title: Random Projections on Manifolds of Symmetric Positive Definite Matrices for Image Classification

abstract: Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance. Taking into account manifold geometry is typically done via (1) embedding the manifolds in tangent spaces, or (2) embedding into Reproducing Kernel Hilbert Spaces (RKHS). While embedding into tangent spaces allows the use of existing Euclidean-based learning algorithms, manifold shape is only approximated which can cause loss of discriminatory information. The RKHS approach retains more of the manifold structure, but may require non-trivial effort to kernelise Euclidean-based learning algorithms. In contrast to the above approaches, in this paper we offer a novel solution that allows SPD matrices to be used with unmodified Euclidean-based learning algorithms, with the true manifold shape well-preserved. Specifically, we propose to project SPD matrices using a set of random projection hyperplanes over RKHS into a random projection space, which leads to representing each matrix as a vector of projection coefficients. Experiments on face recognition, person re-identification and texture classification show that the proposed approach outperforms several recent methods, such as Tensor Sparse Coding, Histogram Plus Epitome, Riemannian Locality Preserving Projection and Relational Divergence Classification.


title: Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks

abstract: Scalability properties of deep neural networks raise key research questions, particularly as the problems considered become larger and more challenging. This paper expands on the idea of conditional computation introduced by Bengio, et. al., where the nodes of a deep network are augmented by a set of gating units that determine when a node should be calculated. By factorizing the weight matrix into a low-rank approximation, an estimation of the sign of the pre-nonlinearity activation can be efficiently obtained. For networks using rectified-linear hidden units, this implies that the computation of a hidden unit with an estimated negative pre-nonlinearity can be ommitted altogether, as its value will become zero when nonlinearity is applied. For sparse neural networks, this can result in considerable speed gains. Experimental results using the MNIST and SVHN data sets with a fully-connected deep neural network demonstrate the performance robustness of the proposed scheme with respect to the error introduced by the conditional computation process.


title: Deep Learning for Detection of Object-Based Forgery in Advanced Video

abstract: Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN) to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.


title: GOVERNANCE FOR MOBILE SERVICE PLATFORMS: A LITERATURE REVIEW AND RESEARCH AGENDA

abstract: Mobile service platforms are IT-based marketplaces that have become the source of competitive advantages. Aligning the interests of stakeholders by establishing effective governance mechanisms is central to the success of mobile service platforms. This phenomenon ignites research in many disciplines, which results in a fragmented understanding of mobile service platforms. This paper is a first step towards establishing a comprehensive understanding of the role of governance mechanisms in mobile service platforms. We review the literature and develop a theoretical modular framework which provides an outline for the analysis and conceptualization of mobile service platform governance. This also provides help to identify promising research avenues.


title: Search-Based Software Testing: Past, Present and Future

abstract: Search-Based Software Testing is the use of a meta-heuristic optimizing search technique, such as a Genetic Algorithm, to automate or partially automate a testing task, for example the automatic generation of test data. Key to the optimization process is a problem-specific fitness function. The role of the fitness function is to guide the search to good solutions from a potentially infinite search space, within a practical time limit. Work on Search-Based Software Testing dates back to 1976, with interest in the area beginning to gather pace in the 1990s. More recently there has been an explosion of the amount of work. This paper reviews past work and the current state of the art, and discusses potential future research areas and open problems that remain in the field.


title: Doppler images of intra-pulmonary shunt within atelectasis in anesthetized children

abstract: Doppler images of pulmonary vessels in pulmonary diseases associated with subpleural consolidations have been described. Color Doppler easily identifies such vessels within consolidations while spectral Doppler analysis allows the differentiation between pulmonary and bronchial arteries. Thus, Doppler helps in diagnosing the nature of consolidations. To our knowledge, Doppler analysis of pulmonary vessels within anesthesia-induced atelectasis has never been described before. The aim of this case series is to demonstrate the ability of lung ultrasound to detect the shunting of blood within atelectatic lung areas in anesthetized children. Three anesthetized and mechanically ventilated children were scanned in the supine position using a high-resolution linear probe of 6–12 MHz. Once subpleural consolidations were detected in the most dependent posterior lung regions, the probe was rotated such that its long axis followed the intercostal space. In this oblique position, color Doppler mapping was performed to detect blood flow within the consolidation. Thereafter, pulsed waved spectral Doppler was applied in the previously identified vessels during a short expiratory pause, which prevented interferences from respiratory motion. Different flow patterns were identified which corresponded to both, pulmonary and bronchial vessels. Finally, a lung recruitment maneuver was performed which leads to the complete resolution of the aforementioned consolidation thereby confirming the pathophysiological entity of anesthesia-induced atelectasis. Lung ultrasound is a non-invasive imaging tool that not only enables the diagnosis of anesthesia-induced atelectasis in pediatric patients but also analysis of shunting blood within this consolidation.


title: Learning what is where from social observations

abstract: Learning What is Where from Social Observations Julian Jara-Ettinger (jjara@mit.edu) Chris L. Baker (clbaker@mit.edu) Joshua B. Tenenbaum (jbt@mit.edu) Department of Brain and Cognitive Sciences, MIT Cambridge, MA 02139 Abstract Gopnik & Meltzoff, 1997). Implicitly you assume basic as- pects of rationality in the person you see crossing the street: that they want to cross safely, that they update their beliefs about the presence of oncoming cars based on what they can see, and that they plan their actions appropriately to achieve their goals given their beliefs. If they are clearly paying at- tention to the side of the street you cannot see, and they are walking across unhurriedly and unworriedly, it is then a good bet that no traffic is headed imminently toward them; if they are jumping or dashing out of the way, that is another story. Accounts of distinctively human cognition often empha- size the sophisticated representational power of people’s ToM, as in the capacity to represent arbitrary belief states, false beliefs as well as true ones, and predict how people will act accordingly. But just as or more important is the sophis- ticated inferential power of ToM: how we can learn about the contents of other agents’ mental states, or even the structure of the world, by reasoning backwards to the best explanations of agents’ observed behaviors. This kind of inverse reasoning underlies not only the traffic example above, but many other situations of practical importance for everyday cognition. For example, if you see people filing out of a new restaurant with contented looks, it is a good bet the food inside is satisfying. If you see someone enter the restaurant with an expression of eager anticipation, then exit a moment later and start looking for a different place to eat, you might guess that the restau- rant is unexpectedly closed – or perhaps he mistook it for a different place. If your friend the foodie goes far out of his way while visiting a new city to visit a particular restaurant, you can bet that place is one of the city’s best. In this paper, we present a computational model of this social-learning capacity – inferring the world’s state from ob- serving other agents’ behavior, guided by ToM. Similar in- ferential abilities have been studied in infants (Csibra, Bir´o, Ko´os, & Gergely, 2003) and adults (Goodman, Baker, & Tenenbaum, 2009), and the latter paper presented a compu- tational model similar to ours in key respects (but focused on causal learning). Our work is the first to test people’s social learning against rational model predictions in a large- scale quantitative experiment, showing that people can form surprisingly accurate fine-grained beliefs about the relative probabilities of different possible worlds from sparse social observations – just a single agent moving along a single goal- directed path of intentional action. We contrast our model with a non-intentional, non-ToM account based on low-level features of the agent’s motion. Even when we introduce many free parameters in the form of variable feature weights, Observing the actions of other people allows us to learn not only about their mental states, but also about hidden aspects of a shared environmental situation – things we cannot see, but they can, and that influence their behavior in predictable ways. This paper presents a computational model of how peo- ple can learn about the world through these social inferences, supported by the same Theory of Mind (ToM) that enables rep- resenting and reasoning about an agent’s mental states such as beliefs, desires, and intentions. The model is an extension of the Bayesian Theory of Mind (BToM) model of Baker et al. (2011), which treats observed intentional actions as the output of an approximately rational planning process and then rea- sons backwards to infer the most likely inputs to the agent’s planner – in this case, the locations and states of utility sources (potential goal objects) in the environment. We conducted a large-scale experiment comparing the world-state inferences of the BToM model and those of human subjects, given observa- tions of agents moving along various trajectories in simple spa- tial environments. The model quantitatively predicts subjects’ graded beliefs about possible world states with high accuracy – and substantially better than a non-mentalistic feature-based model with many more free parameters. These results show the power of social learning for acquiring surprisingly fine-grained knowledge about the world. Keywords: Social Cognition; Theory of Mind; Social Learn- ing; Reinforcement Learning Introduction The most obvious way to learn about the world is by direct observation. You may believe there is a Starbucks across the street from your office because you have passed it many times, and believe it is open at this moment because you just passed by a few minutes ago and saw a number of people go- ing in and out. But many aspects of the world are unobserv- able and must be inferred indirectly, often based on observing the actions of other people who know or perceive what you do not. Consider the situation of driving or biking and need- ing to turn left at an intersection onto a busy street, across oncoming traffic. Of course before turning you will check to see whether there are any cars coming down the busy street from the left, but suppose there is a large truck parked on the street, blocking your view so that you cannot see whether there is any oncoming traffic. You may inch out slowly until you can see, but you may also observe what other drivers or pedestrians are doing. If they are in a position to see the on- coming cars that you cannot, and if they are crossing the busy street at the same point you wish to turn, then it is a good bet that your turn would also be safe. Making such a judgment is literally betting your life on a mental model of another person’s cognitive processes – a Theory of Mind (ToM) (e.g. Dennett, 1987; Wellman, 1990;


title: Impact of early treatment programs on HIV epidemics: An immunity-based mathematical model.

