Query: GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation

Relevant passages:
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture <=SEP=> In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling. We use a multiscale convolutional network that is able to adapt easily to each task using only small modifications, regressing from the input image to the output map directly. Our method progressively refines predictions using a sequence of scales, and captures many image details without any superpixels or low-level segmentation. We achieve state-of-the-art performance on benchmarks for all three tasks.

ParseNet: Looking Wider to See Better <=SEP=> We present a technique for adding global context to deep convolutional networks for semantic segmentation. The approach is simple, using the average feature for a layer to augment the features at each location. In addition, we study several idiosyncrasies of training, significantly increasing the performance of baseline networks (e.g. from FCN [19]). When we add our proposed global feature, and a technique for learning normalization parameters, accuracy increases consistently even over our improved versions of the baselines. Our proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow and PASCAL-Context with small additional computational cost over baselines, and near current state-of-theart performance on PASCAL VOC 2012 semantic segmentation with a simple approach. Code is available at https: //github.com/weiliu89/caffe/tree/fcn.

Coupled depth learning <=SEP=> In this paper we propose a method for estimating depth from a single image using a coarse to fine approach. We argue that modeling the fine depth details is easier after a coarse depth map has been computed. We express a global (coarse) depth map of an image as a linear combination of a depth basis learned from training examples. The depth basis captures spatial and statistical regularities and reduces the problem of global depth estimation to the task of predicting the input-specific coefficients in the linear combination. This is formulated as a regression problem from a holistic representation of the image. Crucially, the depth basis and the regression function are coupled and jointly optimized by our learning scheme. We demonstrate that this results in a significant improvement in accuracy compared to direct regression of depth pixel values or approaches learning the depth basis disjointly from the regression function. The global depth estimate is then used as a guidance by a local refinement method that introduces depth details that were not captured at the global level. Experiments on the NYUv2 and KITTI datasets show that our method outperforms the existing state-of-the-art at a considerably lower computational cost for both training and testing.

Shape, Illumination, and Reflectance from Shading <=SEP=> A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely on multiple observations of the same scene to overconstrain the problem. Recovering these same properties from a single image seems almost impossible in comparison-there are an infinite number of shapes, paint, and lights that exactly reproduce a single image. However, certain explanations are more likely than others: surfaces tend to be smooth, paint tends to be uniform, and illumination tends to be natural. We therefore pose this problem as one of statistical inference, and define an optimization problem that searches for the most likely explanation of a single image. Our technique can be viewed as a superset of several classic computer vision problems (shape-from-shading, intrinsic images, color constancy, illumination estimation, etc) and outperforms all previous solutions to those constituent problems.

Multi-Scale Context Aggregation by Dilated Convolutions <=SEP=> State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction problems such as semantic segmentation are structurally different from image classification. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multiscale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy.

An overview on crime prediction methods <=SEP=> In the recent past, crime analyses are required to reveal the complexities in the crime dataset. This process will help the parties that involve in law enforcement in arresting offenders and directing the crime prevention strategies. The ability to predict the future crimes based on the location, pattern and time can serve as a valuable source of knowledge for them either from strategic or tactical perspectives. Nevertheless, to predict future crime accurately with a better performance, it is a challenging task because of the increasing numbers of crime in present days. Therefore, crime prediction method is important to identify the future crime and reduces the numbers of crime. Currently, some researchers have been conducted a study to predict crime based on particular inputs. The performance of prediction models can be evaluated using a variety of different prediction methods such as support vector machine, multivariate time series and artificial neural network. However, there are still some limitations on their findings to provide an accurate prediction for the location of crimes. A large number of research papers on this topic have already been published previously. Thus, in this paper, we thoroughly review each of them and summarized the outcomes. Our objective is to identify current implementations of crime prediction method and the possibility to enhance it for future needs.

