bluuebunny/crossref_metadata_2025_split huggingface dataset:
Supported Employment im Routinebetrieb: Evaluation des Berner Job Coach Placement-Programms 2005 – 2016
Authors: Richter Dirk , Hunziker Markus , Hoffmann Holger 
Published: May 2019

Zusammenfassung
Ziel Die Studie berichtet über die Evaluation des Berner Job Coach Placement-Programms im Routinebetrieb.
Methode Administrative Daten der Teilnehmenden aus den Jahren 2005 – 2016 wurden hinsichtlich der Integration in den allgemeinen Arbeitsmarkt und hinsichtlich der Aufnahme einer regulären Beschäftigung analysiert.
Ergebnisse 46 % aller Teilnehmenden (N = 420) erreichten das primäre Erfolgsziel der Integration in den allgemeinen Arbeitsmarkt, 33 % schafften den Übergang in eine reguläre Anstellung.
Diskussion Das Berner Job Coach Placement Programm erreicht auch im Routinebetrieb eine im internationalen Vergleich sehr gute Integrationsquote.
Targeted Neonatal Echocardiography-Guided Therapy in Vein of Galen Aneurysmal Malformation: A Report of Two Cases with a Review of Physiology and Approach to Management
Authors: Giesinger R. E. , Elsayed Y. N. , Castaldo M. P. , McNamara P. J. 
Published: May 2019

AbstractVein of Galen malformation results in predictable changes in physiology which exist on a continuum. Severe pulmonary hypertension may present as hypoxemia; however, excessive reduction in pulmonary vascular resistance may precipitate progressive pulmonary overcirculation and impaired systemic blood flow. Right ventricular performance and the patency and direction of the ductus arteriosus may play a crucial role in postductal organ perfusion. Physiological stabilization may be complex and variable over time. The utilization of targeted neonatal echocardiography to guide treatment decisions may improve the ability to provide therapy tailored to the specific disease pathophysiology and monitor serially as conditions change. An enhanced approach to physiological stabilization may reduce the risk of unexpected decompensation and allow for thoughtful, controlled endovascular embolization in appropriate candidates.
Radioscapholunate Fusion for Radiocarpal Osteoarthritis: Prognostic Factors of Clinical and Radiographic Outcomes
Authors: Degeorge Benjamin , Montoya-Faivre David , Dap François , Dautel Gilles , Coulet Bertrand , Chammas Michel 
Published: May 2019

Abstract
Background Radioscapholunate (RSL) fusion is a surgical option to manage radiocarpal osteoarthritis. Many authors upgrade the procedure by adding distal scaphoid excision with or without excision of the triquetrum. Our objective was to identify the prognostic factors for good clinical and radiographic outcomes of RSL arthrodesis.
Purpose Distal scaphoid excision improves both scaphotrapeziotrapezoidal osteoarthritis and radiocarpal fusion, and excision of the triquetrum is not critical to achieving satisfactory outcomes.
Methods In this retrospective and bicentric investigation, all wrists were managed with RSL fusion for posttraumatic radiocarpal osteoarthritis. A total of 85 patients were included and evaluated at the revision by clinical (pain, wrist motion, and strength), functional (Disabilities of the Arm, Shoulder, and Hand questionnaire, Patient-Related Wrist Evaluation score, and Modified Mayo Wrist Score), and radiological (scaphotrapeziotrapezoidal, midcarpal osteoarthritis, and radiocarpal nonunion) examinations. We assessed prognostic factors for clinical and radiographic (osteoarthritis and nonunions) outcomes.
Results The average follow-up was 9.1 years (1–21.4). Work-related accidents adversely impacted the clinical outcomes, and distal scaphoid excision significantly improved them and decreased scaphotrapeziotrapezoidal osteoarthritis and nonunion. Neither distal scaphoid excision nor excision of the triquetrum influenced midcarpal osteoarthritis. Radiocarpal fusion was significantly promoted by memory staples and bone grafting.
Discussion Distal scaphoid excision should be preferred to improve the functional results while decreasing scaphotrapeziotrapezoidal osteoarthritis and radiocarpal nonunion rates. Excision of the triquetrum appears to be an alternative to radioulnar resection–arthroplasty to solve ulnocarpal impaction syndrome. Strict surgical procedure must be observed to promote RSL fusion combining solid bone fixation and the use of bone graft.
Level of evidence This is a level IV, case series, retrospective series.
Diagnostic Ultrasound Detection of Changes in Femoral Muscle Mass Recovery after Tibial Plateau Levelling Osteotomy in Dogs
Authors: Frank Ilan , Duerr Felix , Zanghi Brian , Middleton Rondo , Lang Linda 
Published: May 2019