abstract: While studies on pre-exposure prophylaxis (PrEP) and post-exposure prophylaxis (PEP) have demonstrated substantial advantages in controlling HIV transmission, the overall benefits of the programs with early initiation of antiretroviral therapy (ART) have not been fully understood and are still on debate. Here, we develop an immunity-based (CD4+ T cell count based) mathematical model to study the impacts of early treatment programs on HIV epidemics and the overall community-level immunity. The model is parametrized using the HIV prevalence data from South Africa and fully analyzed for stability of equilibria and infection persistence criteria. Using our model, we evaluate the effects of early treatment on the new infection transmission, disease death, basic reproduction number, HIV prevalence, and the community-level immunity. Our model predicts that the programs with early treatments significantly reduce the new infection transmission and increase the community-level immunity, but the treatments alone may not be enough to eliminate HIV epidemics. These findings, including the community-level immunity, might provide helpful information for proper implementation of HIV treatment programs.


title: A dataset for forgery detection and spotting in document images

abstract: In the last decades, the explosion of the volume of digital document images, and the development of consumer tools to modify these images, has lead to a huge increase on reported fraudulent document cases. This situation has promoted the development of automatic methods for both preventing forgeries in modified documents and detecting them. However, document forensics is a sensitive topic. Data is usually either private or unlabeled, and most of the reported works are commonly evaluated on datasets with a restricted access. In this paper we present a new public dataset made of a corpus of 477 corrupted payslips in which near 6000 characters were forged. Provided with a reliable groundtruth, we expect this dataset to be useful for many works in the digital forensics research domain.


title: An Ontology Engineering Approach to Gamify Collaborative Learning Scenarios

abstract: The design of collaborative learning (CL) scenarios that increase both students’ learning and motivation is a challenge that the CSCL community has been addressing in the past few years. On one hand, CSCL design (i.e. scripts) has been shown to be effective to support meaningful interactions and better learning. On the other hand, scripted collaboration often does not motivate students to participate in the CL process, which makes more difficult the use of group activities over time. To deal with the problem of motivation, researchers and educators are now looking at gamification techniques to engage students. Gamification is an interesting concept that deals with the introduction and use of game design elements in a proper way to satisfy individual motivational needs. The use of gamification in educational settings is a complex task that requires, from instructional designers, knowledge about game elements (such as leaderboards and point systems), game design (e.g. how to combine game elements) and their impact on motivation and learning. Today, to the best of our knowledge, there are no approaches for the formal systematization of the instructional design knowledge about gamification and its application in CL scenarios. Thus, to address this issue, we have applied ontological engineering techniques to develop an Ontology called OntoGaCLeS. In this paper, we present the main concepts and ontological structure used to represent gamified CL scenarios. In this ontology, we formalize the representation of gamification concepts and explain how they affect motivation in the context of collaborative learning. Particularly, we will focus on the definition of player roles and gameplay strategies. Furthermore, to show the utility of our approach, we illustrate how to use our ontology to define a personalized gamification model that is used to gamify a CL scenario based on motivational needs and individual traits of learners in a group.


title: Interactive Volume Visualization of General Polyhedral Grids

abstract: This paper presents a novel framework for visualizing volumetric data specified on complex polyhedral grids, without the need to perform any kind of a priori tetrahedralization. These grids are composed of polyhedra that often are non-convex and have an arbitrary number of faces, where the faces can be non-planar with an arbitrary number of vertices. The importance of such grids in state-of-the-art simulation packages is increasing rapidly. We propose a very compact, face-based data structure for representing such meshes for visualization, called two-sided face sequence lists (TSFSL), as well as an algorithm for direct GPU-based ray-casting using this representation. The TSFSL data structure is able to represent the entire mesh topology in a 1D TSFSL data array of face records, which facilitates the use of efficient 1D texture accesses for visualization. In order to scale to large data sizes, we employ a mesh decomposition into bricks that can be handled independently, where each brick is then composed of its own TSFSL array. This bricking enables memory savings and performance improvements for large meshes. We illustrate the feasibility of our approach with real-world application results, by visualizing highly complex polyhedral data from commercial state-of-the-art simulation packages.


title: Activity Classification in Independent Living Environment with JINS MEME Eyewear

abstract: The population of older adults relative to the total population is rising rapidly worldwide, and this contributes to an increased burden on healthcare systems. Older adults with complex needs are often limited in their ability to perform basic daily activities, and they may require task-specific supports. With continuous health-monitoring systems, the ability to recognize people's activities in their homes can enable automated assisted living systems, caregivers and clinicians to provide suitable adaptive care. With the advent of miniaturized sensing technology, which can be wearable, it is now possible to collect and store data on different aspects of human movement under realistic independent living conditions.In our most recent Smart Condo™ study, twenty-six participants spent one two-hour session in the one-bedroom living environment, either alone or in pairs, and performed a scripted protocol of activities of daily living. Twelve of these participants wore the commercial smart eyewear device JINS MEME, which collected electrooculography, accelerometer and gyroscope data throughout their sessions. In this paper, we describe our method for offline classification of the participants' activities. We show that this method yields equal or better results with a variety of activities compared to approaches that involve more restrictive wearable device setups. The results demonstrate the suitability of JINS MEME for recognition of activities of daily living and identify limitations associated with the current model.


title: The advantages and disadvantages of being polyploid

abstract: Polyploids — organisms that have multiple sets of chromosomes — are common in certain plant and animal taxa, and can be surprisingly stable. The evidence that has emerged from genome analyses also indicates that many other eukaryotic genomes have a polyploid ancestry, suggesting that both humans and most other eukaryotes have either benefited from or endured polyploidy. Studies of polyploids soon after their formation have revealed genetic and epigenetic interactions between redundant genes. These interactions can be related to the phenotypes and evolutionary fates of polyploids. Here, I consider the advantages and challenges of polyploidy, and its evolutionary potential.


title: Link Prediction Across Multiple Social Networks

abstract: The problem of link prediction has been studied extensively in literature. There are various versions of the link prediction problem \textit{e.g.,} link existence problem, link removal problem, predicting edge weights over time etc. In this paper we describe a new type of link prediction problem called the Inter-network link-prediction problem where the task is to predict links \textit{across} different networks. Thus given a set of nodes which participate in multiple networks the task is to determine if one can predict the edges that occur in one network by only using node attribute and edge information from other networks. We use insights from theories of evolution of social communication networks and the MTML framework to derive models which can be used to make link predictions across networks. For the experiments data from different \textit{types} of social networks from a Massively Multiplayer Online Role Playing Game (MMORPG) is used.


title: Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language

abstract: This paper addresses the problem of manipulating images using natural language description. Our task aims to semantically modify visual attributes of an object in an image according to the text describing the new visual appearance. Although existing methods synthesize images having new attributes, they do not fully preserve text-irrelevant contents of the original image. In this paper, we propose the text-adaptive generative adversarial network (TAGAN) to generate semantically manipulated images while preserving text-irrelevant contents. The key to our method is the text-adaptive discriminator that creates word-level local discriminators according to input text to classify fine-grained attributes independently. With this discriminator, the generator learns to generate images where only regions that correspond to the given text are modified. Experimental results show that our method outperforms existing methods on CUB and Oxford-102 datasets, and our results were mostly preferred on a user study. Extensive analysis shows that our method is able to effectively disentangle visual attributes and produce pleasing outputs.


Bidirectional Generative Adversarial Networks for Neural Machine Translation


title: Guided filtering for PRNU-based localization of small-size image forgeries

abstract: PRNU-based techniques guarantee a good forgery detection performance irrespective of the specific type of forgery. The presence or absence of the camera PRNU pattern is detected by a correlation test. Given the very low power of the PRNU signal, however, the correlation must be averaged over a pretty large window, reducing the algorithm's ability to reveal small forgeries. To improve resolution, we estimate correlation with a spatially adaptive filtering technique, with weights computed over a suitable pilot image. Implementation efficiency is achieved by resorting to the recently proposed guided filters. Experiments prove that the proposed filtering strategy allows for a much better detection performance in the case of small forgeries.


title: Data Exchange Platform to Fight Insurance Fraud on Blockchain

abstract: The Paper introduces the concept of Blockchain and its application in sharing fraud intelligence data in Insurance marketplace.


title: Chemical Reactant Recommendation Using a Network of Organic Chemistry

abstract: This paper focuses on the overall task of recommending to the chemist candidate molecules (reactants) necessary to synthesize a given target molecule (product), which is a novel application as well as an important step for the chemist to find a synthesis route to generate the product. We formulate this task as a link-prediction problem over a so-called Network of Organic Chemistry (NOC) that we have constructed from 8 million chemical reactions described in the US patent literature between 1976 and 2013. We leverage state-of-the-art factorization algorithms for recommender systems to solve this task. Our empirical evaluation demonstrates that Factorization Machines, trained with chemistry-specific knowledge, outperforms current methods based on similarity of chemical structures.


