title: An Overview on Wireless Sensor Networks Technology and Evolution

abstract: Wireless sensor networks (WSNs) enable new applications and require non-conventional paradigms for protocol design due to several constraints. Owing to the requirement for low device complexity together with low energy consumption (i.e., long network lifetime), a proper balance between communication and signal/data processing capabilities must be found. This motivates a huge effort in research activities, standardization process, and industrial investments on this field since the last decade. This survey paper aims at reporting an overview of WSNs technologies, main applications and standards, features in WSNs design, and evolutions. In particular, some peculiar applications, such as those based on environmental monitoring, are discussed and design strategies highlighted; a case study based on a real implementation is also reported. Trends and possible evolutions are traced. Emphasis is given to the IEEE 802.15.4 technology, which enables many applications of WSNs. Some example of performance characteristics of 802.15.4-based networks are shown and discussed as a function of the size of the WSN and the data type to be exchanged among nodes.


Untreated ruptures of the anterior cruciate ligament.


title: Neuropathological stageing of Alzheimer-related changes

abstract: Eighty-three brains obtained at autopsy from nondemented and demented individuals were examined for extracellular amyloid deposits and intraneuronal neurofibrillary changes. The distribution pattern and packing density of amyloid deposits turned out to be of limited significance for differentiation of neuropathological stages. Neurofibrillary changes occurred in the form of neuritic plaques, neurofibrillary tangles and neuropil threads. The distribution of neuritic plaques varied widely not only within architectonic units but also from one individual to another. Neurofibrillary tangles and neuropil threads, in contrast, exhibited a characteristic distribution pattern permitting the differentiation of six stages. The first two stages were characterized by an either mild or severe alteration of the transentorhinal layer Pre-alpha (transentorhinal stages I-II). The two forms of limbic stages (stages III-IV) were marked by a conspicuous affection of layer Pre-alpha in both transentorhinal region and proper entorhinal cortex. In addition, there was mild involvement of the first Ammon's horn sector. The hallmark of the two isocortical stages (stages V-VI) was the destruction of virtually all isocortical association areas. The investigation showed that recognition of the six stages required qualitative evaluation of only a few key preparations.


title: AffectAura: an intelligent system for emotional memory

abstract: We present AffectAura, an emotional prosthetic that allows users to reflect on their emotional states over long periods of time. We designed a multimodal sensor set-up for continuous logging of audio, visual, physiological and contextual data, a classification scheme for predicting user affective state and an interface for user reflection. The system continuously predicts a user's valence, arousal and engage-ment, and correlates this with information on events, communications and data interactions. We evaluate the interface through a user study consisting of six users and over 240 hours of data, and demonstrate the utility of such a reflection tool. We show that users could reason forward and backward in time about their emotional experiences using the interface, and found this useful.


title: Neuro-Fuzzy Systems : A Survey

abstract: – The techniques of artificial intelligence based in fuzzy logic and neural networks are many times applied together. The reasons to combine these two paradigms come out of the difficulties and inherent limitations of each isolated paradigm. Generically, when they are used in a combined way, they are called Neuro-Fuzzy Systems. This term, however, is many times used to assign a specific type of system that integrates both techniques. This type of system is characterised for a fuzzy system where fuzzy sets and fuzzy rules are adjusted using input output patterns. There are several different implementations of neuro-suzzy systems, therefore each author defined its own model. This article summarizes a general vision of the area describing the most known hybrid neuro-fuzzy techniques, its advantages and disadvantages.


title: An edge-directed bicubic interpolation algorithm

abstract: Image interpolation is a technique of producing a highresolution image from its low-resolution counterpart, which is often required in many image processing tasks. In this paper, we propose an edge-directed bicubic convolution (BC) interpolation. The proposed method can well adapt to the varying edge structures of images. The experimental results show that it reduces common artifacts such as blurring, blocking and ringing etc. and significantly outperforms some existing interpolation methods (including BC interpolation) in terms of both subjective and objective measures.


title: Analytical approach for optimal design of a line-start internal permanent magnet synchronous motor

abstract: The work described in this paper deals with the analytical design and optimization of a line-start permanent magnet synchronous motor (LSPM) with a series magnetic circuit structure. The design approach considers a LSPM as an induction motor (IM) combined with a permanent magnet rotor arrangement and takes into account the characteristics of both asynchronous and synchronous regimes.


abstract: We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally, we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm. This review adds to the earlier version, Murtagh F, Contreras P. Algorithms for hierarchical clustering: an overview, Wiley Interdiscip Rev: Data Mining Knowl Discov 2012, 2, 86–97. WIREs Data Mining Knowl Discov 2017, 7:e1219. doi: 10.1002/widm.1219 ::: ::: This article is categorized under: ::: ::: Algorithmic Development > Hierarchies and Trees ::: Technologies > Classification ::: Technologies > Structure Discovery and Clustering


The detection of fraud in mobile phone networks


title: Database of iris printouts and its application: Development of liveness detection method for iris recognition

abstract: Liveness detection (often referred to as presentation attack detection) is the ability to detect artificial objects presented to a biometric device with an intention to subvert the recognition system. This paper presents the database of iris printout images with a controlled quality, and its fundamental application, namely development of liveness detection method for iris recognition. The database gathers images of only those printouts that were accepted by an example commercial camera, i.e. the iris template calculated for an artefact was matched to the corresponding iris reference of the living eye. This means that the quality of the employed imitations is not accidental and precisely controlled. The database consists of 729 printout images for 243 different eyes, and 1274 images of the authentic eyes, corresponding to imitations. It may thus serve as a good benchmark for at least two challenges: a) assessment of the liveness detection algorithms, and b) assessment of the eagerness of matching real and fake samples by iris recognition methods. To our best knowledge, the iris printout database of such properties is the first worldwide published as of today. In its second part, the paper presents an example application of this database, i.e. the development of liveness detection method based on iris image frequency analysis. We discuss how to select frequency windows and regions of interest to make the method sensitive to “alien frequencies” resulting from the printing process. The proposed method shows a very promising results, since it may be configured to achieve no false alarms when the rate of accepting the iris printouts is approximately 5% (i.e. 95% of presentation attack trials are correctly identified). This favorable compares to the results of commercial equipment used in the database development, as this device accepted all the printouts used. The method employs the same image as used in iris recognition process, hence no investments into the capture devices is required, and may be applied also to other carriers for printed iris patterns, e.g. contact lens.


title: Posterior regularization for structured latent variable models

abstract: We present posterior regularization, a probabilistic framework for structured, weakly supervised learning. Our framework efficiently incorporates indirect supervision via constraints on posterior distributions of probabilistic models with latent variables. Posterior regularization separates model complexity from the complexity of structural constraints it is desired to satisfy. By directly imposing decomposable regularization on the posterior moments of latent variables during learning, we retain the computational efficiency of the unconstrained model while ensuring desired constraints hold in expectation. We present an efficient algorithm for learning with posterior regularization and illustrate its versatility on a diverse set of structural constraints such as bijectivity, symmetry and group sparsity in several large scale experiments, including multi-view learning, cross-lingual dependency grammar induction, unsupervised part-of-speech induction, and bitext word alignment.


title: Estimation of the dietary nutrient profile of free-roaming feral cats: possible implications for nutrition of domestic cats

abstract: Cats are strict carnivores and in the wild rely on a diet solely based on animal tissues to meet their specific and unique nutritional requirements. Although the feeding ecology of cats in the wild has been well documented in the literature, there is no information on the precise nutrient profile to which the cat's metabolism has adapted. The present study aimed to derive the dietary nutrient profile of free-living cats. Studies reporting the feeding habits of cats in the wild were reviewed and data on the nutrient composition of the consumed prey items obtained from the literature. Fifty-five studies reported feeding strategy data of cats in the wild. After specific exclusion criteria, twenty-seven studies were used to derive thirty individual dietary nutrient profiles. The results show that feral cats are obligatory carnivores, with their daily energy intake from crude protein being 52 %, from crude fat 46 % and from N-free extract only 2 %. Minerals and trace elements are consumed in relatively high concentrations compared with recommended allowances determined using empirical methods. The calculated nutrient profile may be considered the nutrient intake to which the cat's metabolic system has adapted. The present study provides insight into the nutritive, as well as possible non-nutritive aspects of a natural diet of whole prey for cats and provides novel ways to further improve feline diets to increase health and longevity.


title: Empowerment -- an Introduction

abstract: Is it better for you to own a corkscrew or not? If asked, you as a human being would likely say “yes”, but more importantly, you are somehow able to make this decision. You are able to decide this, even if your current acute problems or task do not include opening a wine bottle. Similarly, it is also unlikely that you evaluated several possible trajectories your life could take and looked at them with and without a corkscrew, and then measured your survival or reproductive fitness in each. When you, as a human cognitive agent, made this decision, you were likely relying on a behavioural “proxy”, an internal motivation that abstracts the problem of evaluating a decision impact on your overall life, but evaluating it in regard to some simple fitness function. One example would be the idea of curiosity, urging you to act so that your experience new sensations and learn about the environment. On average, this should lead to better and richer models of the world, which give you a better chance of reaching your ultimate goals of survival and reproduction.


