title: Detecting the Magnitude of Events from News Articles

abstract: Forced migration is increasingly becoming a global issue of concern. In this paper, we present an effective model of targeted event detection, as an essential step towards the forced migration detection problem. To date, most of the the approaches deal with the event detection in a general setting with the main objective of detecting the presence or onset of an event. However, we focus on analyzing the magnitude of a given event from a collection of text documents such as news articles from multiple sources. We use violence as an illustration as it is one of the most critical factors of forced migration. The recent advancements in semantic similarity measures are adopted to obtain relevant violence scores for each word in the vocabulary of news articles in an unsupervised manner. The resulting scores are then used to compute the average daily violence scores over a period of three months. Evaluation of the proposed model against a manually annotated data set yields a Pearson's correlation of 0.8. We also include a case study exploring the relationship between violence and key events.


title: SearchTogether: an interface for collaborative web search

abstract: Studies of search habits reveal that people engage in many search tasks involving collaboration with others, such as travel planning, organizing social events, or working on a homework assignment. However, current Web search tools are designed for a single user, working alone. We introduce SearchTogether, a prototype that enables groups of remote users to synchronously or asynchronously collaborate when searching the Web. We describe an example usage scenario, and discuss the ways SearchTogether facilitates collaboration by supporting awareness, division of labor, and persistence. We then discuss the findings of our evaluation of SearchTogether, analyzing which aspects of its design enabled successful collaboration among study participants.


title: PaRRT: Parallel rapidly exploring random tree (RRT) based on MapReduce

abstract: The basic Rapidly Exploring Random Trees (RRT) method is recognized as a very effective solution to resolve motion planning problems. The large scale of data provided by the emerging of smart paradigm makes the running of RRT algorithm a challenging task. In fact, research is increasingly oriented towards the parallelization of RRT. In this paper, we propose a new extension of RRT by using MapReduce, the programming model designed to perform parallel distributed computing on very large datasets. The experimental results demonstrate that our PaRRT can scale well and efficiently large data structures.


From crisis to choice : Managing change in shrinking cities


title: GeoCueDepth: Exploiting geometric structure cues to estimate depth from a single image

abstract: Depth estimation from a single image is very challenging due to the inherent ambiguity of mapping a color image to a depth map. Previous work tackles this problem by exploiting various levels of features with multi-scale deep convolutional neural networks. However, most of the local geometric structure related monocular depth cues are lost when being propagated through convolutional neural network. Moreover, the error of depth cues related to local geometric structures is not considered in the loss function. In this work, we propose the GeoCueDepth convolutional neural network to exploit local geometric structure cues and propose a training loss that takes the geometric error into consideration, which significantly improve the performance of depth prediction in both accuracy and sharpness. Experiments show that the proposed method achieves 0.122 average relative error and 0.078 square relative error on the NYU Depth v2 data set, which outperforms state-of-the-art monocular depth estimation approaches.


title: Measuring the Accuracy of Object Detectors and Trackers

abstract: The accuracy of object detectors and trackers is most commonly evaluated by the Intersection over Union (IoU) criterion. To date, most approaches are restricted to axis-aligned or oriented boxes and, as a consequence, many datasets are only labeled with boxes. Nevertheless, axis-aligned or oriented boxes cannot accurately capture an object's shape. To address this, a number of densely segmented datasets has started to emerge in both the object detection and the object tracking communities. However, evaluating the accuracy of object detectors and trackers that are restricted to boxes on densely segmented data is not straightforward. To close this gap, we introduce the relative Intersection over Union (rIoU) accuracy measure. The measure normalizes the IoU with the optimal box for the segmentation to generate an accuracy measure that ranges between 0 and 1 and allows a more precise measurement of accuracies. Furthermore, it enables an efficient and easy way to understand scenes and the strengths and weaknesses of an object detection or tracking approach. We display how the new measure can be efficiently calculated and present an easy-to-use evaluation framework. The framework is tested on the DAVIS and the VOT2016 segmentations and has been made available to the community.


title: Cross-Corpus Training with TreeLSTM for the Extraction of Biomedical Relationships from Text

abstract: A bottleneck problem in machine learning-based relationship extraction (RE) algorithms, and particularly of deep learning-based ones, is the availability of training data in the form of annotated corpora. For specific domains, such as biomedicine, the long time and high expertise required for the development of manually annotated corpora explain that most of the existing one are relatively small (i.e., hundreds of sentences). Beside, larger corpora focusing on general or domain-specific relationships (such as citizenship or drug-drug interactions) have been developed. In this paper, we study how large annotated corpora developed for alternative tasks may improve the performances on biomedicine related tasks, for which few annotated resources are available. We experiment two deep learning-based models to extract relationships from biomedical texts with high performance. The first one combine locally extracted features using a Convolutional Neural Network (CNN) model, while the second exploit the syntactic structure of sentences using a Recursive Neural Network (RNN) architecture. Our experiments show that, contrary to the former, the latter benefits from a cross-corpus learning strategy to improve the performance of relationship extraction tasks. Indeed our approach leads to the best published performances for two biomedical RE tasks, and to state-of-the-art results for two other biomedical RE tasks, for which few annotated resources are available (less than 400 manually annotated sentences). This may be particularly impactful in specialized domains in which training resources are scarce, because they would benefit from the training data of other domains for which large annotated corpora does exist.