title: Periocular fillers and related anatomy.

abstract: Aging of the periocular area involves changes of the skin, muscle, fat, and bones. Facial fillers can be helpful in minimizing these changes by restoring youthful fullness to periocular areas that have undergone volume loss or loss of support. Physicians should understand the complicated anatomy surrounding the eyes, both to understand the aging process and to minimize treatment complications.


title: Multiple input converters for fuel cells

abstract: Multilevel converters have been used previously to integrate several fuel cell modules for higher power applications. Some previous publications have also shown improvements in fuel cell utilization by exploiting the static characteristics of fuel cells, which show more than a 30% difference in the output voltage between no-load to full-load conditions. This paper first describes standard fuel cell power electronics interfaces, then reviews a few of the multi input systems and explores additional configurations.


title: Hierarchical Multimodal LSTM for Dense Visual-Semantic Embedding

abstract: We address the problem of dense visual-semantic embedding that maps not only full sentences and whole images but also phrases within sentences and salient regions within images into a multimodal embedding space. Such dense embeddings, when applied to the task of image captioning, enable us to produce several region-oriented and detailed phrases rather than just an overview sentence to describe an image. Specifically, we present a hierarchical structured recurrent neural network (RNN), namely Hierarchical Multimodal LSTM (HM-LSTM). Compared with chain structured RNN, our proposed model exploits the hierarchical relations between sentences and phrases, and between whole images and image regions, to jointly establish their representations. Without the need of any supervised labels, our proposed model automatically learns the fine-grained correspondences between phrases and image regions towards the dense embedding. Extensive experiments on several datasets validate the efficacy of our method, which compares favorably with the state-of-the-art methods.


title: Self-assembly of neural networks viewed as swarm intelligence

abstract: While self-assembly is a fairly active area of research in swarm intelligence, relatively little attention has been paid to the issues surrounding the construction of network structures. In this paper we extend methods developed previously for controlling collective movements of agent teams to serve as the basis for self-assembly or “growth” of networks, using neural networks as a concrete application to evaluate our approach. Our central innovation is having network connections arise as persistent “trails” left behind moving agents, trails that are reminiscent of pheromone deposits made by agents in ant colony optimization models. The resulting network connections are thus essentially a record of agent movements. We demonstrate our model’s effectiveness by using it to produce two large networks that support subsequent learning of topographic and feature maps. Improvements produced by the incorporation of collective movements are also examined through computational experiments. These results indicate that methods for directing collective movements can be adopted to facilitate network self-assembly.


title: New fine line fabrication technology on glass-cloth prepreg without insulating films for PKG substrate

abstract: As electronic parts increase its performance and miniaturize in its size, package substrates are demanded to be thinner and higher in density. But higher density substrates using insulating films give higher warpage values when they are very thin, due to its low modulus. Packages with glass-cloth prepreg as its outer-layer have lower warpage, but making a 40 μm pitch package with them are difficult because it cannot adapt to the semi-additive method. Conventional insulating films can be applied to the semi-additive method; however, a process of roughing the surface of the insulating films by chemicals is required, causing the limitation of material of insulating films. Therefore, we developed a new technology of semi-additive primer (SAPP) with glass-cloth prepregs which allows higher density and lower warpage for substrates. Under 30 μm pitch wiring of SAPP applied substrates were easier compared to conventional substrates made from the insulating film semi-additive method. Also, the roughening process (Ra=0.50–0.60 μm) of conventional insulating films to achieve adhesion strength with plated copper makes high density wiring difficult. But the new SAPP system has a surface roughness of Ra <0.25 μm with high adhesive strength which allows easy high density wiring. It has been made clear that by providing a new adhesive-agent layer, peel strength equivalent to that of the roughened insulating film (0.7 kN/m or more) can be obtained with sufficient reliability. The thinner package boards consisting of our new low-CTE and high modulus insulation prepreg E-700G(R) and SAPP have lower warpage than that of build-up structure and coreless structure using conventional insulating films. The package substrates made of SAPP with E-700G(R) core show low coefficient of thermal expansion (9–10 ppm/°C) and high modulus of tensile elasticity (32 GPa), with 20 μm pitch wiring possible.


Using Models at Run Time to Detect Incomplete and Inconsistent Requirements


title: Example-based facial sketch generation with non-parametric sampling

abstract: In this paper, we present an example-based facial sketch system. Our system automatically generates a sketch from an input image, by learning from example sketches drawn with a particular style by an artist. There are two key elements in our system: a non-parametric sampling method and a flexible sketch model. Given an input image pixel and its neighborhood, the conditional distribution of a sketch point is computed by querying the examples and finding all similar neighborhoods. An "expected sketch image" is then drawn from the distribution to reflect the drawing style. Finally, facial sketches are obtained by incorporating the sketch model. Experimental results demonstrate the effectiveness of our techniques.


title: Investigation on Current Crowding Effect in IGBTs

abstract: This paper investigates the influences of various topside cell structures of insulated gate bipolar transistor (IGBT) on the current crowding effect during high current turn-OFF by symmetrical multicell numerical simulations. It is observed that lower breakdown electric field in the drift region can reduce the negative differential resistance in the forward blocking curve and therefore suppress the current crowding effect during high current turn-OFF. We refer to this characteristic as the self-suppressing current crowding effect (SSCCE). Different topside cell structures of IGBT have considerable influence on the SSCCE. A modest increment of the SSCCE by proper topside structure design provides an alternative solution to improve the ruggedness of IGBT, especially for the strong punchthrough or low current gain IGBTs.


title: Extracting Product Features And Opinions From Reviews

abstract: Consumers are often forced to wade through many on-line reviews in order to make an informed product choice. This paper introduces Opine, an unsupervised information-extraction system which mines reviews in order to build a model of important product features, their evaluation by reviewers, and their relative quality across products.Compared to previous work, Opine achieves 22% higher precision (with only 3% lower recall) on the feature extraction task. Opine's novel use of relaxation labeling for finding the semantic orientation of words in context leads to strong performance on the tasks of finding opinion phrases and their polarity.


title: Creating Diversity In Ensembles Using Artificial Data

abstract: Abstract The diversity of an ensemble of classifiers is known to be an important factor in determining its generalization error. We present a new method for generating ensembles, D ecorate (Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples), that directly constructs diverse hypotheses using additional artificially constructed training examples. The technique is a simple, general meta-learner that can use any strong learner as a base classifier to build diverse committees. Experimental results using decision-tree induction as a base learner demonstrate that this approach consistently achieves higher predictive accuracy than the base classifier, Bagging and Random Forests. D ecorate also obtains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets.


title: Golden retriever: a Java based open source image retrieval engine

abstract: Golden Retriever Image Retrieval Engine (GRire) is an open source light weight Java library developed for Content Based Image Retrieval (CBIR) tasks, employing the Bag of Visual Words (BOVW) model. It provides a complete framework for creating CBIR system including image analysis tools, classifiers, weighting schemes etc., for efficient indexing and retrieval procedures. Its eminent feature is its extensibility, achieved through the open source nature of the library as well as a user-friendly embedded plug-in system. GRire is available on-line along with install and development documentation on http://www.grire.net and on its Google Code page http://code.google.com/p/grire. It is distributed either as a Java library or as a standalone Java application, both GPL licensed.


title: Reflectance scanning: estimating shading frame and BRDF with generalized linear light sources

abstract: We present a generalized linear light source solution to estimate both the local shading frame and anisotropic surface reflectance of a planar spatially varying material sample. We generalize linear light source reflectometry by modulating the intensity along the linear light source, and show that a constant and two sinusoidal lighting patterns are sufficient for estimating the local shading frame and anisotropic surface reflectance. We propose a novel reconstruction algorithm based on the key observation that after factoring out the tangent rotation, the anisotropic surface reflectance lies in a low rank subspace. We exploit the differences in tangent rotation between surface points to infer the low rank subspace and fit each surface point's reflectance function in the projected low rank subspace to the observations. We propose two prototype acquisition devices for capturing surface reflectance that differ on whether the camera is fixed with respect to the linear light source or fixed with respect to the material sample. We demonstrate convincing results obtained from reflectance scans of surfaces with different reflectance and shading frame variations.


title: Adaptive matching for expert systems with uncertain task types

abstract: A matching in a two-sided market often incurs an externality: a matched resource may become unavailable to the other side of the market, at least for a while. This is especially an issue in online platforms involving human experts as the expert resources are often scarce. The efficient utilization of experts in these platforms is made challenging by the fact that the information available about the parties involved is usually limited. ::: To address this challenge, we develop a model of a task-expert matching system where a task is matched to an expert using not only the prior information about the task but also the feedback obtained from the past matches. In our model the tasks arrive online while the experts are fixed and constrained by a finite service capacity. For this model, we characterize the maximum task resolution throughput a platform can achieve. We show that the natural greedy approaches where each expert is assigned a task most suitable to her skill is suboptimal, as it does not internalize the above externality. We develop a throughput optimal backpressure algorithm which does so by accounting for the `congestion' among different task types. Finally, we validate our model and confirm our theoretical findings with data-driven simulations via logs of Math.StackExchange, a StackOverflow forum dedicated to mathematics.


