nils-herrmann/clean_scirepeval huggingface dataset:
DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real world applications. In this work, we introduce DeepFashion1, a large scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion.
The MegaFace Benchmark: 1 Million Faces for Recognition at Scale
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Recent face recognition experiments on a major benchmark (LFW  show stunning performance a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evaluations at the million scale (LFW includes only 13K photos of 5K people) To this end, we have assembled the MegaFace dataset and created the first MegaFace challenge. Our dataset includes One Million photos that capture more than 690K different individuals. The challenge evaluates performance of algorithms with increasing numbers of "distractors" (going from 10 to 1M) in the gallery set. We present both identification and verification performance, evaluate performance with respect to pose and a persons age, and compare as a function of training data size #photos and #people) We report results of state of the art and baseline algorithms. The MegaFace dataset, baseline code, and evaluation scripts, are all publicly released for further experimentations1.
NetVLAD: CNN Architecture for Weakly Supervised Place Recognition
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following three principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end to end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval. The layer is readily pluggable into any CNN architecture and amenable to training via backpropagation. Second, we develop a training procedure, based on a new weakly supervised ranking loss, to learn parameters of the architecture in an end to end manner from images depicting the same places over time downloaded from Google Street View Time Machine. Finally, we show that the proposed architecture significantly outperforms non learnt image representations and off the shelf CNN descriptors on two challenging place recognition benchmarks, and improves over current state of the art compact image representations on standard image retrieval benchmarks.
Dynamic Image Networks for Action Recognition
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

We introduce the concept of dynamic image, a novel compact representation of videos useful for video analysis especially when convolutional neural networks (CNNs) are used. The dynamic image is based on the rank pooling concept and is obtained through the parameters of a ranking machine that encodes the temporal evolution of the frames of the video. Dynamic images are obtained by directly applying rank pooling on the raw image pixels of a video producing a single RGB image per video. This idea is simple but powerful as it enables the use of existing CNN models directly on video data with fine tuning. We present an efficient and effective approximate rank pooling operator, speeding it up orders of magnitude compared to rank pooling. Our new approximate rank pooling CNN layer allows us to generalize dynamic images to dynamic feature maps and we demonstrate the power of our new representations on standard benchmarks in action recognition achieving state of the art performance.
From passive to interactive object learning and recognition through self identification on a humanoid robot
Venue: Auton. Robots

Service robots, working in evolving human environments, need the ability to continuously learn to recognize new objects. Ideally, they should act as humans do, by observing their environment and interacting with objects, without specific supervision. Taking inspiration from infant development, we propose a developmental approach that enables a robot to progressively learn objects appearances in a social environment: first, only through observation, then through active object manipulation. We focus on incremental, continuous, and unsupervised learning that does not require prior knowledge about the environment or the robot. In the first phase, we analyse the visual space and detect proto objects as units of attention that are learned and recognized as possible physical entities. The appearance of each entity is represented as a multi view model based on complementary visual features. In the second phase, entities are classified into three categories: parts of the body of the robot, parts of a human partner, and manipulable objects. The categorization approach is based on mutual information between the visual and proprioceptive data, and on motion behaviour of entities. The ability to categorize entities is then used during interactive object exploration to improve the previously acquired objects models. The proposed system is implemented and evaluated with an iCub and a Meka robot learning 20 objects. The system is able to recognize objects with 88.5 success and create coherent representation models that are further improved by interactive learning.
El aprovechamiento una alternativa social y ambiental para el manejo de los residuos solidos reciclables para la jurisdiccion de la corporacion autonoma regional de las cuencas de los Rios Negro Nare "Cornare

En el desarrollo de toda actividad humana se generan residuos solidos que segun su naturaleza se clasifican en no peligrosos y peligrosos, entre los no peligrosos se cuentan los organicos y/o biodegradables, reciclables y/o aprovechables, ordinarios o inertes; los residuos peligrosos, son los que presentan riesgos potenciales por tanto la gestion de estos debe estar encaminada a proteger los seres humanos y al medio ambiente. Dentro de los residuos reciclables y/o aprovechables se encuentran, el plastico, papel y carton, el vidrio, los metales, y los organicos; estos residuos son susceptibles de incorporarse a procesos productivos o de ser reutilizados en su funcion original o en alguna relacionada, mediante procesos simples de adecuacion y/o transformacion. Una vez analizada la capacidad de generacion, se encontro que en la region del oriente antioqueno se cuenta con gran potencial de residuos solidos que pueden ser aprovechables, permitiendo asi disminuir la demanda de los recursos naturales, los residuos que se llevan a los rellenos sanitarios, y la emision de gases efecto invernadero (GEI) causantes del cambio climatico. Frente a la composicion de los Residuos Solidos Domesticos (RSD) los que mas se producen son los residuos organicos, seguidos de los plasticos, papel y carton, lo que evidencia una oportunidad para su recuperacion previa, comercializacion y aprovechamiento de los residuos reciclables.
Evaluation des effets de taille et d'architecture sur les proprietes mecaniques et electriques de fils composites metalliques cuivre/niobium fabriques par deformation plastique severe