Identifying attack and support argumentative relations using deep learning


title: Gait ataxia-specific cerebellar influences and their rehabilitation

abstract: It is well known that the cerebellum is important for movement control and plays a critical role in balance and locomotion. As such, one of the most characteristic and sensitive signs of cerebellar damage is gait ataxia. However, characterizing ataxic gait is no easy task, because gait patterns are highly variable. This vari- ability seems to result from the interaction of different factors, namely, (1) the primary motor deficits in balance control and multi-joint coordination and oculomotor dys- function, (2) the safety strategies used, and (3) inaccurate adjustments in patients with loss of balance. In this report, we review different approaches to analyzing ataxic gait and studies to identify and quantify the different fac- tors contributing to this movement disorder. We also dis- cuss the influence of the cerebellum in adaptive locomotor control, the interaction between cognitive load and gait in dual-task paradigms, and the recent advances in rehabilitation of gait and posture for patients with cere- bellar degeneration. In the second part, we discuss open questions concerning cerebellar mechanisms in multi- joint coordination during different walking conditions. Fur- thermore, we point out potential future directions in motor rehabilitation, with the objective of identifying predictors of rehabilitation outcome and the development of individ- ualized training programs that potentially involve rehabili- tation technology. V C 2013 International Parkinson and Movement Disorder Society


title: On the use of SNMP as a management interface for virtual networks

abstract: Virtual networks emerged as an alternative for network infrastructure provision. However, the growth in users demand for shared resources over network elements increase allocation complexity. Thus, enabling effective management on each node is key to the global network operation. This work investigate the use of SNMP as a management interface for virtual routers in order to support automation and provide optimal use of physical assets. The solution was evaluated in two virtualization platforms (Xen and VMware) in order the verify the advantages and limitations of the proposed management interface.


title: Complications of Canine Tibial Tuberosity Avulsion Fractures

abstract: Proximal tibial plateau deformity, with consequent tilting of the joint surface, patellar luxation, and implant failure complicated tibial tuberosity avulsions that were repaired surgically.


Dynamic factor models


title: A 15-V Bidirectional Ultrasound Interface Analog Front-End IC for Medical Imaging Using Standard CMOS Technology

abstract: A high-voltage (HV) interface analog front-end (AFE) integrated circuit (IC) for medical ultrasound imaging applications using 0.18-μm standard CMOS process is presented. The proposed AFE IC includes a HV pulser in the transmit path that safely generates up to 15-Vpp of unipolar pulses at 2.6 MHz, a HV switch for isolation between transmitter and receiver frontend parts, and a 95.1-dBΩ low-power transimpedance preamplifier with 12-MHz bandwidth and 3.5-pA/√Hz input referred noise in the receive path operating at a low 1.1-V supply voltage. Both the pulser and the switch utilize dynamically-gate-biased stacked 3.3-V transistors to enable HV operation without compromising device reliability. The implemented single-channel AFE IC prototype intended for interfacing capacitive micromachined ultrasound transducer consumes 0.15 mm2 of core die area, making it feasible to be applied for various multi-array medical ultrasound imaging systems.


title: Blending Images for Texturing 3D Models

abstract: This paper describes a novel system for building seamless texture maps for a surface of arbitrary topology from real images of the object taken with a standard digital camera and uncontrolled lighting. In our application we wish to take a sparse set of real images of a 3D object, and apply the images to an approximate surface model of the object to generate a high quality textured model. In practice the measured colour and intensity for a surface element observed in different photographic images will not agree. This is due to the interaction between real world lighting effects (such as highlights and specularities) and variations in the camera gain settings as well as registration and surface modelling errors. We describe a new automatic approach that extends a classical 2D image blending technique to a 3D surface, which produces high quality photo-realistic results at a low computational cost.


title: Comparing Reconstruction Algorithms for Electrical Impedance Tomography

abstract: An improved electrical impedance tomographic reconstruction algorithm is presented that is generally guaranteed to converge. The algorithm is attractive for several reasons. A modified Newton?Raphson method varies a finite-element model of resistivities to fit a set of voltage measurements in a least-squared sense. Two procedures for calculating the Jacobian matrix are derived. One is standard, while the other is based on the compensation theorem. This second procedure is more efficient for computations, and just as accurate as the standard one. The inherent ill-conditioning in the approximate Hessian matrix of the linearized system is eliminated using the Marquardt method. Results from two-dimensional computer simulations are compared to four other reconstruction algorithms, which are based on methods proposed by other authors. The modified Newton?Raphson method provided significantly better reconstructions than any of the other methods. The algorithms compared are the perturbation, equipotential, iterative-equipotential, and the double-constraint methods. The modified Newton?Raphson method was found to be sensitive to measurement error, but future work in designing electrode-probing configurations is expected to reduce this sensitivity.


title: Anywhere surface touch: utilizing any surface as an input area

abstract: The current trend towards smaller and smaller mobile devices may cause considerable difficulties in using them. In this paper, we propose an interface called Anywhere Surface Touch, which allows any flat or curved surface in a real environment to be used as an input area. The interface uses only a single small camera and a contact microphone to recognize several kinds of interaction between the fingers of the user and the surface. The system recognizes which fingers are interacting and in which direction the fingers are moving. Additionally, the fusion of vision and sound allows the system to distinguish the contact conditions between the fingers and the surface. Evaluation experiments showed that users became accustomed to our system quickly, soon being able to perform input operations on various surfaces.


title: Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic

abstract: MapReduce is by far one of the most successful realizations of large-scale data-intensive cloud computing platforms. MapReduce automatically parallelizes computation by running multiple map and/or reduce tasks over distributed data across multiple machines. Hadoop is an open source implementation of MapReduce. When Hadoop schedules reduce tasks, it neither exploits data locality nor addresses partitioning skew present in some MapReduce applications. This might lead to increased cluster network traffic. In this paper we investigate the problems of data locality and partitioning skew in Hadoop. We propose Center-of-Gravity Reduce Scheduler (CoGRS), a locality-aware skew-aware reduce task scheduler for saving MapReduce network traffic. In an attempt to exploit data locality, CoGRS schedules each reduce task at its center-of-gravity node, which is computed after considering partitioning skew as well. We implemented CoGRS in Hadoop-0.20.2 and tested it on a private cloud as well as on Amazon EC2. As compared to native Hadoop, our results show that CoGRS minimizes off-rack network traffic by averages of 9.6% and 38.6% on our private cloud and on an Amazon EC2 cluster, respectively. This reflects on job execution times and provides an improvement of up to 23.8%.