title: Automated detection and classification of cryptographic algorithms in binary programs through machine learning

abstract: Threats from the internet, particularly malicious software (i.e., malware) often use cryptographic algorithms to disguise their actions and even to take control of a victim's system (as in the case of ransomware). Malware and other threats proliferate too quickly for the time-consuming traditional methods of binary analysis to be effective. By automating detection and classification of cryptographic algorithms, we can speed program analysis and more efficiently combat malware. ::: This thesis will present several methods of leveraging machine learning to automatically discover and classify cryptographic algorithms in compiled binary programs. ::: While further work is necessary to fully evaluate these methods on real-world binary programs, the results in this paper suggest that machine learning can be used successfully to detect and identify cryptographic primitives in compiled code. Currently, these techniques successfully detect and classify cryptographic algorithms in small single-purpose programs, and further work is proposed to apply them to real-world examples.


title: WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation

abstract: This paper introduces WILDCAT, a deep learning method which jointly aims at aligning image regions for gaining spatial invariance and learning strongly localized features. Our model is trained using only global image labels and is devoted to three main visual recognition tasks: image classification, weakly supervised object localization and semantic segmentation. WILDCAT extends state-of-the-art Convolutional Neural Networks at three main levels: the use of Fully Convolutional Networks for maintaining spatial resolution, the explicit design in the network of local features related to different class modalities, and a new way to pool these features to provide a global image prediction required for weakly supervised training. Extensive experiments show that our model significantly outperforms state-of-the-art methods.


title: Post-Traumatic Stress Disorder in Sexually Abused Children

abstract: Abstract Thirty-one sexually abused children were studied using structured interviews and standardized instruments to determine the frequency of post-traumatic stress disorder (PTSD) and associated symptoms. Of these sexually abused children, 48.4% met DSM-III-R PTSD criteria; 75% of children abused by natural fathers, 67% abused by strangers, and 25% of those abused by trusted adults met criteria as opposed to none of the children abused by an older child. Many children not meeting full DSM-III-R criteria exhibited partial PTSD symptoms. No relationship was observed between the time lapsed since last abusive episode and the development of PTSD. Three standardized instruments (CDI, SEI, and STAIC), although useful in identifying non-PTSD related symptoms, were not useful in differentiating PTSD. The CBCL detected group differences, with PTSD children exhibiting significantly more symptoms of externalizing and internalizing behaviors.


title: Monolayer Single-Crystal 1T′-MoTe2Grown by Chemical Vapor Deposition Exhibits Weak Antilocalization Effect

abstract: Growth of transition metal dichalcogenide (TMD) monolayers is of interest due to their unique electrical and optical properties. Films in the 2H and 1T phases have been widely studied but monolayers of some 1T′-TMDs are predicted to be large-gap quantum spin Hall insulators, suitable for innovative transistor structures that can be switched via a topological phase transition rather than conventional carrier depletion [Qian et al. Science 2014, 346, 1344−1347]. Here we detail a reproducible method for chemical vapor deposition of monolayer, single-crystal flakes of 1T′-MoTe2. Atomic force microscopy, Raman spectroscopy, X-ray photoelectron spectroscopy, and transmission electron microscopy confirm the composition and structure of MoTe2 flakes. Variable temperature magnetotransport shows weak antilocalization at low temperatures, an effect seen in topological insulators and evidence of strong spin–orbit coupling. Our approach provides a pathway to systematic investigation of monolayer, single-crystal 1T′-Mo...


title: Visual Navigation Using Projection of Spatial Right-Angle In Indoor Environment

abstract: Helping robots understand indoor scenes has considerable value in computer vision. However, due to the diversity of indoor scenes, understanding them remains a big challenge. There are many spatial right-angles in indoor scenes. These spatial right-angles are projected into diverse 2D projections. These projections can be considered a composition of a pair of lines (line-pairs). Given the vanishing points (VPs), line segments can be assigned to 1 of 3 main orthogonal directions. The line-pairs (intersection of 2 lines), such that each of them converges to a different VP, are likely to be the projection of a spatial right-angle onto the image plane. These projections may enable us to estimate their original orientation and position in 3D scenes. In this paper, we presented a method to efficiently understand indoor scenes from a single image, without training or any knowledge of the camera's internal calibration. Through geometric inference of line-pairs, it is possible to find these spatial right-angle projections. Then, these projections can be assigned to different clusters, and the line that lies in the neighbor-cluster helps us estimate the layout of the indoor scene. The proposed approach required no prior training. We compared the room layout estimated by our algorithm against the room box ground truth, measuring the percentage of pixels that were correctly classified. These experiments showed that our method estimated not only room layout, but also details of the indoor scene.


Uptane : Securing Software Updates for Automobiles


title: Combining latent factor model with location features for event-based group recommendation

abstract: Groups play an essential role in many social websites which promote users' interactions and accelerate the diffusion of information. Recommending groups that users are really interested to join is significant for both users and social media. While traditional group recommendation problem has been extensively studied, we focus on a new type of the problem, i.e., event-based group recommendation. Unlike the other forms of groups, users join this type of groups mainly for participating offline events organized by group members or inviting other users to attend events sponsored by them. These characteristics determine that previously proposed approaches for group recommendation cannot be adapted to the new problem easily as they ignore the geographical influence and other explicit features of groups and users. In this paper, we propose a method called Pairwise Tag enhAnced and featuRe-based Matrix factorIzation for Group recommendAtioN (PTARMIGAN), which considers location features, social features, and implicit patterns simultaneously in a unified model. More specifically, we exploit matrix factorization to model interactions between users and groups. Meanwhile, we incorporate their profile information into pairwise enhanced latent factors respectively. We also utilize the linear model to capture explicit features. Due to the reinforcement between explicit features and implicit patterns, our approach can provide better group recommendations. We conducted a comprehensive performance evaluation on real word data sets and the experimental results demonstrate the effectiveness of our method.


title: Understanding the Role of Body Movement in Player Engagement

abstract: The introduction of full-body controllers has made computer games more accessible and promises to provide a more natural and engaging experience to players. However, the relationship between body movement and game engagement is not yet well understood. In this article, I consider how body movement affects the player's experience during game play. I start by presenting a taxonomy of body movements observed during game play. These are framed in the context of a body of previously published research that is then embedded into a novel model of engagement. This model describes the relationship between the taxonomy of movement and the type of engagement that each class of movement facilitates. I discuss the factors that may inhibit or enhance such relationship. Finally, I conclude by considering how the proposed model could lead to a more systematic and effective use of body movement for enhancing game experience.


title: Eon Mode: Bringing the Vertica Columnar Database to the Cloud

abstract: The Vertica Analytic Database is a powerful tool for high performance, large scale SQL analytics. Historically, Vertica has managed direct-attached disk for performance and reliability, at a cost of product complexity and scalability. Eon mode is a new architecture for Vertica that places the data on a reliable shared storage, matching the original architecture's performance on existing workloads and supporting new workloads. While the design reuses Vertica's optimizer and execution engine, the metadata, storage, and fault tolerance mechanisms are re-architected to enable and take advantage of shared storage. A sharding mechanism distributes load over the nodes while retaining the capability of running node-local table joins. Running on Amazon EC2 compute and S3 storage, Eon mode demonstrates good performance, superior scalability, and robust operational behavior. With these improvements, Vertica delivers on the promise of cloud economics, consuming only the compute and storage resources needed, while supporting efficient elasticity.


abstract: A direct CPW-to-rectangular waveguide transition using a dipole slot antenna is proposed in this paper. The advantage of this transition is that no intermediate transition and vias are needed in this transition. Parameter studies demonstrate that a transition having a fractional bandwidth of 22.85%, for which the return loss is larger than 15 dB, is achieved in the X-band (8.2–12.4 GHz). In order to verify our results, we fabricate and measure a back-to-back transition for which the measurement results agree well with the simulation results.


title: DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications

abstract: This paper introduces DuReader, a new large-scale, open-domain Chinese ma- chine reading comprehension (MRC) dataset, designed to address real-world MRC. DuReader has three advantages over previous MRC datasets: (1) data sources: questions and documents are based on Baidu Search and Baidu Zhidao; answers are manually generated. (2) question types: it provides rich annotations for more question types, especially yes-no and opinion questions, that leaves more opportunity for the research community. (3) scale: it contains 200K questions, 420K answers and 1M documents; it is the largest Chinese MRC dataset so far. Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements. To help the community make these improvements, both DuReader and baseline systems have been posted online. We also organize a shared competition to encourage the exploration of more models. Since the release of the task, there are significant improvements over the baselines.


