Miracle-dz/UniFAR-FTUs huggingface dataset:
Explicablility as Minimizing Distance from Expected Behavior

In order to have effective human-AI collaboration, it is necessary to address how the AI agent's behavior is being perceived by the humans-in-the-loop. When the agent's task plans are generated without such considerations, they may often demonstrate inexplicable behavior from the human's point of view. This problem may arise due to the human's partial or inaccurate understanding of the agent's planning model. This may have serious implications from increased cognitive load to more serious concerns of safety around a physical agent. In this paper, we address this issue by modeling plan explicability as a function of the distance between a plan that agent makes and the plan that human expects it to make. We learn a regression model for mapping the plan distances to explicability scores of plans and develop an anytime search algorithm that can use this model as a heuristic to come up with progressively explicable plans. We evaluate the effectiveness of our approach in a simulated autonomous car domain and a physical robot domain.
Internet-Based Distance Education–The Administrator's Perspective

Abstract In this study, the authors sought to investigate the status of distance education (DE) at 68 higher education institutions. Through a survey of DE program directors, the authors gathered information on size, target audience, media used, evaluation methods, student characteristics, student and faculty perceptions, and administrative support. Results reveal that the programs were still primarily asynchronous in their course delivery and needed more staffing and technical support and that program coordinators were experimenting with alternative forms of evaluation.
Measuring and Pricing Qos

Abstract The authors study a scalable pricing framework for QoS enabled networks that support real-time, adjustable real-time, and non-realtime traffic. The scheme, which belongs to usage-based methods, is independent of the underlying network and the mechanisms for QoS provisioning. The framework is credit based, ensuring the fairness, comprehensibility, and predictability of usage price. It also provides a means for the network providers to ensure, with high probability, cost recovery and profit, competitiveness of prices, and encouragement of client behaviours that will enhance the network's efficiency. This is achieved by appropriate charging mechanisms and the provision of suitable incentives. Simulation results suggest that users have better overall satisfaction, that providers are able to recover costs; and that better network utilization is achieved while reduced call blocking probability is observed. The implementation and usage costs of the framework are low.
Disease burden and cost-of-illness of food-related pathogens in the Netherlands

Op verzoek van het ministerie van VWS onderzoekt het RIVM de maatschappelijke kosten van 14 ziekteverwekkers; vanaf 2011 wordt dit jaarlijks in kaart gebracht. De infecties die de ziekteverwekkers veroorzaken, kunnen worden overgedragen via voedsel, de mens, het milieu of dieren (zoonosen). De geschatte kosten van de 14 ziekteverwekkers bedroegen in 2011 416 miljoen euro. De meeste mensen worden ziek van een besmetting met het norovirus, de Campylobacter-bacterie en het rotavirus waardoor deze de hoogste kosten met zich meebrengen. Als naar de kosten per patient wordt gekeken, zijn deze het hoogst bij een besmetting door de bacterie Listeria monocytogenes, de parasiet Toxoplasma gondii, en het hepatitis E-virus omdat deze relatief ernstige ziekteverschijnselen veroorzaken. Onderverdeling kosten van voedselinfecties Meer dan 40 procent van de totale kosten die de onderzochte ziekteverwekkers met zich meebrengen wordt via voedsel veroorzaakt (168 miljoen euro in 2011). De overige kosten worden toegeschreven aan de overdracht van mens op mens (28 procent), blootstelling via het milieu (15 procent) of via contacten tussen dieren en mensen (7 procent). De resterende 9 procent van de kosten is gerelateerd aan reizen naar het buitenland. De kosten van voedselinfecties zijn nader gespecificeerd. Ruim de helft (51 procent oftewel 86 miljoen euro) van de kosten van voedselinfecties worden veroorzaakt door producten van dierlijke oorsprong, zoals vlees, eieren en zuivelproducten. Vis, fruit en groenten, dranken, graanproducten en andere niet-gespecificeerde voedselgroepen veroorzaken respectievelijk 8, 6, 2, 5 en 14 procent van de ziektekosten toegeschreven aan voedsel. Onderverdeling soorten kosten De onderzoekers hebben de maatschappelijke kosten van de 14 ziekteverwekkers onderverdeeld in drie categorieen. Ten eerste zijn er de kosten voor consulten aan artsen, ziekenhuisopnamen en medicijnen, de 'directe medische kosten'. Deze bedragen minder dan 25 procent van alle kosten. Daarnaast bestaan ze uit kosten die door de patient zelf worden betaald, zoals reiskosten van en naar de arts (directe niet-medische kosten). Deze zijn laag. Als derde post zijn er de kosten die voortvloeien uit productiviteitsverliezen vanwege werkverzuim van de patienten en speciaal onderwijs na neurologische aandoeningen (indirecte niet-medische kosten). Deze post is het meest substantieel en bedraagt bijna 75 procent van de totale kosten.
Is Malnutrition Still a Serious Problem in India? A Comprehensive Review