Organizational Use of Big Data and Competitive Advantage - Exploration of Antecedents <=SEP=> The use of Big Data can hold considerable benefits for organizations, but there is as yet little research on the impacts of Big Data use on key variables such as competitive advantage. The antecedents to Big Data use aiming at competitive advantage are also poorly understood. Drawing from prior research on the ICT-competitiveness link, this research examined the relationship of organizational use of Big Data and competitive advantage – as well as certain hypothesized antecedents to this relationship. Data was collected from a nationwide survey in Japan which was addressed to Big Data users in private companies. The result shows that the competitiveness is influenced by the systematic and extensive use of Big Data in the organization, and top management understanding for Big Data use. Antecedents of systematic and extensive Big Data use were found to be the cross-functional activities of analysts, proactive attitude of users, secured data management, and human resources. Future research should strive to verify these respondent opinions using longitudinal approaches based on objective financial data.

Back to the Future: Current-Mode Processor in the Era of Deeply Scaled CMOS <=SEP=> This paper explores the use of MOS current-mode logic (MCML) as a fast and low noise alternative to static CMOS circuits in microprocessors, thereby improving the performance, energy efficiency, and signal integrity of future computer systems. The power and ground noise generated by an MCML circuit is typically 10-100× smaller than the noise generated by a static CMOS circuit. Unlike static CMOS, whose dominant dynamic power is proportional to the frequency, MCML circuits dissipate a constant power independent of clock frequency. Although these traits make MCML highly energy efficient when operating at high speeds, the constant static power of MCML poses a challenge for a microarchitecture that operates at the modest clock rate and with a low activity factor. To address this challenge, a single-core microarchitecture for MCML is explored that exploits the C-slow retiming technique, and operates at a high frequency with low complexity to save energy. This design principle contrasts with the contemporary multicore design paradigm for static CMOS that relies on a large number of gates operating in parallel at the modest speeds. The proposed architecture generates 10-40× lower power and ground noise, and operates within 13% of the performance (i.e., 1/ExecutionTime) of a conventional, eight-core static CMOS processor while exhibiting 1.6× lower energy and 9% less area. Moreover, the operation of an MCML processor is robust under both systematic and random variations in transistor threshold voltage and effective channel length.

Marine Litter Distribution and Density in European Seas, from the Shelves to Deep Basins <=SEP=> Anthropogenic litter is present in all marine habitats, from beaches to the most remote points in the oceans. On the seafloor, marine litter, particularly plastic, can accumulate in high densities with deleterious consequences for its inhabitants. Yet, because of the high cost involved with sampling the seafloor, no large-scale assessment of distribution patterns was available to date. Here, we present data on litter distribution and density collected during 588 video and trawl surveys across 32 sites in European waters. We found litter to be present in the deepest areas and at locations as remote from land as the Charlie-Gibbs Fracture Zone across the Mid-Atlantic Ridge. The highest litter density occurs in submarine canyons, whilst the lowest density can be found on continental shelves and on ocean ridges. Plastic was the most prevalent litter item found on the seafloor. Litter from fishing activities (derelict fishing lines and nets) was particularly common on seamounts, banks, mounds and ocean ridges. Our results highlight the extent of the problem and the need for action to prevent increasing accumulation of litter in marine environments.

The development of integrated career portal in university using agile methodology <=SEP=> A gap between university graduate's quality and industry sector demand is one of the challenge university has to tackle, which makes university graduates absorption in job market slows down. One of the tools that can be used to bridge between graduates and companies is an integrated career portal. This portal can be optimized to help industry find the employees they need from the alumni from university. On the other hand, this tool can help graduates to get job that suits their competency. By using the portal, alumni can hopefully minimize the number of unemployed university graduates and accelerate their absorption into job market. The development of this career portal will use agile methodology. Questionnaire was distributed to alumni and employers to collect information about what user need in the portal. The result of this research can create a career portal which integrated business process and social media.

Internet of Things security and forensics: Challenges and opportunities <=SEP=> The Internet of Things (IoT) envisions pervasive, connected, and smart nodes interacting autonomously while offering all sorts of services. Wide distribution, openness and relatively high processing power of IoT objects made them an ideal target for cyber attacks. Moreover, as many of IoT nodes are collecting and processing private information, they are becoming a goldmine of data for malicious actors. Therefore, security and specifically the ability to detect compromised nodes, together with collecting and preserving evidences of an attack or malicious activities emerge as a priority in successful deployment of IoT networks. In this paper, we first introduce existing major security and forensics challenges within IoT domain and then briefly discuss about papers published in this special issue targeting identified challenges.

