Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel geographic routing algorithm for ad hoc networks based on localized Delaunay triangulation

With the development of ad hoc networks, some researchers proposed several geometric routing protocols which depend on the planarization of the network connectivity graph to guarantee the delivery of the packet between any pair of nodes in the network. In this paper, we proposed a new online routing algorithm GLNFR (greedy and local neighbor face routing) for finding paths between the nodes of the ad hoc networks by storing a small amount of local face information at each node. The localized Delaunay triangulation was used to be the backbone of wireless network on which the GLNFR routing algorithm runs. It has the better scalability and adaptability for the change of ad hoc networks. Experiment on NS have been conducted. The results show that the delivery rate of packets and routing protocol message cost under such novel routing protocols performs better than others proposed before.
Person re-identification employing 3D scene information

Abstract. The person reidentification task applied in a real-world scenario is addressed. Finding people in a network of cameras is challenging due to significant variations in lighting conditions, different color responses, and different camera viewpoints. State-of-the-art algorithms are likely to fail due to serious perspective and pose changes. Most of the existing approaches try to cope with all these changes by applying metric learning tools to find a transfer function between a camera pair while ignoring the body alignment issue. Additionally, this transfer function usually depends on the camera pair and requires labeled training data for each camera. This might be unattainable in a large camera network. We employ three-dimensional scene information for minimizing perspective distortions and estimating the target pose. The estimated pose is further used for splitting a target trajectory into reliable chunks, each one with a uniform pose. These chunks are matched through a network of cameras using a previously learned metric pool. However, instead of learning transfer functions that cope with all appearance variations, we propose to learn a generic metric pool that only focuses on pose changes. This pool consists of metrics, each one learned to match a specific pair of poses and not being limited to a specific camera pair. Automatically estimated poses determine the proper metric, thus improving matching. We show that metrics learned using only a single camera can significantly improve the matching across the whole camera network, providing a scalable solution. We validated our approach on publicly available datasets, demonstrating increase in the reidentification performance.
Septic loosening due to Brucella melitensis after bilateral knee prosthesis and two-stage total knee prosthesis revision.

OZET Infection after total knee replacement is a serious complication associated with a high incidence of morbidity. A prosthetic infection due to Brucella melitensis is a very rare condition. In this study, we describe a case of brucellosis infection with septic loosening of both knee joints after bilaterally cemented total knee prosthesis application; this is rarely encountered in the literature. The patient diagnosis was established by clinical findings (fever, joint swelling, etc.) and supported by laboratory findings and analysis of joint fluid obtained from joint aspiration. Plain radiography and bone scintigraphy confirmed septic loosening. In this case, we performed total knee prosthesis revision bilaterally after antibiotic treatment. We preferred two-stage surgery for the revision. In the first stage, the infected prosthesis was removed and a Teicoplanin-loaded bone cement spacer was inserted in both knees. Twenty weeks later, the second stage was performed. During the 24-month follow-up period, no complications were seen.
The role of patient management in medical expertise development: Extending the contemporary theory

Medical expertise research has focused on why experienced physicians display remarkable performance in solving clinical problems and how this competency develops over many years of education and practice. The contemporary theory on the development of expertise in medicine proposes that students progress through different stages of knowledge restructuring (i.e., encapsulation and illness script) in which their knowledge is finely tuned towards practical situations. Although research in clinical case paradigm has provided us with a better understanding of the differences between students and doctors, it has almost exclusively focused on diagnostic performance, and patient management has received substantially less attention. This reflects an incomplete picture of a medical expert, as management is not only a crucial step in patient workup, but it is also highly connected to experience with patients. In their daily practice, doctors always diagnose and manage patients concurrently; hence, after a large number of patient encounters in practical settings, these two types of knowledge become integrated into one another. In order to incorporate management into contemporary theory, it is proposed that it should be considered as a part of the illness script structure. In the way towards expertise, knowledge about patient management becomes more prominent, and so an investigation of management knowledge could ultimately enable us to broaden our understanding of medical expertise.
Performance comparison of congestion aware multi-path routing (with load balancing) and ordinary DSR