Les fils composites Cu/Nb etudies ici sont d'excellents candidats pour les bobines non destructives generant des champs magnetiques pulses intenses (B 100T) Ils sont fabriques par Accumulative Drawing and Bundling (ADB) et sont constitue de renforts continus de Nb dans une matrice multi echelles de Cu. Ces travaux rentrent dans le cadre du projet METAFORES (ANR 12 BS09 0002) visant l'evaluation des effets de taille et d'architecture sur les proprietes des conducteurs Cu/Nb. L'objectif principal consiste donc a caracteriser leur microstructure et leurs proprietes a chaque etape de la fabrication par differentes techniques de caracterisation. Des essais mecaniques et electriques montrent une augmentation de la limite d'elasticite avec l'affinement de la microstructure tout en conservant une conductivite electrique adequate. Les etudes de la texture globale par DRX ont mis en evidence trois composantes de texture de fibre dont deux pour la matrice de Cu et et une composante unique pour le Nb. On retrouve ces trois composantes de texture dans les analyses locales (EBSD) cependant les proportions relatives des composantes du Cu varient en fonction du nombre de cycles ADB.Les essais de deformation in situ sous neutrons ont mis en evidence des comportements elasto plastique et purement elastique des familles de grains {111} du Cu et {110} du Nb respectivement, quels que soient les echantillons. Pour la famille {200} du Cu, le comportement mecanique varie en fonction du nombre de cycles ADB. Tous ces resultats viendront nourrir les simulations effectuees dans le cadre du projet METAFORES (These de Tang Gu, ENSAM Paris/Mines ParisTech)
Microparticles as controlled drug delivery carrier for the treatment of ulcerative colitis: A brief review
Venue: Saudi pharmaceutical journal SPJ the official publication of the Saudi Pharmaceutical Society

Ulcerative colitis is the chronic relapsing multifactorial gastrointestinal inflammatory bowel disease, which is characterized by bloody or mucus diarrhea, tenesmus, bowel dystension, anemia. The annual incidence of ulcerative colitis in Asia, North America and Europe was found to be 6.3, 19.2 and 24.3 per 100,000 person years. The major challenge in the treatment of ulcerative colitis is appropriate local targeting and drug related side effects. To overcome these challenges, microparticulate systems seem to be a promising approach for controlled and sustained drug release after oral administration. The main goal of this article is to explore the role of microparticles in ulcerative colitis for the appropriate targeting of drugs to colon. There are different approaches which have been studied over the last decade, including prodrugs, polymeric approach, time released system, pH sensitive system, which show the site specific drug delivery to colon. Among these approaches, microparticulate drug delivery system has been gaining an immense importance for local targeting of drug to colon at a controlled and sustained rate. Combined approaches such as pH dependent and time dependent system provide the maximum release of drug into colon via oral route. This article embraces briefly about pathophysiology, challenges and polymeric approaches mainly multiparticulate systems for site specific drug delivery to colon in sustained and controlled manner so that drug related side effects by reducing dosage frequency can be minimized.
Colon targeted oral drug delivery system based on alginate chitosan microspheres loaded with icariin in the treatment of ulcerative colitis.
Venue: International journal of pharmaceutics

In recent years, oral colon specific drug delivery system has been paid more attention in the treatment of inflammatory bowel disease (IBD) As the special pH condition in gastrointestinal tract, the challenge for treatment of IBD was that the colon drug delivery system should endure the low pH in stomach and release drugs quickly in high pH in colon. Icariin with the poor solubility and low bioavailability limited the treatment of many diseases in clinic. In this study, the protective mechanism of alginate chitosan microspheres loaded with icariin were investigated with trinitrobenzene sulfonic acid (TNBS)/ethanol induced colonic mucosal injury in rats. The results of drug release showed that the icariin loaded into microspheres released only 10% in simulated gastric fluid and a high amount of 65.6% released in simulated colonic fluid. The fluorescence tracer indicated high retention of targeted microspheres more than 12h in colon. The microspheres loaded with icariin could not only reduce the colonic injury by decreasing the colon mucosa damage index in rats, but also reduce the inflammatory response by reducing the production and gene expression of inflammatory mediators and cytokines in colonic mucosa. All the results indicate that targeted microspheres loaded with icariin could exert the colon protective effects through reducing the inflammatory response, which would be developed as a potential drug controlled release system for treatment of ulcerative colitis.
Teachers Cognition of Teaching English Pronunciation Techniques A Mixed Method Approach pdf