Malnutrition is a leading cause of health-related distress in developing countries and is caused mainly due to protein–energy imbalance and deficiency of micronutrients. It can be a crucial risk factor of physical ailment and death around the globe. Every year thousands of people, including young children and pregnant females get affected . Protein-energy imbalance causes two of the most common types of malnutrition, namely, marasmus (simple starvation) and kwashiorkor (protein deficient diet). Characteristic manifestations of malnutrition in developing countries are seen due to deficiencies in micronutrients like iron, zinc, iodine and vitamin A. If poor diet along with infectious disease is highly prevalent at a regular interval, it results into a vicious impact. Although better diagnosis and treatment for malnutrition have been developed recently, patients belonging to rural areas have lesser access to such options due to minimal health service schemes. Strategies for preventing protein–energy imbalance range from promotion of breast-feeding to the use of food supplementations. Besides, micronutrient deficiencies can be addressed by planning strategies for a proper diet-plan. For example diversification of dietary materials using domestic gardens and livestock. Use of iodized salt is a great success globally, but supplementation of other nutrients is yet to cover vulnerable communities properly. For implementation of these strategies effectively, nutrition related educational campaigns and proper healthcare initiatives must be taken into stride. To eradicate malnutrition related problems, we must address poverty, which is clearly associated with the scarce supply of food and nutrition, educate people regarding the importance of nutrients in food, organize fundraisers and other programs to address the situation at hand, give extra attention to the extremely poverty stricken regions within a country and prioritize malnutrition related diseases as much as infectious diseases so that they can be eradicated once and for all.
Parler en amour et parler de l’amour : Normativité relationnelle et conversations quotidiennes

Une part significative de nos echanges et interactions quotidiennes est menagee a la question de l’amour et des rapports amoureux. C’est en effet par l’interaction et la conversation quotidienne que l’on entre en relation, que l’on parle de notre relation, que l’on parle des situations des autres, etc. A l’aide d’une methodologie novatrice, le debriefing conversationnel, nous avons eu l’occasion de nous pencher sur les thematiques pregnantes de la normativite et de l’individualite contemporaine presentes dans les recits de conversation de participants varies. Nous proposons de presenter nos resultats de recherche selon trois moments de l’interaction qui ressortent de notre terrain d’enquete : 1) Comment parle-t-on de l’amour ? 2) Comment parle-t-on en amour ? et finalement : 3) Comment recoit-on ces conversations en amour et sur l’amour ? Nous aimerions ainsi mieux comprendre comment et de quelles manieres les experiences de sociabilite contemporaines contribuent a construire les normes amoureuses.
Acute appendicitis perforating into the bladder mimicking cystitis