title: Learning from streaming data with concept drift and imbalance: an overview

abstract: The primary focus of machine learning has traditionally been on learning from data assumed to be sufficient and representative of the underlying fixed, yet unknown, distribution. Such restrictions on the problem domain paved the way for development of elegant algorithms with theoretically provable performance guarantees. As is often the case, however, real-world problems rarely fit neatly into such restricted models. For instance class distributions are often skewed, resulting in the “class imbalance” problem. Data drawn from non-stationary distributions is also common in real-world applications, resulting in the “concept drift” or “non-stationary learning” problem which is often associated with streaming data scenarios. Recently, these problems have independently experienced increased research attention, however, the combined problem of addressing all of the above mentioned issues has enjoyed relatively little research. If the ultimate goal of intelligent machine learning algorithms is to be able to address a wide spectrum of real-world scenarios, then the need for a general framework for learning from, and adapting to, a non-stationary environment that may introduce imbalanced data can be hardly overstated. In this paper, we first present an overview of each of these challenging areas, followed by a comprehensive review of recent research for developing such a general framework.


title: Research advances in intelligent collision avoidance and adaptive cruise control

abstract: This paper looks into recent developments and research trends in collision avoidance/warning systems and automation of vehicle longitudinal/lateral control tasks. It is an attempt to provide a bigger picture of the very diverse, detailed and highly multidisciplinary research in this area. Based on diversely selected research, this paper explains the initiatives for automation in different levels of transportation system with a specific emphasis on the vehicle-level automation. Human factor studies and legal issues are analyzed as well as control algorithms. Drivers' comfort and well being, increased safety, and increased highway capacity are among the most important initiatives counted for automation. However, sometimes these are contradictory requirements. Relying on an analytical survey of the published research, we will try to provide a more clear understanding of the impact of automation/warning systems on each of the above-mentioned factors. The discussion of sensory issues requires a dedicated paper due to its broad range and is not addressed in this paper.


Comparison of Feature Detection and Matching Approaches : SIFT and SURF


title: Gasoline classification using near infrared (NIR) spectroscopy data: Comparison of multivariate techniques

abstract: Abstract Near infrared (NIR) spectroscopy is a non-destructive (vibrational spectroscopy based) measurement technique for many multicomponent chemical systems, including products of petroleum (crude oil) refining and petrochemicals, food products (tea, fruits, e.g., apples, milk, wine, spirits, meat, bread, cheese, etc.), pharmaceuticals (drugs, tablets, bioreactor monitoring, etc.), and combustion products. In this paper we have compared the abilities of nine different multivariate classification methods: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), soft independent modeling of class analogy (SIMCA), partial least squares (PLS) classification, K -nearest neighbor (KNN), support vector machines (SVM), probabilistic neural network (PNN), and multilayer perceptron (ANN-MLP) – for gasoline classification. Three sets of near infrared (NIR) spectra (450, 415, and 345 spectra) were used for classification of gasolines into 3, 6, and 3 classes, respectively, according to their source (refinery or process) and type. The 14,000–8000 cm −1 NIR spectral region was chosen. In all cases NIR spectroscopy was found to be effective for gasoline classification purposes, when compared with nuclear magnetic resonance (NMR) spectroscopy or gas chromatography (GC). KNN, SVM, and PNN techniques for classification were found to be among the most effective ones. Artificial neural network (ANN-MLP) approach based on principal component analysis (PCA), which was believed to be efficient, has shown much worse results. We hope that the results obtained in this study will help both further chemometric (multivariate data analysis) investigations and investigations in the sphere of applied vibrational (infrared/IR, near-IR, and Raman) spectroscopy of sophisticated multicomponent systems.


Thermoelectric Properties of PEDOT/PSS-Halloysite Nanotubes (HNTs) Hybrid Films


Analytical Longitudinal and Lateral Models of a Spherical Rolling Robot


An Improved PSO Clustering Algorithm Based on Affinity Propagation


title: Automatic number plate recognition system using modified stroke width transform

abstract: Automatic number plate recognition(ANPR) techniques occupy a significant role in intelligent traffic management systems. Most of the existing ANPR systems do not perform well under conditions like high vehicle speed, varying illuminations, changing back ground etc. In this paper we propose an ANPR technique which efficiently solves the above issues using the stroke width transform. In our method, first we pre-process the image to remove blurring artifacts. Then the significant edges in the image are detected and a new image is formed by grouping the connecting rays. This is followed by morphological processing which detects the letters in the image. Then an appropriate Optical Character Recognizer (OCR) recovers the final result. Experimental results show that the proposed method work effectively under varying light conditions and even in the presence of shadows.


title: Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression.

abstract: In this paper, we revisit batch state estimation through the lens of Gaussian process (GP) regression. We consider continuous-discrete estimation problems wherein a trajectory is viewed as a one-dimensional GP, with time as the independent variable. Our continuous-time prior can be defined by any linear, time-varying stochastic differential equation driven by white noise; this allows the possibility of smoothing our trajectory estimates using a variety of vehicle dynamics models (e.g., ‘constant-velocity’). We show that this class of prior results in an inverse kernel matrix (i.e., covariance matrix between all pairs of measurement times) that is exactly sparse (block-tridiagonal) and that this can be exploited to carry out GP regression (and interpolation) very efficiently. Though the prior is continuous, we consider measurements to occur at discrete times. When the measurement model is also linear, this GP approach is equivalent to classical, discrete-time smoothing (at the measurement times). When the measurement model is nonlinear, we iterate over the whole trajectory (as is common in vision and robotics) to maximize accuracy. We test the approach experimentally on a simultaneous trajectory estimation and mapping problem using a mobile robot dataset.