Auto cropping for digital photographs <=SEP=> In this paper, we propose an effective approach to the nearly untouched problem, still photograph auto cropping, which is one of the important features to automatically enhance photographs. To obtain an optimal result, we first formulate auto cropping as an optimization problem by defining an energy function, which consists of three sub models: composition sub model, conservative sub model, and penalty sub model. Then, particle swarm optimization (PSO) is employed to obtain the optimal solution by maximizing the objective function. Experimental results and user studies over hundreds of photographs show that the proposed approach is effective and accurate in most cases, and can be used in many practical multimedia applications.

Amplified Boomerang Attacks Against Reduced-Round MARS and Serpent <=SEP=> We introduce a new cryptanalytic technique based on Wagner’s boomerang and inside-out attacks. We first describe this new attack in terms of the original boomerang attack, and then demonstrate its use on reduced-round variants of the MARS core and Serpent. Our attack breaks eleven rounds of the MARS core with 2 chosen plaintexts, 2 memory, and 2 partial decryptions. Our attack breaks eight rounds of Serpent with 2 chosen plaintexts, 2 memory, and 2 partial decryptions.

The naturalness of reproduced high dynamic range images <=SEP=> The problem of visualizing high dynamic range images on the devices with restricted dynamic range has recently gained a lot of interest in the computer graphics community. Various so-called tone mapping operators have been proposed to face this issue. The field of tone mapping assumes thorough knowledge of both the objective and subjective attributes of an image. However, there no published analysis of such attributes exists so far. In this paper, we present an overview of image attributes which are used extensively in different tone mapping methods. Furthermore, we propose a scheme of relationships between these attributes, leading to the definition of an overall quality measure which we call naturalness. We present results of the subjective psychophysical testing that we have performed to prove the proposed relationship scheme. Our effort sets the stage for well-founded quality comparisons between tone mapping operators. By providing good definitions of the different attributes, comparisons, be they user-driven or fully automatic, are made possible at all.

Real-Time Ray Tracing with CUDA <=SEP=> The graphics processors (GPUs) have recently emerged as a low-cost alternative for parallel programming. Since modern GPUs have great computational power as well as high memory bandwidth, running ray tracing on them has been an active field of research in computer graphics in recent years. Furthermore, the introduction of CUDA, a novel GPGPU architecture, has removed several limitations that the traditional GPU-based ray tracing suffered. In this paper, an implementation of high performance CUDA ray tracing is demonstrated. We focus on the performance and show how our design choices in various optimization lead to an implementation that outperforms the previous works. For reasonably complex scenes with simple shading, our implementation achieves the performance of 30 to 43 million traced rays per second. Our implementation also includes the effects of recursive specular reflection and refraction, which were less discussed in previous GPU-based ray tracing works.

Are men really more ‘ oriented ’ toward short-term mating than women ? <=SEP=> According to Sexual Strategies Theory (D.M. Buss and D.P. Schmitt 1993), both men and women possess psychological adaptations for short-term mating. However, men may possess three adaptations that make it seem as though they are generally more ‘oriented’ toward short-term mating than women: (1) Men possess greater desire for short-term sexual relationships than women; (2) Men prefer larger numbers of sexual partners over time than women; and (3) Men require less time before consenting to sex than women. We review a wide body of psychological theory and evidence that corroborates the presence of these adaptations in men’s short-term sexual psychology. We also correct some recurring misinterpretations of Sexual Strategies Theory, such as the mistaken notion that women are designed solely for long-term mating. Finally, we document how the observed sex differences in short-term mating complement some feminist theories and refute competing evolutionary theories of human sexuality. Psychology, Evolution & Gender 3.3 December 2001 pp. 211–239 Psychology, Evolution & Gender ISSN 1461-6661 print/ISSN 1470-1073 online © 2001 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/1461666011011933 1