Ad hoc networks are useful for providing communication support where no fixed infrastructure exists or the deployment of a fixed infrastructure is not economically profitable, and movement of communicating parties are allowed. Due to the dynamic topology, developing better routing protocol became a challenging task. The Dynamic Source Routing (DSR) protocol is a simple and efficient routing protocol designed specifically for use in multi-hop wireless ad hoc networks of mobile nodes. Multi-path routing protocol is one of the approaches used to have less overhead, better bandwidth cost by distributing the load among a set of paths . However, due to interfering in the channels of the paths, multi-path increases the end to end delay and do not work well in highly congested networks. In this paper we propose a congestion aware multi-path Dynamic Source Routing Protocol. we generate a set of disjoint multi-paths and handle the problem of end to end delay using the correlation factor measurement. A set of path selection criteria have been used to improve the end to end delay. The simulation experiments are conducted using ns-2 network simulator. We will compare the performance of the suggested algorithm to the performance of the conventional Dynamic Source Routing Protocol (DSR).
Real-time action recognition using a multilayer descriptor with variable size

Abstract. Video analysis technology has become less expensive and more powerful in terms of storage resources and resolution capacity, promoting progress in a wide range of applications. Video-based human action detection has been used for several tasks in surveillance environments, such as forensic investigation, patient monitoring, medical training, accident prevention, and traffic monitoring, among others. We present a method for action identification based on adaptive training of a multilayer descriptor applied to a single classifier. Cumulative motion shapes (CMSs) are extracted according to the number of frames present in the video. Each CMS is employed as a self-sufficient layer in the training stage but belongs to the same descriptor. A robust classification is achieved through individual responses of classifiers for each layer, and the dominant result is used as a final outcome. Experiments are conducted on five public datasets (Weizmann, KTH, MuHAVi, IXMAS, and URADL) to demonstrate the effectiveness of the method in terms of accuracy in real time.
Comparative study on texture features for fingerprint recognition: application to the BioHashing template protection scheme

In order to ensure privacy constraints in biometrics, new algorithms called template protection schemes have been proposed in the last ten years in the literature. Most of these algorithms require as input a feature having a fixed size. Texture features can be used within this context for fingerprints as the number of minutiae varies in general for different captures. BioHashing is a two authentication factor algorithm that can be used to enhance to ensure privacy while using biometrics. In this work, we compare different recent texture features from the literature within the BioHashing scheme while considering many constraints: efficiency, maximal representation size and constant size description. Experiments are conducted on three fingerprint databases from the FVC competition. Results permit us to conclude on the texture features to be used within this context.
Effective real-time vehicle tracking using discriminative sparse coding on local patches

Abstract. A visual tracking framework that provides an object detector and tracker, which focuses on effective and efficient visual tracking in surveillance of real-world intelligent transport system applications, is proposed. The framework casts the tracking task as problems of object detection, feature representation, and classification, which is different from appearance model-matching approaches. Through a feature representation of discriminative sparse coding on local patches called DSCLP, which trains a dictionary on local clustered patches sampled from both positive and negative datasets, the discriminative power and robustness has been improved remarkably, which makes our method more robust to a complex realistic setting with all kinds of degraded image quality. Moreover, by catching objects through one-time background subtraction, along with offline dictionary training, computation time is dramatically reduced, which enables our framework to achieve real-time tracking performance even in a high-definition sequence with heavy traffic. Experiment results show that our work outperforms some state-of-the-art methods in terms of speed, accuracy, and robustness and exhibits increased robustness in a complex real-world scenario with degraded image quality caused by vehicle occlusion, image blur of rain or fog, and change in viewpoint or scale.
Improving health care encounters and communication with transgender patients

Abstract Background/objectives Transgender persons represent a generally ill-served or underserved population. This marginalized group continues to experience considerable difficulty in obtaining culturally competent health care despite recommendations by professional organizations and introduction of antidiscrimination legislation. This review examines communication and procedural barriers to transgender health care and suggests practical steps to help ameliorate disparities and unequal treatment. Methods Publications addressing health disparities and communication and procedural barriers to health care of transgender persons were identified by a structured search of electronic databases. The most pertinent articles are reviewed and consolidated with the authors’ collective experiences. Results To improve their interactions and communication with transgender persons, health care providers can take a variety of practical steps in several key areas: office environment, registration forms, initial interview and assessment, confidentiality, personnel training, awareness of and compliance with applicable antidiscrimination legislation, health insurance-related issues, and outreach and transgender health promotion. Conclusions Enhancing patient satisfaction through culturally competent health care, quality assurance, and patient feedback is critical to creating open lines of communication between practitioner and patient and fostering a favorable context for transgender patient care.
Human action recognition with group lasso regularized-support vector machine