title: Eigentaste: A Constant Time Collaborative Filtering Algorithm

abstract: Eigentaste is a collaborative filtering algorithm that uses i>universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clustering of users and rapid computation of recommendations. For a database of i>n users, standard nearest-neighbor techniques require i>O(i>n) processing time to compute recommendations, whereas Eigentaste requires i>O(1) (constant) time. We compare Eigentaste to alternative algorithms using data from i>Jester, an online joke recommending system. ::: ::: Jester has collected approximately 2,500,000 ratings from 57,000 users. We use the Normalized Mean Absolute Error (NMAE) measure to compare performance of different algorithms. In the Appendix we use Uniform and Normal distribution models to derive analytic estimates of NMAE when predictions are random. On the Jester dataset, Eigentaste computes recommendations two orders of magnitude faster with no loss of accuracy. Jester is online at: http://eigentaste.berkeley.edu


title: Video Registration to SfM Models

abstract: Registering image data to Structure from Motion (SfM) point clouds is widely used to find precise camera location and orientation with respect to a world model. In case of videos one constraint has previously been unexploited: temporal smoothness. Without temporal smoothness the magnitude of the pose error in each frame of a video will often dominate the magnitude of frame-to-frame pose change. This hinders application of methods requiring stable poses estimates (e.g. tracking, augmented reality). We incorporate temporal constraints into the image-based registration setting and solve the problem by pose regularization with model fitting and smoothing methods. This leads to accurate, gap-free and smooth poses for all frames. We evaluate different methods on challenging synthetic and real street-view SfM data for varying scenarios of motion speed, outlier contamination, pose estimation failures and 2D-3D correspondence noise. For all test cases a 2 to 60-fold reduction in root mean squared (RMS) positional error is observed, depending on pose estimation difficulty. For varying scenarios, different methods perform best. We give guidance which methods should be preferred depending on circumstances and requirements.


title: Fast Smoke Detection for Video Surveillance Using CUDA

abstract: Smoke detection is a key component of disaster and accident detection. Despite the wide variety of smoke detection methods and sensors that have been proposed, none has been able to maintain a high frame rate while improving detection performance. In this paper, a smoke detection method for surveillance cameras is presented that relies on shape features of smoke regions as well as color information. The method takes advantage of the use of a stationary camera by using a background subtraction method to detect changes in the scene. The color of the smoke is used to assess the probability that pixels in the scene belong to a smoke region. Due to the variable density of the smoke, not all pixels of the actual smoke area appear in the foreground mask. These separate pixels are united by morphological operations and connected-component labeling methods. The existence of a smoke region is confirmed by analyzing the roughness of its boundary. The final step of the algorithm is to check the density of edge pixels within a region. Comparison of objects in the current and previous frames is conducted to distinguish fluid smoke regions from rigid moving objects. Some parts of the algorithm were boosted by means of parallel processing using compute unified device architecture graphics processing unit, thereby enabling fast processing of both low-resolution and high-definition videos. The algorithm was tested on multiple video sequences and demonstrated appropriate processing time for a realistic range of frame sizes.


title: Teenage communication in the Instant Messaging era

abstract: Since the mid-1990s, teenagers have migrated from email and chat rooms to instant messaging (IM). We observed this change in data from 60 interviews with teens and their families conducted from 1996 to 2002 and a national survey of teenagers in 2002. We examined the content of conversations, communication partners, and conversation multitasking. While using IM, teens mainly talk to friends from daily life and rarely with those they met online. In terms of both partners and content, IM conversations are more like face-to-face visits than email exchanges. Teens engage in IM to be “with” friends. Conversations often consist of inconsequential small talk, but can offer opportunities for more substantial social support. Despite these similarities, teens report they enjoy IM conversations far less than they enjoy face-to-face visits and phone conversations. We offer some design recommendations focused on the integration of IM-like interactions with other online and real world communication.


title: Electronic media use and sleep in school-aged children and adolescents: A review

abstract: Electronic media have often been considered to have a negative impact on the sleep of children and adolescents, but there are no comprehensive reviews of research in this area. The present study identified 36 papers that have investigated the relationship between sleep and electronic media in school-aged children and adolescents, including television viewing, use of computers, electronic gaming, and/or the internet, mobile telephones, and music. Many variables have been investigated across these studies, although delayed bedtime and shorter total sleep time have been found to be most consistently related to media use. A model of the mechanisms by which media use may affect sleep is presented and discussed as a vehicle for future research.


title: IT-Enabled Healthcare Integration: The Case of National Electronic Health Records in Singapore

abstract: Information Technology (IT) is considered as an important enabler towards achieving integrated health care. Consequently, Electronic Health Record (EHR) systems, which serve as the foundation for integrated care across organizations, regions, or nations, have received much attention globally. However, many EHR systems have failed to be implemented successfully. The buy-in of different stakeholders has been identified as an important factor to smoothen the IT healthcare integration process. By employing stakeholder and institutional theories as theoretical lenses to examine the influence of different stakeholders in EHR implementation, we aim to form a deeper understanding of how EHR initiatives can be managed for healthcare integration. A positivist, exploratory, case study approach was chosen to analyze the till date successful implementation of the National Electronic Health Record (NEHR) initiative in Singapore. Based on both primary and secondary data, we report preliminary findings about the salience and institutional influences for key stakeholders in this context. This study contributes to research by extending stakeholder theory through including the dynamic view of institutional theory. Moreover, it can provide practictioners with insights to implement EHR programs for healthcare integration effectively.


title: DRAW: Deep Networks for Recognizing Styles of Artists Who Illustrate Children's Books

abstract: This paper is motivated from a young boy's capability to recognize an illustrator's style in a totally different context. In the book "We are All Born Free" [1], composed of selected rights from the Universal Declaration of Human Rights interpreted by different illustrators, the boy was surprised to see a picture similar to the ones in the "Winnie the Witch" series drawn by Korky Paul (Figure [1]). The style was noticeable in other characters of the same illustrator in different books as well. The capability of a child to easily spot the style was shown to be valid for other illustrators such as Axel Scheffler and Debi Gliori. The boy's enthusiasm let us to start the journey to explore the capabilities of machines to recognize the style of illustrators. We collected pages from children's books to construct a new illustrations dataset consisting of about 6500 pages from 24 artists. We exploited deep networks for categorizing illustrators and with around 94% classification performance our method over-performed the traditional methods by more than 10%. Going beyond categorization we explored transferring style. The classification performance on the transferred images has shown the ability of our system to capture the style. Furthermore, we discovered representative illustrations and discriminative stylistic elements.


title: Infant grasp learning: a computational model

abstract: This paper presents ILGM (the Infant Learning to Grasp Model), the first computational model of infant grasp learning that is constrained by the infant motor development literature. By grasp learning we mean learning how to make motor plans in response to sensory stimuli such that open-loop execution of the plan leads to a successful grasp. The open-loop assumption is justified by the behavioral evidence that early grasping is based on open-loop control rather than on-line visual feedback. Key elements of the infancy period, namely elementary motor schemas, the exploratory nature of infant motor interaction, and inherent motor variability are captured in the model. In particular we show, through computational modeling, how an existing behavior (reaching) yields a more complex behavior (grasping) through interactive goal-directed trial and error learning. Our study focuses on how the infant learns to generate grasps that match the affordances presented by objects in the environment. ILGM was designed to learn execution parameters for controlling the hand movement as well as for modulating the reach to provide a successful grasp matching the target object affordance. Moreover, ILGM produces testable predictions regarding infant motor learning processes and poses new questions to experimentalists.


title: Hepatitis B virus and HIV infection.

abstract: Approximately 5 to 10% of human immunodeficiency virus- (HIV-) infected persons worldwide have chronic hepatitis B virus (HBV). The management of these patients merits special attention. They experience a faster progression to cirrhosis and more frequent liver-related death than HBV-monoinfected individuals. For this reason, therapy for both HIV and HBV is a priority in most cases. Some antivirals (i.e., tenofovir, lamivudine, emtricitabine) are active against both viruses and should be part of the antiretroviral treatment choice. However, drugs such as entecavir, telbivudine, or adefovir are active against HBV and may display some residual activity against HIV, occasionally leading to the selection of resistance mutations in the HIV polymerase, as is clearly shown with entecavir. Thus, they should be used only in the context of potent antiretroviral treatment. In this review, the authors will provide updated information on the natural history of HIV/HBV coinfected patients, when and which drugs should be used in treatment, and the concern about selection of drug resistance and vaccine escape mutants.


title: Dense Multimodal Fusion for Hierarchically Joint Representation

abstract: Multiple modalities can provide more valuable information than single one by describing the same contents in various ways. Hence, it is highly expected to learn effective joint representation by fusing the features of different modalities. However, previous methods mainly focus on fusing the shallow features or high-level representations generated by unimodal deep networks, which only capture part of the hierarchical correlations across modalities. In this paper, we propose to densely integrate the representations by greedily stacking multiple shared layers between different modality-specific networks, which is named as Dense Multimodal Fusion (DMF). The joint representations in different shared layers can capture the correlations in different levels, and the connection between shared layers also provides an efficient way to learn the dependence among hierarchical correlations. These two properties jointly contribute to the multiple learning paths in DMF, which results in faster convergence, lower training loss, and better performance. We evaluate our model on three typical multimodal learning tasks, including audiovisual speech recognition, cross-modal retrieval, and multimodal classification. The noticeable performance in the experiments demonstrates that our model can learn more effective joint representation.