Abstract
Objective The goal of this study was to develop a clinically feasible ultrasound (US) protocol that can detect changes in thigh muscle mass in dogs after stifle surgery. The primary aim of this study was to compare previously described US measurement locations of the canine thigh for detecting changes in muscle mass in dogs recovering from tibial plateau levelling osteotomy (TPLO).
Study Design This was a prospective, exploratory pilot study. Adult dogs (n = 7) undergoing pet-owner elected TPLO were enrolled. Twelve different US measurements were performed in triplicate by a single experienced observer. Measurements were performed at 0, 2, 4 and 8 weeks after surgery at a proximal and distal location along the femur. Data from all available time points and locations were analysed for the main effect of time within modalities.
Results A total of 1,008 US measurements were performed. Measurements of the transverse sectional area of the rectus femoris muscle detected significant (p  ≤  0.05) muscle loss between weeks 0 and 2 at the lateral and medial aspects of the distal location (19% and 15% respectively). Measurements of the thigh muscle thickness were significantly (p < 0.01) increased between 2nd- and 8th- week time points at the lateral aspect of the proximal location (26%).
Conclusion The proximal femoral location, measured from the lateral aspect, appears to be the most suitable US measurement for detecting increases in femoral muscle mass in dogs recovering from TPLO. The provided pilot data suggest that further research evaluating this outcome measure is indicated.
Nosocomial Infection Outbreak with Enterobacter aerogenes at a Neonatal Intensive Care Unit and Its Outcomes
Authors: Ulubaş Işık Dilek , Bas Ahmet Yagmur , Kulali Ferit , Ozcan Beyza , Unal Sezin , Yucel Husniye , Demirel Nihal 
Published: May 2019

Objective Health care associated infections (HAIs) in the neonatal intensive care unit (NICU) are a significant cause of neonatal morbidity and mortality. Enterobacter aerogenes, a gram-negative bacterium, rarely causes HAI in the pediatric and NICUs. We report the case of a HAI outbreak of E. aerogenes in an NICU and discuss the clinical spectrum and outcome of the affected preterm newborns and successful prevention measures.
Materials and Methods A retrospective study was conducted in a level III NICU of Etlik Zubeyde Hanım Women's Health Teaching and Research Hospital. The infants with blood cultures positive for E. aerogenes during the outbreak were enrolled. Perinatal risk factors, laboratory findings, symptoms, treatment, and prognosis were recorded. Clinical applications and prevention measures were identified.
Results A preterm infant of 27 weeks of gestation who was diagnosed on the eighth postnatal day was the initial case of the reported outbreak of health care associated E. aerogenes infection. Subsequently, 12 more infants were diagnosed in the next 3 months. Infants' mean gestational age was 27.1 ± 2.3 weeks, and mean birthweight was 902 ± 161 g. The clinical symptoms at presentation were respiratory distress, sclerema, circulatory failure, and shock, which appeared at a mean age of 7.6 ± 5.8 days. Analysis of E. aerogenes strains revealed that all strains were of the same clonal type. Eight patients died in follow-up due to E. aerogenes septicemia during this outbreak. The mean interval between the onset of symptoms and death was 1.5 ± 1 days.
Conclusion Outbreaks of health care associated E. aerogenes infection result in a high mortality rate among very low birthweight infants. Awareness of adjustable risk factors and preventive measures to control the outbreak decreases both morbidity and mortality.
Pott's Disease: An Emerging Source of Potentially Inappropriate Treatment
Authors: Esposito Silvia , Moscatelli Marco , Schiariti Marco Paolo , Viganò Ilaria , Pantaleoni Chiara , Marucci Gianluca 
Published: May 2019

