title: Trading Data in Good Faith: Integrating Truthfulness and Privacy Preservation in Data Markets

abstract: As a significant business paradigm, many online information platforms have emerged to satisfy society's needs for person-specific data, where a service provider collects raw data from data contributors, and then offers value-added data services to data consumers. However, in the data trading layer, the data consumers face a pressing problem, i.e., how to verify whether the service provider has truthfully collected and processed data? Furthermore, the data contributors are usually unwilling to reveal their sensitive personal data and real identities to the data consumers. In this paper, we propose TPDM, which efficiently integrates Truthfulness and Privacy preservation in Data Markets. TPDM is structured internally in an Encrypt-then-Sign fashion, using somewhat homomorphic encryption and identitybased signature. It simultaneously facilitates batch verification, data processing, and outcome verification, while maintaining identity preservation and data confidentiality. We also instantiate TPDM with a profile-matching service, and extensively evaluate its performance on Yahoo! Music ratings dataset. Our evaluation results show that TPDM achieves several desirable properties, while incurring low computation and communication overheads when supporting a large-scale data market.


title: Segmentation of blood vessels from red-free and fluorescein retinal images

abstract: The morphology of the retinal blood vessels can be an important indicator for diseases like diabetes, hypertension and retinopathy of prematurity (ROP). Thus, the measurement of changes in morphology of arterioles and venules can be of diagnostic value. Here we present a method to automatically segment retinal blood vessels based upon multiscale feature extraction. This method overcomes the problem of variations in contrast inherent in these images by using the first and second spatial derivatives of the intensity image that gives information about vessel topology. This approach also enables the detection of blood vessels of different widths, lengths and orientations. The local maxima over scales of the magnitude of the gradient and the maximum principal curvature of the Hessian tensor are used in a multiple pass region growing procedure. The growth progressively segments the blood vessels using feature information together with spatial information. The algorithm is tested on red-free and fluorescein retinal images, taken from two local and two public databases. Comparison with first public database yields values of 75.05% true positive rate (TPR) and 4.38% false positive rate (FPR). Second database values are of 72.46% TPR and 3.45% FPR. Our results on both public databases were comparable in performance with other authors. However, we conclude that these values are not sensitive enough so as to evaluate the performance of vessel geometry detection. Therefore we propose a new approach that uses measurements of vessel diameters and branching angles as a validation criterion to compare our segmented images with those hand segmented from public databases. Comparisons made between both hand segmented images from public databases showed a large inter-subject variability on geometric values. A last evaluation was made comparing vessel geometric values obtained from our segmented images between red-free and fluorescein paired images with the latter as the ‘‘ground truth’’. Our results demonstrated that borders found by our method are less biased and follow more consistently the border of the vessel and therefore they yield more confident geometric values. � 2006 Elsevier B.V. All rights reserved.


Geospatial analysis from space: Advanced approaches for data processing, information extraction and interpretation


Plagiarism Detection Using Artificial Intelligence Technique In Multiple Files


title: Extraordinary Optical Transmission through Sub-wavelength Hole Arrays.

abstract: The desire to use and control photons in a manner analogous to the control of electrons in solids has inspired great interest in such topics as the localization of light, microcavity quantum electrodynamics and near-field optics1,2,3,4,5,6. A fundamental constraint in manipulating light is the extremely low transmittivity of apertures smaller than the wavelength of the incident photon. While exploring the optical properties of submicrometre cylindrical cavities in metallic films, we have found that arrays of such holes display highly unusual zero-order transmission spectra (where the incident and detected light are collinear) at wavelengths larger than the array period, beyond which no diffraction occurs. In particular, sharp peaks in transmission are observed at wavelengths as large as ten times the diameter of the cylinders. At these maxima the transmission efficiency can exceed unity (when normalized to the area of the holes), which is orders of magnitude greater than predicted by standard aperture theory. Our experiments provide evidence that these unusual optical properties are due to the coupling of light with plasmons — electronic excitations — on the surface of the periodically patterned metal film. Measurements of transmission as a function of the incident light angle result in a photonic band diagram. These findings may find application in novel photonic devices.


CAPACITY-CONSTRAINTS ON E-COMMERCE IN PAKISTAN


title: Integration of SNPs-FMRI-methylation data with sparse multi-CCA for schizophrenia study

abstract: Schizophrenia (SZ) is a complex mental disorder associated with genetic variations, brain development and activities, and environmental factors. There is an increasing interest in combining genetic, epigenetic and neuroimaging datasets to explore different level of biomarkers for the correlation and interaction between these diverse factors. Sparse Multi-Canonical Correlation Analysis (sMCCA) is a powerful tool that can analyze the correlation of three or more datasets. In this paper, we propose the sMCCA model for imaging genomics study. We show the advantage of sMCCA over sparse CCA (sCCA) through the simulation testing, and further apply it to the analysis of real data (SNPs, fMRI and methylation) from schizophrenia study. Some new genes and brain regions related to SZ disease are discovered by sMCCA and the relationships among these biomarkers are further discussed.


title: Subpixel image scaling for color‐matrix displays

abstract: The perceived resolution of matrix displays increases when the relative position of the color subpixels is taken into account. Subpixel-rendering algorithms are being used to convert an input image to subpixel-corrected display images. This paper deals with the consequences of the subpixel structure and the theoretical background of the resolution gain. We will show that this theory allows a low-cost implementation in an image scaler. This leads to high flexibility, allowing different subpixel arrangements and a simple control over the trade-off between perceived resolution and color errors.


title: Prevalence and Effect of Cyberbullying on Children and Young People: A Scoping Review of Social Media Studies.

abstract: IMPORTANCE ::: Social media has had a profound effect on how children and adolescents interact. While there are many benefits to the use of social media, cyberbullying has emerged as a potential harm, raising questions regarding its influence on mental health. ::: ::: ::: OBJECTIVE ::: To review existing publications that examine the health-related effects of cyberbullying via social media among children and adolescents. ::: ::: ::: EVIDENCE REVIEW ::: We searched 11 electronic databases from January 1, 2000, through January 17, 2012 (updated June 24, 2014). Studies were screened by 2 independent reviewers and were included if they reported primary research, described or evaluated the use of a social media tool in the context of cyberbullying, and were conducted with children or adolescents. Data were extracted by 1 reviewer and verified by a second. All studies were assessed by 2 reviewers for methodological quality using the Mixed Methods Appraisal Tool. Results were not pooled owing to heterogeneity in study objectives and outcomes; a narrative analysis is presented. ::: ::: ::: FINDINGS ::: Thirty-six studies in 34 publications were included. Most were conducted in the United States (21 [58.3%]), sampled middle and high school populations (24 [66.7%]), and included adolescents who were 12 to 18 years of age (35 [97.2%]). The median reported prevalence of cyberbullying was 23.0% (interquartile range, 11.0%-42.6%). Five studies reported inconsistent and/or weak correlations between cyberbullying and anxiety. Ten studies found a statistically significant association between cyberbullying and report of depression. Five studies investigated self-harm or suicidality, with conflicting results. Results indicate that the most common reason for cyberbullying is relationship issues, with girls most often being the recipients. Responses to cyberbullying are most often passive, with a pervasive lack of awareness or confidence that anything can be done. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: There is a consistent relationship across studies between cyberbullying and depression among children and adolescents; however, the evidence of the effect of cyberbullying on other mental health conditions is inconsistent. This review provides important information that characterizes cyberbullying within the context of social media, including attributes of the recipients and perpetrators, reasons for and the nature of bullying behaviors, and how recipients react to and manage bullying behaviors. This information is critical to the development of effective prevention and management strategies.


title: Identifying expressions of emotion in text

abstract: Finding emotions in text is an area of research with wide-ranging applications. We describe an emotion annotation task of identifying emotion category, emotion intensity and the words/phrases that indicate emotion in text. We introduce the annotation scheme and present results of an annotation agreement study on a corpus of blog posts. The average inter-annotator agreement on labeling a sentence as emotion or non-emotion was 0.76. The agreement on emotion categories was in the range 0.6 to 0.79; for emotion indicators, it was 0.66. Preliminary results of emotion classification experiments show the accuracy of 73.89%, significantly above the baseline.


title: Reading order independent grouping proof for RFID tags

abstract: Using RFID to verify the simultaneous presence of more than one tag has generated interest since the Yoking Proof mechanism was proposed by A. Juels (2004). Various protocols have been proposed ever since. However, all these protocols require that the tags are ordered in a certain way. This article proposes an order-independent protocol which improves the efficiency and reduced failure rates. Privacy is also protected by not sending the identity in plain text.


title: Diffusion maps for edge-aware image editing

abstract: Edge-aware operations, such as edge-preserving smoothing and edge-aware interpolation, require assessing the degree of similarity between pairs of pixels, typically defined as a simple monotonic function of the Euclidean distance between pixel values in some feature space. In this work we introduce the idea of replacing these Euclidean distances with diffusion distances, which better account for the global distribution of pixels in their feature space. These distances are approximated using diffusion maps: a set of the dominant eigenvectors of a large affinity matrix, which may be computed efficiently by sampling a small number of matrix columns (the Nyström method). We demonstrate the benefits of using diffusion distances in a variety of image editing contexts, and explore the use of diffusion maps as a tool for facilitating the creation of complex selection masks. Finally, we present a new analysis that establishes a connection between the spatial interaction range between two pixels, and the number of samples necessary for accurate Nyström approximations.