The Effects of Moderate Aerobic Exercise on Memory Retention and Recall


title: Combining Qualitative and Quantitative Sampling, Data Collection, and Analysis Techniques in Mixed-Method Studies

abstract: Researchers have increasingly turned to mixed-method techniques to expand the scope and improve the analytic power of their studies. Yet there is still relatively little direction on and much confusion about how to combine qualitative and quantitative techniques. These techniques are neither paradigm- nor method-linked; researchers' orientations to inquiry and their methodological commitments will influence how they use them. Examples of sampling combinations include criterion sampling from instrument scores, random purposeful sampling, and stratified purposeful sampling. Examples of data collection combinations include the use of instruments for fuller qualitative description, for validation, as guides for purposeful sampling, and as elicitation devices in interviews. Examples of data analysis combinations include interpretively linking qualitative and quantitative data sets and the transformation processes of qualitizing and quantitizing.


title: Performance Analysis of Proposed OpenFlow-Based Network Architecture Using Mininet

abstract: Future internet is required to be flexible, scalable, reliable, secure and having other advanced features. Software Defined Networking (SDN) fulfills most of the task to achieve require functioning. OpenFlow technology is the first standard for SDN which executes the protocols based on the required application. It is also capable for controlling multiple hardware or software switches from a single controller. This can be achieved by the separation of data plane and control plane from a network core devices. In this paper, a proposed architecture of an OpenFlow based small campus network is deployed. The performance analysis for a custom designed network model is achieved by executing few networking test on a defined nodes using network control protocols. The performance analysis of bandwidth, throughput and other network parameters between nodes for Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) connection is also performed. A custom model has been designed using a prototype network emulator that is Mininet and the code for network topology is executed in Python script.


title: Image-Based Lunar Surface Reconstruction

abstract: For the creation of a realistic 3 meter-sized relief globe of the Moon, a detailed height map of the entire lunar surface is required. Available height measurements of the Moon's surface are too coarse by a factor of 15 for this purpose. The only publicly available source of high-resolution information are photographic images from the Lunar Orbiter IV mission in 1967. We present a shape-from-shading approach to plausibly increase the resolution of existing low-resolution height data, based on a single high-resolution photographic mosaic image of the Moon. The presented reconstruction approach is designed to be robust with respect to frequent imperfections of the photographic imagery. Aside from the automatic reconstruction of a complete detailed lunar surface height map, we give a qualitative validation by the reconstruction of lunar surface details from close-up photographs of the Apollo 15 landing site.


title: An intelligent classification model for phishing email detection

abstract: Phishing attacks are one of the trending cyber-attacks that apply socially engineered messages that are communicated to people from professional hackers aiming at fooling users to reveal their sensitive information, the most popular communication channel to those messages is through users’ emails. This paper presents an intelligent classification model for detecting phishing emails using knowledge discovery, data mining and text processing techniques. This paper introduces the concept of phishing terms weighting which evaluates the weight of phishing terms in each email. The pre-processing phase is enhanced by applying text stemming and WordNet ontology to enrich the model with word synonyms. The model applied the knowledge discovery procedures using five popular classification algorithms and achieved a notable enhancement in classification accuracy; 99.1% accuracy was achieved using the Random Forest algorithm and 98.4% using J48, which is –to our knowledge- the highest accuracy rate for an accredited data set. This paper also presents a comparative study with similar proposed classification techniques.


title: Quality of Life of Women with Urinary Incontinence: A Systematic Literature Review

abstract: PURPOSE ::: The purpose of this study was to review studies that have examined the quality of life of women with urinary incontinence. ::: ::: ::: MATERIALS AND METHODS ::: A review was conducted that used the databases PubMED, Proquest, CINAHL, and Sciencedirect. Articles were included that were published in English between 2005 and 2010 the key words use were urinary incontinence, women, and quality of life. ::: ::: ::: RESULTS ::: A total of 18 studies were identified, and the prevalence of urinary incontinence varied depending on the definition of incontinence used and the age of the population studied. The Incontinence Quality of Life (I-QoL), Incontinence Impact Questionnaire-short form (IIQ-7), and King's Health Questionnaire (KHQ) were the most commonly used instruments. Demographic, medical, physical, psychological, health, and intervention factors were reported as influencing factors on the quality of life of women with incontinence. Age, severity of urinary incontinence, type of urinary incontinence, number of urinary incontinence episodes, body weight, stress, and help-seeking behavior were statistically significant variables influencing quality of life. ::: ::: ::: CONCLUSION ::: Future studies are needed to identify factors related to quality of life among women with incontinence and to use validated instruments according to specific subjects.


title: Timeline Generation through Evolutionary Trans-Temporal Summarization

abstract: We investigate an important and challenging problem in summary generation, i.e., Evolutionary Trans-Temporal Summarization (ETTS), which generates news timelines from massive data on the Internet. ETTS greatly facilitates fast news browsing and knowledge comprehension, and hence is a necessity. Given the collection of time-stamped web documents related to the evolving news, ETTS aims to return news evolution along the timeline, consisting of individual but correlated summaries on each date. Existing summarization algorithms fail to utilize trans-temporal characteristics among these component summaries. We propose to model trans-temporal correlations among component summaries for timelines, using inter-date and intra-date sentence dependencies, and present a novel combination. We develop experimental systems to compare 5 rival algorithms on 6 instinctively different datasets which amount to 10251 documents. Evaluation results in ROUGE metrics indicate the effectiveness of the proposed approach based on trans-temporal information.