title: Saliency Detection: A Spectral Residual Approach

abstract: The ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of this basic intelligent behavior still remains a challenge. This paper presents a simple method for the visual saliency detection. Our model is independent of features, categories, or other forms of prior knowledge of the objects. By analyzing the log-spectrum of an input image, we extract the spectral residual of an image in spectral domain, and propose a fast method to construct the corresponding saliency map in spatial domain. We test this model on both natural pictures and artificial images such as psychological patterns. The result indicate fast and robust saliency detection of our method.


title: A Large Scale Corpus of Gulf Arabic

abstract: Most Arabic natural language processing tools and resources are developed to serve Modern Standard Arabic (MSA), which is the official written language in the Arab World. Some Dialectal Arabic varieties, notably Egyptian Arabic, have received some attention lately and have a growing collection of resources that include annotated corpora and morphological analyzers and taggers. Gulf Arabic, however, lags behind in that respect. In this paper, we present the Gumar Corpus, a large-scale corpus of Gulf Arabic consisting of 110 million words from 1,200 forum novels. We annotate the corpus for sub-dialect information at the document level. We also present results of a preliminary study in the morphological annotation of Gulf Arabic which includes developing guidelines for a conventional orthography. The text of the corpus is publicly browsable through a web interface we developed for it.


title: Generative Image Inpainting with Contextual Attention

abstract: Recent deep learning based approaches have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can generate visually plausible image structures and textures, but often create distorted structures or blurry textures inconsistent with surrounding areas. This is mainly due to ineffectiveness of convolutional neural networks in explicitly borrowing or copying information from distant spatial locations. On the other hand, traditional texture and patch synthesis approaches are particularly suitable when it needs to borrow textures from the surrounding regions. Motivated by these observations, we propose a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions. The model is a feedforward, fully convolutional neural network which can process images with multiple holes at arbitrary locations and with variable sizes during the test time. Experiments on multiple datasets including faces (CelebA, CelebA-HQ), textures (DTD) and natural images (ImageNet, Places2) demonstrate that our proposed approach generates higher-quality inpainting results than existing ones. Code, demo and models are available at: https://github.com/JiahuiYu/generative_inpainting.


title: A Chinese Mind-Body Exercise Improves Self-Control of Children with Autism: A Randomized Controlled Trial

abstract: Self-control problems commonly manifest as temper outbursts and repetitive/rigid/impulsive behaviors, in children with autism spectrum disorders (ASD), which often contributes to learning difficulties and caregiver burden. The present study aims to compare the effect of a traditional Chinese Chan-based mind-body exercise, Nei Yang Gong, with that of the conventional Progressive Muscle Relaxation (PMR) technique in enhancing the self-control of children with ASD. Forty-six age- and IQ-matched ASD children were randomly assigned to receive group training in Nei Yang Gong (experimental group) or PMR (control group) twice per week for four weeks. The participants' self-control was measured by three neuropsychological tests and parental rating on standardized questionnaires, and the underlying neural mechanism was assessed by the participants' brain EEG activity during an inhibitory-control task before and after intervention. The results show that the experimental group demonstrated significantly greater improvement in self-control than the control group, which concurs with the parental reports of reduced autistic symptoms and increased control of temper and behaviors. In addition, the experimental group showed enhanced EEG activity in the anterior cingulate cortex, a region that mediates self-control, whereas the PMR group did not. The present findings support the potential application of Chinese Chan-based mind-body exercises as a form of neuropsychological rehabilitation for patients with self-control problems. Chinese Clinical Trial Registry; Registration No.: ChiCTR-TRC-12002561; URL: www.chictr.org.


title: Fundamental challenges in mobile computing

abstract: Abstract : This paper is an answer to the question: 'What is unique and conceptually different about mobile computing?' The paper begins by describing a set of constraints intrinsic to mobile computing, and examining the impact of these constraints on the design of distributed systems. Next, it summarizes the key results of the Coda and Odyssey systems. Finally, it describes the research opportunities in five important topics relevant to mobile computing: caching metrics, semantic callbacks and validators, resource revocation, analysis of adaptation, and global estimation from local observations.


title: Comparison of visible and infra-red imagery for face recognition

abstract: This paper presents initial results in a study comparing the effectiveness of visible and infra-red (IR) imagery for detecting and recognizing faces in areas where personnel identification is critical, (e.g. airports and secure buildings). We compare the effectiveness of visible versus IR imagery by running three face recognition algorithms on a database of images collected for this study. There are both IR and visible images for each person in the database collected using the same scenarios. We used three very different feature-extraction and decision-making algorithms for our study to insure that the comparisons would not depend on a particular processing technique. We also present recognition results when visible and infra-red decision metrics are fused. The recognition results show that both visible and IR imagery perform similarly across algorithms and that fusion of IR and visible imagery as a viable means of enhancing performance beyond that of either acting alone. We examine the relative importance of different regions of the face for recognition. We also discuss practical issues of implementation, along with plans for the next phase of the study, face detection in an uncontrolled environment. Preliminary face detection results are presented.


title: DBpedia spotlight: shedding light on the web of documents

abstract: Interlinking text documents with Linked Open Data enables the Web of Data to be used as background knowledge within document-oriented applications such as search and faceted browsing. As a step towards interconnecting the Web of Documents with the Web of Data, we developed DBpedia Spotlight, a system for automatically annotating text documents with DBpedia URIs. DBpedia Spotlight allows users to configure the annotations to their specific needs through the DBpedia Ontology and quality measures such as prominence, topical pertinence, contextual ambiguity and disambiguation confidence. We compare our approach with the state of the art in disambiguation, and evaluate our results in light of three baselines and six publicly available annotation systems, demonstrating the competitiveness of our system. DBpedia Spotlight is shared as open source and deployed as a Web Service freely available for public use.


Complete Algorithms


title: Learning social network embeddings for predicting information diffusion

abstract: Analyzing and modeling the temporal diffusion of information on social media has mainly been treated as a diffusion process on known graphs or proximity structures. The underlying phenomenon results however from the interactions of several actors and media and is more complex than what these models can account for and cannot be explained using such limiting assumptions. We introduce here a new approach to this problem whose goal is to learn a mapping of the observed temporal dynamic onto a continuous space. Nodes participating to diffusion cascades are projected in a latent representation space in such a way that information diffusion can be modeled efficiently using a heat diffusion process. This amounts to learning a diffusion kernel for which the proximity of nodes in the projection space reflects the proximity of their infection time in cascades. The proposed approach possesses several unique characteristics compared to existing ones. Since its parameters are directly learned from cascade samples without requiring any additional information, it does not rely on any pre-existing diffusion structure. Because the solution to the diffusion equation can be expressed in a closed form in the projection space, the inference time for predicting the diffusion of a new piece of information is greatly reduced compared to discrete models. Experiments and comparisons with baselines and alternative models have been performed on both synthetic networks and real datasets. They show the effectiveness of the proposed method both in terms of prediction quality and of inference speed.


title: Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter

abstract: This brief communication presents preliminary findings on automated Twitter accounts distributing links to scientific articles deposited on the preprint repository arXiv. It discusses the implication of the presence of such bots from the perspective of social media metrics altmetrics, where mentions of scholarly documents on Twitter have been suggested as a means of measuring impact that is both broader and timelier than citations. Our results show that automated Twitter accounts create a considerable amount of tweets to scientific articles and that they behave differently than common social bots, which has critical implications for the use of raw tweet counts in research evaluation and assessment. We discuss some definitions of Twitter cyborgs and bots in scholarly communication and propose distinguishing between different levels of engagement-that is, differentiating between tweeting only bibliographic information to discussing or commenting on the content of a scientific work.


title: Modelling of trends in Twitter using retweet graph dynamics

abstract: In this paper we model user behaviour in Twitter to capture the emergence of trending topics. For this purpose, we first extensively analyse tweet datasets of several different events. In particular, for these datasets, we construct and investigate the retweet graphs. We find that the retweet graph for a trending topic has a relatively dense largest connected component (LCC). Next, based on the insights obtained from the analyses of the datasets, we design a mathematical model that describes the evolution of a retweet graph by three main parameters. We then quantify, analytically and by simulation, the influence of the model parameters on the basic characteristics of the retweet graph, such as the density of edges and the size and density of the LCC. Finally, we put the model in practice, estimate its parameters and compare the resulting behavior of the model to our datasets.


title: An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis

abstract: In this paper, we explore theoretical properties of training a two-layered ReLU network $g(\mathbf{x}; \mathbf{w}) = \sum_{j=1}^K \sigma(\mathbf{w}_j^T\mathbf{x})$ with centered $d$-dimensional spherical Gaussian input $\mathbf{x}$ ($\sigma$=ReLU). We train our network with gradient descent on $\mathbf{w}$ to mimic the output of a teacher network with the same architecture and fixed parameters $\mathbf{w}^*$. We show that its population gradient has an analytical formula, leading to interesting theoretical analysis of critical points and convergence behaviors. First, we prove that critical points outside the hyperplane spanned by the teacher parameters ("out-of-plane") are not isolated and form manifolds, and characterize in-plane critical-point-free regions for two ReLU case. On the other hand, convergence to $\mathbf{w}^*$ for one ReLU node is guaranteed with at least $(1-\epsilon)/2$ probability, if weights are initialized randomly with standard deviation upper-bounded by $O(\epsilon/\sqrt{d})$, consistent with empirical practice. For network with many ReLU nodes, we prove that an infinitesimal perturbation of weight initialization results in convergence towards $\mathbf{w}^*$ (or its permutation), a phenomenon known as spontaneous symmetric-breaking (SSB) in physics. We assume no independence of ReLU activations. Simulation verifies our findings.