title: Comparison of Classification and Ranking Approaches to Pronominal Anaphora Resolution in Czech

abstract: In this paper we compare two Machine Learning approaches to the task of pronominal anaphora resolution: a conventional classification system based on C5.0 decision trees, and a novel perceptron-based ranker. We use coreference links annotated in the Prague Dependency Treebank 2.0 for training and evaluation purposes. The perceptron system achieves f-score 79.43% on recognizing coreference of personal and possessive pronouns, which clearly outperforms the classifier and which is the best result reported on this data set so far.


title: Entity Matching: How Similar Is Similar

abstract: Entity matching that finds records referring to the same entity is an important operation in data cleaning and integration. Existing studies usually use a given similarity function to quantify the similarity of records, and focus on devising index structures and algorithms for efficient entity matching. However it is a big challenge to define "how similar is similar" for real applications, since it is rather hard to automatically select appropriate similarity functions. In this paper we attempt to address this problem. As there are a large number of similarity functions, and even worse thresholds may have infinite values, it is rather expensive to find appropriate similarity functions and thresholds. Fortunately, we have an observation that different similarity functions and thresholds have redundancy, and we have an opportunity to prune inappropriate similarity functions. To this end, we propose effective optimization techniques to eliminate such redundancy, and devise efficient algorithms to find the best similarity functions. The experimental results on both real and synthetic datasets show that our method achieves high accuracy and outperforms the baseline algorithms.


title: Investigating the success of ERP systems: Case studies in three Taiwanese high-tech industries

abstract: The measurement of enterprise resource planning (ERP) systems success or effectiveness is critical to our understanding of the value and efficacy of ERP investment and managerial actions. Whether traditional information systems success models can be extended to investigating ERP systems success is yet to be investigated. This paper proposes a partial extension and respecification of the DeLone and MacLean model of IS success to ERP systems. The purpose of the present research is to re-examine the updated DeLone and McLean model [W. DeLone, E. McLean, The DeLone McLean model of information system success: a ten-year update, Journal of Management Information Systems 19 (4) (2003) 3-9] of ERP systems success. The updated DeLone and McLean model was applied to collect data from the questionnaires answered by 204 users of ERP systems at three high-tech firms in Taiwan. Finally, this study suggests that system quality, service quality, and information quality are most important successful factors.


title: Patterns of authority: A structural basis for political inquiry

abstract: 1,175,160. Fishing reels. ETABS. CARPANO & PONS. 25 Oct., 1968 [25 Oct., 1968], No. 50772/68. Heading A1A. One end flange of a fishing reel is so shaped that it may be supported on the arm of the user.


title: Matsumoto and Ekman's Japanese and Caucasian Facial Expressions of Emotion (JACFEE): Reliability data and cross-national differences.

abstract: Substantial research has documented the universality of several emotional expressions. However, recent findings have demonstrated cultural differences in level of recognition and ratings of intensity. When testing cultural differences, stimulus sets must meet certain requirements. Matsumoto and Ekman's Japanese and Caucasian Facial Expressions of Emotion (JACFEE) is the only set that meets these requirements. The purpose of this study was to obtain judgment reliability data on the JACFEE, and to test for possible cross-national differences in judgments as well. Subjects from Hungary, Japan, Poland, Sumatra, United States, and Vietnam viewed the complete JACFEE photo set and judged which emotions were portrayed in the photos and rated the intensity of those expressions. Results revealed high agreement across countries in identifying the emotions portrayed in the photos, demonstrating the reliability of the JACFEE. Despite high agreement, cross-national differences were found in the exact level of agreement for photos of anger, contempt, disgust, fear, sadness, and surprise. Cross-national differences were also found in the level of intensity attributed to the photos. No systematic variation due to either preceding emotion or presentation order of the JACFEE was found. Also, we found that grouping the countries into a Western/Non-Western dichotomy was not justified according to the data. Instead, the cross-national differences are discussed in terms of possible sociopsychological variables that influence emotion judgments.


title: Angiogenesis biomarkers and their targeting ligands as potential targets for tumor angiogenesis

abstract: Angiogenesis is known as one of the hallmarks in cancer which could play a key role in providing oxygen and nutrients for tumor cells. It has been shown that tumor cannot grow without sufficient development of new blood vessels. Accordingly, targeting angiogenesis, especially endothelial cells, could be considered as a common therapeutic target in tumors and more investigation on already existing biomarkers and potentially new biomarkers of endothelial cells seems to be necessary in cancer therapy. Moreover, the use of effective targeting approaches such as proteins and peptides, aptamers, and small molecules is an important step for targeting biomarkers associated with endothelial cells and angiogenesis in cancer therapy. These agents are FDA approved, or are currently under investigation in pre-clinical and clinical studies. Among various biomarkers for angiogenesis microRNAs are suitable candidates for target therapy. These molecules play key roles in tumor angiogenesis which exert their effect via targeting a variety of cellular and molecular pathways involved in tumor angiogenesis. Here, we summarize a variety of biomarkers which their expressions or their functions could change the function of endothelial cells in tumor microenvironments. Moreover, we highlighted various therapeutic agents which could target these biomarkers.


NONLINEAR MODELING OF A GUITAR LOUDSPEAKER CABINET


title: Closed-form information-theoretic divergences for statistical mixtures

abstract: Statistical mixtures such as Rayleigh, Wishart or Gaussian mixture models are commonly used in pattern recognition and signal processing tasks. Since the Kullback-Leibler divergence between any two such mixture models does not admit an analytical expression, the relative entropy can only be approximated numerically using time-consuming Monte-Carlo stochastic sampling. This drawback has motivated the quest for alternative information-theoretic divergences such as the recent Jensen-Renyi, Cauchy-Schwarz, or total square loss divergences that bypass the numerical approximations by providing exact analytic expressions. In this paper, we state sufficient conditions on the mixture distribution family so that these novel non-KL statistical divergences between any two such mixtures can be expressed in generic closed-form formulas.


title: LMS-based calibration of pipelined ADCs including linear and nonlinear errors

abstract: A least mean square (LMS) based calibration algorithm is proposed to calibrate most known error sources in 1.5 bit/stage pipelined ADCs, known to be immune to moderate comparator offsets. The error sources include linear gain errors, reference voltage errors, systematic offset errors and amplifier non-linear errors of each pipeline stage. LMS is used to estimate the error parameters in the digital domain. After estimation the proposed algorithm calibrate the pipelined ADC using the estimated parameter errors. Simulation results show that the proposed algorithm can improve the ENOB from 6.6 bits to 13.9 bits for a 14 bit 1.5 bits/stage pipelined ADC.


title: Load Balancing in Cloud Computingusing Modified Throttled Algorithm

abstract: Load balancing is one of the critical components for efficient operations in the cloud computing environment. In recent years many clients from all over the world are demanding the various services at rapid rate. Many algorithms have been designed to carry out the client's request towards the cloud nodes. Accordingly the cloud computing platform will dynamically configure its servers and these servers may be present physically or virtually in the computing environment. Hence, selecting the virtual machines or servers has to be scheduled properly by using an appropriate load balancing approach. In the present work, a local optimized load balancing approach is proposed for distributing of incoming jobs uniformly among the servers or virtual machines. Further, the performance is analyzed using CloudAnalyst simulator and compared with existing Round Robin and Throttled algorithms. Simulation results have demonstrated that the proposed algorithm has distributed the load uniformly among virtual machines.


title: Performance analysis of support vector machines classifiers in breast cancer mammography recognition

abstract: Support vector machine (SVM) is a supervised machine learning approach that was recognized as a statistical learning apotheosis for the small-sample database. SVM has shown its excellent learning and generalization ability and has been extensively employed in many areas. This paper presents a performance analysis of six types of SVMs for the diagnosis of the classical Wisconsin breast cancer problem from a statistical point of view. The classification performance of standard SVM (St-SVM) is analyzed and compared with those of the other modified classifiers such as proximal support vector machine (PSVM) classifiers, Lagrangian support vector machines (LSVM), finite Newton method for Lagrangian support vector machine (NSVM), Linear programming support vector machines (LPSVM), and smooth support vector machine (SSVM). The experimental results reveal that these SVM classifiers achieve very fast, simple, and efficient breast cancer diagnosis. The training results indicated that LSVM has the lowest accuracy of 95.6107 %, while St-SVM performed better than other methods for all performance indices (accuracy = 97.71 %) and is closely followed by LPSVM (accuracy = 97.3282). However, in the validation phase, the overall accuracies of LPSVM achieved 97.1429 %, which was superior to LSVM (95.4286 %), SSVM (96.5714 %), PSVM (96 %), NSVM (96.5714 %), and St-SVM (94.86 %). Value of ROC and MCC for LPSVM achieved 0.9938 and 0.9369, respectively, which outperformed other classifiers. The results strongly suggest that LPSVM can aid in the diagnosis of breast cancer.


title: Participation in School-Based Extracurricular Activities and Adolescent Adjustment

abstract: This paper examines the association between participation in school-based extracurricular activities (ECAs) and adolescent adjustment (drinking, marijuana use, grades, academic attitudes and academic aspirations) among students from six high schools. Three major issues were addressed: the potential confounding of selective EGA participation by better adjusted students and measures of adjustment, variability in the strength of the association between ECA participation and adjustment as a function of adolescent demographic characteristics and activity type, and the role of peers as mediators of the association between ECA participation and adjustment. Adolescents who participated in ECAs reported higher grades, more positive attitudes toward schools, and higher academic aspirations once demographic characteristics and prior adjustment were controlled. Alcohol and marijuana use were not independently associated with ECA participation. The ECA-adjustment association did not vary by demographic characteristics...


title: On the Security of Pay-per-Click and Other Web Advertising Schemes

abstract: We present a hit inflation attack on pay-per-click Web advertising schemes. Our attack is virtually impossible for the program provider to detect conclusively, regardless of whether the provider is a third-party ‘ad network’ or the target of the click itself. If practiced widely, this attack could accelerate a move away from pay-per-click programs and toward programs in which referrers are paid only if the referred user subsequently makes a purchase (pay-per-sale) or engages in other substantial activity at the target site (pay-per-lead). We also briefly discuss the lack of auditability inherent in these schemes. © 1999 Published by Elsevier Science B.V. All rights reserved.