title: Context Tree for Adaptive Session-based Recommendation

abstract: There has been growing interests in recent years from both practical and research perspectives for session-based recommendation tasks as long-term user profiles do not often exist in many real-life recommendation applications. In this case, recommendations for user's immediate next actions need to be generated based on patterns in anonymous short sessions. An often overlooked aspect is that new items with limited observations arrive continuously in many domains (e.g. news and discussion forums). Therefore, recommendations need to be adaptive to such frequent changes. In this paper, we benchmark a new nonparametric method called context tree (CT) against various state-of-the-art methods on extensive datasets for session-based recommendation task. Apart from the standard static evaluation protocol adopted by previous literatures, we include an adaptive configuration to mimic the situation when new items with limited observations arrives continuously. Our results show that CT outperforms two best-performing approaches (recurrent neural network; heuristic-based nearest neighbor) in majority of the tested configurations and datasets. We analyze reasons for this and demonstrate that it is because of the better adaptation to changes in the domain, as well as the remarkable capability to learn static sequential patterns. Moreover, our running time analysis illustrates the efficiency of using CT as other nonparametric methods.


title: “Drunk in Love”: The Portrayal of Risk Behavior in Music Lyrics

abstract: The current study investigated the prevalence of multiple risk behaviors in popular music lyrics as well as the contexts within which they occur. We conducted a content analysis of the top 20 Billboard songs from 2009 to 2013 in the genres of rap, country, adult contemporary, rock, R&B/hip-hop, and pop, coding for the presence of alcohol, marijuana, nonmarijuana drugs, and sex as well as the contexts intoxication, binging/addiction, partying/socializing, disregard for consequences, and emotional states. The contexts relationship status and degradation were also coded for when sex was present. Of the 600 songs, 212 mentioned sexual behaviors, which were most frequent in rap and R&B/hip-hop. Alcohol was the next most frequent risk behavior, again with greatest mention in rap and R&B/hip-hop. Alcohol, marijuana, and nonmarijuana drugs were most often associated with positive emotions, and sex was most often described within the context of casual relationships. Alcohol and sex were associated with disregard f...


title: QCCE: Quality constrained co-saliency estimation for common object detection

abstract: Despite recent advances in joint processing of images, sometimes it may not be as effective as single image processing for object discovery problems. In this paper while aiming for common object detection, we attempt to address this problem by proposing a novel QCCE: Quality Constrained Co-saliency Estimation method. The approach here is to iteratively update the saliency maps through co-saliency estimation depending upon quality scores, which indicate the degree of separation of foreground and background likelihoods (the easier the separation, the higher the quality of saliency map). In this way, joint processing is automatically constrained by the quality of saliency maps. Moreover, the proposed method can be applied to both unsupervised and supervised scenarios, unlike other methods which are particularly designed for one scenario only. Experimental results demonstrate superior performance of the proposed method compared to the state-of-the-art methods.


The''echo state''approach to analysing and training recurrent neural networks


title: Foursquare to the Rescue: Predicting Ambulance Calls Across Geographies

abstract: Understanding how ambulance incidents are spatially distributed can shed light to the epidemiological dynamics of geographic areas and inform healthcare policy design. Here we analyze a longitudinal dataset of more than four million ambulance calls across a region of twelve million residents in the North West of England. With the aim to explain geographic variations in ambulance call frequencies, we employ a wide range of data layers including open government datasets describing population demographics and socio-economic characteristics, as well as geographic activity in online services such as Foursquare. Working at a fine level of spatial granularity we demonstrate that daytime population levels and the deprivation status of an area are the most important variables when it comes to predicting the volume of ambulance calls at an area. Foursquare check-ins on the other hand complement these government sourced indicators, offering a novel view to population nightlife and commercial activity locally. We demonstrate how check-in activity can provide an edge when predicting certain types of emergency incidents in a multi-variate regression model.


title: Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunications Industry

abstract: We explore techniques from statistical machine learning to predict churn and, based on these predictions, to determine what incentives should be offered to subscribers to improve retention and maximize profitability to the carrier. The techniques include legit regression, decision trees, neural networks, and boosting. Our experiments are based on a database of nearly 47000 USA domestic subscribers and includes information about their usage, billing, credit, application, and complaint history. Our experiments show that under a wide variety of assumptions concerning the cost of intervention and the retention rate resulting from intervention, using predictive techniques to identify potential churners and offering incentives can yield significant savings to a carrier. We also show the importance of a data representation crafted by domain experts. Finally, we report on a real-world test of the techniques that validate our simulation experiments.


title: PyNN: A Common Interface for Neuronal Network Simulators

abstract: Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization, and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN.


title: Fast and robust super-resolution

abstract: In the last two decades, many papers have been published, proposing a variety methods of multiframe resolution enhancement. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose a different implementation using L/sub 1/ norm minimization and robust regularization to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation, and results in sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other robust super-resolution methods.


title: Deficits in facial affect recognition among antisocial populations: A meta-analysis

abstract: Individuals with disorders marked by antisocial behavior frequently show deficits in recognizing displays of facial affect. Antisociality may be associated with specific deficits in identifying fearful expressions, which would implicate dysfunction in neural structures that subserve fearful expression processing. A meta-analysis of 20 studies was conducted to assess: (a) if antisocial populations show any consistent deficits in recognizing six emotional expressions; (b) beyond any generalized impairment, whether specific fear recognition deficits are apparent; and (c) if deficits in fear recognition are a function of task difficulty. Results show a robust link between antisocial behavior and specific deficits in recognizing fearful expressions. This impairment cannot be attributed solely to task difficulty. These results suggest dysfunction among antisocial individuals in specified neural substrates, namely the amygdala, involved in processing fearful facial affect.


title: N-gram-based Machine Translation

abstract: This article describes in detail an n-gram approach to statistical machine translation. This approach consists of a log-linear combination of a translation model based on n-grams of bilingual units, which are referred to as tuples, along with four specific feature functions. Translation performance, which happens to be in the state of the art, is demonstrated with Spanish-to-English and English-to-Spanish translations of the European Parliament Plenary Sessions (EPPS).


title: Advanced compiler optimizations for supercomputers

abstract: Compilers for vector or multiprocessor computers must have certain optimization features to successfully generate parallel code.


title: A hybrid convolutional neural networks with extreme learning machine for WCE image classification

abstract: Wireless Capsule Endoscopy (WCE) is considered as a promising technology for non-invasive gastrointestinal disease examination. This paper studies the classification problem of the digestive organs for wireless capsule endoscopy (WCE) images aiming at saving the review time of doctors. Our previous study has proved the Convolutional Neural Networks (CNN)-based WCE classification system is able to achieve 95% classification accuracy in average, but it is difficult to further improve the classification accuracy owing to the variations of individuals and the complex digestive tract circumstance. Research shows that there are two possible approaches to improve classification accuracy: to extract more discriminative image features and to employ a more powerful classifier. In this paper, we propose to design a WCE classification system by a hybrid CNN with Extreme Learning Machine (ELM). In our approach, we construct the CNN as a data-driven feature extractor and the cascaded ELM as a strong classifier instead of the conventional used full-connection classifier in deep CNN classification system. Moreover, to improve the convergence and classification capability of ELM under supervision manner, a new initialization is employed. Our developed WCE image classification system is named as HCNN-NELM. With about 1 million real WCE images (25 examinations), intensive experiments are conducted to evaluate its performance. Results illustrate its superior performance compared to traditional classification methods and conventional CNN-based method, where about 97.25% classification accuracy can be achieved in average.


title: Controlling privacy in recommender systems

abstract: Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences. In this paper, we explore a two-tiered notion of privacy where there is a small set of "public" users who are willing to share their preferences openly, and a large set of "private" users who require privacy guarantees. We show theoretically and demonstrate empirically that a moderate number of public users with no access to private user information already suffices for reasonable accuracy. Moreover, we introduce a new privacy concept for gleaning relational information from private users while maintaining a first order deniability. We demonstrate gains from controlled access to private user preferences.


title: Probabilistic Semantics and Pragmatics Uncertainty in Language and Thought

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


title: Graph Connectivity in Noisy Sparse Subspace Clustering

abstract: Subspace clustering is the problem of clustering data points into a union of low-dimensional linear/affine subspaces. It is the mathematical abstraction of many important problems in computer vision, image processing and machine learning. A line of recent work (4, 19, 24, 20) provided strong theoretical guarantee for sparse subspace clustering (4), the state-of-the-art algorithm for subspace clustering, on both noiseless and noisy data sets. It was shown that under mild conditions, with high probability no two points from different subspaces are clustered together. Such guarantee, however, is not sufficient for the clustering to be correct, due to the notorious "graph connectivity problem" (15). In this paper, we investigate the graph connectivity problem for noisy sparse subspace clustering and show that a simple post-processing procedure is capable of delivering consistent clustering under certain "general position" or "restricted eigenvalue" assumptions. We also show that our condition is almost tight with adversarial noise perturbation by constructing a counter-example. These results provide the first exact clustering guarantee of noisy SSC for subspaces of dimension greater then 3.