title: The use of 3D surface scanning for the measurement and assessment of the human foot

abstract: BackgroundA number of surface scanning systems with the ability to quickly and easily obtain 3D digital representations of the foot are now commercially available. This review aims to present a summary of the reported use of these technologies in footwear development, the design of customised orthotics, and investigations for other ergonomic purposes related to the foot.MethodsThe PubMed and ScienceDirect databases were searched. Reference lists and experts in the field were also consulted to identify additional articles. Studies in English which had 3D surface scanning of the foot as an integral element of their protocol were included in the review.ResultsThirty-eight articles meeting the search criteria were included. Advantages and disadvantages of using 3D surface scanning systems are highlighted. A meta-analysis of studies using scanners to investigate the changes in foot dimensions during varying levels of weight bearing was carried out.ConclusionsModern 3D surface scanning systems can obtain accurate and repeatable digital representations of the foot shape and have been successfully used in medical, ergonomic and footwear development applications. The increasing affordability of these systems presents opportunities for researchers investigating the foot and for manufacturers of foot related apparel and devices, particularly those interested in producing items that are customised to the individual. Suggestions are made for future areas of research and for the standardization of the protocols used to produce foot scans.


title: The influence of avatars on online consumer shopping behavior

abstract: An impediment to Web-based retail sales is the impersonal nature of Web-based shopping. A solution to this problem is to use an avatar to deliver product information. An avatar is a graphic representation that can be animated by means of computer technology. Study 1 shows that using an avatar sales agent leads to more satisfaction with the retailer, a more positive attitude toward the product, and a greater purchase intention. Study 2 shows that an attractive avatar is a more effective sales agent at moderate levels of product involvement, but an expert avatar is a more effective sales agent at high levels of product involvement.


Task-structure analysis for knowledge modeling


title: Biology and Applications of CRISPR Systems: Harnessing Nature’s Toolbox for Genome Engineering

abstract: Bacteria and archaea possess a range of defense mechanisms to combat plasmids and viral infections. Unique among these are the CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR associated) systems, which provide adaptive immunity against foreign nucleic acids. CRISPR systems function by acquiring genetic records of invaders to facilitate robust interference upon reinfection. In this Review, we discuss recent advances in understanding the diverse mechanisms by which Cas proteins respond to foreign nucleic acids and how these systems have been harnessed for precision genome manipulation in a wide array of organisms.


title: LegalRuleML: Design Principles and Foundations

abstract: This tutorial presents the principles of the OASIS LegalRuleML applied to the legal domain and discusses why, how, and when LegalRuleML is well-suited for modelling norms. To provide a framework of reference, we present a comprehensive list of requirements for devising rule interchange languages that capture the peculiarities of legal rule modelling in support of legal reasoning. The tutorial comprises syntactic, semantic, and pragmatic foundations, a LegalRuleML primer, as well as use case examples from the legal domain.


title: Local Metamaterial-Based Waveguides in Gaps Between Parallel Metal Plates

abstract: This letter presents a new metamaterial-based waveguide technology referred to as ridge gap waveguides. The main advantages of the ridge gap waveguides compared to hollow waveguides are that they are planar and much cheaper to manufacture, in particular at high frequencies such as for millimeter and sub- millimeter waves. The latter is due to the fact that there are no mechanical joints across which electric currents must float. The gap waveguides have lower losses than microstrip lines, and they are completely shielded by metal so no additional packaging is needed, in contrast to the severe packaging problems associated with microstrip circuits. The gap waveguides are realized in a narrow gap between two parallel metal plates by using a texture or multilayer structure on one of the surfaces. The waves follow metal ridges in the textured surface. All wave propagation in other directions is prohibited (in cutoff) by realizing a high surface impedance (ideally a perfect magnetic conductor) in the textured surface at both sides of all ridges. Thereby, cavity resonances do not appear either within the band of operation. The present letter introduces the gap waveguide and presents some initial simulated results.


title: Subjective well-being and social media use: Do personality traits moderate the impact of social comparison on Facebook?

abstract: The purpose of the study was to explore whether personality traits moderate the association between social comparison on Facebook and subjective well-being, measured as both life satisfaction and eudaimonic well-being. Data were collected via an online questionnaire which measured Facebook use, social comparison behavior and personality traits for 337 respondents. The results showed positive associations between Facebook intensity and both measures of subjective well-being, and negative associations between Facebook social comparison and both measures of subjective well-being. Personality traits were assessed by the Reinforcement Sensitivity Theory personality questionnaire, which revealed that Reward Interest was positively associated with eudaimonic well-being, and Goal-Drive Persistence was positively associated with both measures of subjective well-being. Impulsivity was negatively associated with eudaimonic well-being and the Behavioral Inhibition System was negatively associated with both measures of subjective well-being. Interactions between personality traits and social comparison on Facebook indicated that for respondents with high Goal-Drive Persistence, Facebook social comparison had a positive association with eudaimonic well-being, thus confirming that some personality traits moderate the association between Facebook social comparison and subjective well-being. The results of this study highlight how individual differences in personality may impact how social comparison on Facebook affects individuals' subjective well-being. Analysis of the association between social comparison on Facebook and well-being.The potential moderating role of personality traits was explored.Subjective well-being was assessed as life satisfaction and eudaimonic well-being.Facebook social comparison was negatively associated with subjective well-being.Goal-Drive Persistence moderates this relationship.


title: Directer: A Parallel and Directed Fuzzing based on Concolic Execution

abstract: Fuzzing is a widely used technology to find vulnerabilities, but the current technology is mostly based on coverage and there are relatively few research in the field of directed fuzzing. In this paper, a parallelized testing technique combining directed fuzzing and concolic execution will be proposed. It extracts path space within the level of basic block in the function call chain through the program control flow analysis and function call relationship. Concolic execution is used to implement the target reachable paths guidance, in order to achieve the goal of rapid arrival. In the experimental stage, the developed Directer was used to test on LAVA dataset, which shows better performance than the existing fuzzers.


Cookie Hijacking in the Wild : Security and Privacy Implications


title: Point Cloud GAN

abstract: Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show a straightforward extension of existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data. We propose a two fold modification to GAN algorithm for learning to generate point clouds (PC-GAN). First, we combine ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process. A key component of our method is that we train a posterior inference network for the hidden variables. Second, instead of using only state-of-the-art Wasserstein GAN objective, we propose a sandwiching objective, which results in a tighter Wasserstein distance estimate than the commonly used dual form. Thereby, PC-GAN defines a generic framework that can incorporate many existing GAN algorithms. We validate our claims on ModelNet40 benchmark dataset. Using the distance between generated point clouds and true meshes as metric, we find that PC-GAN trained by the sandwiching objective achieves better results on test data than the existing methods. Moreover, as a byproduct, PC- GAN learns versatile latent representations of point clouds, which can achieve competitive performance with other unsupervised learning algorithms on object recognition task. Lastly, we also provide studies on generating unseen classes of objects and transforming image to point cloud, which demonstrates the compelling generalization capability and potentials of PC-GAN.


title: Generative Adversarial Networks for Unpaired Voice Transformation on Impaired Speech

abstract: This paper focuses on using voice conversion (VC) to improve the speech intelligibility of surgical patients who have had parts of their articulators removed. Due to the difficulty of data collection, VC without parallel data is highly desired. Although techniques for unparallel VC, for example, CycleGAN, have been developed, they usually focus on transforming the speaker identity, and directly transforming the speech of one speaker to that of another speaker and as such do not address the task here. In this paper, we propose a new approach for unparallel VC. The proposed approach transforms impaired speech to normal speech while preserving the linguistic content and speaker characteristics. To our knowledge, this is the first end-to-end GAN-based unsupervised VC model applied to impaired speech. The experimental results show that the proposed approach outperforms CycleGAN.


title: Hybrid Knowledge Routed Modules for Large-scale Object Detection

abstract: The dominant object detection approaches treat the recognition of each region separately and overlook crucial semantic correlations between objects in one scene. This paradigm leads to substantial performance drop when facing heavy long-tail problems, where very few samples are available for rare classes and plenty of confusing categories exists. We exploit diverse human commonsense knowledge for reasoning over large-scale object categories and reaching semantic coherency within one image. Particularly, we present Hybrid Knowledge Routed Modules (HKRM) that incorporates the reasoning routed by two kinds of knowledge forms: an explicit knowledge module for structured constraints that are summarized with linguistic knowledge (e.g. shared attributes, relationships) about concepts; and an implicit knowledge module that depicts some implicit constraints (e.g. common spatial layouts). By functioning over a region-to-region graph, both modules can be individualized and adapted to coordinate with visual patterns in each image, guided by specific knowledge forms. HKRM are light-weight, general-purpose and extensible by easily incorporating multiple knowledge to endow any detection networks the ability of global semantic reasoning. Experiments on large-scale object detection benchmarks show HKRM obtains around 34.5% improvement on VisualGenome (1000 categories) and 30.4% on ADE in terms of mAP. Codes and trained model can be found in https://github.com/chanyn/HKRM.