Classification algorithm in Data mining : An Overview


title: Variations in Sense of Place Across Immigrant Status and Gender in Hamilton, Ontario; Saskatoon, Saskatchewan; and, Charlottetown, Prince Edward Island, Canada

abstract: Past research in Hamilton, Ontario has found that age and longevity of residence are positively associated with evaluations of sense of place (SoP); further, evaluations of SoP between immigrants and Canadian-born individuals have shown no clear pattern (Williams et al. 2010; Williams and Kitchen 2012). This paper builds on this work by further examining evaluations of SoP among both immigrants and Canadian-born residents and across gender in Hamilton, while expanding the study to two other small-to-medium sized cities: Saskatoon, Saskatchewan and, Charlottetown, Prince Edward Island. This paper has two objectives: (1) to establish measures of SoP across immigrant status and gender in Hamilton, Saskatoon, and Charlottetown; and, (2) to determine how SoP varies according to immigrant status, length of residence in Canada, age, income, and neighbourhood length of residence across the three city sites. Telephone survey data (n = 1,132) was used to compare evaluations of SoP across various groups and to construct an ordered logistic regression model for SoP. Results suggest that immigrants tended to rate their SoP lower than their Canadian-born counterparts. Hamilton residents were found to rate their SoP lowest, followed by Saskatoon residents and, finally, Charlottetown residents. Younger individuals, those with lower income levels, and those with shorter neighbourhood residency in the cities concerned were more likely to have lower evaluations of SoP. This research suggests that greater attention is needed to nurture immigrants’ connection with their new home.


title: Information security behavior: Recognizing the influencers

abstract: With the wide spread use of Internet, comes an increase in Information Security threats. To protect against these threats, technology alone have been found not enough as it can be misused by users and become vulnerable to various threats, thus, losing its usefulness. This is evident as users tend to use weak passwords, open email attachments without checking and do not set correct security settings. However, especially with the continuously evolving threat landscape, one cannot assume that users are always motivated to learn about Information Security and practice it. Actually, there are situations of an aware user who knows how to protect himself but, simply, chooses not to, because they do not care, usability problems or because they do not consider themselves as targets. Thus, understanding human security behavior is vital for ensuring an efficient Information Security environment that cannot depend on technology only. Although a number of psychological theories and models, such as Protection Motivation Theory and Technology Acceptance Model, were used in the literature to interpret these behaviors, they tend to assess users' intensions rather than actual behavior. The aim of this paper is to understand and assess these behaviors from a holistic view by finding the significant factors that influence them and how to best assist users to protect themselves. To accomplish this, a systematic literature review was conducted where relevant literature was sought in a number of academic digital databases. As a result, several key behavioral influencers were identified to be essential to consider when educating and directing users' security behavior. Further to that, a number of Information Security awareness approaches were proposed that may transform the user from being ill-informed into a security-minded user who is able to make an informed decision.


title: Targeting hypoxia in cancer therapy

abstract: Hypoxia is common in developing and advanced tumours and could therefore provide a means for drug selectivity. As discussed in this Review, this can be achieved either by using prodrugs that are activated in hypoxic regions, or by directly targeting hypoxia-induced signalling pathways that confer survival to tumour cells.


title: Variance stabilization applied to microarray data calibration and to the quantification of differential expression

abstract: We introduce a statistical model for microarray gene expression data that comprises data calibration, the quantification of differential expression, and the quantification of measurement error. In particular, we derive a transformation h for intensity measurements, and a difference statistic Deltah whose variance is approximately constant along the whole intensity range. This forms a basis for statistical inference from microarray data, and provides a rational data pre-processing strategy for multivariate analyses. For the transformation h, the parametric form h(x)=arsinh(a+bx) is derived from a model of the variance-versus-mean dependence for microarray intensity data, using the method of variance stabilizing transformations. For large intensities, h coincides with the logarithmic transformation, and Deltah with the log-ratio. The parameters of h together with those of the calibration between experiments are estimated with a robust variant of maximum-likelihood estimation. We demonstrate our approach on data sets from different experimental platforms, including two-colour cDNA arrays and a series of Affymetrix oligonucleotide arrays.


title: Maximizing product adoption in social networks

abstract: One of the key objectives of viral marketing is to identify a small set of users in a social network, who when convinced to adopt a product will influence others in the network leading to a large number of adoptions in an expected sense. The seminal work of Kempe et al. [13] approaches this as the problem of influence maximization. This and other previous papers tacitly assume that a user who is influenced (or, informed) about a product necessarily adopts the product and encourages her friends to adopt it. However, an influenced user may not adopt the product herself, and yet form an opinion based on the experiences of her friends, and share this opinion with others. Furthermore, a user who adopts the product may not like the product and hence not encourage her friends to adopt it to the same extent as another user who adopted and liked the product. This is independent of the extent to which those friends are influenced by her. Previous works do not account for these phenomena. We argue that it is important to distinguish product adoption from influence. We propose a model that factors in a user's experience (or projected experience) with a product. We adapt the classical Linear Threshold (LT) propagation model by defining an objective function that explicitly captures product adoption, as opposed to influence. We show that under our model, adoption maximization is NP-hard and the objective function is monotone and submodular, thus admitting an approximation algorithm. We perform experiments on three real popular social networks and show that our model is able to distinguish between influence and adoption, and predict product adoption much more accurately than approaches based on the classical LT model.


title: Structure identification in complete rule-based fuzzy systems

abstract: The identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory. There are numerous approaches to the issue of parameter optimization within a fixed fuzzy system structure but no reliable method to obtain the optimal topology of the fuzzy system from a set of input-output data. This paper presents a reliable method to obtain the structure of a complete rule-based fuzzy system for a specific approximation accuracy of the training data, i.e., it can decide which input variables must be taken into account in the fuzzy system and how many membership functions (MFs) are needed in every selected input variable in order to reach the approximation target with the minimum number of parameters.


title: A Theoretical and Practical Implementation Tutorial on Topic Modeling and Gibbs Sampling

abstract: This technical report provides a tutorial on the theoretical details of probabilistic topic modeling and gives practical steps on implementing topic models such as Latent Dirichlet Allocation (LDA) through the Markov Chain Monte Carlo approximate inference algorithm Gibbs Sampling.


title: Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

abstract: Abstract In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients who underwent invasive FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). As ischemic changes are expected to appear locally, the LV myocardium is divided into a number of spatially connected clusters, and statistics of the encodings are computed as features. Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted features. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Twenty CCTA images were used to train the LV myocardium encoder. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 ± 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis. This might reduce the number of patients undergoing unnecessary invasive FFR measurements.


title: Lateral epicondylitis: a review of pathology and management.

abstract: Lateral epicondylitis, or 'tennis elbow', is a common condition that usually affects patients between 35 and 55 years of age. It is generally self-limiting, but in some patients it may continue to cause persistent symptoms, which can be refractory to treatment. This review discusses the mechanism of disease, symptoms and signs, investigations, current management protocols and potential new treatments.


title: A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation

abstract: We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image. We show that, though it is widely adopted for ease of modeling, the log-transformed image for this task is not ideal. Based on the previous investigation of the logarithmic transformation, a new weighted variational model is proposed for better prior representation, which is imposed in the regularization terms. Different from conventional variational models, the proposed model can preserve the estimated reflectance with more details. Moreover, the proposed model can suppress noise to some extent. An alternating minimization scheme is adopted to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed model with its algorithm. Compared with other variational methods, the proposed method yields comparable or better results on both subjective and objective assessments.