title: Classification and discrimination of focal and non-focal EEG signals based on deep neural network

abstract: In this paper, a new model of focal and non-focal electroencephalography classification is carried out using a deep neural network (DNN). The Convolution Architecture For Feature Extraction (Caffe) framework with three different models (LeNet, AlexNet, and GoogLeNet) are applied, where the DNN is trained with different training epoch values (TEs). The performance of discriminating the focal and non-focal EEG signals using soft-max classifier is investigated. This classification serves medical specialists for taking a surgery decision of focal epilepsy patient. In this work, the EEG signals acquired from EEG database in literature works for five epilepsy patients are used for examining the proposed scheme. The results demonstrate a significant performance in terms of the classification accuracy and the remarkable short running time, via few numbers of the training epochs (TEs). However, the first model (LeNet) displays the best performance. Overall, the proposed classification approach provides a better performance as compared with the existing state-of-the-art techniques. Classification accuracy result is 100% for LeNet model at TE=2, while the accuracy of AlexNet reaches to 100% at TE=5, and finally, GoogLeNet touches an accuracy of 100% at TE=10.


Simple Inductance Formulas for Radio Coils


title: Automatic Indexing Based on Bayesian Inference Networks

abstract: In this paper, a Bayesian inference network model for automatic indexing with index terms (descriptors) from a prescribed vocabulary is presented. It requires an indexing dictionary with rules mapping terms of the respective subject field onto descriptors and inverted lists for terms occuring in a set of documents of the subject field and descriptors manually assigned to these documents. The indexing dictionary can be derived automatically from a set of manually indexed documents. An application of the network model is described, followed by an indexing example and some experimental results about the indexing performance of the network model.


title: Hydraulic System of Locomotion

abstract: Spiders lack extensor muscles in the main joints of their walking legs (femur–patella and tibia–metatarsus joint, respectively). These leg joints are extended by a rise of hemolymph pressure in the prosoma, caused by contraction of prosomal dorsoventrally running muscles. This leads to volume decrease in the prosoma (made possible by soft and flexible pleurae) and to hemolymph flow into the legs. Inside the legs, hemolymph flows in channels (lacunae) whose cross sections are strongly influenced by intrinsic muscle activity. In hydraulic leg joints, only flexor muscles occur that act antagonistically to hemolymph pressure. The articular membranes in leg joints are bellows-like folded, in this way preventing unfavourable torques opposing joint extension. During maximum activity, perfusion of the prosoma stops because prosomal pressure exceeds systolic heart pressure. In the opisthosoma, a subepidermal muscle-sheet (“abdominal sac”) and several other muscles generate hemolymph pressure that is considerably lower than prosomal pressure during high activity, but may be very close to it during rest or moderate activity. Opisthosomal pressures are sometimes linked to prosomal ones, but sometimes not. This implies (contrary to previous opinions) a temporary hydrostatic separation between pro- and opisthosoma, probably due to a muscular valve at the anterior end of the pedicel. In some spiders, the prosoma forms a stiff capsule unable to generate hemolymph pressure. It is suggested that these spiders produce hemolymph pressure for leg extension mainly in the opisthosoma by contraction of a strongly modified abdominal sac.


title: Machine Learning for Seismic Signal Processing: Phase Classification on a Manifold

abstract: In this research, we consider the supervised learning problem of seismic phase classification. In seismology, knowledge of the seismic activity arrival time and phase leads to epicenter localization and surface velocity estimates useful in developing seismic early warning systems and detecting man-made seismic events. Formally, the activity arrival time refers to the moment at which a seismic wave is first detected and the seismic phase classifies the physics of the wave. We propose a new perspective for the classification of seismic phases in three-channel seismic data collected within a network of regional recording stations. Our method extends current techniques and incorporates concepts from machine learning. Machine learning techniques attempt to leverage the concept of "learning'' the patterns associated with different types of data characteristics. In this case, the data characteristics are the seismic phases. This concept makes sense because the characteristics of the phase types are dictated by the physics of wave propagation. Thus by "learning'' a signature for each type of phase, we can apply classification algorithms to identify the phase of incoming data from a database of known phases observed over the recording network. Our method first uses a multi-scale feature extraction technique for clustering seismic data on low-dimensional manifolds. We then apply kernel ridge regression on each feature manifold for phase classification. In addition, we have designed an information theoretic measure used to merge regression scores across the multi-scale feature manifolds. Our approach complements current methods in seismic phase classification and brings to light machine learning techniques not yet fully examined in the context of seismology. We have applied our technique to a seismic data set from the Idaho, Montana, Wyoming, and Utah regions collected during 2005 and 2006. This data set contained compression wave and surface wave seismic phases. Through cross-validation, our method achieves a 74.6% average correct classification rate when compared to analyst classifications.


title: Integration of Radio Frequency Identification and Wireless Sensor Networks

abstract: iii OZ v DEDICATION vii ACKNOWLEDGMENTS viii LIST OF TABLES xii LIST OF FIGURES xiii LIST OF SYMBOLS/ABBREVIATIONS xv


title: Exploring Shared Structures and Hierarchies for Multiple NLP Tasks

abstract: Designing shared neural architecture plays an important role in multi-task learning. The challenge is that finding an optimal sharing scheme heavily relies on the expert knowledge and is not scalable to a large number of diverse tasks. Inspired by the promising work of neural architecture search (NAS), we apply reinforcement learning to automatically find possible shared architecture for multi-task learning. Specifically, we use a controller to select from a set of shareable modules and assemble a task-specific architecture, and repeat the same procedure for other tasks. The controller is trained with reinforcement learning to maximize the expected accuracies for all tasks. We conduct extensive experiments on two types of tasks, text classification and sequence labeling, which demonstrate the benefits of our approach.


title: Big data analytics for disaster response and recovery through sentiment analysis

abstract: Abstract Big data created by social media and mobile networks provide an exceptional opportunity to mine valuable insights from them. This information is harnessed by business entities to measure the level of customer satisfaction but its application in disaster response is still in its inflection point. Social networks are increasingly used for emergency communications and help related requests. During disaster situations, such emergency requests need to be mined from the pool of big data for providing timely help. Though government organizations and emergency responders work together through their respective national disaster response framework, the sentiment of the affected people during and after the disaster determines the success of the disaster response and recovery process. In this paper, we propose a big data driven approach for disaster response through sentiment analysis. The proposed model collects disaster data from social networks and categorize them according to the needs of the affected people. The categorized disaster data are classified through machine learning algorithm for analyzing the sentiment of the people. Various features like, parts of speech and lexicon are analyzed to identify the best classification strategy for disaster data. The results show that lexicon based approach is suitable for analyzing the needs of the people during disaster. The practical implication of the proposed methodology is the real-time categorization and classification of social media big data for disaster response and recovery. This analysis helps the emergency responders and rescue personnel to develop better strategies for effective information management of the rapidly changing disaster environment.


title: Microfluidic synthesis of advanced microparticles for encapsulation and controlled release.

abstract: We describe droplet microfluidic strategies used to fabricate advanced microparticles that are useful structures for the encapsulation and release of actives; these strategies can be further developed to produce microparticles for advanced drug delivery applications. Microfluidics enables exquisite control in the fabrication of polymer vesicles and thermosensitive microgels from single and higher-order multiple emulsion templates. The strategies used to create the diversity of microparticle structures described in this review, coupled with the scalability of microfluidics, will enable fabrication of large quantities of novel microparticle structures that have potential uses in controlled drug release applications.


title: Intervening in ageing to prevent the diseases of ageing

abstract: Increases in human lifespan worldwide have revealed that advancing age is the predominant risk factor for major life-threatening diseases. Recent work has shown that ageing in diverse animals, including humans, is malleable to specific types of genetic mutation, diet, and drugs that can extend lifespan and improve health during ageing. These findings point to the prospect of broad-spectrum preventive medicine for the diseases of ageing based on intervention in relevant aspects of the ageing process itself.


title: Translating emotion theory and research into preventive interventions.