title: Fast level set image and video segmentation using new evolution indicator operators

abstract: We propose an effective level set evolution method for robust object segmentation in real images. We construct an effective region indicator and an multiscale edge indicator, and use these two indicators to adaptively guide the evolution of the level set function. The multiscale edge indicator is defined in the gradient domain of the multiscale feature-preserving filtered image. The region indicator is built on the similarity map between image pixels and user specified interest regions, where the similarity map is computed using Gaussian Mixture Models (GMM). Then we combine these two methods to develop a new mixing edge stop function, which makes the level set method more robust to initial active contour setting, and forces the level set to evolve adaptively based on the image content. Furthermore, we apply an acceleration approach to speed up our evolution process, which yields real time segmentation performance. Finally, we extend the proposed approach to video segmentation for achieving effective target tracking results. As the results show, our approach is effective for image and video segmentation and works well to accurately detect the complex object boundaries in real-time.


title: Acing the IOC Game: Toward Automatic Discovery and Analysis of Open-Source Cyber Threat Intelligence

abstract: To adapt to the rapidly evolving landscape of cyber threats, security professionals are actively exchanging Indicators of Compromise (IOC) (e.g., malware signatures, botnet IPs) through public sources (e.g. blogs, forums, tweets, etc.). Such information, often presented in articles, posts, white papers etc., can be converted into a machine-readable OpenIOC format for automatic analysis and quick deployment to various security mechanisms like an intrusion detection system. With hundreds of thousands of sources in the wild, the IOC data are produced at a high volume and velocity today, which becomes increasingly hard to manage by humans. Efforts to automatically gather such information from unstructured text, however, is impeded by the limitations of today's Natural Language Processing (NLP) techniques, which cannot meet the high standard (in terms of accuracy and coverage) expected from the IOCs that could serve as direct input to a defense system. In this paper, we present iACE, an innovation solution for fully automated IOC extraction. Our approach is based upon the observation that the IOCs in technical articles are often described in a predictable way: being connected to a set of context terms (e.g., "download") through stable grammatical relations. Leveraging this observation, iACE is designed to automatically locate a putative IOC token (e.g., a zip file) and its context (e.g., "malware", "download") within the sentences in a technical article, and further analyze their relations through a novel application of graph mining techniques. Once the grammatical connection between the tokens is found to be in line with the way that the IOC is commonly presented, these tokens are extracted to generate an OpenIOC item that describes not only the indicator (e.g., a malicious zip file) but also its context (e.g., download from an external source). Running on 71,000 articles collected from 45 leading technical blogs, this new approach demonstrates a remarkable performance: it generated 900K OpenIOC items with a precision of 95% and a coverage over 90%, which is way beyond what the state-of-the-art NLP technique and industry IOC tool can achieve, at a speed of thousands of articles per hour. Further, by correlating the IOCs mined from the articles published over a 13-year span, our study sheds new light on the links across hundreds of seemingly unrelated attack instances, particularly their shared infrastructure resources, as well as the impacts of such open-source threat intelligence on security protection and evolution of attack strategies.


title: Learning to Compose Words into Sentences with Reinforcement Learning

abstract: We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences. In contrast with prior work on tree-structured models in which the trees are either provided as input or predicted using supervision from explicit treebank annotations, the tree structures in this work are optimized to improve performance on a downstream task. Experiments demonstrate the benefit of learning task-specific composition orders, outperforming both sequential encoders and recursive encoders based on treebank annotations. We analyze the induced trees and show that while they discover some linguistically intuitive structures (e.g., noun phrases, simple verb phrases), they are different than conventional English syntactic structures.


title: Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

abstract: Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations.


title: Using Student Performance to Assess CS Unplugged Activities in a Classroom Environment

abstract: Computer Science Unplugged activities have been shown to be successful in increasing student interest in computer science when used in outreach and after school events. There is less research available on adapting these extra-curricular activities for use in a classroom setting, where there are more students and the activities must support educational goals, not just changes in attitude. We describe our work in updating several existing CS Unplugged activities as well as introducing some new activities for use in an American middle school classroom. One challenge when using CS Unplugged activities is to determine what, if anything, students are learning. In this paper we detail one approach that links the updated activities to computational thinking skills, then incorporates worksheets where students illustrate their understanding.


title: Vision-based real-time position control of a semi-automated system for robot-assisted joint fracture surgery

abstract: PURPOSE ::: Joint fracture surgery quality can be improved by robotic system with high-accuracy and high-repeatability fracture fragment manipulation. A new real-time vision-based system for fragment manipulation during robot-assisted fracture surgery was developed and tested. ::: ::: ::: METHODS ::: The control strategy was accomplished by merging fast open-loop control with vision-based control. This two-phase process is designed to eliminate the open-loop positioning errors by closing the control loop using visual feedback provided by an optical tracking system. Evaluation of the control system accuracy was performed using robot positioning trials, and fracture reduction accuracy was tested in trials on ex vivo porcine model. ::: ::: ::: RESULTS ::: The system resulted in high fracture reduction reliability with a reduction accuracy of 0.09 mm (translations) and of [Formula: see text] (rotations), maximum observed errors in the order of 0.12 mm (translations) and of [Formula: see text] (rotations), and a reduction repeatability of 0.02 mm and [Formula: see text]. ::: ::: ::: CONCLUSIONS ::: The proposed vision-based system was shown to be effective and suitable for real joint fracture surgical procedures, contributing a potential improvement of their quality.