title: Self-Affirmation Theory 1 The Psychology of Self-Defense: Self-Affirmation Theory

abstract: In major league baseball, a hitter could have a long and productive career by maintaining a .300 average, that is, by getting a base hit 30% of the time. A great deal of money could be earned and fame accrued. Yet the other 70% of the time, this player would have failed. The vast majority of attempts to hit the ball would result in ‘‘making an out’’ and thus pose a potential threat to the player’s sense of personal worth and social regard. Like major league baseball players, people in contemporary society face innumerable failures and self‐threats. These include substandard performance on the job or in class, frustrated goals or aspirations, information challenging the validity of long‐held beliefs, illness, the defeat of one’s political party in an election or of one’s favorite sports team in a playoV, scientific evidence suggesting that one is engaging in risky health behavior, negative feedback at work or in school, rejection in a romantic relationship, real and perceived social slights, interpersonal and intergroup conflict, the misbehavior of one’s child, the loss of a loved one, and so on. In the course of a given day, the potential number of events that could threaten people’s ‘‘moral and adaptive adequacy’’—their sense of themselves as good, virtuous, successful, and able to control important life outcomes (Steele, 1988)—seems limitless and likely to exceed the small number of events that aYrm it. A major undertaking for most people is to sustain self‐integrity when faced with the inevitable setbacks and disappointments of daily life—the 70% of the time 183


title: Multi-Digit Recognition Using A Space Displacement Neural Network

abstract: We present a feed-forward network architecture for recognizing an unconstrained handwritten multi-digit string. This is an extension of previous work on recognizing isolated digits. In this architecture a single digit recognizer is replicated over the input. The output layer of the network is coupled to a Viterbi alignment module that chooses the best interpretation of the input. Training errors are propagated through the Viterbi module. The novelty in this procedure is that segmentation is done on the feature maps developed in the Space Displacement Neural Network (SDNN) rather than the input (pixel) space.


title: Joint Hand Detection and Rotation Estimation Using CNN

abstract: Hand detection is essential for many hand related tasks, e.g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction. However, hand detection in uncontrolled environments is challenging due to the flexibility of wrist joint and cluttered background. We propose a deep learning based approach which detects hands and calibrates in-plane rotation under supervision at the same time. To guarantee the recall, we propose a context aware proposal generation algorithm which significantly outperforms the selective search. We then design a convolutional neural network(CNN) which handles object rotation explicitly to jointly solve the object detection and rotation estimation tasks. Experiments show that our method achieves better results than state-of-the-art detection models on widely-used benchmarks such as Oxford and Egohands database. We further show that rotation estimation and classification can mutually benefit each other.


title: A histogram specification technique for dark image enhancement using a local transformation method

abstract: Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques.


title: Cyber Attack Surface Analysis of Advanced Metering Infrastructure

abstract: Cyber attacks on critical infrastructure have been an issue of importance in industry over the past several years, as well as a focus of academic research. Cyber attacks of various types and magnitude have been on the rise targeted specifically at power grids. A successful attack on the power grid could result in significant impacts including grid shutdown, cascading failures, damage to the infrastructure, and potential harm to people. Power grid infrastructure are critical in nature, they enable operations for residential, commercial, industrial, and government users across critical infrastructure such as water, communication, banking, transportation, manufacturing, and more. The compromise of these operations introduces threats, which span from economic to public safety. The Advanced Metering Infrastructure (AMI) is now being rapidly deployed throughout the power grid, and is an enabling technology for smart grid. Identifying the attack surface is a necessary step in achieving cyber security in smart grids and AMI. The purpose of this paper is to quantify and examine the cyber attack surface of AMI in order to lay the foundation for mitigating approaches to AMI cyber security.


title: Comparing Apples to Apple: The Effects of Stemmers on Topic Models

abstract: Rule-based stemmers such as the Porter stemmer are frequently used to preprocess English corpora for topic modeling. In this work, we train and evaluate topic models on a variety of corpora using several different stemming algorithms. We examine several different quantitative measures of the resulting models, including likelihood, coherence, model stability, and entropy. Despite their frequent use in topic modeling, we find that stemmers produce no meaningful improvement in likelihood and coherence and in fact can degrade topic stability.


title: Using Argumentation to Control Lexical Choice: A Functional Unification Implementation

abstract: This thesis presents new surface generation techniques that improve on both aspects of surface generation: (1) lexical choice, which consists of choosing words and their associated syntactic structures and (2) syntactic realization, which consists of combining these partial structures into grammatical sentences. The thesis investigates the impact of the pragmatic situation on surface generation. Because surface generation depends directly on many aspects of the situation, these new techniques allow a purely conceptual input to be expressed by a greater variety of linguistic forms and with more sensitivity to pragmatic factors than was previously possible. ::: Specifically, this research focuses on the impact on lexical choice of one part of the pragmatic situation: the speaker's argumentative intent, i.e., the goal of the speaker to convince the hearer of a certain conclusion. The argumentative intent can be realized by a variety of evaluative expressions appearing at various ranks in the syntactic structure. This thesis describes the selection of four classes of evaluative expressions: judgment determiners (i.e., many), scalar adjectives (i.e., difficult), connotative verbs (i.e., require, enjoy) and argumentative connectives (i.e., but, so). These four classes were not addressed in previous generators. ::: The scFUF formalism is introduced in this thesis to address the issue of complex constraint interaction arising when performing lexical choice under pragmatic constraints. scFUF is derived from Functional Unification Grammars (scFUGs), a formalism previously used for syntactic realization only. scFUF extends scFUGs by providing new mechanisms for control, for expressing hierarchical relations and for using modular knowledge sources. These extensions make scFUF capable of handling lexical choice. In addition, this thesis describes the use of scFUF to develop scSURGE, one of the largest and most widely used syntactic realization grammar available. ::: Finally these new generation techniques are applied to the implementation of scADVISOR II, a question-answering system helping students to choose classes for a semester. In particular, scADVISOR II uses the increased expressive flexibility provided by these techniques to convince a student to choose different classes by presenting the same objective data, retrieved from an underlying knowledge base, in different linguistic forms using evaluative expressions.


title: Multivariate k-nearest neighbour regression for time series data — A novel algorithm for forecasting UK electricity demand

abstract: The k-nearest neighbour (k-NN) algorithm is one of the most widely used benchmark algorithms in classification, supported by its simplicity and intuitiveness in finding similar instances in multivariate and large-dimensional feature spaces of arbitrary attribute scales. In contrast, only few scientific studies of k-NN exist in forecasting time series data, which have mainly assessed various distance metrics to identify similar univariate time series motifs in past data. In electricity load forecasting, k-NN studies are limited to identifying past motifs of the same dependent variable to match future realisations, in a non-causal approach to forecasting. However, causal information in the form of deterministic calendar information is readily available on past and future time series motifs, allowing the distinction between load profiles of working days, weekends and bank-holidays to be encoded as binary dummy variables, and to be efficiently included in the search for similar neighbours. In this paper, we propose a multivariate k-NN regression method for forecasting the electricity demand in the UK market which utilises binary dummy variables as a second feature to categorise the day being forecasted as a working day or a non-working day. We assess the efficacy of this approach in a robust empirical evaluation using UK electricity load data. The approach shows improvements beyond conventional k-NN approaches and accuracy beyond that of simple statistical benchmark methods.


title: A HIERARCHICAL INTRUSION DETECTION ARCHITECTURE FOR WIRELESS SENSOR NETWORKS

abstract: Networks protection against different types of attacks is one of most important posed issue into the network and information security application domains. This problem on Wireless Sensor Networks (WSNs), in attention to their special properties, has more importance. Now, there are some of proposed architectures and guide lines to protect Wireless Sensor Networks (WSNs) against different types of intrusions; but any one of them do not has a comprehensive view to this problem and they are usually designed and implemented in single-purpose; but, the proposed design in this paper tries to has been a comprehensive view to this issue by presenting a complete and comprehensive Intrusion Detection Architecture (IDA). The main contribution of this architecture is its hierarchical structure; i.e., it is designed and applicable, in one or two levels, consistent to the application domain and its required security level. Focus of this paper is on the clustering WSNs, designing and deploying Cluster-based Intrusion Detection System (CIDS) on cluster-heads and Wireless Sensor Network wide level Intrusion Detection System (WSNIDS) on the central server. Suppositions of the WSN and Intrusion Detection Architecture (IDA) are: static and heterogeneous network, hierarchical and clustering structure, clusters' overlapping and using hierarchical routing protocol such as LEACH, but along with minor changes. Finally, the proposed idea has been verified by designing a questionnaire, representing it to some (about 50 people) experts and then, analyzing and evaluating its acquired results.