abstract: Scientific advances in the field of emotions suggest a framework for conceptualizing the emotion-related aspects of prevention programs that aim to enhance children’s socioemotional competence and prevent the emergence of behavior problems and psychopathology. A conception of emotions as inherently adaptive and motivational and the related empirical evidence from several disciplines and specialities suggest 7 principles for developing preventive interventions: the utilization of positive and negative emotions, emotion modulation as a mediator of emotion utilization, emotion patterns in states and traits, different processes of emotion activation, emotion communication in early life, and the development of connections for the modular and relatively independent emotions and cognitive systems. Each principle’s practical implications and application in current prevention programs are discussed. Almost from its beginning, psychology has consisted of both a science and a profession, and an uneasy relation between the two. William James (1890/1950), who defined the field more than a century ago, had a lab and saw patients, but the relations between his science and practice have remained largely unknown (Taylor, 1999). This uneasy relation between the major aspects of psychology continued through the 20th century, leaving a wide gulf between the science and practice of psychology (Weisz, Donenberg, Han, & Weiss, 1995). Experts have noted the dearth of mental health projects that integrate theory, findings from basic research, intervention, and careful evaluation of treatments or programs (Cicchetti & Toth, 1999; Coie et al., 1993). Recent efforts to encourage and support work on translating basic research into treatment and prevention programs (National Advisory Mental Health Council’s Behavioral Science Workgroup, 2000) offer a bright promise for helping close the gap between science and practice. In the present article, I attempt to translate emotion theory and research into principles for developing preventive interventions in the first 12 years of life. I focus on deriving theoretical principles for programs intended to enhance socioemotional competence and prevent the development of behavior problems and psychopathology. Some of the principles may prove applicable to other types of interventions and at any age, but they stem from theory and research on infants and children. In keeping with concepts in “prevention science” (Coie et al., 1993), I draw from emotion theory and research in attempting to derive principles to guide the selection and shaping of techniques that address fundamental causal processes in the early development of risk and protective factors. Many widely used prevention programs share this goal. However, when evaluated with stringent criteria, such as objective assessment of long-term behavioral outcomes and theoretical explanations of change, very few programs merit high ratings (Bear, Webster-Stratton, Furlong, & Rhee, 2000). Many widely used school-based programs have not yet identified precisely what factors mediate change and how they do it, and these achievements probably will not happen until there are closer relations between science and practice.


A Kind of D-Amino Acid Oxidase Inhibitor, Sodium Benzoate, Might Relieve Panic Symptoms in a First-Episode, Drug-Naïve Panic-Disorder Patient


title: A review of recent development in smart grid and micro-grid laboratories

abstract: In recent decades, smart grid have become increasingly attractive to both energy producers and consumers. Amongst the main challenges for the successful realization of smart grid includes the integration of renewable energy resources, real time demand response and management of intermittent energy resources. Apart from smart grid, the development of micro-grids should take into consideration of issues such as the system performance, modeling, monitoring and controlling of the micro-grids. In particular, the recent advancements in information and communication technologies (ICTs) could facilitate the effective development of the future micro-grid system. This paper presents an overview of smart grids features and highlights the recent development of micro-grid laboratories in Europe, US and Japan.


Design of Neuro-Fuzzy System Controller for DC ServomotorBased Satellite Tracking System


title: Application of nitric oxide and calcium nitrate enhances tolerance of wheat seedlings to salt stress

abstract: Nitric oxide and calcium are two kinds of signaling molecules that are the key secondary messenger. An experiment was conducted in saline culture solution to examine the cross-talk interactions of nitric oxide and calcium in common wheat under NaCl stress. Addition of 100 mM NaCl reduced plant growth, decreased the chlorophyll content and root activity, increased the reactive oxygen species (such as \( {\text{O}}_{2}^{ \cdot - } \) and H2O2) level, lipid peroxidation and electrolyte leakage, and suppressed the activities of antioxidant enzymes in wheat seedlings. Moreover, high Na+ resulted in the deficiency of mineral nutrients. While addition of 0.10 mM sodium nitroprusside (a NO donor) and/or 17.50 mM Ca(NO3)2 (Na/Ca = 5) alleviated a decline in chlorophyll content, root activity and soluble sugar content, stimulated the activities of antioxidant enzymes such as superoxide dismutase, peroxidase and catalase and decreased the electrolyte leakage, lipid peroxidation, H2O2 content, \( {\text{O}}_{2}^{ \cdot - } \) generation rate. Furthermore, the concentrations of potassium (K), calcium (Ca), magnesium (Mg), zinc (Zn), iron (Fe) and copper (Cu) concentrations in the leaves and roots of wheat were increased, indicating that application of Ca(NO3)2 facilitated the maintenance of ion homeostasis, while the exogenous NO improved the uptake of Ca. The results suggest that the nitric oxide and calcium are effective in alleviating the adverse effects of salt stress: the nitric oxide is more actively involved in antioxidant system while the calcium is more beneficial to ion homeostasis. When applied in combination, Ca(NO3)2 could counteract the toxicity of exogenous NO to the root, while the exogenous NO may promote the selectivity of uptake and transport of Ca2+.


title: Scaffolds in tissue engineering bone and cartilage.

abstract: Musculoskeletal tissue, bone and cartilage are under extensive investigation in tissue engineering research. A number of biodegradable and bioresorbable materials, as well as scaffold designs, have been experimentally and/or clinically studied. Ideally, a scaffold should have the following characteristics: (i) three-dimensional and highly porous with an interconnected pore network for cell growth and flow transport of nutrients and metabolic waste; (ii) biocompatible and bioresorbable with a controllable degradation and resorption rate to match cell/tissue growth in vitro and/or in vivo; (iii) suitable surface chemistry for cell attachment, proliferation, and differentation and (iv) mechanical properties to match those of the tissues at the site of implantation. This paper reviews research on the tissue engineering of bone and cartilage from the polymeric scaffold point of view.


title: A Research of Maize Disease Image Recognition of Corn Based on BP Networks

abstract: A Research of maize disease image recognition of corn leaf based on image processing and analysis, which is to study diseases of image classification. According to the texture characteristics of corn diseases, it uses YCbCr color space technology to segment disease spot, and uses the cooccurrence matrix spatial gray level layer to extract disease spot texture feature, and uses BP neural network to class the maize disease. Application YCbCr color space technology segmented disease spot, and using the co-occurrence matrix spatial gray level layer extracted disease spot texture feature of using BP neural network, on maize disease classification identification. On VC++ platform, do experiments for the study design recognition algorithm, the experimental results show that the algorithm can effectively identify the disease image, the accuracy was as high as 98% or more, the study provided the theoretical basis to cognition of maize leaf disease. the image re of maize leaf disease image recognition to provide a theoretical basis.


title: Oral cancer prediction using gene expression profiling and machine learning

abstract: Oral premalignant lesion (OPL) patients have a high risk of developing oral cancer. In this study we investigate using machine learning techniques with gene expression profiling to predict the possibility of oral cancer development in OPL patients. Four classification techniques were used: support vector machine (SVM), Regularized Least Squares (RLS), multi-layer perceptron (MLP) with back propagation and deep neural network (DNN). Fisher discriminate analysis was used to select relevant features from the gene expression array. The results show high accuracy (96%) using DNN and 94% accuracy using SVM and MLP with one sample cross validation. Furthermore, we achieved the same results using 10-fold cross validation.


title: ZeroN: mid-air tangible interaction enabled by computer controlled magnetic levitation

abstract: This paper presents ZeroN, a new tangible interface element that can be levitated and moved freely by computer in a three dimensional space. ZeroN serves as a tangible rep-resentation of a 3D coordinate of the virtual world through which users can see, feel, and control computation. To ac-complish this, we developed a magnetic control system that can levitate and actuate a permanent magnet in a pre-defined 3D volume. This is combined with an optical tracking and display system that projects images on the levitating object. We present applications that explore this new interaction modality. Users are invited to place or move the ZeroN object just as they can place objects on surfaces. For example, users can place the sun above physical objects to cast digital shadows, or place a planet that will start revolving based on simulated physical conditions. We describe the technology and interaction scenarios, discuss initial observations, and outline future development.


title: Analyzing reviews and code of mobile apps for better release planning

abstract: The mobile applications industry experiences an unprecedented high growth, developers working in this context face a fierce competition in acquiring and retaining users. They have to quickly implement new features and fix bugs, or risks losing their users to the competition. To achieve this goal they must closely monitor and analyze the user feedback they receive in form of reviews. However, successful apps can receive up to several thousands of reviews per day, manually analysing each of them is a time consuming task. To help developers deal with the large amount of available data, we manually analyzed the text of 1566 user reviews and defined a high and low level taxonomy containing mobile specific categories (e.g. performance, resources, battery, memory, etc.) highly relevant for developers during the planning of maintenance and evolution activities. Then we built the User Request Referencer (URR) prototype, using Machine Learning and Information Retrieval techniques, to automatically classify reviews according to our taxonomy and recommend for a particular review what are the source code files that need to be modified to handle the issue described in the user review. We evaluated our approach through an empirical study involving the reviews and code of 39 mobile applications. Our results show a high precision and recall of URR in organising reviews according to the defined taxonomy.


title: Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition

abstract: Integration of multiple microphone data is one of the key ways to achieve robust speech recognition in noisy environments or when the speaker is located at some distance from the input device. Signal processing techniques such as beamforming are widely used to extract a speech signal of interest from background noise. These techniques, however, are highly dependent on prior spatial information about the microphones and the environment in which the system is being used. In this work, we present a neural attention network that directly combines multi-channel audio to generate phonetic states without requiring any prior knowledge of the microphone layout or any explicit signal preprocessing for speech enhancement. We embed an attention mechanism within a Recurrent Neural Network (RNN) based acoustic model to automatically tune its attention to a more reliable input source. Unlike traditional multi-channel preprocessing, our system can be optimized towards the desired output in one step. Although attention-based models have recently achieved impressive results on sequence-to-sequence learning, no attention mechanisms have previously been applied to learn potentially asynchronous and non-stationary multiple inputs. We evaluate our neural attention model on the CHiME-3 challenge task, and show that the model achieves comparable performance to beamforming using a purely data-driven method.


title: Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning

abstract: Long Short-Term Memory (LSTM) is a popular approach to boosting the ability of Recurrent Neural Networks to store longer term temporal information. The capacity of an LSTM network can be increased by widening and adding layers. However, usually the former introduces additional parameters, while the latter increases the runtime. As an alternative we propose the Tensorized LSTM in which the hidden states are represented by tensors and updated via a cross-layer convolution. By increasing the tensor size, the network can be widened efficiently without additional parameters since the parameters are shared across different locations in the tensor; by delaying the output, the network can be deepened implicitly with little additional runtime since deep computations for each timestep are merged into temporal computations of the sequence. Experiments conducted on five challenging sequence learning tasks show the potential of the proposed model.