title: Domain-specific iterative readability computation

abstract: We present a new algorithm to measure domain-specific readability. It iteratively computes the readability of domain-specific resources based on the difficulty of domain-specific concepts and vice versa, in a style reminiscent of other bipartite graph algorithms such as Hyperlink-Induced Topic Search (HITS) and the Stochastic Approach for Link-Structure Analysis (SALSA). While simple, our algorithm outperforms standard heuristic measures and remains competitive among supervised-learning approaches. Moreover, it is less domain-dependent and portable across domains as it does not rely on an annotated corpus or expensive expert knowledge that supervised or domain-specific methods require.


title: Continuous Space Translation Models for Phrase-Based Statistical Machine Translation

abstract: This paper presents a new approach to perform the estimation of the translation model probabilities of a phrase-based statistical machine translation system. We use neural networks to directly learn the translation probability of phrase pairs using continuous representations. The system can be easily trained on the same data used to build standard phrase-based systems. We provide experimental evidence that the approach seems to be able to infer meaningful translation probabilities for phrase pairs not seen in the training data, or even predict a list of the most likely translations given a source phrase. The approach can be used to rescore n-best lists, but we also discuss an integration into the Moses decoder. A preliminary evaluation on the English/French IWSLT task achieved improvements in the BLEU score and a human analysis showed that the new model often chooses semantically better translations. Several extensions of this work are discussed.


title: Neural correlates of mindfulness meditation-related anxiety relief

abstract: Anxiety is the cognitive state related to the inability to control emotional responses to perceived threats. Anxiety is inversely related to brain activity associated with the cognitive regulation of emotions. Mindfulness meditation has been found to regulate anxiety. However, the brain mechanisms involved in meditation-related anxiety relief are largely unknown. We employed pulsed arterial spin labeling MRI to compare the effects of distraction in the form of attending to the breath (ATB; before meditation training) to mindfulness meditation (after meditation training) on state anxiety across the same subjects. Fifteen healthy subjects, with no prior meditation experience, participated in 4 d of mindfulness meditation training. ATB did not reduce state anxiety, but state anxiety was significantly reduced in every session that subjects meditated. Meditation-related anxiety relief was associated with activation of the anterior cingulate cortex, ventromedial prefrontal cortex and anterior insula. Meditation-related activation in these regions exhibited a strong relationship to anxiety relief when compared to ATB. During meditation, those who exhibited greater default-related activity (i.e. posterior cingulate cortex) reported greater anxiety, possibly reflecting an inability to control self-referential thoughts. These findings provide evidence that mindfulness meditation attenuates anxiety through mechanisms involved in the regulation of self-referential thought processes.


title: Emerging treatments for PTSD ☆

abstract: Recent innovations in posttraumatic stress disorder (PTSD) research have identified new treatments with significant potential, as well as novel enhancements to empirically-validated treatments. This paper reviews emerging psychotherapeutic and pharmacologic interventions for the treatment of PTSD. It examines the evidence for a range of interventions, from social and family-based treatments to technological-based treatments. It describes recent findings regarding novel pharmacologic approaches including propranolol, ketamine, prazosin, and methylenedioxymethamphetamine. Special emphasis is given to the description of virtual reality and D-cycloserine as enhancements to prolonged exposure therapy.


title: Multiple-instance ranking: Learning to rank images for image retrieval

abstract: We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training examples and then use the learned model to rank new images. Unlike previous work on image retrieval, which usually coarsely divide the images into relevant and irrelevant images and learn a binary classifier, we learn the ranking model from image pairs with preference relations. In addition to the relevance of images, we are further interested in what portion of the image is of interest to the user. Therefore, we consider images represented by sets of regions and propose multiple-instance rank learning based on the max margin framework. Three different schemes are designed to encode the multiple-instance assumption. We evaluate the performance of the multiple-instance ranking algorithms on real-word images collected from Flickr - a popular photo sharing service. The experimental results show that the proposed algorithms are capable of learning effective ranking models for image retrieval.


title: Why cybercrime?

abstract: How do cybercrime markets emerge, evolve, and persist? How can cybercrime be prevented, decreased and mitigated? Extant anti-cybercrime efforts have concentrated on deterrence through criminal prosecution and technical mitigation. Deterrence-only strategies, however, may be more expensive for the network than for attackers, particularly considering the asymmetric nature of computer security. The implicit assumption of deterrence, i.e. criminals are strictly self-optimizing rational agents cognizant only of a cost-benefit function, is contentious. Criminal actions are constrained/enabled by the institutional structures of their immediate neighborhood. Exposure to crime (or probability of victimization is similarly influenced. Thus, this paper examines the respective economic, structural, and cultural theories in criminology and explores their relevance online. We discuss the implications for technical solutions, security design, as well as public policy. An intuitive position is to lower the entry cost of legal enterprise, and thereby increase the opportunity cost of cybercrime engagement. We also discuss solutions that allow simultaneous investments (to reduce crime online) by both public and private entities, while mitigating for potential moral hazard. Our concluding argument, then, is for complementing deterrence with policy solutions that preemptively engage potential criminals as legitimate market stakeholders. In addition to the explicit examination of deterrence theories of cybercrime, this work offers a broader consideration of cybercrime that is grounded in theories of crime offline.


title: Optimal section thickness for detection of polyps at MR: resolution phantom study

abstract: Purpose ::: To determine (1) the sensitivity for detection of small polyps with varying MR slice thicknesses using a resolution phantom; (2) reader confidence in polyp detection; and (3) image acquisition time.


title: FinSAL: FinFET-Based Secure Adiabatic Logic for Energy-Efficient and DPA Resistant IoT Devices

abstract: With the emergence of Internet of Things (IoT), there is an urgent need to design energy-efficient and secure IoT devices. For example, IoT devices such as radio frequency identification tags and wireless sensor nodes employ AES cryptographic module that are susceptible to differential power analysis (DPA) attacks. With the scaling of technology, leakage power in the cryptographic device increases, which increases their vulnerability to DPA attack. This paper presents a novel FinFET-based secure adiabatic logic (FinSAL), that is energy-efficient and DPA-immune. The proposed adiabatic FinSAL is used to design logic gates such as buffers, XOR, and NAND. Further, the logic gates based on adiabatic FinSAL are used to implement a positive polarity Reed Muller architecture-based S-box circuit. SPICE simulations at 12.5 MHz show that adiabatic FinSAL (20-nm FinFET technology) S-box circuit saves up to 81% of energy per cycle as compared to the conventional S-box circuit implemented using FinFET (20-nm FinFET technology). Further, the security of adiabatic FinSAL S-box circuit has been evaluated by performing the DPA attack through SPICE simulations. We proved that the FinSAL S-box circuit is resistant to a DPA attack through a developed DPA attack flow applicable to SPICE simulations. Further, the impact of FinSAL on hardware security at different technology nodes of FinFETs (7, 10, 14, and 16 nm) are evaluated. From the simulation results, FinSAL gates at 14-nm FinFET offer superior security with optimum power consumption, therefore is the best candidate to design low-power secure IoT devices.


title: KANE's method for dynamic modeling

abstract: Kane's method is becoming increasingly popular among the dynamists because of its advantages over the Newton-Euler and Lagrange. Many of the literatures have used Kane's method successfully to model the dynamic systems but none has provided a detailed and quick explanation on how to implement the method on a system. This paper provides a fast and easy guide to learn the Kane's method, especially for the beginners. A dynamic model of 3 DOF kinematic chain has been developed as an example to demonstrate the detailed steps involved in modeling using Kane's method. The formulated model is then verified successfully using Lagrange method.


title: Bioactive fungal polysaccharides as potential functional ingredients in food and nutraceuticals.

abstract: Fungal bioactive polysaccharides deriving mainly from the Basidiomycetes family (and some from the Ascomycetes) and medicinal mushrooms have been well known and widely used in far Asia as part of traditional diet and medicine, and in the last decades have been the core of intense research for the understanding and the utilization of their medicinal properties in naturally produced pharmaceuticals. In fact, some of these biopolymers (mainly β-glucans or heteropolysaccharides) have already made their way to the market as antitumor, immunostimulating or prophylactic drugs. The fact that many of these biopolymers are produced by edible mushrooms makes them also very good candidates for the formulation of novel functional foods and nutraceuticals without any serious safety concerns, in order to make use of their immunomodulating, anticancer, antimicrobial, hypocholesterolemic, hypoglycemic and health-promoting properties. This article summarizes the most important properties and applications of bioactive fungal polysaccharides and discusses the latest developments on the utilization of these biopolymers in human nutrition.