AbstractSpinal Tuberculosis in children is uncommon, even more so in cases of involvement of posterior vertebral elements, and its diagnosis is often delayed. Here we report the case of a young female presenting neuroradiological features and clinical symptoms suspicious for malignant tumor. Histological examination of biopsy specimen evidenced a Pott's disease. We highlight the importance of suspecting this disorder in children with both aspecific systemic and neurological symptoms, in order to reach a timely diagnosis for appropriate and targeted intervention, avoiding the risk of overtreatment and malpractice claims.
Image Super-Resolution based NCSs Model with Packet Loss
Authors: Jiang Changjiang , Li Hantao , Zhou Shangbo 
Published: May 2019

Abstract
There are many ways to super-resolution reconstruction of a single image. This method require the association between low-resolution images and high-resolution images, and has achieved good results in different applications. This article also pursues the correlation between low-resolution images and high-resolution images, but we combine images with linear networks to explore the possibilities of combining the two domains. In order to achieve the purpose of restoring image details, we used a method of combining image and network, different from the traditional single-picture super-resolution, we using network delay and packet loss compensation, fully retain the data packets before and after, and find the internal similarity from low-resolution images. Experiments show that inputting images into a network system is affected by system packet loss and has a large impact. It also shows that it is feasible and effective to combine images with network packet loss in the networked control systems model, and specific image effects will be shown in the paper. Our algorithm has achieve a certain expected effect, by superimposing the image super-resolution reconstruction is more extensive, and it is also convenient for future research based on image super-resolution.
New Methods on Image Analysis, Two Applications: An Augmented Reality Application for Heartbeat Sound Analysis And A MRI Brain Injury Image Analysis
Authors: Lechuga L J , Yamamoto T , López O , Lechuga G 
Published: May 2019

Abstract
Traditional statistical methods have become insufficient when applied to image analysis. The increasing size of data volume and its complexity demands new statistical approaches and algorithms. Current methods imply losing intrinsic data structures, for example when data comes from multiway arrays. In this work we concentrate in two applications i) A pre-diagnostic smartphone application for detection of cardiovascular abnormalities through the analysis of heartbeat sounds and the use of augmented reality for displaying valuable information to the end user in an immersive experience. Using the latest augmented reality smartphone applications, a digital stethoscope, heartbeat audios and classification using neural networks, we measure a user’s heartbeat and output in real time, a pre-diagnostic of their current cardiovascular health. ii) A study concerning comatose patients, based on a Diffusion Tensor Image Magnetic Resonance Imaging (MRI) dataset that predicts long-term outcome for patients having suffered a brain traumatic injury. MRI images were obtained from 104 comatose patients, 65 with positive outcome and 39 with negative outcome, 39 controls were used. The fact that each volumetric image led into a 143x255726x4 tensor input, is used to briefly explain how new multiway methods could be useful in image analysis methods.
Video-based Person Re-identification Based on Feature Learning of Valid Regions and Distance Fusion
Authors: Yang Danmei , Qi Meibin , Wu Jingjing , Jiang Jianguo 
Published: May 2019

Abstract
Video-based person re-identification (re-id) is a very challenging problem because of the occlusion and the changes of viewpoints, pedestrian postures and illumination. Most existing methods of video-based person re-id usually concatenates the extracted appearance and space-time features directly, which do not consider the discrepancy of different features fully. In order to deal with this problem well, we propose a simple but effective method based on feature learning of valid regions and distance fusion, which combines three distances. The first distance is local distance of valid regions calculated using the Gaussian of Gaussian(GOG) feature. Pedestrian images are divided in horizontal directions, and then regions with smaller distances are selected as valid ones to be reserved and regions with larger distances are as invalid ones to be removed due to occlusions and posture changes. The other two distances are obtained by independent metric learning using the histogram of oriented gradient 3D (HOG3D) feature and the Local Maximal Occurrence (LOMO) feature. The three distances are added as the final distance between pedestrian pair, so the matching rank of gallery is obtained. Extensive experiments are performed on the iLIDS-VID and PRID-2011 datasets, and the results prove the effectiveness of our methods.
Multi-info Fusion Based Video Recommendation System
Authors: Liu Yang , Zhang Guijuan , Jin Xiaoning , Yuan Haifeng 
Published: May 2019