Perforation of the appendix can lead to periappendiceal abscess or diffuse peritonitis. The major reason for appendiceal perforation is delay in diagnosis and treatment. We report this rare case of perforated appendix into the bladder, which caused acute abdomen. We also present a review of the literature. The patient underwent an emergency operation in which the appendicitis was found to have perforated into the bladder and produced symptoms that mimicked cystitis. The terminal ileum and the cecum were seriously inflamed. The patient underwent a limited right hemicolectomy, closure of the bladder with a serosal patch, closure of the transverse colon and an end‐ileostomy. Differential diagnosis of acute appendicitis especially in women is very difficult because the surgeon must always consider and look for many other conditions that can mimic appendicitis. From a management viewpoint, the various diagnoses can be divided into those that require surgery and those that do not. The present case was an uncommon case of acute appendicitis where, unfortunately, its primary symptoms and laboratory data had led to the wrong diagnosis of cystitis, which did not require surgery.
Learning Principal Orientations Descriptor for Action Recognition

In this paper, we propose an unsupervised learning based representation method, named principal orientations descriptor (POD), to describe the local and statistic characteristics for action recognition. Unlike hand-crafted features which require high prior knowledge, our POD is learned from raw pixels and reflects the distribution of principal orientations around the motion trajectories. Different from deep learning based features which are based on a large number of labeled data, our POD is learned in an unsupervised learning manner. We learn POD in the spatial domain and the temporal domain based on the same motion trajectories individually, which makes POD have the ability to describe both the spatial and the temporal information along the same trajectories. To evaluate the performance of POD, we conduct experiments on two challenging action datasets: Hollywood2 and HMDB51. The results show that our method is competitive to the existing methods.
Les «élites» polítiques de la Catalunya democràtica

es hipbtesis bbiques de la teoria elitista clbsica porten a suposar una profunda continuitat en les caracteristiques estructurals del grup dirigent (en sentit ampli), fins i tot a traves dels canvis del sistema politic, i fortes similituds entre les diverses forces actuants en el sistema politic. Per dir-ho amb poques paraules, ni el canvi ni els partits son massa importants: , coincidirien a dir analistes reaccionaris i anarquistes primitius. L'experikncia del que ha succe'it a Catal.unya des de la transicio democrhtica pot ser vista com un bon exercici de contrast d'aquelles hipbtesis. En concret, i pel que aqui interessa, el canvi politic ha implicat en el nostre pais:
Motion Vector Based Data Association for On-Line Multi-object Tracking

On-line multi-object tracking needs to solve the data association problem on each new frame in time-critical video analysis applications. However, associating the new detection responses and existing trajectories under the tracking-by-detection framework is faced with challenges such as mis-detections and false alarms. In order to build a more reliable frame-by-frame association with the given detection results in applications where precision is primarily required, we design a strong associating constraint based on motion vectors computed from uniformly sampled keypoints in the scene while considering spatial information at the same time. With the optical flow analysis between the two successive frames, we propose a new cost function for building the association matrix and solve the multi-object tracking problem in an on-line form. Experimental results on challenging benchmark datasets show that our method achieves overall state-of-the-art performance, especially effective in reducing false alarms.
Real-Time Traffic Sign Classification Using Combined Convolutional Neural Networks

The traffic sign recognition system inside the vehicle plays an important role and could guarantee the safety of human life on the road since it feedbacks road information to the driver in time. Benefited from learning features of the traffic sign, the convolutional neural network (CNN) has been widely used in traffic sign recognition with a high accuracy. However, the different kinds of traffic signs appear to distinctive features. A deep and high complexity neural network with a larger number of parameters is usually required to adapt the distinctive features, while it tends to be time-consuming and can not meet real-time requirement. In this paper, we firstly divide traffic signs into hierarchal structure according to the types of features, and then use a combined CNNs (CCNN) to adapt the hierarchical traffic signs, where the probabilities of superclass and subclass the sign belongs to are calculated using two CNNs with a simple network. Finally, classifying of the sign can be achieved by the weighted output of the two CNNs, and a low complexity sign recognition system could be obtained. Simulation results on the GTSRB database show that the proposed method achieves comparable accuracy and less time-consuming to the state-of-the-art methods.
Learning Autoencoder of Attribute Constraint for Zero-Shot Classification