title: Biomimetic growth of apatite on electrolytic TiO2 coatings in simulated body fluid

abstract: Abstract Titanium and titanium alloys for implants have been widely applied to the orthopaedic and dental fields, due to their excellent corrosion resistance, good mechanical properties and biocompatibility. However, the near bioinert and metallic ions release are still the major problems in the clinical failure. For these reasons, modification of biomaterials surface properties, which support bioactivity and corrosion resistance, should be one of the key objectives in the design of the next generation of orthopaedic/dental implants. Cathodic electrolytic deposition of anatase TiO2 coating has been successfully deposited on pure titanium substrate. After annealed at 300 °C for 1 h, the coating were further condensed into anatase TiO2, and gradually transformed into rutile TiO2 at 500 °C. The dynamic cyclic polarization tests indicated that dense and nanocrystalline anatase TiO2 coated effectively improved corrosion resistance to avoid aggression of fluoride ion. For immersion tests, the matching structure of anatase TiO2 and apatite may play an important role in the apatite formation ability. Moreover, the cell culture results also indicated that nanocrystalline anatase TiO2 not only promoted cells differentiation, but also appeared more bioactive while maintaining non-toxicity. Obviously, the materials characteristics such as crystal structure, surface morphology and corrosion resistance may play an important role in the osteointegration.


title: A framework for sentiment analysis with opinion mining of hotel reviews

abstract: The rapid increase in mountains of unstructured textual data accompanied by proliferation of tools to analyse them has opened up great opportunities and challenges for text mining research. The automatic labelling of text data is hard because people often express opinions in complex ways that are sometimes difficult to comprehend. The labelling process involves huge amount of efforts and mislabelled datasets usually lead to incorrect decisions. In this paper, we design a framework for sentiment analysis with opinion mining for the case of hotel customer feedback. Most available datasets of hotel reviews are not labelled which presents a lot of works for researchers as far as text data pre-processing task is concerned. Moreover, sentiment datasets are often highly domain sensitive and hard to create because sentiments are feelings such as emotions, attitudes and opinions that are commonly rife with idioms, onomatopoeias, homophones, phonemes, alliterations and acronyms. The proposed framework is termed sentiment polarity that automatically prepares a sentiment dataset for training and testing to extract unbiased opinions of hotel services from reviews. A comparative analysis was established with Naive Bayes multinomial, sequential minimal optimization, compliment Naive Bayes and Composite hypercubes on iterated random projections to discover a suitable machine learning algorithm for the classification component of the framework.


title: High-speed 10-bit LCD column driver with a split DAC and a class-AB output buffer

abstract: We propose a high-speed rail-to-rail 10-bit column driver with a reduced die area for LCD-TV applications. This column driver combines an 8-bit resistor-string digital-to-analog converter (R-DAC), constructed using a hybrid-type decoder to reduce the RC time delay and die area, with a 2-bit interpolation DAC. In the output buffer, error amplifiers drive a column line so as to realize a highspeed rail-to-rail drive. Gamma-corrected output voltages are generated by the resistor string of R-DAC, which contains resistors of unequal values that match the inverse of the liquid crystal transmittance-voltage characteristic. A prototype 10- bit LCD column driver was designed and fabricated using a 0.3 mum CMOS technology, and has a settling time of within 2 mus and a quiescent current of 5.4 muA per channel.


A Critical Exploratory Analysis of Black Girls' Achievement in 8th grade U.S. History


title: Evo-ROS: integrating evolution and the robot operating system

abstract: In this paper, we describe the Evo-ROS framework, which is intended to help bridge the gap between the evolutionary and traditional robotics communities. Evo-ROS combines an evolutionary algorithm with individual physics-based evaluations conducted using the Robot Operating System (ROS) and the Gazebo simulation environment. Our goals in developing Evo-ROS are to (1) provide researchers in evolutionary robotics with access to the extensive support for real-world components and capabilities developed by the ROS community and (2) enable ROS developers, and more broadly robotics researchers, to take advantage of evolutionary search during design and testing. We describe the details of the Evo-ROS structure and operation, followed by presentation of a case study using Evo-ROS to optimize placement of sonar sensors on unmanned ground vehicles that can experience reduced sensing capability due to component failures and physical damage. The case study provides insights into the current capabilities and identifies areas for future enhancements.


title: Pyogenic Granuloma of Nasal Septum: A Case Report and Review of Literature

abstract: Pyogenic granuloma ::: is a rapidly growing hyperplastic, vascular proliferation of the skin or mucous ::: membrane. A benign lesion of unknown aetiology commonly associated with ::: pregnancy, oral contraceptives and trauma. While lesions occur frequently in ::: oral cavity, occurrence in the nasal septum is rarely reported. We report a ::: case of 38-year-old male (known case of active pulmonary tuberculosis on ::: anti-tubercular therapy) who presented with unilateral pyogenic granuloma of ::: the anterior nasal septum with unilateral nasal obstruction. We emphasize that ::: the rarely seen lobular capillary hemangioma (pyogenic granuloma) must be kept ::: in mind in the differential diagnosis of a rapidly growing mass of the nasal ::: cavity, even without history of epistaxis. Here we report an uncommon case and ::: review current literature regarding aetiology, site and role of rifampicin in ::: pyogenic granuloma.


title: Banishing the Homunculus: Making Working Memory Work

abstract: The prefrontal cortex has long been thought to subserve both working memory and "executive" function, but the mechanistic basis of their integrated function has remained poorly understood, often amounting to a homunculus. This paper reviews the progress in our laboratory and others pursuing a long-term research agenda to deconstruct this homunculus by elucidating the precise computational and neural mechanisms underlying these phenomena. We outline six key functional demands underlying working memory, and then describe the current state of our computational model of the prefrontal cortex and associated systems in the basal ganglia (BG). The model, called PBWM (prefrontal cortex, basal ganglia working memory model), relies on actively maintained representations in the prefrontal cortex, which are dynamically updated/gated by the basal ganglia. It is capable of developing human-like performance largely on its own by taking advantage of powerful reinforcement learning mechanisms, based on the midbrain dopaminergic system and its activation via the basal ganglia and amygdala. These learning mechanisms enable the model to learn to control both itself and other brain areas in a strategic, task-appropriate manner. The model can learn challenging working memory tasks, and has been corroborated by several important empirical studies.


title: Modeling , Control , and Flight Testing of a Small Ducted Fan Aircraft

abstract: Small ducted fan autonomous vehicles have potential for several applications, especially for mi ssions in urban environments. This paper discusses the use of dynamic inversion with neural network adaptation to provide an adaptive controller for the GTSpy, a small ducted fan autonomous vehicle based on the Micro Autonomous Systems’ Helispy. This app roach allows utilization of the entire low speed flight envelope with a relatively poorly understood vehicle. A simulator model is constructed from a force and moment analysis of the vehicle, allowing for a validation of the controller in preparation for flight testing. Data from flight testing of the system is provided. Nomenclature B A A 2 1 ˆ , ˆ , ˆ = linearized vehicle dynamics a


title: 100 MHz isolated DC-DC resonant converter using spiral planar PCB transformer

abstract: This paper describes a very high frequency (VHF) isolated dc-dc resonant converter operating at 100 MHz switching frequency with a rated output power of 2 W. The power stage consists of two half-bridge GaN chips with integrated gate drivers, one operating as a dc-ac inverter on the primary side, the other operating as an ac-dc synchronous rectifier on the secondary side. Isolation is provided by a spiral planar printed circuit board (PCB) transformer designed and optimized using 3D finite element modeling (FEM) tools and radio-frequency (RF) circuit design software. Simulation and experimental results are presented for a prototype step-down converter operating from 20 V input voltage. Experimental results show a peak efficiency of 80.5% at 1.8 W output power.


title: Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach

abstract: A novel framework using a Bayesian approach for content-based phishing web page detection is presented. Our model takes into account textual and visual contents to measure the similarity between the protected web page and suspicious web pages. A text classifier, an image classifier, and an algorithm fusing the results from classifiers are introduced. An outstanding feature of this paper is the exploration of a Bayesian model to estimate the matching threshold. This is required in the classifier for determining the class of the web page and identifying whether the web page is phishing or not. In the text classifier, the naive Bayes rule is used to calculate the probability that a web page is phishing. In the image classifier, the earth mover's distance is employed to measure the visual similarity, and our Bayesian model is designed to determine the threshold. In the data fusion algorithm, the Bayes theory is used to synthesize the classification results from textual and visual content. The effectiveness of our proposed approach was examined in a large-scale dataset collected from real phishing cases. Experimental results demonstrated that the text classifier and the image classifier we designed deliver promising results, the fusion algorithm outperforms either of the individual classifiers, and our model can be adapted to different phishing cases.


title: www.elsevier.com/locate/patcog Genetic watermarking based on transform-domain techniques

abstract: Abstract An innovative watermarking scheme based on genetic algorithms (GA) in the transform domain is proposed. It is robust against watermarking attacks, which are commonly employed in the literature. In addition, the watermarked image quality is also considered. In this paper, we employ GA for optimizing both the fundamentally conflicting requirements. Watermarking with GA is easy for implementation. We also examine the effectiveness of our scheme by checking the fitness function in GA, which includes both factors related to robustness and invisibility. Simulation results also show both the robustness under attacks, and the improvement in watermarked image quality with GA.