Abstract
The great progress in recommendation system help users discover more interesting items that satisfy their appetites. Considering the video recommendation is an increasing popular sub-field of recommendation, but the traditional recommendation techniques such as Collaborative Filtering and Content-based model simply exploit one information source that limits its performance. In this paper, we proposed a Multi-info fusion based recommendation system which integrates several different information sources to comprehensively model the similarity between videos. The information sources including the common user-item rating data and video’s textual content that consists of video’s genres and textual description. Experimental results on a public dataset show that the proposed system is of high quality and achieves significant improvements over the traditional Collaborative Filtering techniques.
An Improved Visual Background Extraction Algorithm Combining Depth Information
Authors: Yang Feng , Jin Shijun , Ye Wenwu 
Published: May 2019

Abstract
ViBe is one of the most commonly used background subtraction method, which conducts foreground detection on each frame pixel-by-pixel. When the algorithm is applied to video sequence with large depth differences such as forest fire surveillance, the problem of traditional ViBe is appearing. If the algorithm parameters of different depth are the same, it will inevitably lead to two situations: the nearby shaking of trees is easy to cause false detection because of the large pixel area, and the distant smoke is easy to miss as it occupies small pixels of the image. To solve this problem, an improved visual background extraction algorithm combining depth information is proposed. The method calculates the dark channel information as depth information, and a conversion function is designed to adjust sensitivity to accommodate moving targets with huge depth difference. The experimental results demonstrate that the improved ViBe algorithm has better performance than traditional ViBe algorithm in forest fire surveillance. In addition, the improvement of the algorithm does not excessively increase the computational time-consuming.
Correspondence Between 3D Shapes Based on Improved Functional Map
Authors: Yang Jun , Li Longjie , Obaseki Alexander Jesuorobo 
Published: May 2019

Abstract
We propose a shape matching algorithm based on an improved functional map in order to calculate correspondence between two given 3D non-rigid shapes, in which shape correspondence can be represented as a mapping function of mixed transformation matrix B and calibration matrix P of basis functions. First, Laplace matrix is calculated and a new matrix is constructed as basis matrix of function space by using eigenvectors of Laplace matrix after eigen-decomposition. Then, a calibration algorithm based on statistical covariance is proposed to calculate the matrix P that is used to calibrate basis matrices of function space of two shapes. Finally, the matrix B is optimized by an improved ICP(Iterative Closest Point) algorithm in order to calculate shape correspondence with the matrix P. Experimental results show that the proposed algorithm avoids excessive initial conditions, obtains accurate shape correspondence and significantly solves symmetry ambiguities of 3D shapes during matching process.
Real World Hand Gesture Interaction in Virtual Reality
Authors: Zhang Qi , Zhu Wenzhe , Zhu Qing 
Published: May 2019

Abstract
With the development of virtual reality, the appropriate interaction technology has become the focus of practitioners. Today’s virtual reality interaction is mostly through the interaction of traditional electronic devices, such as handles and other products, temporarily solve the problem of interaction, but also make the user from the virtual world is taken out, immersion greatly reduced. The paper uses hand gestures to interact with virtual reality. By using skin detection in YCbCr color space and indicates the location of hand to determine the approximate range of hand. Then, the histograms of oriented gradient (HOG) which has been widely used in target detection research in recent years is used to extract gesture features, and applies support vector machine (SVM) method to achieve the real-time hand gesture recognition. Finally, experiments prove that the proposed method is accurate and stable in virtual reality interaction.
An Efficient Method for Automatic Generation of Labanotation Based on Bi-Directional LSTM
Authors: Zhang Xueyan , Miao Zhenjiang , Yang Xiaonan , Zhang Qiang 
Published: May 2019

Abstract
Labanotation uses a variety of graphic symbols to analyse and record human movements accurately and flexibly, which is an important means to protect traditional dance. In this paper, we introduce an efficient method for automatic generation of Labanotation from motion capture data by identifying human movements with bidirectional LSTM network (Bi-LSTM). Up to our knowledge, this is the first time that Bi-LSTM network has been introduced to the field of Labanotation generation. Compared with previous methods, Bi-LSTM used in our human movements recognition system learns context information for sequential data from not only the past but also the future directions. Combined with a newly designed discriminative skeleton-topologic feature, our approach has the ability to generate more accurate Labanotation than previous work. Experiment results on two public motion capture datasets show that our method outperforms state-of-the-art methods, demonstrating its effectiveness.
DCRN: Densely Connected Refinement Network for Object Detection
Authors: Gao Shihui , Miao Zhenjiang , Zhang Qiang , Li Qingyu 
Published: May 2019