The goal of zero-shot classification (ZSC) isto classify target classes precisely based on learning asemantic mapping from a feature space to a semanticknowledge space. However, the learned semantic mappingis only concerned with predicting source classes. Applyingthe semantic mapping to target classes directly will sufferfrom the semantic shift problem. In this paper, we proposea novel method called autoencoder of attribute constraint(AOAC) to settle this problem. In AOAC, we adopt theencoder-decoder paradigm to learn the semantic mapping.Additionally, we take the inaccurate attributes of sourceimages into consideration and generate virtual data to solveit. The experimental results on two challenging datasetsshow that our proposed AOAC can resolve the semanticshift problem effectively and also improve the computationalspeed significantly.
Preemptive Ilioinguinal- Iliohypogastric Nerve Block versus Intravenous Morphine for Postoperative Pain Control after Unilateral Inguinal Herniotomy in Children: A Comparative Study

Abstract Background: Inguinal hernia repair in children is one of the commonest surgical procedures. As a day-case procedure, it requires adequate post-operative pain control. A prospective randomized double-blinded study was designed to compare the effectiveness of postoperative pain control and incidence of complications between combined ilioinguinal/iliohypogastric nerve block and intravenous morphine. Methods: Sixty patients aged 2-12 years were randomly allocated to two groups of thirty. One group received intravenous morphine 100 microgram/kg before skin incision and the other had an ilioinguinal and iliohypogastric nerve block with 0.5% bupivacaine (0.25 ml/kg) also before skin incision. All patients have received standardized anaesthesia. Pain was assessed using 0- 10 scale at 0.30 minutes and 1, 2, 3 and 4 hours postoperative. Time to first analgesia, number of paracetamol doses and the incidence of respiratory depression, vomiting and itching were assessed during the first 24 hours. Results: During the first four postoperative hours, the two groups had identical pain scores. Time to first analgesia and number of doses of the rescue drug for residual pain during the first 24 postoperative hours was equal for the two groups. None of the 60 patients had respiratory depression but the morphine group patients were found to have more episodes of vomiting (P<0.05), no significant differences in itching (p=NS). Conclusion: We concluded that both intravenous morphine (100 microgram/kg) and ilioinguinal- iliohypogastric nerve block have the same analgesic effect for postherniorrhaphy pain in children with higher incidence of vomiting in morphine treated patients. Keywords: Morphine, Nerve block, Hernia repair, Pain.
Pixel-Based Color Model for Robust Tracking

In this paper, we propose a Pixel-based Color Model (PCM) that can deal with the problems of deformation and occlusion in visual object tracking. We use a pixel-based classifier and a feature mapping technique that can distinguish the target pixels from the background. The PCM method is generic as it can be incorporated into any tracker that fails to deal with the problems of deformation and occlusion. The base tracker in this paper is a correlation filter which has achieved great performance in recent years. Compared with the existing trackers, the Pixel-based Color Model combined with Correlation Filter (PCMCF) has several advantages: (1) By learning the color model of the target, it can deal with the problem of deformation. (2) As the model is pixel-based, it is less sensitive to occlusion. (3) The adaptive learning strategy can avoid model drift when the target is partially or fully occluded. (4) When the target recovers from fully occlusion, the color model will find it immediately. Both quantitative and qualitative evaluations have been conducted, and it shows that the proposed PCM method can improve the performance of the base tracker, and the PCMCF algorithm is superior to most state-of-the-art trackers especially in deformation and occlusion.
A New GVF Arrow Pattern for Character Segmentation from Double Line License Plate Images

License plate recognition is a live problem for several developing countries because of its many challenges. One of such challenges is character segmentation from double lines (alphabets in one line and numerals on another line) license plate images, where we can see touching between adjacent characters (horizontally) and lines (vertically). This is the major cause for poor recognition performance. Therefore, we propose a novel technique based on Gradient Vector Flow (GVF) to segment characters from double line license plate images. The proposed technique explores a new GVF arrow pattern, which represents spaces between lines and characters based on the fact that the force in concavity created between characters and lines according to the fact that curved shaped characters attract GVF arrows in unique fashion. This observation leads to find seed space patches for segmentation. The spatial coordinates of seed space patches are passed through Hough transform to find line separators. Next, the proposed technique searches for seed space patches, which are perpendicular to line separators to find character separators. Experimental results on double line license plate images show that the proposed technique is robust to touching, rotations, scaling, distortion, and outperforms the existing character segmentation methods. The recognition experiments before and after segmentation show that the proposed segmentation is significant in improving license plate recognition rate.
Marginal Deep Architectures