Strategic planning and corporate performance . What is the relationship ?


title: Exploring consumers' use behavior on internet finance in China: From the perspective of the financial products similar to Yu'e Bao

abstract: The purpose of this paper is to explore the key factors affecting consumer's use behavior on financial products similar to Yu'e Bao. Considering the financial market and policy environment in China, theoretical model is founded based on extending Unified Theory of Acceptance and Use of Technology (UTAUT) and the hypotheses are put forward. Then, to test the model and hypotheses, 199 valid data is collected by online survey and analyzed with the help of software SPSS 19.0 and AMOS 17.0. The results show that performance expectancy, social influence, trust, government e-readiness and facilitating conditions have positive effects on user's usage intention towards financial products similar to Yu'e Bao. Then the corresponding countermeasures and advices are put forward. The findings will not only help Chinese internet financial companies to improve their service, but also provide specific directions to regularize the industry of internet finance in China.


title: Learning to Align Images Using Weak Geometric Supervision

abstract: Image alignment tasks require accurate pixel correspondences, which are usually recovered by matching local feature descriptors. Such descriptors are often derived using supervised learning on existing datasets with ground truth correspondences. However, the cost of creating such datasets is usually prohibitive. In this paper, we propose a new approach to align two images related by an unknown 2D homography where the local descriptor is learned from scratch from the images and the homography is estimated simultaneously. Our key insight is that a siamese convolutional neural network can be trained jointly while iteratively updating the homography parameters by optimizing a single loss function. Our method is currently weakly supervised because the input images need to be roughly aligned. ::: We have used this method to align images of different modalities such as RGB and near-infra-red (NIR) without using any prior labeled data. Images automatically aligned by our method were then used to train descriptors that generalize to new images. We also evaluated our method on RGB images. On the HPatches benchmark, our method achieves comparable accuracy to deep local descriptors that were trained offline in a supervised setting.


title: Blockchain and IoT Integration: A Systematic Survey

abstract: The Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes IoT vulnerable to privacy and security threats. With its "security by design," Blockchain (BC) can help in addressing major security requirements in IoT. BC capabilities like immutability, transparency, auditability, data encryption and operational resilience can help solve most architectural shortcomings of IoT. This article presents a comprehensive survey on BC and IoT integration. The objective of this paper is to analyze the current research trends on the usage of BC-related approaches and technologies in an IoT context. This paper presents the following novelties, with respect to related work: (i) it covers different application domains, organizing the available literature according to this categorization, (ii) it introduces two usage patterns, i.e., device manipulation and data management (open marketplace solution), and (iii) it reports on the development level of some of the presented solutions. We also analyze the main challenges faced by the research community in the smooth integration of BC and IoT, and point out the main open issues and future research directions. Last but not least, we also present a survey about novel uses of BC in the machine economy.


Sanctions of International Arbitration


title: Cross Media Entity Extraction and Linkage for Chemical Documents

abstract: Text and images are two major sources of information in scientific literature. Information from these two media typically reinforce and complement each other, thus simplifying the process for human to extract and comprehend information. However, machines cannot create the links or have the semantic understanding between images and text. We propose to integrate text analysis and image processing techniques to bridge the gap between the two media, and discover knowledge from the combined information sources, which would be otherwise lost by traditional single-media based mining systems. The focus is on the chemical entity extraction task because images are well known to add value to the textual content in chemical literature. Annotation of US chemical patent documents demonstrates the effectiveness of our proposal.


Local accuracy and global consistency for efficient SLAM


title: Quantum Theory, the Church-Turing principle and the universal quantum computer

abstract: It is argued that underlying the Church-Turing hypothesis there is an implicit physical assertion. Here, this assertion is presented explicitly as a physical principle: ‘every finitely realizable physical system can be perfectly simulated by a universal model computing machine operating by finite means’. Classical physics and the universal Turing machine, because the former is continuous and the latter discrete, do not obey the principle, at least in the strong form above. A class of model computing machines that is the quantum generalization of the class of Turing machines is described, and it is shown that quantum theory and the ‘universal quantum computer’ are compatible with the principle. Computing machines resembling the universal quantum computer could, in principle, be built and would have many remarkable properties not reproducible by any Turing machine. These do not include the computation of non-recursive functions, but they do include ‘quantum parallelism’, a method by which certain probabilistic tasks can be performed faster by a universal quantum computer than by any classical restriction of it. The intuitive explanation of these properties places an intolerable strain on all interpretations of quantum theory other than Everett’s. Some of the numerous connections between the quantum theory of computation and the rest of physics are explored. Quantum complexity theory allows a physically more reasonable definition of the ‘complexity’ or ‘knowledge’ in a physical system than does classical complexity theory.


Identifying Purchase Intent from Social Posts


title: A theory of formal synthesis via inductive learning

abstract: Formal synthesis is the process of generating a program satisfying a high-level formal specification. In recent times, effective formal synthesis methods have been proposed based on the use of inductive learning. We refer to this class of methods that learn programs from examples as formal inductive synthesis. In this paper, we present a theoretical framework for formal inductive synthesis. We discuss how formal inductive synthesis differs from traditional machine learning. We then describe oracle-guided inductive synthesis (OGIS), a framework that captures a family of synthesizers that operate by iteratively querying an oracle. An instance of OGIS that has had much practical impact is counterexample-guided inductive synthesis (CEGIS). We present a theoretical characterization of CEGIS for learning any program that computes a recursive language. In particular, we analyze the relative power of CEGIS variants where the types of counterexamples generated by the oracle varies. We also consider the impact of bounded versus unbounded memory available to the learning algorithm. In the special case where the universe of candidate programs is finite, we relate the speed of convergence to the notion of teaching dimension studied in machine learning theory. Altogether, the results of the paper take a first step towards a theoretical foundation for the emerging field of formal inductive synthesis.


title: Hardware-enforced Protection against Software Reverse-Engineering based on an Instruction Set Encoding

abstract: Software programs are prone to reverse-engineering. Protection usually consists either in obfuscation or Randomized Instruction Set Emulation (RISE). In this article, we explore a mixed software/hardware RISE suitable for embedded systems. This solution is very easy to implement on any open CPU core (LEON, openRISC, LatticeMicro32, etc.), as it implies only localized changes at the latest stage of the code execution hardware, which makes Dallas and DMA attacks unsuccessful. Similarly, alternations in the software development flow are minor and straightforward. All in one, our study shows that an easy protection can be attained at virtually no overhead cost if both the hardware and the software are customized.


title: Generating Consistent Land Surface Temperature and Emissivity Products Between ASTER and MODIS Data for Earth Science Research

abstract: Land surface temperature and emissivity (LST&E) products are generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the National Aeronautics and Space Administration's Terra satellite. These products are generated at different spatial, spectral, and temporal resolutions, resulting in discrepancies between them that are difficult to quantify, compounded by the fact that different retrieval algorithms are used to produce them. The highest spatial resolution MODIS emissivity product currently produced is from the day/night algorithm, which has a spatial resolution of 5 km. The lack of a high-spatial-resolution emissivity product from MODIS limits the usefulness of the data for a variety of applications and limits utilization with higher resolution products such as those from ASTER. This paper aims to address this problem by using the ASTER Temperature Emissivity Separation (TES) algorithm, combined with an improved atmospheric correction method, to generate the LST&E products for MODIS at 1-km spatial resolution and for ASTER in a consistent manner. The rms differences between the ASTER and MODIS emissivities generated from TES over the southwestern U.S. were 0.013 at 8.6 μm and 0.0096 at 11 μm, with good correlations of up to 0.83. The validation with laboratory-measured sand samples from the Algodones and Kelso Dunes in CA showed a good agreement in spectral shape and magnitude, with mean emissivity differences in all bands of 0.009 and 0.010 for MODIS and ASTER, respectively. These differences are equivalent to approximately 0.6 K in the LST for a material at 300 K and at 11 μm.


title: Revealing the buried penis in adults.

abstract: INTRODUCTION ::: Several surgical solutions have been proposed for resolving penile concealment with successful outcomes. Those include liposuction, adhesiolysis, and suprapubic lipectomy through the abdominal crease. Nevertheless, some limitations exist and compromise the results of surgical correction. ::: ::: ::: AIM ::: This work presents our technique for revealing the hidden penis, addressing the limitations of existing methods for surgical correction. ::: ::: ::: METHODS ::: Sixty-four adult males with buried penis were operated upon. The penis was revealed by the combination of adhesiolysis, suprapubic and lateral lipectomy, anchoring the penoscrotal and penopubic junctions, and skin coverage by a local flap. ::: ::: ::: MAIN OUTCOME MEASURES ::: Penile length in the flaccid and erect states. ::: ::: ::: RESULTS ::: Average postoperative length in the flaccid state was approximately 7 cm +/- 1.3 (a 293% increase) and in the erect state was 18.4 cm +/- 2.9 (185.7% increase), compared with preoperative length of 1.8 cm +/- 0.4 in the flaccid state and 6.4 cm +/- 1.6 in the erect state. Minor complications occurred. There was no deterioration in sexual function. ::: ::: ::: CONCLUSION ::: Revealing the concealed penis is a complicated procedure. The outcome may be improved by implementing a radical approach to tissue excision, providing adequate skin coverage, and anchoring the penile shaft, skin, and subcutaneous tissues in the revealed state to prevent relapse.


title: Fourth Order Criteria for Blind Sources Separation

abstract: Various criteria based on contrast functions as well as higher order statistics are used for solving the problem of blind separation of sources. In the paper, for the case of instantaneous mixtures of two sources, it is proved that the minimization or the cancellation of a really simple criterion, a fourth-order cross-cumulant, leads to a set of solutions whose spurious ones can be simply cancelled by using a decorrelation. >


title: Prediction of the Distribution of Perceived Music Emotions Using Discrete Samples

abstract: Typically, a machine learning model of automatic music emotion recognition is trained to learn the relationship between music features and perceived emotion values. However, simply assigning an emotion value to a clip in the training phase does not work well because the perceived emotion of a clip varies from person to person. To resolve this problem, we propose a novel approach that represents the perceived emotion of a clip as a probability distribution in the emotion plane. In addition, we develop a methodology that predicts the emotion distribution of a clip by estimating the emotion mass at discrete samples of the emotion plane. We also develop model fusion algorithms to integrate different perceptual dimensions of music listening and to enhance the modeling of emotion perception. The effectiveness of the proposed approach is validated through an extensive performance study. An average R2 statistics of 0.5439 for emotion prediction is achieved. We also show how this approach can be applied to enhance our understanding of music emotion.


