2016 IEEE Second International Conference on Multimedia Big Data (BigMM)

Educational Data Mining and Learning Analytics are two growing fields of study, trying to make sense of education data and to improve teaching and learning experience. We study dropout prediction in Massively Open Online Courses (MOOCS) where the goal is given student's learning behavior log data in one month, to predict whether students would drop out in next ten days. We collect 39 courses data from XuetangX platform, which is based on the open source Edx platform. We describe our complete approach to cope with drop out prediction task, including data extraction from Edx platform, data preprocessing, feature engineering and performance test on several supervised classification model such as SVM, Logistics Regression, Random Forest and Gradient Boosting Decision Tree. We achieve 88% accuracy in dropout prediction task with GBDT model.
Shortwave infrared hyperspectral reflectance imaging for cotton foreign matter classification
Venue: Comput. Electron. Agric.

A non contact near infrared imaging technique was investigated to classify cotton foreign matter.A total of 11 botanical and 5 non botanical foreign matter materials were studied.Linear discriminant analysis achieved more than 95% accuracy in cross validation tests.Support vector machine achieved satisfactory classification results at the pixel level on images.The non contact hyperspectral imaging technique is promising for online classification. Cotton contaminants seriously reduce the commercial value of cotton lint and further degrade the quality of textile products. This research aims to investigate the potential of a non contact technique, i.e. liquid crystal tunable filter (LCTF) hyperspectral imaging, to inspect foreign matter on the surface of cotton lint. The foreign matter samples used in this study included 11 types of botanical foreign matter and 5 types of non botanical foreign matter. Hyperspectral images of the foreign matter were acquired using a LCTF hyperspectral imaging system with a spectral range from 900 to 1700nm. The mean spectra of the foreign matter and lint samples were extracted manually from the images. Linear discriminant analysis was applied to classify different types of foreign matter and cotton lint according to their spectral features. Classification accuracies of 96.5% and 95.1% were achieved with leave one out and four fold cross validation, respectively. For pixel level image classification, a majority of the pixels for different types of foreign matter were classified correctly by a support vector machine, using the top features of the minimum noise fraction transformation. The results demonstrate that non contact liquid crystal tunable filter hyperspectral imaging is a promising method to discriminate foreign matter materials from cotton lint.
An Efficient and Effective Automatic Recognition System for Online Recognition of Foreign Fibers in Cotton
Venue: IEEE Access

Preventing foreign fibers from being mixed with cotton is essential for producing high quality cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This paper proposes an efficient recognition system to accurately recognize foreign fibers mixed in cotton. The core component of the proposed system is an efficient classifier based on the kernel extreme learning machine (KELM) A two step grid search strategy, which integrates a coarse search with a fine search, is adopted to train an optimal KELM recognition model. The resultant model is compared with the support vector machine and extreme learning machine on a real data set using tenfold cross validation analysis. The experimental results show that the proposed recognition system can achieve classification accuracy as high as 93.57% which is superior to the other two state of the art systems. The results clearly confirm the superiority of the developed model in terms of classification accuracy. Promisingly, the proposed system can serve as a new candidate of powerful foreign fiber recognition systems with excellent performance.
'Your Foreign Plants are Very Delicate' Peasant Crop Ecologies and the Subversion of Colonial Cotton Designs in Dharwar, western India, 1830 1880.

As in other cotton growing agrarian environments colonised by Europeans,1 the onset of British colonial rule in rural western India was accompanied by pressures on local peasants to adopt and cultivate new foreign varieties of cotton so as to transform the species into one of the main commodities for the export market. Moreover, the potential of raw cotton to generate high land revenue assessments in western India was held to be considerable; indeed, the entire structure of colonial rule at local rural level was organised around the levy and collection of land revenue, which comprised the colonial state's primary source of  In 1835, revenue officials of the Government of Bombay, anxious to find out why the raiyats (peasant cultivators) of the district of Dharwar had not taken advantage of opportunities to grow 'superior' allegedly more profitable, foreign (mainly American) cottons, in preference to their 'country cotton' sent out local mamlatdars (junior indigenous revenue officials) to each taluka (district administrative sub division) to investigate the possible reasons. The responses of the peasants articulated their understandings of the social and ecological dynamics that lay at the heart of their farming practices.