title: Mining Online User-Generated Content: Using Sentiment Analysis Technique to Study Hotel Service Quality

abstract: This study aims to look beyond the quantitative summary to provide a more comprehensive view of online user-generated content. We obtain a unique and extensive dataset of online user reviews for hotels across various review sites and over a long time periods. We use the sentiment analysis technique to decompose user reviews into five dimensions to measure hotel service quality. Those dimensions are then incorporated into econometrics models to examine their effect in shaping users' overall evaluation and content generating behavior. The results suggest that different dimensions of user reviews have significantly differential impact in forming user evaluation and driving content generation.


title: Analogical problem solving

abstract: Abstract The use of an analogy from a semantically distant domain to guide the problemsolving process was investigated. The representation of analogy in memory and processes involved in the use of analogies were discussed theoretically and explored in five experiments. In Experiment I oral protocols were used to examine the processes involved in solving a problem by analogy. In all experiments subjects who first read a story about a military problem and its solution tended to generate analogous solutions to a medical problem (Duncker's “radiation problem”), provided they were given a hint to use the story to help solve the problem. Transfer frequency was reduced when the problem presented in the military story was substantially disanalogous to the radiation problem, even though the solution illustrated in the story corresponded to an effective radiation solution (Experiment II). Subjects in Experiment III tended to generate analogous solutions to the radiation problem after providing their own solutions to the military problem. Subjects were able to retrieve the story from memory and use it to generate an analogous solution, even when the critical story had been memorized in the context of two distractor stories (Experiment IV). However, when no hint to consider the story was given, frequency of analogous solutions decreased markedly. This decrease in transfer occurred when the story analogy was presented in a recall task along with distractor stories (Experiment IV), when it was presented alone, and when it was presented in between two attempts to solve the problem (Experiment V). Component processes and strategic variations in analogical problem solving were discussed. Issues related to noticing analogies and accessing them in memory were also examined, as was the relationship of analogical reasoning to other cognitive tasks.


title: Compact and power-efficient 5 GHz full-duplex design utilizing the 180° ring hybrid coupler

abstract: This paper presents a compact and power-efficient 5 Ghz in-band full-duplex (FD) design in ANSYS HFSS using the 180-degree ring hybrid coupler. The proposed design achieves an excellent isolation of 57dB by taking advantage of destructive interference between two radiating antennas attached to the coupler, leading to a large reduction in self-interference. The design is passive and hence overcomes additional power requirement for adaptive channel estimation. In addition, it has a very workable physical size for the desired frequency of operation. The proposed FD design is therefore compact and power-efficient, which can be used in mobile devices, such as cell phones or tablet/phablet devices for a more flexible and greater contention of scarce RF resources.


title: Estimating Coloured 3D Face Models from Single Images: An Example Based Approach

abstract: In this paper we present a method to derive 3D shape and surface texture of a human face from a single image. The method draws on a general flexible 3D face model which is “learned” from examples of individual 3D-face data (Cyberware-scans). In an analysis-by-synthesis loop, the flexible model is matched to the novel face image.


Complete traumatic hemipelvectomy: Report of a case after 19 years of follow-up


title: When the brain plays music: auditory–motor interactions in music perception and production

abstract: Music performance is both a natural human activity, present in all societies, and one of the most complex and demanding cognitive challenges that the human mind can undertake. Unlike most other sensory-motor activities, music performance requires precise timing of several hierarchically organized actions, as well as precise control over pitch interval production, implemented through diverse effectors according to the instrument involved. We review the cognitive neuroscience literature of both motor and auditory domains, highlighting the value of studying interactions between these systems in a musical context, and propose some ideas concerning the role of the premotor cortex in integration of higher order features of music with appropriately timed and organized actions.


title: M-Rated Video Games and Aggressive or Problem Behavior Among Young Adolescents

abstract: This research examined the potential relationship between adolescent problem behaviors and amount of time spent with violent electronic games. Survey data were collected from 1,254 7th and 8th grade students in two states. A “dose” of exposure to Mature-rated games was calculated using Entertainment Software Rating Board ratings of titles children reported playing “a lot in the past six months,” and average days per week of video game play. Analyses were conducted using simultaneous logistic regression for binary outcome variables, and simultaneous multiple linear regression for continuous outcome variables, controlling for a series of potential confounders. M-rated game dose predicted greater risk for bullying (p < .01) and physical fights (p < .001), but not for delinquent behaviors or being a victim of bullies. When analyzed separately, these associations became weaker for boys and stronger for girls.


title: ADVANCES IN IMAGE PROCESSING FOR DETECTION OF PLANT DISEASES

abstract: The studies of plant trait/disease refer to the studies of visually observable patterns of a particular plant. Nowadays crops face many traits/diseases. Damage of the insect is one of the major trait/disease. Insecticides are not always proved efficient because insecticides may be toxic to some kind of birds. It also damages natural animal food chains. A common practice for plant scientists is to estimate the damage of plant (leaf, stem) because of disease by an eye on a scale based on percentage of affected area. It results in subjectivity and low throughput. This paper provides a advances in various methods used to study plant diseases/traits using image processing. The methods studied are for increasing throughput & reducing subjectiveness arising from human experts in detecting the plant diseases.


title: Event Pattern Matching over Graph Streams

abstract: A graph is a fundamental and general data structure underlying all data applications. Many applications today call for the management and query capabilities directly on graphs. Real time graph streams, as seen in road networks, social and communication networks, and web requests, are such applications. Event pattern matching requires the awareness of graph structures, which is different from traditional complex event processing. It also requires a focus on the dynamicity of the graph, time order constraints in patterns, and online query processing, which deviates significantly from previous work on subgraph matching as well. We study the semantics and efficient online algorithms for this important and intriguing problem, and evaluate our approaches with extensive experiments over real world datasets in four different domains.