In recent years, a great number of attempts have been made on teachers' cognition with the aim of understanding the complications reinforcing the teachers' cognitions and their classroom practices. Such studies shed light on how teachers' cognitions expand over time and how they are reflected in their classroom practices. The aim of the present study was to investigate Iranian EFL teachers' cognition particularly in terms of the pronunciation techniques they apply in the oral communication classrooms and their knowledge about their language learners' characteristics. To achieve the goals of the study, the cognitions of five English teachers in the oral communication classrooms were explored. The teachers were requested to answer two semi structured interviews to obtain the data about their cognitions regarding the pronunciation techniques. Furthermore, their students were asked to fill out a questionnaire to express their opinions about the techniques applied by their teachers during instruction of English pronunciation. The qualitative and quantitative results showed that there was an intricate relationship between language teachers' experience with their cognitions about their language learners. Moreover, those teachers who were in higher level language courses showed to have broader cognitions about both the techniques they used in classrooms and the language learners' characteristics as well.
Family centered care from the perspective of nurses in the Neonatal Intensive Care Unit

to describe the challenges and strategies for implementation of Family centered Care in the in the care of newborns hospitalized in the Intensive Care Unit from the perspective of nurses.  qualitative study conducted in the Neonatal Intensive Care Unit of a public hospital. Fourteen nurses were interviewed. We used the technique of thematic content analysis.  the main aspects hindering the implementation of Family centered Care were: lack of material resources, absence of parents in the unit and shortage of staff, which influenced directly in the assistance provided to the newborn and the family. To minimize the challenges, nurses mentioned strategies such as creating and updating protocols, training, continuing education programs, optimizing communication and meetings with families.  the main challenges are related to human and material resources and infrastructure. Strategies mentioned were teamwork and continuous improvement.
Semi Supervised Learning with Bidirectional Adaptive Pairwise Encoding
Venue: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)

In contrast to classic supervised learning methods that demand pre defined class labels, pairwise encoding or side information encoding merely requires pairwise similarity information to drive feature learning, which makes it very appealing for many fundamental tasks such as dimensionality reduction and semi supervised learning. In this paper, we present a novel bimarginal pairwise encoding model, along with deep autoencoder, to learn nonlinear embedding for the aforementioned tasks. The new method learns powerful features that preserve critical pairwise information in a semi supervised manner. It has achieved better performance on the well known yet hard to make improvement benchmark MINIST compared with other methods in the same category, i.e. Autoencoder  Invariant Mapping for Dimensionality Reduction  Neighborhood Component Analysis  and Fixed Bi Margin Pairwise Encoding [11]
ANALISA KEKUATAN MEKANIK SAMBUNGAN LAS METODE MIG( METAL INERT GAS) DAN METODE FSW( FRICTION STIR WELDING) 800 RPM PADA ALUMUNIUM TIPE 5083

Pemilihan metode pengelasan yang efektif berguna untuk industri galangan. Khususnya galangan kapal aluminium. Maka dari itu penelitian ini bertujuan untuk membandingkan dua metode pengelasan yang berbeda yaitu metode MIG (Metal Inert Gas) dan metode FSW (Friction Stir Welding) Material Alumunium yang digunakan adalah Al 5083. Pada pengelasan MIG (Metal Inert Gas) menggunakan jenis sambungan pengelasan single V butt joint dengan sudut 60deg. Tegangan dan arus yang digunakan adalah 22V dan 220 A dengan elektroda ER 5356. Dan pada pengelasan FSW (Friction Stir Welding) menggunakan putaran tool 800 RPM dan Travel Speed 30 mm/min. Pengujian di lakukan uji tarik dengan menggunakan standar ASTM E 8M 00b. Dari hasil penelitian ini menunjukan bahwa pada metode pengelasan MIG (Metal Inert Gas) menghasilkan pengelasan yang lebih sempurna atau lebih baik dengan memberikan nilai Tegangan Tarik maksimal yaitu 99,35 N/mm 2 dan nilai Regangan sebesar 1,9% Sedangkan pada metode FSW (Friction Stir Welding) hanya memperoleh nilai Tegangan Tarik maksimal yaitu 29,62 N/mm 2 dan nilai Regangan sebesar 0,5% Selain meggunakan hasil Uji Lab juga dilakukan analisa menggunakan software Ansys LS Dyna dengan hasil kekuatan Tarik tertinggi pada pengelasan MIG (Metal Inert Gas) sebesar 72,6 N/mm 2 dan pada pengelasan FSW (Friction Stir Welding) sebesar 25,9 N/mm 2
The entomopathogen Beauveria bassiana has epiphytic and endophytic activity against the tomato leaf miner Tuta absoluta