Automatic Detection of Respiration Rate From Ambulatory Single-Lead ECG <=SEP=> Ambulatory electrocardiography is increasingly being used in clinical practice to detect abnormal electrical behavior of the heart during ordinary daily activities. The utility of this monitoring can be improved by deriving respiration, which previously has been based on overnight apnea studies where patients are stationary, or the use of multilead ECG systems for stress testing. We compared six respiratory measures derived from a single-lead portable ECG monitor with simultaneously measured respiration air flow obtained from an ambulatory nasal cannula respiratory monitor. Ten controlled 1-h recordings were performed covering activities of daily living (lying, sitting, standing, walking, jogging, running, and stair climbing) and six overnight studies. The best method was an average of a 0.2-0.8 Hz bandpass filter and RR technique based on lengthening and shortening of the RR interval. Mean error rates with the reference gold standard were plusmn4 breaths per minute (bpm) (all activities), plusmn2 bpm (lying and sitting), and plusmn1 breath per minute (overnight studies). Statistically similar results were obtained using heart rate information alone (RR technique) compared to the best technique derived from the full ECG waveform that simplifies data collection procedures. The study shows that respiration can be derived under dynamic activities from a single-lead ECG without significant differences from traditional methods.

Location and Time Aware Social Collaborative Retrieval for New Successive Point-of-Interest Recommendation <=SEP=> In location-based social networks (LBSNs), new successive point-of-interest (POI) recommendation is a newly formulated task which tries to regard the POI a user currently visits as his POI-related query and recommend new POIs the user has not visited before. While carefully designed methods are proposed to solve this problem, they ignore the essence of the task which involves retrieval and recommendation problem simultaneously and fail to employ the social relations or temporal information adequately to improve the results.
 In order to solve this problem, we propose a new model called location and time aware social collaborative retrieval model (LTSCR), which has two distinct advantages: (1) it models the location, time, and social information simultaneously for the successive POI recommendation task; (2) it efficiently utilizes the merits of the collaborative retrieval model which leverages weighted approximately ranked pairwise (WARP) loss for achieving better top-n ranking results, just as the new successive POI recommendation task needs. We conducted some comprehensive experiments on publicly available datasets and demonstrate the power of the proposed method, with 46.6% growth in Precision@5 and 47.3% improvement in Recall@5 over the best previous method.

MOBILE LEARNING FOR EDUCATION : BENEFITS AND CHALLENGES <=SEP=> Education and training is the process by which the wisdom, knowledge and skills of one generation are passed on to the next. Today there are two form s f education and training: conventional education and distance education. Mobile learning, or "M-Lear ning", offers modern ways to support learning process through mobile devices, such as handheld an d tablet computers, MP3 players, smart phones and mobile phones. This document introduces the sub ject of mobile learning for education purposes. It examines what impact mobile devices have had on teaching and learning practices and goes on to look at the opportunities presented by the use of d igital media on mobile devices. The main purpose of this paper is to describe the current state of mobi le learning, benefits, challenges, and it’s barrier s to support teaching and learning. Data for this paper w re collected through bibliographic and internet research from January to March 2013. Four key areas will be addressed in this paper: 1. An analysis of Mobile Learning. 2. Differentiating E-Learning from Mobile Learning 3. Value and Benefits of Mobile Learning 4. Challenges and Barriers of M obile Learning: Study showed that M-Learning as a Distance learning brought great benefits to socie ty include : Training when it is needed, Training a t any time; Training at any place; Learner-centred co ntent; Avoidance of re-entry to work problems; Training for taxpayers, and those fully occupied du ring university lectures and sessions at training centres; and The industrialisation of teaching and learning. And also, notebooks, mobile Tablets, iPod touch, and iPads are very popular devices for mobil e learning because of their cost and availability o f apps. --------------------------------

Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection <=SEP=> In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.

Learning Filter Banks Using Deep Learning For Acoustic Signals <=SEP=> Designing appropriate features for acoustic event recognition tasks is an active field of research. Expressive features should both improve the performance of the tasks and also be interpret-able. Currently, heuristically designed features based on the domain knowledge requires tremendous effort in hand-crafting, while features extracted through deep network are difficult for human to interpret. In this work, we explore the experience guided learning method for designing acoustic features. This is a novel hybrid approach combining both domain knowledge and purely data driven feature designing. Based on the procedure of log Mel-filter banks, we design a filter bank learning layer. We concatenate this layer with a convolutional neural network (CNN) model. After training the network, the weight of the filter bank learning layer is extracted to facilitate the design of acoustic features. We smooth the trained weight of the learning layer and re-initialize it in filter bank learning layer as audio feature extractor. For the environmental sound recognition task based on the Urbansound8K dataset [1], the experience guided learning leads to a 2% accuracy improvement compared with the fixed feature extractors (the log Mel-filter bank). The shape of the new filter banks are visualized and explained to prove the effectiveness of the feature design process.