title: Deconvolution of astronomical images using the multiscale maximum entropy method

abstract: Following the ideas of Bontekoe et al. who noticed that the classical Maximum Entropy Method (MEM) had diculties to eciently restore high and low spatial frequency structure in an image at the same time, we use the wavelet transform, a mathematical tool to decompose a signal into dierent frequency bands. We introduce the concept of multi-scale entropy of an image, leading to a better restoration at all spatial frequencies. This deconvolution method is flux conservative and the use of a multiresolution support solves the problem of MEM to choose the parameter, i.e. the relative weight between the goodness-of-t and the entropy. We show that our algorithm is ecient too for ltering astronomical images. A range of practical examples illustrate this approach.


title: Automatic facial expression recognition on a single 3D face by exploring shape deformation

abstract: Facial expression recognition has many applications in multimedia processing and the development of 3D data acquisition techniques makes it possible to identify expressions using 3D shape information. In this paper, we propose an automatic facial expression recognition approach based on a single 3D face. The shape of an expressional 3D face is approximated as the sum of two parts, a basic facial shape component (BFSC) and an expressional shape component (ESC). The BFSC represents the basic face structure and neutral-style shape and the ESC contains shape changes caused by facial expressions. To separate the BFSC and ESC, our method firstly builds a reference face for each input 3D non-neutral face by a learning method, which well represents the basic facial shape. Then, based on the BFSC and the original expressional face, a facial expression descriptor is designed. The surface depth changes are considered in the descriptor. Finally, the descriptor is input into an SVM to recognize the expression. Unlike previous methods which recognize a facial expression with the help of manually labeled key points and/or a neutral face, our method works on a single 3D face without any manual assistance. Extensive experiments are carried out on the BU-3DFE database and comparisons with existing methods are conducted. The experimental results show the effectiveness of our method.


A Context-aware Natural Language Generation Dataset for Dialogue Systems


title: The FDA-approved natural product dihydroergocristine reduces the production of the Alzheimer’s disease amyloid-β peptides

abstract: Known gamma-secretase inhibitors or modulators display an undesirable pharmacokinetic profile and toxicity and have therefore not been successful in clinical trials for Alzheimer's disease (AD). So far, no compounds from natural products have been identified as direct inhibitors of gamma-secretase. To search for bioactive molecules that can reduce the amount of amyloid-beta peptides (A beta) and that have better pharmacokinetics and an improved safety profile, we completed a screen of similar to 400 natural products by using cell-based and cell-free gamma-secretase activity assays. We identified dihydroergocristine (DHEC), a component of an FDA-(Food and Drug Administration)-approved drug, to be a direct inhibitor of gamma-secretase. Micromolar concentrations of DHEC substantially reduced A beta levels in different cell types, including a cell line derived from an AD patient. Structure-activity relationship studies implied that the key moiety for inhibiting gamma-secretase is the cyclized tripeptide moiety of DHEC. A Surface Plasmon Resonance assay showed that DHEC binds directly to gamma-secretase and Nicastrin, with equilibrium dissociation constants (K-d) of 25.7 nM and 9.8 mu M, respectively. This study offers DHEC not only as a new chemical moiety for selectively modulating the activity of gamma-secretase but also a candidate for drug repositioning in Alzheimer's disease.


title: Applications of transcranial direct current stimulation in children and pediatrics

abstract: Transcranial direct current stimulation (tDCS) is a neuromodulatory noninvasive brain stimulation tool with potential to increase or reduce regional and remote cortical excitability. Numerous studies have shown the ability of this technique to induce neuroplasticity and to modulate cognition and behavior in adults. Clinical studies have also demonstrated the ability of tDCS to induce therapeutic effects in several central nervous system disorders. However, knowledge about its ability to modulate brain functions in children or induce clinical improvements in pediatrics is limited. The objective of this review is to describe relevant data of some recent studies that may help to understand the potential of this technique in children with specific regard to effective and safe treatment of different developmental disorders in pediatrics. Overall, the results show that standard protocols of tDCS are well tolerated by children and have promising clinical effects. Nevertheless, treatment effects seem to be partially heterogeneous, and a case of a seizure in a child with previous history of infantile spasms and diagnosed epilepsy treated with tDCS for spasticity was reported. Further research is needed to determine safety criteria for tDCS use in children and to elucidate the particular neurophysiological changes induced by this neuromodulatory technique when it is applied in the developing brain.


title: Effects of LO Phase and Amplitude Imbalances and Phase Noise on $M$-QAM Transceiver Performance

abstract: This paper presents a rigorous analytical model for analyzing the effects of local oscillator output imperfections such as phase/amplitude imbalances and phase noise on M-ary quadrature amplitude modulation (M-QAM) transceiver performances. A closed-form expression of the error vector magnitude (EVM) and an analytic expression of the symbol error rate (SER) are derived considering a single carrier, linear transceiver link with additive white Gaussian noise channel. The proposed analytical model achieves a good agreement with the simulation results based on Monte-Carlo method. The proposed QAM imperfection analysis model provides an efficient means for system and circuit designers to analyze the wireless transceiver performances and properly trade-off and specify the transceiver block specifications.


title: Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

abstract: Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* - "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison.


title: Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information

abstract: Hesitant fuzzy set (HFS), which allows the membership degree of an element to a set represented by several possible values, is considered as a powerful tool to express uncertain information in the process of multi-attribute decision making (MADM) problems. In this paper, we develop a novel approach based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and the maximizing deviation method for solving MADM problems, in which the evaluation information provided by the decision maker is expressed in hesitant fuzzy elements and the information about attribute weights is incomplete. There are two key issues being addressed in this approach. The first one is to establish an optimization model based on the maximizing deviation method, which can be used to determine the attribute weights. According to the idea of the TOPSIS of Hwang and Yoon [1], the second one is to calculate the relative closeness coefficient of each alternative to the hesitant positive-ideal solution, based on which the considered alternatives are ranked and then the most desirable one is selected. An energy policy selection problem is used to illustrate the detailed implementation process of the proposed approach, and demonstrate its validity and applicability. Finally, the extended results in interval-valued hesitant fuzzy situations are also pointed out.