Distributed Vector Representations for Unsupervised Automatic Short Answer Grading.


title: Counting people in crowds with a real-time network of simple image sensors

abstract: Estimating the number of people in a crowded environment is a central task in civilian surveillance. Most vision-based counting techniques depend on detecting individuals in order to count, an unrealistic proposition in crowded settings. We propose an alternative approach that directly estimates the number of people. In our system, groups of image sensors segment foreground objects from the background, aggregate the resulting silhouettes over a network, and compute a planar projection of the scene's visual hull. We introduce a geometric algorithm that calculates bounds on the number of persons in each region of the projection, after phantom regions have been eliminated. The computational requirements scale well with the number of sensors and the number of people, and only limited amounts of data are transmitted over the network. Because of these properties, our system runs in real-time and can be deployed as an untethered wireless sensor network. We describe the major components of our system, and report preliminary experiments with our first prototype implementation.


title: The effectiveness of a bibliotherapy in increasing the self-management ability of slightly to moderately frail older people

abstract: OBJECTIVE ::: Self-management ability (SMA) is the ability to obtain those resources necessary for the production of well-being. With age, SMA becomes increasingly important, if one has a large variety of resources, physical and psychosocial losses due to the aging process can be substituted or compensated for. This study examined whether an increase in SMA would ensure sustainable levels of positive well-being among slightly to moderately frail older people. ::: ::: ::: METHODS ::: A bibliotherapy was developed to increase the SMA of slightly to moderately frail older people, and to help these persons to sustain a certain level of well-being. The effectiveness of this bibliotherapy was examined by comparing the SMA, mastery, and subjective well-being of 97 older people participating in the bibliotherapy to those of 96 older people in a delayed-treatment control condition. ::: ::: ::: RESULTS ::: The bibliotherapy resulted in a significant increase in SMA and mastery compared to the delayed-treatment control condition, and for SMA, this effect still existed 6 months after the intervention. The increase in SMA among older people who received the bibliotherapy prevented a decline in well-being as expected, but only in the short-term. ::: ::: ::: CONCLUSION ::: The current findings show that it is possible to counteract an age-related decline in well-being, even with only slight to moderate levels of frailty. ::: ::: ::: PRACTICE IMPLICATIONS ::: Cheap and easily accessible interventions, like the self-management bibliotherapy described in this article, may provide a useful addition to more traditional gerontological interventions.


title: Advertising in a pervasive computing environment

abstract: The advent of the internet has revolutionized the field of advertising by providing a whole new path for reaching potential customers. Studies show that online advertising is, on the whole, extremely effective and that consumer acceptance of online advertising is comparable to traditional media[7][8]. One of the reasons for the high effectiveness of online advertising is that users interact with the web at a far more personal and intimate level than they interact with other advertising media like the radio or television. Pervasive computing environments deal with users at an even more intimate level; hence such environments are even better advertising platforms than the web. Pervasive environments allow the delivery of relevant advertising in suitable ways to selected consumers. In this paper, we examine some of the possibilities of pervasive advertising as well as some of the issues involved.


Beyond User Preferences: The Short-Term Behaviour Modelling


title: A Hundred Days of Continuous Integration

abstract: Many agile teams use continuous integration (CI). It is one of the extreme programming practices and has been broadly adopted by the community [1]. Just how effective is it? Does the effort of maintaining the CI server and fixing build breaks save time compared to a lengthier check-in process that attempts to never break the build? While much anecdotal evidence exists as to the benefits of CI there is very little in the way of data to support this. How do you convince teams and management that itpsilas worth adopting and how best to do it? This report outlines our experience with CI in a distributed team environment and attempts to answer these questions.


title: Collaborative topic ranking: leveraging item meta-data for sparsity reduction

abstract: Pair-wise ranking methods have been widely used in recommender systems to deal with implicit feedback. They attempt to discriminate between a handful of observed items and the large set of unobserved items. In these approaches, however, user preferences and item characteristics cannot be estimated reliably due to overfitting given highly sparse data. To alleviate this problem, in this paper, we propose a novel hierarchical Bayesian framework which incorporates "bag-of-words" type meta-data on items into pair-wise ranking models for one-class collaborative filtering. The main idea of our method lies in extending the pair-wise ranking with a probabilistic topic modeling. Instead of regularizing item factors through a zero-mean Gaussian prior, our method introduces item-specific topic proportions as priors for item factors. As a by-product, inter-pretable latent factors for users and items may help explain recommendations in some applications. We conduct an experimental study on a real and publicly available dataset, and the results show that our algorithm is effective in providing accurate recommendation and interpreting user factors and item factors.


title: Experiments in Microblog Summarization

abstract: Abstract —This paper presents algorithms for summarizingmicroblog posts. In particular, our algorithms process collectionsof short posts on speciﬁc topics on the well-known site calledTwitter and create short summaries from these collections ofposts on a speciﬁc topic. The goal is to produce summariesthat are similar to what a human would produce for the samecollection of posts on a speciﬁc topic. We evaluate the summariesproduced by the summarizing algorithms, compare them withhuman-produced summaries and obtain excellent results. I. I NTRODUCTION Twitter, the microblogging site started in 2006, has becomea social phenomenon, with more than 20 million visitors eachmonth. While the majority posts are conversational or notvery meaningful, about 3.6% of the posts concern topics ofmainstream news 1 . At the end of 2009, Twitter had 75 millionaccount holders, of which about 20% are active 2 . There areapproximately 2.5 million Twitter posts per day 3 . To helppeople who read Twitter posts or tweets, Twitter provides ashort list of popular topics called


title: Using VTLN for Broadcast News transcription

abstract: A coating system which contains a bond coating and a thermal barrier coating is applied to metal surfaces such as turbine blades and which provides both low thermal conductivity and improved adherence when exposed to high temperature gases or liquids. The bond coating contains NiCrAlY and the thermal barrier coating contains a reflective oxide. The reflective oxides ZrO2-Y2O3 and ZrO2-MgO have demonstrated significant utility in high temperature turbine applications.


title: Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection

abstract: We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state-of-the-art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large windows (receptive fields). However, this success comes at a cost, since the associated loss of effecive spatial resolution washes out high-frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge detection, thus making class-boundaries explicit in the model, First, we construct a comparatively simple, memory-efficient model by adding boundary detection to the Segnet encoder-decoder architecture. Second, we also include boundary detection in FCN-type models and set up a high-end classifier ensemble. We show that boundary detection significantly improves semantic segmentation with CNNs. Our high-end ensemble achieves > 90% overall accuracy on the ISPRS Vaihingen benchmark.


title: Apertium: a free/open-source platform for rule-based machine translation

abstract: Apertium is a free/open-source platform for rule-based machine translation. It is being widely used to build machine translation systems for a variety of language pairs, especially in those cases (mainly with related-language pairs) where shallow transfer suffices to produce good quality translations, although it has also proven useful in assimilation scenarios with more distant pairs involved. This article summarises the Apertium platform: the translation engine, the encoding of linguistic data, and the tools developed around the platform. The present limitations of the platform and the challenges posed for the coming years are also discussed. Finally, evaluation results for some of the most active language pairs are presented. An appendix describes Apertium as a free/open-source project.