Improved Algorithm for Intrusion Detection Using Genetic Algorithm and SNORT <=SEP=> Intrusion Detection Systems (IDSs) detects the network attacks by scanning each data packets. There are various approaches were done for making better IDS, though traditional IDS approaches are insufficient in terms of automating detection of attacks as attackers evolved. This research is focused on Genetic Algorithm approach used in Intrusion Detection. This research shows that how the traditional SNORT and Genetic Algorithm combined together so that the number of rules of SNORT is decreased. Experimental results Shows That IDS using Genetic Algorithm and SNORT can decrease the amount of detection time, CPU Utilization and Memory Utilization. Keywords—Genetic Algorithm, SNORT, Fitness Function,

Probabilistic Semantics and Pragmatics: Uncertainty in Language and Thought <=SEP=> Language is used to communicate ideas. Ideas are mental tools for coping with a complex and uncertain world. Thus human conceptual structures should be key to language meaning, and probability—the mathematics of uncertainty— should be indispensable for describing both language and thought. Indeed, probabilistic models are enormously useful in modeling human cognition (Tenenbaum et al., 2011) and aspects of natural language (Bod et al., 2003; Chater et al., 2006). With a few early exceptions (e.g. Adams, 1975; Cohen, 1999b), probabilistic tools have only recently been used in natural language semantics and pragmatics. In this chapter we synthesize several of these modeling advances, exploring a formal model of interpretation grounded, via lexical semantics and pragmatic inference, in conceptual structure. Flexible human cognition is derived in large part from our ability to imagine possibilities (or possible worlds). A rich set of concepts, intuitive theories, and other mental representations support imagining and reasoning about possible worlds—together we will call these the conceptual lexicon. We posit that this collection of concepts also forms the set of primitive elements available for lexical semantics: word meanings can be built from the pieces of conceptual structure. Larger semantic structures are then built from word meanings by composition, ultimately resulting in a sentence meaning which is a phrase in the “language of thought” provided by the conceptual lexicon. This expression is truth-functional in that it takes on a Boolean value for each imagined world, and it can thus be used as the basis for belief updating. However, the connection between cognition, semantics, and belief is not direct: because language must flexibly adapt to the context of communication, the connection between lexical representation and interpreted meaning is mediated by pragmatic inference.

Clinical and symptomatological reflections: the fascial system <=SEP=> Every body structure is wrapped in connective tissue, or fascia, creating a structural continuity that gives form and function to every tissue and organ. Currently, there is still little information on the functions and interactions between the fascial continuum and the body system; unfortunately, in medical literature there are few texts explaining how fascial stasis or altered movement of the various connective layers can generate a clinical problem. Certainly, the fascia plays a significant role in conveying mechanical tension, in order to control an inflammatory environment. The fascial continuum is essential for transmitting muscle force, for correct motor coordination, and for preserving the organs in their site; the fascia is a vital instrument that enables the individual to communicate and live independently. This article considers what the literature offers on symptoms related to the fascial system, trying to connect the existing information on the continuity of the connective tissue and symptoms that are not always clearly defined. In our opinion, knowing and understanding this complex system of fascial layers is essential for the clinician and other health practitioners in finding the best treatment strategy for the patient.

Towards Online-Recognition with Deep Bidirectional LSTM Acoustic Models <=SEP=> Online-Recognition requires the acoustic model to provide posterior probabilities after a limited time delay given the online input audio data. This necessitates unidirectional modeling and the standard solution is to use unidirectional long short-term memory (LSTM) recurrent neural networks (RNN) or feedforward neural networks (FFNN). It is known that bidirectional LSTMs are more powerful and perform better than unidirectional LSTMs. To demonstrate the performance difference, we start by comparing several different bidirectional and unidirectional LSTM topologies. Furthermore, we apply a modification to bidirectional RNNs to enable online-recognition by moving a window over the input stream and perform one forwarding through the RNN on each window. Then, we combine the posteriors of each forwarding and we renormalize them. We show in experiments that the performance of this online-enabled bidirectional LSTM performs as good as the offline bidirectional LSTM and much better than the unidirectional LSTM.