Abstract. The bag-of-visual-words (BOVW) and Fisher kernel are two popular models in human action recognition, and support vector machine (SVM) is the most commonly used classifier for the two models. We show two kinds of group structures in the feature representation constructed by BOVW and Fisher kernel, respectively, since the structural information of feature representation can be seen as a prior for the classifier and can improve the performance of the classifier, which has been verified in several areas. However, the standard SVM employs L2-norm regularization in its learning procedure, which penalizes each variable individually and cannot express the structural information of feature representation. We replace the L2-norm regularization with group lasso regularization in standard SVM, and a group lasso regularized-support vector machine (GLRSVM) is proposed. Then, we embed the group structural information of feature representation into GLRSVM. Finally, we introduce an algorithm to solve the optimization problem of GLRSVM by alternating directions method of multipliers. The experiments evaluated on KTH, YouTube, and Hollywood2 datasets show that our method achieves promising results and improves the state-of-the-art methods on KTH and YouTube datasets.
Super-resolving barcode images with an edge-preserving variational Bayesian framework

Abstract. Limited resolution, blurring, warping, and additive noise associated with image acquisition and storage often make the barcode image degraded, generating low-resolution (LR) barcode images. Barcodes in degraded LR images are difficult to recognize. The goal of this paper is to introduce the potential of super-resolution (SR) technique in conquering the aforementioned challenges, and a variational Bayesian SR method is proposed in this work. Different from the previous work, here, the high-resolution barcode image is estimated through its corresponding posterior probability distribution. The barcode image is made of some homogeneous regions separated by sharp edges, and sometimes it is anisotropic. A universal prior probability distribution was proposed for the barcode image by considering these characteristics. Mathematically, the efficiency of this prior distribution is demonstrated, which can preserve sharp edges and suppress artifacts in the reconstructed barcode images. Moreover, by using the variational Bayesian framework, the motion parameters and hyperparameters can be estimated accurately and efficiently, ensuring the success of the SR technique. In order to overcome the difficulty caused by nonlinearity, the Taylor expansion method is introduced to solve the proposed SR problem. Eventually, the simulated and real data experiments show the encouraging performance of the proposed SR method. It increases certainly the reconstruction quality, and could be considerably robust against blur and noise. It is believed that the variational SR technique in the barcode auto-identification technique should open a further perspective of coping with technology challenge.
Prevalencia de fibrilación auricular en la población española de 60 o más años de edad. Estudio PREV-ICTUS

Introduction and objectives. The aims of this study were to determine the prevalence of atrial fibrillation in individuals aged 60 years or more in Spain using a random sample of the population and to identify associated factors. Methods. An analysis of the PREV-ICTUS study, a randomized cross-sectional population-based study of individuals aged 60 years or more, was carried out. Data on demographic variables, cardiovascular risk factors, and cardiovascular disease were obtained from medical records. The diagnosis of atrial fibrillation was based on the patient’s medical history and an electrocardiogram performed during the study. Results. In the 7108 individuals studied (mean age, 71.9 [7.1] years, 53.6% female), the prevalence of atrial fibrillation was 8.5% (95% confidence interval [CI], 7.99.2%). It was higher in males (9.3% vs 7.9% in females; P=.036) and increased from 4.2% in individuals aged 6064 years to 16.5% in those aged 85 years or more (χ 2 test for linear trend, P<.001). Multivariate analysis showed that existing cardiovascular disease, hypertension, age, and left ventricular hypertrophy had the strongest associations with atrial fibrillation. Although there was a strong relationship between hypertension and atrial fibrillation (odds ratio, 2.53; 95% CI, 1.60-4.01), no association was found between poor blood pressure control and atrial fibrillation. A weak association with diabetes mellitus was found only when arterial pressure was included in the model, but not when a diagnosis of hypertension was included.
Elimination of character-resembling anomalies within a detected region using density-dependent reference point construction in an automated license plate recognition system

Abstract. The problem of eliminating character-resembling blobs on a detected region in the plate detection stage of an automated license plate recognition system is addressed. The proposed method amplifies the slight differences between the noncharacter blobs (anomalies) and the character blobs (true signal) to enhance the tractability. This method postulates on two propositions: (1) the anomalies are usually located around the true signal and the suspected anomalies and (2) blobs should be given less emphasis in computing a reference point. The first proposition is based on prior knowledge and observation; the second proposition is based on the fact that a reference point that takes anomalies into account is contaminated and thus misleading. The gist of the method mainly focuses on the methodology to emphasize the blobs differently in accordance to their location in computing the reference point that approximates the representative value of true signal properties more accurately, thus giving the effect of amplifying the slight differences. The performance of the method is evaluated on both its capability and consistency in solving certain types of anomalies.
Local and global regularized concept factorization for image clustering