Distributed Data Mining in Credit Card Fraud Detection


title: PRECISE GLOBAL DEM GENERATION BY ALOS PRISM

abstract: Abstract. The Japan Aerospace Exploration Agency (JAXA) generated the global digital elevation/surface model (DEM/DSM) and orthorectified image (ORI) using the archived data of the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS, nicknamed "Daichi"), which was operated from 2006 to 2011. PRISM consisted of three panchromatic radiometers that acquired along-track stereo images. It had a spatial resolution of 2.5 m in the nadir-looking radiometer and achieved global coverage, making it a suitable potential candidate for precise global DSM and ORI generation. In the past 10 years or so, JAXA has conducted the calibration of the system corrected standard products of PRISM in order to improve absolute accuracies as well as to validate the high-level products such as DSM and ORI. In this paper, we introduce an overview of the global DEM/DSM dataset generation project, including a summary of ALOS and PRISM, in addition to the global data archive status. It is also necessary to consider data processing strategies, since the processing capabilities of the level 1 standard product and the high-level products must be developed in terms of both hardware and software to achieve the project aims. The automatic DSM/ORI processing software and its test processing results are also described.


title: Dynamic asset allocation for varied financial markets under regime switching framework

abstract: Asset allocation among diverse financial markets is essential for investors especially under situations such as the financial crisis of 2008. Portfolio optimization is the most developed method to examine the optimal decision for asset allocation. We employ the hidden Markov model to identify regimes in varied financial markets; a regime switching model gives multiple distributions and this information can convert the static mean–variance model into an optimization problem under uncertainty, which is the case for unobservable market regimes. We construct a stochastic program to optimize portfolios under the regime switching framework and use scenario generation to mathematically formulate the optimization problem. In addition, we build a simple example for a pension fund and examine the behavior of the optimal solution over time by using a rolling-horizon simulation. We conclude that the regime information helps portfolios avoid risk during left-tail events.


title: The Achilles heel of OAuth: a multi-platform study of OAuth-based authentication

abstract: Websites and mobile applications today increasingly utilize OAuth for authorization and authentication. Major companies such as Facebook, Google and Twitter all provide OAuth services. The usage of OAuth for authorization is well documented and has been studied by many researchers. However, little work has been done to specify or analyze the usage of OAuth for authentication. Given that many developers have employed OAuth for authentication on multiple platforms, we believe it is imperative to conduct a study to understand how developers customize OAuth for authentication on different platforms. In this paper, we analyze how popular applications on the Web, Android and iOS platform authenticate users with OAuth. Our approach is to dissect the traffic from an attacker's perspective to recover the authentication mechanisms employed by the apps and identify exploitable vulnerabilities. The results show that OAuth-based authentication mechanisms employed by these applications lack sufficient verification and suffer from many vulnerabilities. Closer examination reveals that developers have different tendencies to authenticate users with OAuth on different platforms, and 32.9%, 47.1% and 41.6% of the analyzed mechanisms on the three platforms are vulnerable. We then categorize the root causes of these vulnerabilities and make practical recommendations for developers to help design and implement robust authentication mechanisms with OAuth.


title: EmotiW 2016: video and group-level emotion recognition challenges

abstract: This paper discusses the baseline for the Emotion Recognition in the Wild (EmotiW) 2016 challenge. Continuing on the theme of automatic affect recognition `in the wild', the EmotiW challenge 2016 consists of two sub-challenges: an audio-video based emotion and a new group-based emotion recognition sub-challenges. The audio-video based sub-challenge is based on the Acted Facial Expressions in the Wild (AFEW) database. The group-based emotion recognition sub-challenge is based on the Happy People Images (HAPPEI) database. We describe the data, baseline method, challenge protocols and the challenge results. A total of 22 and 7 teams participated in the audio-video based emotion and group-based emotion sub-challenges, respectively.


title: Energy efficient data encoding in DRAM channels exploiting data value similarity

abstract: As DRAM data bandwidth increases, tremendous energy is dissipated in the DRAM data bus. To reduce the energy consumed in the data bus, DRAM interfaces with asymmetric termination, such as Pseudo Open Drain (POD) and Low Voltage Swing Terminated Logic (LVSTL), have been adopted in modern DRAMs. In interfaces using asymmetric termination, the amount of termination energy is proportional to the hamming weight of the data words. In this work, we propose Bitwise Difference Encoding (BD-Encoding), which decreases the hamming weight of data words, leading to a reduction in energy consumption in the modern DRAM data bus. Since smaller hamming weight of the data words also reduces switching activity, switching energy and power noise are also both reduced. BD-Encoding exploits the similarity in data words in the DRAM data bus. We observed that similar data words (i.e. data words whose hamming distance is small) are highly likely to be sent over at similar times. Based on this observation, BD-coder stores the data recently sent over in both the memory controller and DRAMs. Then, BD-coder transfers the bitwise difference between the current data and the most similar data. In an evaluation using SPEC 2006, BD-Encoding using 64 recent data reduced termination energy by 58.3% and switching energy by 45.3%. In addition, 55% of the LdI/dt noise was decreased with BD-Encoding.


title: The specification and enforcement of authorization constraints in workflow management systems

abstract: In recent years, workflow management systems (WFMSs) have gained popularity in both research and commercial sectors. WFMSs are used to coordinate and streamline business processes. Very large WFMSs are often used in organizations with users in the range of several thousands and process instances in the range of tens and thousands. To simplify the complexity of security administration, it is common practice in many businesses to allocate a role for each activity in the process and then assign one or more users to each role—granting an authorization to roles rather than to users. Typically, security policies are expressed as constraints (or rules) on users and roles; separation of duties is a well-known constraint. Unfortunately, current role-based access control models are not adequate to model such constraints. To address this issue we (1) present a language to express both static and dynamic authorization constraints as clauses in a logic program; (2) provide formal notions of constraint consistency; and (3) propose algorithms to check the consistency of constraints and assign users and roles to tasks that constitute the workflow in such a way that no constraints are violated.


title: Robust Incremental SLAM Under Constrained Optimization Formulation

abstract: In this letter, we propose a constrained optimization formulation and a robust incremental framework for the simultaneous localization and mapping problem (SLAM). The new SLAM formulation is derived from the nonlinear least squares (NLS) formulation by mathematically formulating loop-closure cycles as constraints. Under the constrained SLAM formulation, we study the robustness of an incremental SLAM algorithm against local minima and outliers as a constraint/loop-closure cycle selection problem. We find a constraint metric that can predict the objective function growth after including the constraint. By the virtue of the constraint metric, we select constraints into the incremental SLAM according to a least objective function growth principle to increase robustness against local minima and perform $\chi ^2$ difference test on the constraint metric to increase robustness against outliers. Finally, using sequential quadratic programming (SQP) as the solver, an incremental SLAM algorithm (iSQP) is proposed. Experimental validations are provided to illustrate the accuracy of the constraint metric and the robustness of the proposed incremental SLAM algorithm. Nonetheless, the proposed approach is currently confined to datasets with sparse loop-closures due to its computational cost.


title: Learning Multi-scale Block Local Binary Patterns for Face Recognition

abstract: In this paper, we propose a novel representation, calledMultiscale Block Local Binary Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) has been proved to be effective for image representation, but it is too local to be robust. In MB-LBP, the computation is done based on average values of block subregions, instead of individual pixels. In this way, MB-LBP code presents several advantages: (1) It ismore robust than LBP; (2) it encodes not only microstructures but also macrostructures of image patterns, and hence provides a more complete image representation than the basic LBP operator; and (3) MB-LBP can be computed very efficiently using integral images. Furthermore, in order to reflect the uniform appearance of MB-LBP, we redefine the uniform patterns via statistical analysis. Finally, AdaBoost learning is applied to select most effective uniform MB-LBP features and construct face classifiers. Experiments on Face Recognition Grand Challenge (FRGC) ver2.0 database show that the proposed MB-LBP method significantly outperforms other LBP based face recognition algorithms.


title: Total variation regularization of local-global optical flow

abstract: More data fidelity terms in variational optical flow methods improve the estimation's robustness. A robust and anisotropic smoother enhances the specific fill-in process. This work presents a combined local-global (CLG) approach with total variation regularization. The combination of bilateral filtering and anisotropic (image driven) regularization is used to control the propagation phenomena. The resulted method, CLG-TV, is able to compute larger displacements in a reasonable time. The numerical scheme is highly parallelizable and runs in real-time on current generation graphic processing units.