title: High Performance MPI Library for Container-Based HPC Cloud on InfiniBand Clusters

abstract: Virtualization technology has grown rapidly over the past few decades. As a lightweight solution, container-based virtualization provides a promising approach to efficiently build HPC clouds. However, our study shows clear performance bottleneck when running MPI jobs on multi-container environments. This motivates us to first analyze the performance bottleneck for MPI jobs running in different container deployment scenarios. To eliminate performance bottleneck, we propose a high performance locality-aware MPI library, which is able to dynamically detect co-resident containers at runtime. Through this design, the MPI processes in co-resident containers can communicate to each other by shared memory and Cross Memory Attach (CMA) channels instead of the network channel. A comprehensive performance study indicates that compared with the default case, our proposed design can significantly improve the communication performance by up to 9X and 86% in terms of MPI point-to-point and collective operations, respectively. The results for applications demonstrate that the locality-aware design can reduce up to 16% of execution time. The evaluation results also show that by the help of locality-aware design, we can achieve near-native performance in container-based HPC cloud with minor overhead. The proposed locality-aware MPI design reveals significant potential to be utilized to efficiently build large scale container-based HPC clouds.


title: Pedestrian Detection: An Evaluation of the State of the Art

abstract: Pedestrian detection is a key problem in computer vision, with several applications that have the potential to positively impact quality of life. In recent years, the number of approaches to detecting pedestrians in monocular images has grown steadily. However, multiple data sets and widely varying evaluation protocols are used, making direct comparisons difficult. To address these shortcomings, we perform an extensive evaluation of the state of the art in a unified framework. We make three primary contributions: 1) We put together a large, well-annotated, and realistic monocular pedestrian detection data set and study the statistics of the size, position, and occlusion patterns of pedestrians in urban scenes, 2) we propose a refined per-frame evaluation methodology that allows us to carry out probing and informative comparisons, including measuring performance in relation to scale and occlusion, and 3) we evaluate the performance of sixteen pretrained state-of-the-art detectors across six data sets. Our study allows us to assess the state of the art and provides a framework for gauging future efforts. Our experiments show that despite significant progress, performance still has much room for improvement. In particular, detection is disappointing at low resolutions and for partially occluded pedestrians.


title: An improved digital evidence acquisition model for the Internet of Things forensic I: A theoretical framework

abstract: Digital evidence plays a vital role in determining legal case admissibility in electronic- and cyber-oriented crimes. Considering the complicated level of the Internet of Things (IoT) technology, performing the needed forensic investigation will be definitely faced by a number of challenges and obstacles, especially in digital evidence acquisition and analysis phases. Based on the currently available network forensic methods and tools, the performance of IoT forensic will be producing a deteriorated digital evidence trail due to the sophisticated nature of IoT connectivity and data exchangeability via the “things”. In this paper, a revision of IoT digital evidence acquisition procedure is provided. In addition, an improved theoretical framework for IoT forensic model that copes with evidence acquisition issues is proposed and discussed.


title: The STATe of the Neuron Leads to Synaptic Stripping

abstract: In a recent issue of Cell, Di Liberto et al. (2018) elucidate the mechanisms involved in synaptic stripping during viral infection. Infected neurons orchestrate their own synaptic loss downstream of IFNγ signaling, ultimately attracting phagocytic monocytes into the CNS through CCL2 production.


title: RelocNet: Continuous Metric Learning Relocalisation Using Neural Nets

abstract: We propose a method of learning suitable convolutional representations for camera pose retrieval based on nearest neighbour matching and continuous metric learning-based feature descriptors. We introduce information from camera frusta overlaps between pairs of images to optimise our feature embedding network. Thus, the final camera pose descriptor differences represent camera pose changes. In addition, we build a pose regressor that is trained with a geometric loss to infer finer relative poses between a query and nearest neighbour images. Experiments show that our method is able to generalise in a meaningful way, and outperforms related methods across several experiments.


Quadcopter – Obstacle Detection and Collision Avoidance


title: The New Threat of Digital Marketing

abstract: Because of their avid use of new media and their increased spending power, children and teens have become primary targets of a new "media and marketing ecosystem." The digital marketplace is undergoing rapid innovation as new technologies and software applications continue to reshape the media landscape and user behaviors. The advertising industry, in many instances led by food and beverage marketers, is purposefully exploiting the special relationship that youth have with new media, as online marketing campaigns create unprecedented intimacies between adolescents and the brands and products that now literally surround them.


title: 3D Object Detection with Latent Support Surfaces

abstract: We develop a 3D object detection algorithm that uses latent support surfaces to capture contextual relationships in indoor scenes. Existing 3D representations for RGB-D images capture the local shape and appearance of object categories, but have limited power to represent objects with different visual styles. The detection of small objects is also challenging because the search space is very large in 3D scenes. However, we observe that much of the shape variation within 3D object categories can be explained by the location of a latent support surface, and smaller objects are often supported by larger objects. Therefore, we explicitly use latent support surfaces to better represent the 3D appearance of large objects, and provide contextual cues to improve the detection of small objects. We evaluate our model with 19 object categories from the SUN RGB-D database, and demonstrate state-of-the-art performance.


title: Predicting Travel Time Reliability using Mobile Phone GPS Data

abstract: Abstract Estimates of road speeds have become commonplace and central to route planning, but few systems in production provide information about the reliability of the prediction. Probabilistic forecasts of travel time capture reliability and can be used for risk-averse routing, for reporting travel time reliability to a user, or as a component of fleet vehicle decision-support systems. Many of these uses (such as those for mapping services like Bing or Google Maps) require predictions for routes in the road network, at arbitrary times; the highest-volume source of data for this purpose is GPS data from mobile phones. We introduce a method (TRIP) to predict the probability distribution of travel time on an arbitrary route in a road network at an arbitrary time, using GPS data from mobile phones or other probe vehicles. TRIP captures weekly cycles in congestion levels, gives informed predictions for parts of the road network with little data, and is computationally efficient, even for very large road networks and datasets. We apply TRIP to predict travel time on the road network of the Seattle metropolitan region, based on large volumes of GPS data from Windows phones. TRIP provides improved interval predictions (forecast ranges for travel time) relative to Microsoft’s engine for travel time prediction as used in Bing Maps. It also provides deterministic predictions that are as accurate as Bing Maps predictions, despite using fewer explanatory variables, and differing from the observed travel times by only 10.1% on average over 35,190 test trips. To our knowledge TRIP is the first method to provide accurate predictions of travel time reliability for complete, large-scale road networks.


title: Design and Test of a High-Power High-Efficiency Loosely Coupled Planar Wireless Power Transfer System

abstract: In this paper, a high-power high-efficiency wireless-power-transfer system using the class-E operation for transmitter via inductive coupling has been designed and fabricated using the proposed design approach. The system requires no complex external control system but relies on its natural impedance response to achieve the desired power-delivery profile across a wide range of load resistances while maintaining high efficiency to prevent any heating issues. The proposed system consists of multichannels with independent gate drive to control power delivery. The fabricated system is compact and capable of 295 W of power delivery at 75.7% efficiency with forced air cooling and of 69 W of power delivery at 74.2% efficiency with convection cooling. This is the highest power and efficiency of a loosely coupled planar wireless-power-transfer system reported to date.


title: QoS Aware Resource Management for Apache Cassandra

abstract: Apache Cassandra is a distributed database of choice when it comes to big data management with zero downtime, linear scalability, and seamless multiple data center deployment. However, resource allocation for the system during deployment is a major issue which could either lead to bad performance or under-utilization of resources. In this paper we describe a QoS-aware architecture that manages resources for the distributed storage system to proactively and dynamically allocate resources for the distributed storage system to ensure that effective resource utilization and deliver performance according to the specified QoS. The architecture uses machine learning techniques to proactively predict and judge the performance of the system and make decisions for effective resource management for the database. In addition, we propose an adaptive sampling mechanism for classification to ensure that the architecture does not impose an unreasonable overhead on the system. We evaluate and provide results using Yahoo! Cloud Serving Benchmark (YCSB) on the modified Cassandra cluster. Our evaluation shows an increase of the overall utilization of the allocated resources without compromising on the required QoS latency for the overall cluster.


title: Screencasts: How effective are they and how do students engage with them?

abstract: The use of screencasts as an instructional technology is increasing rapidly in higher education. While there appears to be a consensus around students’ satisfaction with the provision of technology-enhanced tools, there is limited evidence revolving around their impact in terms of knowledge acquisition. Moreover, the reasons why students choose to engage (or not) with these resources remain largely unreported. The study assessed the effect of using screencasts on undergraduate students’ understanding and engagement with learning material in one of their modules. Customised screencasts were used as optional additional learning and teaching resources. Grades obtained in a test module (with screencasts) and a control module (without screencasts) were compared to gauge the impact of screencasts on knowledge acquisition. Furthermore, the reasons for students’ engagement (or lack thereof) with the screencasts were explored using questionnaires. A modest but significant impact of screencasts on knowledge acquisi...


title: Can We Teach Computers to Understand Art? Domain Adaptation for Enhancing Deep Networks Capacity to De-Abstract Art

abstract: Abstract Humans comprehend a natural scene at a single glance; painters and other visual artists, through their abstract representations, stressed this capacity to the limit. The performance of computer vision solutions matched that of humans in many problems of visual recognition. In this paper we address the problem of recognizing the genre (subject) in digitized paintings using Convolutional Neural Networks (CNN) as part of the more general dealing with abstract and/or artistic representation of scenes. Initially we establish the state of the art performance by training a CNN from scratch. In the next level of evaluation, we identify aspects that hinder the CNNs' recognition, such as artistic abstraction. Further, we test various domain adaptation methods that could enhance the subject recognition capabilities of the CNNs. The evaluation is performed on a database of 80,000 annotated digitized paintings, which is tentatively extended with artistic photographs, either original or stylized, in order to emulate artistic representations. Surprisingly, the most efficient domain adaptation is not the neural style transfer. Finally, the paper provides an experiment-based assessment of the abstraction level that CNNs are able to achieve.