A License Plate-Recognition Algorithm for Intelligent Transportation System Applications <=SEP=> In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (sliding concentric windows) and connected component analysis in conjunction with a character recognition neural network. The algorithm was tested with 1334 natural-scene gray-level vehicle images of different backgrounds and ambient illumination. The camera focused in the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 1287 over 1334 input images (96.5%). The optical character recognition system is a two-layer probabilistic neural network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1%. The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for the license-plate-recognition algorithm is 86.0%. A review in the related literature presented in this paper reveals that better performance (90% up to 95%) has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low

Semantic Spaces: Measuring the Distance between Different Subspaces <=SEP=> Semantic Space models, which provide a numerical representation of words’ meaning extracted from corpus of documents, have been formalized in terms of Hermitian operators over real valued Hilbert spaces by Bruza et al. [1]. The collapse of a word into a particular meaning has been investigated applying the notion of quantum collapse of superpositional states [2]. While the semantic association between words in a Semantic Space can be computed by means of the Minkowski distance [3] or the cosine of the angle between the vector representation of each pair of words, a new procedure is needed in order to establish relations between two or more Semantic Spaces. We address the question: how can the distance between different Semantic Spaces be computed? By representing each Semantic Space as a subspace of a more general Hilbert space, the relationship between Semantic Spaces can be computed by means of the subspace distance. Such distance needs to take into account the difference in the dimensions between subspaces. The availability of a distance for comparing different Semantic Subspaces would enable to achieve a deeper understanding about the geometry of Semantic Spaces which would possibly translate into better effectiveness in Information Retrieval tasks.

Fracture detection in x-ray images through stacked random forests feature fusion <=SEP=> Bone fractures are among the most common traumas in musculoskeletal injuries. They are also frequently missed during the radiological examination. Thus, there is a need for assistive technologies for radiologists in this field. Previous automatic bone fracture detection work has focused on detection of specific fracture types in a single anatomical region. In this paper, we present a generalized bone fracture detection method that is applicable to multiple bone fracture types and multiple bone structures throughout the body. The method uses features extracted from candidate patches in X-ray images in a novel discriminative learning framework called the Stacked Random Forests Feature Fusion. This is a multilayer learning formulation in which the class probability labels, produced by random forests learners at a lower level, are used to derive the refined class distribution labels at the next level. The candidate patches themselves are selected using an efficient subwindow search algorithm. The outcome of the method is a number of fracture bounding-boxes ranked from the most likely to the least likely to contain a fracture. We evaluate the proposed method on a set of 145 X-rays images. When the top ranking seven fracture bounding-boxes are considered, we are able to capture 81.2% of the fracture findings reported by a radiologist. The proposed method outperforms other fracture detection frameworks that use local features, and single layer random forests and support vector machine classification.

Neural Disambiguation of Causal Lexical Markers Based on Context <=SEP=> Causation is a psychological tool of humans to understand the world and it is projected in natural language. Causation relates two events, so in order to understand the causal relation of those events and the causal reasoning of humans, the study of causality classification is required. We claim that the use of linguistic features may restrict the representation of causality, and dense vector spaces can provide a better encoding of the causal meaning of an utterance. Herein, we propose a neural network architecture only fed with word embeddings for the task of causality classification. Our results show that our claim holds, and we outperform the state-of-the-art on the AltLex corpus. The source code of our experiments is publicly available.1

Chapter 12 DATA MINING IN SOCIAL MEDIA <=SEP=> The rise of online social media is providing a wealth of social network data. Data mining techniques provide researchers and practitioners the tools needed to analyze large, complex, and frequently changing social media data. This chapter introduces the basics of data mining, reviews social media, discusses how to mine social media data, and highlights some illustrative examples with an emphasis on social networking sites and blogs.