title: Placement Strategies for Internet-Scale Data Stream Systems

abstract: Optimally assigning streaming tasks to network machines is a key factor that influences a large data-stream-processing system's performance. Although researchers have prototyped and investigated various algorithms for task placement in data stream management systems, taxonomies and surveys of such algorithms are currently unavailable. To tackle this knowledge gap, the authors identify a set of core placement design characteristics and use them to compare eight placement algorithms. They also present a heuristic decision tree that can help designers judge how suitable a given placement solution might be to specific problems.


title: Framework for Generation of Synthetic Ground Truth Data for Driver Assistance Applications

abstract: High precision ground truth data is a very important factor for the development and evaluation of computer vision algorithms and especially for advanced driver assistance systems. Unfortunately, some types of data, like accurate optical flow and depth as well as pixel-wise semantic annotations are very difficult to obtain.


title: Hybrid order picking strategies for fashion E-commerce warehouse systems

abstract: E-commerce has become an increasingly relevant business in Southeast Asia. Effective warehouse management in terms of order picking is a key competitive advantage in this industry. Fashion products are particularly difficult to efficiently manage in a warehouse as they have high demand variability, with a short shelf-life and very little replenishment. In this work, after a detailed analysis of demand and physical layout of the warehouse, we propose: (1) a new pick list generation algorithm considering aspects such as work balancing and picking time minimization, and (2) a family of picking strategies accounting for possible order configurations and warehouse layout. The main contribution of this work is in the development of hybrid order picking strategies: a combination of zone-based and order-based picking with batching. Simulation is used to assess the performance of these strategies. We have found that these hybrid strategies outperform FIFO order picking often employed in industry.


title: A SURVEY OF ALGORITHMS AND SYSTEMS FOR EXPERT LOCATION IN SOCIAL NETWORKS

abstract: Given a particular task and a set of candidates, one often wants to identify the right expert (or set of experts) that can perform the given task. We call this problem the expert-location problem and we survey its different aspects as they arise in practice. For example, given the activities of candidates within a context (e.g., authoring a document, answering a question), we first describe methods for evaluating the level of expertise for each of them. Often, experts are organized in networks that correspond to social networks or organizational structures of companies. We next devote part of the chapter for describing algorithms that compute the expertise level of individuals by taking into account their position in such a network. Finally, complex tasks often require the collective expertise of more than one experts. In such cases, it is more realistic to require a team of experts that can collaborate towards a common goal. We describe algorithms that identify effective expert teams within a network of experts. The chapter is a survey of different algorithms for expertise evaluation and team identification. We highlight the basic algorithmic problems and give some indicative algorithms that have been developed in the literature. We conclude the chapter by providing a comprehensive overview of real-life systems for expert location.


title: Dijkstra algorithm in parallel- Case study

abstract: This article aims to highlight the usefulness of improving classical algorithms by generating new approaches, efficiency or parallel execution time optimization at all costs. The reconsideration of classical algorithms, but in different testing conditions can help all products that rely on them. Based on the different uses of Dijkstra's algorithm and the new approaches based on the use, it can be said that today this algorithm covers several areas with high utility nowadays. Here there are examples such as GPS systems, which must find roads fast depending on different conditions. Also this algorithm is to be found in the network where we can talk about real priority schemes and data transfer speed that matters a lot today. Even in the top management systems these algorithms, minimum road, can be used with predilection. This article offers an innovative and efficient approach of Dijkstra's algorithm through parallel programming and analysis of the results obtained in different tests.


title: The brain’s conversation with itself: neural substrates of dialogic inner speech

abstract: Inner speech has been implicated in important aspects of normal and atypical cognition, including the development of auditory hallucinations. Studies to date have focused on covert speech elicited by simple word or sentence repetition, while ignoring richer and arguably more psychologically significant varieties of inner speech. This study compared neural activation for inner speech involving conversations (‘dialogic inner speech’) with single-speaker scenarios (‘monologic inner speech’). Inner speech-related activation differences were then compared with activations relating to Theory-of-Mind (ToM) reasoning and visual perspective-taking in a conjunction design. Generation of dialogic (compared with monologic) scenarios was associated with a widespread bilateral network including left and right superior temporal gyri, precuneus, posterior cingulate and left inferior and medial frontal gyri. Activation associated with dialogic scenarios and ToM reasoning overlapped in areas of right posterior temporal cortex previously linked to mental state representation. Implications for understanding verbal cognition in typical and atypical populations are discussed.


title: Characteristics of high performing testers: a case study

abstract: Objective: We studied what are the characteristics of high performing software testers in the industry. Method: We conducted an exploratory case study, collecting data through recorded interviews of one development manager and three testers in each of the three companies, analysis of the defect database, and informal communication within our research partnership with the companies. Results: We found that experience, reflection, motivation and personal characteristics were the top level themes. Experience related to the domain, e.g. processes of the customer, and on the other hand, specialized technical skills, e.g. performance testing, were seen more important than skills of test case design and test planning.


title: Fuzzy Time Series Forecasting Using Percentage Change as the Universe of Discourse

abstract: Since the pioneering work of Zadeh, fuzzy set theory has been applied to a myriad of areas. Song and Chissom introduced the concept of fuzzy time series and applied some methods to the enrollments of the University of Alabama. In recent years, a number of techniques have been proposed for forecasting based on fuzzy set theory methods. These methods have either used enrollment numbers or differences of enrollments as the universe of discourse. We propose using the year to year percentage change as the universe of discourse. In this communication, the approach of Jilani, Burney, and Ardil is modified by using the year to year percentage change as the universe of discourse. We use enrollment figures for the University of Alabama to illustrate our proposed method. The proposed method results in better forecasting accuracy than existing models. Keywords—Fuzzy forecasting, fuzzy time series, fuzzified enrollments, time-invariant model


title: Computational Models for Social Network Analysis: A Brief Survey

abstract: With the exponential growth of online social network services such as Facebook and Twitter, social networks and social medias become more and more important, directly influencing politics, economics, and our daily life. Mining big social networks aims to collect and analyze web-scale social data to reveal patterns of individual and group behaviors. It is an inherently interdisciplinary academic field which emerged from sociology, psychology, statistics, and graph theory. In this article, I briefly survey recent progress on social network mining with an emphasis on understanding the interactions among users in the large dynamic social networks. I will start with some basic knowledge for social network analysis, including methodologies and tools for macro-level, meso-level and microlevel social network analysis. Then I will give an overall roadmap of social network mining. After that, I will describe methodologies for modeling user behavior including state-of-the-art methods for learning user profiles, and introduce recent progress on modeling dynamics of user behaviors using deep learning. Then I will present models and algorithms for quantitative analysis on social interac- tions including homophily and social influence.Finally, I will introduce network structure model including social group formation, and network topology generation. We will introduce recent developed network embedding algorithms for modeling social networks with the embedding techniques. Finally, I will use several concrete examples from Alibaba, the largest online shopping website in the world, and WeChat, the largest social messaging service in China, to explain how online social networks influence our offline world.


Evaluating ensemble classifiers for spam filtering


title: A Carrier-Based PWM Scheme for Neutral Point Voltage Balancing in Three-Level Inverter Extending to Full Power Factor Range

abstract: This paper proposes a unique carrier-based pulse width modulation strategy for a three-level neutral-point-clamped inverter that works satisfactorily and with uniform convergence time over the full power factor (PF) range of the load variation. It has excellent control over capacitor voltages and mitigates low-frequency oscillations in the neutral point for the full PF range. First, an investigation is made to see how the load currents get modulated to generate compensating neutral current causing charging and discharging of dc-link capacitors. It is observed that when an additional modulating voltage signal that is in phase with the corresponding phase current is added in each phase, a neutral current is produced in the right direction which can be used to compensate for any prior unbalance in capacitor voltages. It is demonstrated that the performance of this scheme is insensitive to the load PF variation (full range from zero to unity). It performs satisfactorily throughout the entire linear modulation range and eliminates any undesirable low-frequency harmonic component in the compensating neutral current. The effectiveness of the proposed scheme is verified through simulation and experimental results by varying load PF as well as modulation index with both passive and motor loads.


title: Towards the Ontology Web Search Engine

abstract: The project of the Ontology Web Search Engine is presented in this paper. The main purpose of this paper is to develop such a project that can be easily implemented. Ontology Web Search Engine is software to look for and index ontologies in the Web. OWL (Web Ontology Languages) ontologies are meant, and they are necessary for the functioning of the SWES (Semantic Web Expert System). SWES is an expert system that will use found ontologies from the Web, generating rules from them, and will supplement its knowledge base with these generated rules. It is expected that the SWES will serve as a universal expert system for the average user.


title: A Survey of Biometric Gait Recognition: Approaches, Security and Challenges”, presented at the NIK – 2007 conference

abstract: Biometric systems are becoming increasingly important, since they provide more reliable and efficient means of identity verification. Biometric gait recognition (i.e. recognizing people from the way they walk) is one of the recent attractive topics in biometric research. This paper presents biometric user recognition based on gait. Biometric gait recognition is categorized into three groups based on: machine vision, floor sensor and wearable sensor. An overview of each gait recognition category is presented. In addition, factors that may influence gait recognition are outlined. Furthermore, the security evaluations of biometric gait under various attack scenarios are also presented.