title: A Secure Communication Architecture for Distributed Microgrid Control

abstract: Microgrids are a key component in the evolution of the power grid. Microgrids are required to operate in both grid connected and standalone island mode using local sources of power. A major challenge in implementing microgrids is the communications and control to support transition from grid connected mode and operation in island mode. Here, we propose a secure communication architecture to support microgrid operation and control. A security model, including network, data, and attack models, is defined and a security protocol to address the real-time communication needs of microgrids is proposed. The implementation of the proposed security scheme is discussed and its performance evaluated using theoretical and co-simulation analysis, which shows it to be superior to existing protocols.


title: MPLS-TE and MPLS VPNS with openflow

abstract: We demonstrate MPLS Traffic Engineering (MPLS-TE) and MPLS-based Virtual Private Networks (MPLS VPNs) using OpenFlow [1] and NOX [6]. The demonstration is the outcome of an engineering experiment to answer the following questions: How hard is it to implement a complex control plane on top of a network controller such as NOX? Does the global vantage point in NOX make the implementation easier than the traditional method of implementing it on every switch, embedded in the data plane? We implemented every major feature of MPLS-TE and MPLS-VPN in just 2,000 lines of code, compared to much larger lines of code in the more traditional approach, such as Quagga-MPLS. Because NOX maintains a consistent, up-to-date topology map, the MPLS control plane features are quite simple to implement. And its simplicity makes it easy to extend: We have easily added several new features; something a network operator could do to customize their network to meet their customers' needs. ::: The demo consists of two parts: MPLS-TE services and then MPLS VPN driven by a GUI.


title: Single lens 3D-camera with extended depth-of-field

abstract: Placing a micro lens array in front of an image sensor transforms a normal camera into a single lens 3D camera, ::: which also allows the user to change the focus and the point of view after a picture has been taken. While ::: the concept of such plenoptic cameras is known since 1908, only recently the increased computing power of ::: low-cost hardware and the advances in micro lens array production, have made the application of plenoptic ::: cameras feasible. This text presents a detailed analysis of plenoptic cameras as well as introducing a new type ::: of plenoptic camera with an extended depth of field and a maximal effective resolution of up to a quarter of the ::: sensor resolution.


title: A parametric filtering algorithm for the graph isomorphism problem

abstract: We introduce a new filtering algorithm, called IDL(d)-filtering, for a global constraint dedicated to the graph isomorphism problem—the goal of which is to decide if two given graphs have an identical structure. The basic idea of IDL(d)-filtering is to label every vertex with respect to its relationships with other vertices around it in the graph, and to use these labels to filter domains by removing values that have different labels. IDL(d)-filtering is parameterized by a positive integer value d which gives a limit on the distance between a vertex to be labelled and the set of vertices considered to build its label. We experimentally compare different instantiations of IDL(d)-filtering with state-of-the-art dedicated algorithms and show that IDL(d)-filtering is more efficient on regular sparse graphs and competitive on other kinds of graphs.


title: Adding the Temporal Dimension to Search " A Case Study in Publication Search

abstract: The most well known search techniques are perhaps the PageRank and HITS algorithms. In this paper we argue that these algorithms miss an important dimension, the temporal dimension. Quality pages in the past may not be quality pages now or in the future. These techniques favor older pages because these pages have many in-links accumulated over time. New pages, which may be of high quality, have few or no in-links and are left behind. Research publication search has the same problem. If we use the PageRank or HITS algorithm, those older or classic papers will be ranked high due to the large number of citations that they received in the past. This paper studies the temporal dimension of search in the context of research publication. A number of methods are proposed to deal with the problem based on analyzing the behavior history and the source of each publication. These methods are evaluated empirically. Our results show that they are highly effective.


title: BANK FAILURES, STIGMA MANAGEMENT AND THE ACCOUNTING ESTABLISHMENT.

abstract: Abstract This study considers the responses of individuals and institutions to discreting situations. We suggest that professional privilege and legitimacy depend on the public accounting profession's ability to neutralize and displace the stigma associated with discrediting situations. A case study of the Canadian Commercial Bank failure is used to illustrate the complex interplay of individual and institutional actions, both on the part of individual auditors and the accounting profession, as well as the other involved institutions.


Pattern Rules, Patterns, and Graphs: Analyzing Grade 6 Students' Learning of Linear Functions through the Processes of Webbing, Situated Abstractions, and Convergent Conceptual Change


title: The Spacing Effect and Metacognitive Control

abstract: Research suggests that spaced learning, compared with massed learning, results in superior long-term retention (the spacing effect). Son (2010) identified a potentially important moderator of the spacing effect: metacognitive control. Specifically, when participants chose massed restudy but were instead forced to space the restudy, the spacing effect disappeared in adults (or was reduced in children). This suggests spacing is less effective (or possibly ineffective) if implemented against the wishes of the learner. A closer examination of this paradigm, however, reveals that item-selection issues might alternatively explain the disappearance of the spacing effect. In the current experiments, we replicated the original design demonstrating that an itemselection confound is operating. Furthermore, relative to a more appropriate baseline, the spacing effect was significant and of the same size whether participants’ restudy choices were honored or violated. In this paradigm, metacognitive control does not appear to moderate the spacing effect.


title: A Novel Low-Profile Dual Circularly Polarized Dielectric Resonator Antenna

abstract: A novel dual-port dual-sense circularly polarized (CP) T-shaped dielectric resonator antenna is introduced. The antenna consists of a T-shaped dielectric resonator, a pair of microstrip-fed stubs printed on a substrate with a truncated ground plane, and a decoupling network. Circular polarization is obtained by the structure to excite multiple orthogonal modes. The antenna can radiate left-hand CP (LHCP) or right-hand CP (RHCP) fields depending on the feed port selected. The measured RHCP (LHCP) field at the boresight direction is more than 26 dB (25 dB) stronger than the LHCP (RHCP) counterpart at the center operating frequency. The measured results show that the antenna has usable bandwidths of 17.2% (4.98-5.92 GHz) and 17.9% (5.02-6.03 GHz) and peak gains of 3.2 and 3.3 dBic at two feed ports, respectively. The isolations between two feed ports are better than 22.5 dB over the whole operating band. In addition, the antenna has peak radiation efficiencies of about 89% and 91% for Ports 1 and 2, respectively.


Manifold Learning for Medical Image Registration, Segmentation, and Classification


title: Network intrusion detection for cyber security using unsupervised deep learning approaches

abstract: In the paper, we demonstrate novel approach for network Intrusion Detection System (IDS) for cyber security using unsupervised Deep Learning (DL) techniques. Very often, the supervised learning and rules based approach like SNORT fetch problem to identify new type of attacks. In this implementation, the input samples are numerical encoded and applied un-supervised deep learning techniques called Auto Encoder (AE) and Restricted Boltzmann Machine (RBM) for feature extraction and dimensionality reduction. Then iterative k-means clustering is applied for clustering on lower dimension space with only 3 features. In addition, Unsupervised Extreme Learning Machine (UELM) is used for network intrusion detection in this implementation. We have experimented on KDD-99 dataset, the experimental results show around 91.86% and 92.12% detection accuracy using unsupervised deep learning technique AE and RBM with K-means respectively. The experimental results also demonstrate, the proposed approach shows around 4.4% and 2.95% improvement of detection accuracy using RBM with K-means against only K-mean clustering and Unsupervised Extreme Learning Machine (USELM) respectively.


title: Darwin: A Genomics Co-processor Provides up to 15,000X Acceleration on Long Read Assembly

abstract: Genomics is transforming medicine and our understanding of life in fundamental ways. Genomics data, however, is far outpacing Moore»s Law. Third-generation sequencing technologies produce 100X longer reads than second generation technologies and reveal a much broader mutation spectrum of disease and evolution. However, these technologies incur prohibitively high computational costs. Over 1,300 CPU hours are required for reference-guided assembly of the human genome, and over 15,600 CPU hours are required for de novo assembly. This paper describes "Darwin" --- a co-processor for genomic sequence alignment that, without sacrificing sensitivity, provides up to $15,000X speedup over the state-of-the-art software for reference-guided assembly of third-generation reads. Darwin achieves this speedup through hardware/algorithm co-design, trading more easily accelerated alignment for less memory-intensive filtering, and by optimizing the memory system for filtering. Darwin combines a hardware-accelerated version of D-SOFT, a novel filtering algorithm, alignment at high speed, and with a hardware-accelerated version of GACT, a novel alignment algorithm. GACT generates near-optimal alignments of arbitrarily long genomic sequences using constant memory for the compute-intensive step. Darwin is adaptable, with tunable speed and sensitivity to match emerging sequencing technologies and to meet the requirements of genomic applications beyond read assembly.