The recent introduction and rapid spread of the tomato leaf miner Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) throughout Europe, Africa and the Middle East poses a severe threat to future cultivation of tomato and other Solanaceae. Among the best practicable means for effective and sustainable control of this invasive insect pest are entomopathogenic fungi, which can either prevent a further spread of this insect to new areas or keep population densities below an economic threshold level. Here, we report on the efficacy of a commercially available mycoinsecticide based on the entomopathogen Beauveria bassiana (Bals. Vuill.  In bioassays, high mortality rates and significantly reduced longevity of larvae were obvious when larvae had fed for a period of around 15 days on leaves with B. bassiana propagules present on the surface as an epiphyte with mortality values corrected for variance in control mortality of 90 100% In addition, a second mode of action of B. bassiana against T. absoluta larvae was evident in bioassays in the form of an endophytic establishment of this fungus in treated tomato plants. Longevity of T. absoluta L4 larvae was significantly lower in individuals which had fed on surface sterilized tomato leaves obtained from plants treated 18 days before the bioassay with a B. bassiana suspension compared to larvae feeding on control plants treated with water. Corrected mortality reached values between 30% and 50% for all larval instars. Growth of tomato plants was not inhibited due to colonization by endophytic B. bassiana and a weak systemic translocation of fungal propagules in non treated leaves was evident in the assays. Accordingly, entomopathogenic fungi like B. bassiana express different modes of action again target insect pests, which is of particular relevance for the design of efficient management strategies for invasive pests like T. absoluta.
A fast density based data stream clustering algorithm with cluster centers self determined for mixed data
Venue: Inf. Sci.

Most data streams encountered in real life are data objects with mixed numerical and categorical attributes. Currently most data stream algorithms have shortcomings including low clustering quality, difficulties in determining cluster centers, poor ability for dealing with outliers' issue. A fast density based data stream clustering algorithm with cluster centers automatically determined in the initialization stage is proposed. Based on data attribute relationships analysis, mixed data sets are filed into three types whose corresponding distance measure metrics are designed. Based on field intensity distance distribution graph for each data object, linear regression model and residuals analysis are used to find the outliers of the graph, enabling cluster centers automatic determination. After the cluster centers are found, all data objects can be clustered according to their distance with centers. The data stream clustering algorithm adopts an online/offline two stage processing framework, and a new micro cluster characteristic vector to maintain the arriving data objects dynamically. Micro clusters decay function and deletion mechanism of micro clusters are used to maintain the micro clusters, which reflects the data stream evolution process accurately. Finally, the performances of the proposed algorithm are testified by a series of experiments on real world mixed data sets in comparison with several outstanding clustering algorithms in terms of the clustering purity, efficiency and time complexity.
Learning periodic motions from human demonstrations using transverse contraction analysis
Venue: 2016 American Control Conference (ACC)

In this paper, an algorithm called transverse contracting dynamic system primitive (CDSP) to learn the dynamics of periodic motions from demonstrations is presented. Learning motion plans is essential in making robot programming possible by non expert programmers as well as realizing human robot collaboration. The complex periodic motion of the human arm is generated by an orbitally stable closed loop dynamical system. To capture the complexity, a neural network (NN) model is used to represent the dynamics of the human arm motion states. To take into consideration the orbitally stable nature of the human arm motion dynamics, the unknown motion model learning is subjected to transverse contraction analysis constraints. To learn the model parameters, an optimization problem is formulated by relaxing the non convex transverse contraction constraints using sum of squares (SOS) programming. The NN model is approximated by using a polynomial approximation of the activation function in order to pose the optimization problem as an SOS problem. A negative gradient of a repulsive potential function is added to the learned transverse contracting model to incorporate obstacle avoidance. Experimental results indicate that the model learning via CDSP can generate periodic motions learned from human demonstrated data recorded using Microsoft Kinect sensor. The CDSP algorithm is able to adapt to situations for which the demonstrations are not available, e.g. an obstacle placed in the path. The comparison between the CDSP algorithm and rhythmic dynamic movement primitives (DMPs) shows that the CDSP algorithm performs better in terms of periodic trajectory reproduction accuracy and the time taken by the reproductions to enter the periodic orbit from random initial conditions.