Abstract
Object detection has made great progress in recent years, the two-stage approach achieves high accuracy and the one-stage approach achieves high efficiency. In order to inherit the advantages of both while improving detection performance, this manuscript present a useful method, named Densely Connected Refinement Network (DCRN). It adds the dense connection based on RefineDet. Compare to the RefineDet, our approach can take full advantage of the bottom feature information. DCRN is formed by three interconnected modules, the dense anchor refinement module (DARM), the dense object detection module (DODM) and the dense transfer connection block (DTCB). First module can make better use of the features from different layers to initially adjust anchors by attaching dense connection. The latter module takes the refined anchors to further improve the regression and predict multi-class label. Due to the dense connection in DCRN, the network parameters are reduced and the computing costs of this approach is also saved. Extensive experimental results on PASCAL VOC 2007 and PASCAL VOC 2012 demonstrate that DCRN achieves higher accuracy than the one-stage method and higher efficiency than the two-stage method.
Cerebral Microbleed Detection by Extracting Area and Number from Susceptibility Weighted Imagery Using Convolutional Neural Network
Authors: Sa-ngiem S , Dittakan K , Temkiatvises K , Yaisoongnern S , Kespechara K 
Published: May 2019

Abstract
Cerebral microbleed (CMB) the small vessels in the brain which is one of the major factors used to facilitate in the early stage diagnosis for Alzheimer’s disease detection. In traditional, CMBs detection can be done manually by the neurologists, doctors or specialists. However, the process is time-consuming and the results are not accurate depending on the doctor experiences. Therefore the efficient and reliable of the automatic detection of CMB is needed. This paper proposes a new framework for CMB detection which employs segmentation of the region of interests (ROIs), detection of the CMBs and identification of the area from SWI scan images. Convolutional Neural Network(CNN) is applied to generate the desired models for later prediction. Shape matching mechanism is also applied to identify locations of CMB in the brain. The experimental result shows that the CMB can be classified with a recorded accuracy value of 95.45%. The CMBs were discovered from three different locations include (i) cortical region, (ii) cerebellum and (iii) brainstem with an accuracy value of 100%.
Video Object Detection by Aggregating Features across Adjacent Frames
Authors: Zhang Ruyi , Miao Zhenjiang , Zhang Qiang , Hao Shanshan , Wang Shengbo 
Published: May 2019

AbstractVideo object detection is of great significance for video analysis. In contrast to object detection in still image, video object detection is more challenging which suffers from motion blur, varying view-points/pose, and occlusion. Existing methods utilized temporal information during detection in videos and show improvement over static-image detector. In this paper, we propose a novel method for video object detection that can aggregate features across adjacent frames adaptively as well as capture more global cues so as to be more robust to drastic appearance changes. Initially, current frame feature and warped feature from adjacent frames can be obtained via feature extraction network and optical flow network. Next, a coherence contribution module is designed for adaptively feature aggregation of the two kinds of features obtained from the first step. Finally, the still-image detector which included an extra instance-level module that agregates features from adjacent frames for capturing more global feature is adopted to get the final result. The experimental results evaluated on our method shows leading performance on the ImageNet VID dataset.
Light Corner Based Object Detector with Stacked-ENet Backbones
Authors: Li Qingyu , Miao Zhenjiang 
Published: May 2019

Abstract
Object detection algorithms have made great progress in the past few years because of the development of deep learning. From region selection to anchor box regression, the accuracy of the algorithms becoming more and more accurate, but they’re also far away from vision mechanism of human. Corner based is a new approach for object detection, but methods proposed nowadays are in huge wastage of time and resource, leading them not available in vast bulk of jobs. In this paper, we proposed a method which makes corner-based object detector could inference in real-time. We analyze the disadvantages of existing methods, found out the most consuming parts and the solution for improvements, which finally makes the algorithm efficiency and get a result which is competitive with other existing real-time methods.