Concept factorization (CF), as a popular matrix factorization technique, has recently attracted increasing attention in image clustering, due to the strong ability of dimension reduction and data representation. Existing CF variants only consider the local structure of data, but ignore the global structure information embedded in data, which is very crucial for data representation. To address the above issue, we propose an improved CF method, namely local and global regularized concept factorization (LGCF), by considering the local and global structures simultaneously. Specifically, the local geometric structure is depicted in LGCF via a hypergraph, which is capable of precisely capturing high-order geometrical information. In addition, to discover the global structure, we establish an unsupervised discriminant criterion, which characterizes the between-class scatter and the total scatter of the data with the help of latent features in LGCF. For the formulated LGCF, a multiplicative update rule is developed, and the convergence is rigorously proved. Extensive experiments on several real image datasets demonstrate the superiority of the proposed method over the state-of-the-art methods in terms of clustering accuracy and mutual information.
Permeability of the implanted round window membrane in the cat: an investigation using horseradish peroxidase

The round window membrane of seven cats was implanted with a Silastic™ prosthesis 5 mm long. The membrane's permeability to horseradish peroxidase was investigated at different stages after implantation. All cats were treated with amoxycillin for a period of one week after the operation. Results showed an increased permeability over a period of approximately two weeks following implantation. Thereafter the round window barrier to horseradish peroxidase returned to normal. In all cases cochlear hair cells and auditory nerve fibres were preserved. In cases of spontaneous infection in the bulla the membrane responded with a proliferation of the subepithelial connective tissue, and the formation of protuberances. In these round windows although the permeability was increased the penetration of horseradish peroxidase into the scala tympani was limited. Horseradish peroxidase always passed through the gap between the membrane and the prosthesis. Particles were taken up, however, by the connective tissue envelop.
Exploiting the Associated Information to Locate Mobile Users in Ubiquitous Computing Environment

Although GPS is deemed as ubiquitous outdoor localization technology, we are still far from a similar technology for indoor environments. Though a number of techniques are proposed for indoor localization, they are separated efforts that are way from a real ubiquitous localization system. Our real-world experience from InSpace, a pervasive computing system with wireless devices to provide intelligent services to users, shows that locating mobile users remains very challenging due to various interfering factors. We analyze real traces of mobile phones carried by users and find that mobile users exhibit temporal-spatial stability and neighborhood relativity. Motivated by this observation, we develop a Mobile Boundary Localization approach, MBL, to exploit the associated information to locate mobile users. This localization approach uses different treatment in different conditions and lets each mobile phone try to estimate its possible location range. We have implemented and evaluated MBL by extensive real-world experiments in InSpace and simulations. The results demonstrate that MBL significantly outperforms state-of-the-art localization approaches with more accurate, efficient, and consistent performance. Keywords-Localization; Ubiquitous Computing; Mobile
Structural Way of Thinking as Applied to Quality Assurance Management

Some major structures for improving the quality of software are described. It is noted that defects in software are the result of imperfections in the development process and that high quality is achieved through aiming for a better process. Structures are introduced in the product structure, the product architecture, and the work processes. As people constitute an essential part of the process, the human error problem and work time constancy are considered. Based on these, structures and general principles for improving quality are explained. Progress is attained by education in the broad sense. The present work is written for managers in software fields. >
Self-Compassionate Reactions to Health Threats

Four studies investigated the relationship between self-compassion, health behaviors, and reactions to illness. Participants completed measures of self-compassion, health-related thoughts and feelings, reactions to actual and hypothetical illnesses, and self-regulation. Study 1 revealed that self-compassion was related to health-related cognitions and affect for healthy and unhealthy participants. In Study 2, self-compassion predicted participants’ reactions to actual illnesses beyond the influence of illness severity and other predictors of health behaviors. Self-compassionate people also indicated they would seek medical attention sooner when experiencing symptoms than people lower in self-compassion. Study 3 demonstrated that self-compassion is related to health-promoting behaviors even after accounting for self-regulatory capabilities and illness cognitions. Study 4 revealed that the relationship between self-compassion and health reactions is partially explained by a proactive approach to health, benevolent self-talk, and a motivation toward self-kindness. Overall, these studies demonstrate that self-compassion has important implications for health-promoting behaviors and reactions to illness.