Many deep architectures have been proposed in recent years. To obtain good results, most of the previous deep models need a large number of training data. In this paper, for small and middle scale applications, we propose a novel deep learning framework based on stacked feature learning models. Particularly, we stack marginal Fisher analysis (MFA) layer by layer for the initialization of the deep architecture and call it "Marginal Deep Architectures" (MDA). In the implementation of MDA, the weight matrices of MFA are first learned layer by layer, and then some deep learning techniques are employed to fine tune the deep architecture. To evaluate the effectiveness of MDA, we have compared it with related feature learning methods and deep learning models on six small and middle scale real-world applications. Extensive experiments demonstrate that MDA performs not only better than shallow feature learning models, but also deep learning models in these applications.
Deep Rank Learning for Facial Attractiveness

Predicting facial attractiveness automatically is a rising problem and has received more attention from the computer vision community. Previous works have posed facial attractiveness as a ranking problem. However, extracting features and learning a ranking model are often separated and optimized independently in previous works. In this paper, we propose two End-to-End learning to rank approaches using deep Convolutional Neural Network for fully automatic facial beauty assessment, which are RankSVM CNN and RankNet CNN. To the best of our knowledge, this is the first work to address learning to rank problem for facial beauty by CNN. We proved both the RankNet CNN and the RankSVM CNN are effective and excellent for facial beauty assessment. With a small amount of data, our approaches perform better than any other shallow models we have known. Finally, we have achieved a high prediction correlation, which is 0.469 on the HOTorNOT dataset and 0.815 on the SCUT-FBP dataset.
Age-Invariant Person Identification by Segmentation Verification of Face Image

Face recognition has been a major research theme over the last two decades. There are several problems to be solved to improve the performance of face recognition. Such major problems involve appearance variation due to pose, illumination, expression, and aging. In particular, aging includes internal and external factors that cause facial appearance variation and, consequently, it is the most difficult problem to handle. In this paper, we propose a face recognition method that is robust against facial appearance variation due to aging. The proposed method employs segmentation verification of frontal face images that consists of the following three steps. (1) Face image segmentation generates three regional subimages from the input face image. (2) A matching score is calculated using gradient features from a pair consisting of the input image and a registered image for each of the three generated subimages and original (whole face) image. We obtain four matching scores. (3) The verifying classifier evaluates the matching score vector formed of the matching scores calculated for each of the four images and predicts the a posteriori probability that two matching images belong to the same person. The results of an experimental evaluation with the FGNET and MORPH face aging datasets clarify the effectiveness of the proposed method for age invariant face recognition
Compressed Sensing Natural Imaging via Hadamard-Diagonal Matrix

The measurement matrix is one of the keys of the compressed sensing. However, the existing measurement matrices face the two main problems of the difficult hardware implementation and the low sensing efficiency. In fact, those matrices always ignore the energy concentration characteristic of the natural images in the sparse domain, which greatly limits the sensing efficiency of the measurement matrices and thus the construction efficiency. In this paper, we propose a simple but efficient measurement matrix based on the Hadamard matrix with the consideration of maximizing the energy conservation in the sparse domain, named Hadamard-Diagonal Matrix (HDM). We keep the main sensing rows and columns in the Hadamard matrix with '1' and the others with '0' to keep more energy after the sampling of the natural images in the sparse domain, which increases the sensing efficiency. Meanwhile, the HDM is a binary and sparse matrix which benefits the hardware implementation. The experimental results show that the HDM performs better than some popular existing measurement matrices and is incoherent with different sparse basis.