A Survey on Computer Science Techniques in the FOREX Market: Models and Applications


title: A Study of MatchPyramid Models on Ad-hoc Retrieval

abstract: Deep neural networks have been successfully applied to many text matching tasks, such as paraphrase identification, question answering, and machine translation. Although ad-hoc retrieval can also be formalized as a text matching task, few deep models have been tested on it. In this paper, we study a state-of-the-art deep matching model, namely MatchPyramid, on the ad-hoc retrieval task. The MatchPyramid model employs a convolutional neural network over the interactions between query and document to produce the matching score. We conducted extensive experiments to study the impact of different pooling sizes, interaction functions and kernel sizes on the retrieval performance. Finally, we show that the MatchPyramid models can significantly outperform several recently introduced deep matching models on the retrieval task, but still cannot compete with the traditional retrieval models, such as BM25 and language models.


title: A benchmark 3D fingerprint database

abstract: Fingerprint biometric is one of the most successful biometrics applied in both forensic law enforcement and security applications. Recent developments in fingerprint acquisition technology have resulted in touchless live scan devices that generate 3D representation of fingerprints, and thus can overcome the deformation and smearing problems caused by conventional contact-based acquisition techniques. However, there are yet no 3D full fingerprint databases with their corresponding 2D prints for fingerprint biometric research. This paper presents a 3D fingerprint database we have established in order to investigate the 3D fingerprint biometric comprehensively. It consists of 3D fingerprints as well as their corresponding 2D fingerprints captured by two commercial fingerprint scanners from 150 subjects in Australia. Besides, we have tested the performance of 2D fingerprint verification, 3D fingerprint verification, and 2D to 3D fingerprint verification. The results show that more work is needed to improve the performance of 2D to 3D fingerprint verification. In addition, the database is expected to be released publicly in late 2014.


title: A Simple and Efficient Feature Descriptor for Fast Matching

abstract: A very simple but efficient feature descriptor is proposed for image matching/registration applications where ::: invariance is not important. The descriptor length is only three times the height of the local region in which the ::: descriptor is calculated, and experiments were conducted to compare it to the SURF descriptor. In addition, it ::: is shown, how the sampling can be modified in order to obtain a rotation invariant descriptor, while still keeping ::: it simple and efficient. Examples from stitching in microscopy and stereo processing of pairs of photographs are ::: given to prove the concept.


title: Modeling SmallClient indexing framework for big data analytics

abstract: Continually growing big data by the intervention of electronic and automated devices affects the data retrieval performance of contemporary big data analytics technologies and makes exploration and adoption of improved procedures inevitable. Indexing on big data facilitates analytics in a way that it can store, process, access and analyze given data sets quickly and more efficiently once properly designed. This paper aims to propose a novel mathematical model that introduces an indexing mechanism and ensures improved data retrieval performance on data sets with support to growing volume of big data. The model is composed of three modules: block creation, index creation and query execution. Block creation module improves records access performance while avoiding remote access delays. Index creation module allows maximum possible indexes for big data with minimized indexing overhead. Query execution module performs data search and retrieval operation on user search queries. The evaluation of proposed mathematical model ensures that search performance for both small and big data sets is improved with minimized overhead of data uploading and indexing time. We further verify the results by implementing SmallClient logic on four-node physical cluster that prove the improved performance of proposed approach.


Learning to Classify Text Using Support Vector Machines : Methods , Theory , and Algorithms


title: The Smartphone in Medicine: A Review of Current and Potential Use Among Physicians and Students

abstract: Background: Advancements in technology have always had major impacts in medicine. The smartphone is one of the most ubiquitous and dynamic trends in communication, in which one’s mobile phone can also be used for communicating via email, performing Internet searches, and using specific applications. The smartphone is one of the fastest growing sectors in the technology industry, and its impact in medicine has already been significant. Objective: To provide a comprehensive and up-to-date summary of the role of the smartphone in medicine by highlighting the ways in which it can enhance continuing medical education, patient care, and communication. We also examine the evidence base for this technology. Methods: We conducted a review of all published uses of the smartphone that could be applicable to the field of medicine and medical education with the exclusion of only surgical-related uses. Results: In the 60 studies that were identified, we found many uses for the smartphone in medicine; however, we also found that very few high-quality studies exist to help us understand how best to use this technology. Conclusions: While the smartphone’s role in medicine and education appears promising and exciting, more high-quality studies are needed to better understand the role it will have in this field. We recommend popular smartphone applications for physicians that are lacking in evidence and discuss future studies to support their use. [J Med Internet Res 2012;14(5):e128]


title: Building Concept Representations from Reusable Components

abstract: Our goal is to build knowledge-based systems capable of answering a wide variety of questions, including questions that are unanticipated when the knowledge base is built. For systems to achieve this level of competence and generality, they require the ability to dynamically construct new concept representations, and to do so in response to the questions arLd tasks posed to them. Our approach to meeting this requirement is to build knowledge bases of generalized, representational components, and to develop methods for automatically composing components on demand. This work extends the normal inheritance approach used in frame-based systems, and imports ideas from several different areas of AI, in particular compositional modeling, terminological reasoning, and ontological engineering. The contribution of this work is a novel integration of these methods that improves the efficiency of building knowledge bases and the robustness of using them.


title: Improved ID3 algorithm

abstract: as the classical algorithm of the decision tree classification algorithm, ID3 is famous for the merits of high classifying speed easy, strong learning ability and easy construction. But when use it to classify, there does exist the problem of inclining to chose attributions which has many values, which affects its practicality. This paper for solving the problem a decision tree algorithm based on attribute-importance is proposed. The improved algorithm uses attribute-importance to increase information gain of attribution which has fewer attributions and compares ID3 with improved ID3 by an example. The experimental analysis of the data show that the improved ID3 algorithm can get more reasonable and more effective rules.


title: Vehicle Detection in Aerial Images

abstract: The detection of vehicles in aerial images is widely applied in many applications. Comparing with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem because of small vehicle size and the complex background. In this paper, we propose a novel double focal loss convolutional neural network (DFL-CNN) framework. In the proposed framework, the skip connection is used in the CNN structure to enhance the feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposal network (RPN) and the final classifier. We further introduce the first large-scale vehicle detection dataset ITCVD with ground truth annotations for all the vehicles in the scene. We demonstrate the performance of our model on the existing benchmark German Aerospace Center (DLR) 3K dataset as well as the ITCVD dataset. The experimental results show that our DFL-CNN outperforms the baselines on vehicle detection.


title: High-Precision Disturbance Compensation for a Three-Axis Gyro-stabilized Camera Mount

abstract: This paper presents a compound control strategy for high-precision disturbance compensation in order to improve control accuracy of a three-axis gyro-stabilized camera mount. The influences of unknown disturbances are first analyzed, and the structural properties of the disturbances are given. For these disturbances that can be described by an exosystem, the internal model principle-based controllers are designed to eliminate the influences. For others, a general polynomial function is used to approximate them and a kind of proportional multiple-integral observer-based control method is adopted to estimate and compensate them. Finally, to verify the effectiveness and superiority of the compound control strategy, comparative experiments between the traditional methods and the proposed one have been carried out. Experiments with the proposed method demonstrate stronger disturbance rejection capability and better steady accuracy.


title: Approximate fairness through differential dropping

abstract: Many researchers have argued that the Internet architecture would be more robust and more accommodating of heterogeneity if routers allocated bandwidth fairly. However, most of the mechanisms proposed to accomplish this, such as Fair Queueing [16, 6] and its many variants [2, 23, 15], involve complicated packet scheduling algorithms. These algorithms, while increasingly common in router designs, may not be inexpensively implementable at extremely high speeds; thus, finding more easily implementable variants of such algorithms may be of significant practical value. This paper proposes an algorithm called Approximate Fair Dropping (AFD), which bases its dropping decisions on the recent history of packet arrivals. AFD retains a simple forwarding path and requires an amount of additional state that is small compared to current packet buffers. Simulation results, which we describe here, suggest that the design provides a reasonable degree of fairness in a wide variety of operating conditions. The performance of our approach is aided by the fact that the vast majority of Internet flows are slow but the fast flows send the bulk of the bits. This allows a small sample of recent history to provide accurate rate estimates of the fast flows.


title: Probabilistic Relation Induction in Vector Space Embeddings

abstract: Word embeddings have been found to capture a surprisingly rich amount of syntactic and semantic knowledge. However, it is not yet sufficiently well-understood how the relational knowledge that is implicitly encoded in word embeddings can be extracted in a reliable way. In this paper, we propose two probabilistic models to address this issue. The first model is based on the common relations-as-translations view, but is cast in a probabilistic setting. Our second model is based on the much weaker assumption that there is a linear relationship between the vector representations of related words. Compared to existing approaches, our models lead to more accurate predictions, and they are more explicit about what can and cannot be extracted from the word embedding.