title: End-to-end crowd counting via joint learning local and global count

abstract: Crowd counting is a very challenging task in crowded scenes due to heavy occlusions, appearance variations and perspective distortions. Current crowd counting methods typically operate on an image patch level with overlaps, then sum over the patches to get the final count. In this paper, we propose an end-to-end convolutional neural network (CNN) architecture that takes a whole image as its input and directly outputs the counting result. While making use of sharing computations over overlapping regions, our method takes advantages of contextual information when predicting both local and global count. In particular, we first feed the image to a pre-trained CNN to get a set of high level features. Then the features are mapped to local counting numbers using recurrent network layers with memory cells. We perform the experiments on several challenging crowd counting datasets, which achieve the state-of-the-art results and demonstrate the effectiveness of our method.


title: Detection of and compensation for shadows in colored urban aerial images

abstract: A new method for shadow detection in colored urban aerial images is proposed. First, a new imaging model of shadows is presented, which indicates that hue values of shadowed image areas are larger than those of these areas non-shadowed. Based on this model, a thresholding technique is employed to detect shadowed areas. After detection, the Retinex technique is applied to shadowed and non-shadowed areas individually to compensate for shadows. Experiment results show the effectiveness of the proposed method.


title: Evolutionary Cost-sensitive Extreme Learning Machine and Subspace Extension.

abstract: Conventional extreme learning machines solve a Moore-Penrose generalized inverse of hidden layer activated matrix and analytically determine the output weights to achieve generalized performance, by assuming the same loss from different types of misclassification. The assumption may not hold in cost-sensitive recognition tasks, such as face recognition based access control system, where misclassifying a stranger as a family member and allowed to enter the house may result in more serious disaster than misclassifying a family member as a stranger and not allowed to enter. Though recent cost-sensitive learning can reduce the total loss with a given cost matrix that quantifies how severe one type of mistake against another type of mistake, in many realistic cases the cost matrix is unknown to users. Motivated by these concerns, this paper proposes an evolutionary cost-sensitive extreme learning machine (ECSELM), with the following merits: 1) to our best knowledge, it is the first proposal of cost-sensitive ELM; 2) it well addresses the open issue of how to define the cost matrix in cost-sensitive learning tasks; 3) an evolutionary backtracking search algorithm is induced for adaptive cost matrix optimization. Extensively, an ECSLDA method is generalized by coupling with cost-sensitive subspace learning. Experiments in a variety of cost-sensitive tasks well demonstrate the efficiency and effectiveness of the proposed approaches, specifically, 5%~10% improvements in classification are obtained on several datasets compared with ELMs; the computational efficiency is also 10 times faster than cost-sensitive subspace learning methods.


title: Interactive and automated application of virtual microscopy

abstract: Virtual microscopy can be applied in an interactive and an automated manner. Interactive application is performed in close association to conventional microscopy. It includes image standardization suitable to the performance of an individual pathologist such as image colorization, white color balance, or individual adjusted brightness. The steering commands have to include selection of wanted magnification, easy navigation, notification, and simple measurements (distances, areas). The display of the histological image should be adjusted to the physical limits of the human eye, which are determined by a view angle of approximately 35 seconds. A more sophisticated performance should include acoustic commands that replace the corresponding visual commands. Automated virtual microscopy includes so-called microscopy assistants which can be defined similar to the developed assistants in computer based editing systems (Microsoft Word, etc.). These include an automated image standardization and correction algorithms that excludes images of poor quality (for example uni-colored or out-of-focus images), an automated selection of the most appropriate field of view, an automated selection of the best magnification, and finally proposals of the most probable diagnosis. A quality control of the final diagnosis, and feedback to the laboratory determine the proposed system. The already developed tools of such a system are described in detail, as well as the results of first trials. In order to enhance the speed of such a system, and to allow further user-independent development a distributed implementation probably based upon Grid technology seems to be appropriate. The advantages of such a system as well as the present pathology environment and its expectations will be discussed in detail.


title: Record Linkage: Current Practice and Future Directions

abstract: Record linkage is the task of quickly and accurately identifying records corresponding to the same entity from one or more data sources. Record linkage is also known as data cleaning, entity reconciliation or identification and the merge/purge problem. This paper presents the “standard” probabilistic record linkage model and the associated algorithm. Recent work in information retrieval, federated database systems and data mining have proposed alternatives to key components of the standard algorithm. The impact of these alternatives on the standard approach are assessed. The key question is whether and how these new alternatives are better in terms of time, accuracy and degree of automation for a particular record linkage application.


title: Design of 40–108-GHz Low-Power and High-Speed CMOS Up-/Down-Conversion Ring Mixers for Multistandard MMW Radio Applications

abstract: In this paper, a pair of broadband, low-LO-power, low-dc-power, and high-speed up/down-conversion ring mixers are presented for multistandard millimeter-wave (MMW) radio applications. By employing a weak inversion biasing technique, the ring mixer can operate at a low LO drive level and low dc power while maintaining reasonable conversion gain performance. In addition, an IF transimpedance amplifier (TIA) buffer and wideband RF design are introduced to increase the operation speed of the mixer for MMW wireless Gigabit transmission. Using a 90-nm CMOS low-power process, the up-/down-conversion ring mixers are designed and fabricated based on the presented topology. The down-conversion ring mixer and up-conversion ring mixer exhibit flat measured conversion gain of -1 ± 2 dB and 0 ± 2 dB and dB from 40 to 110 GHz and 40 to 108 GHz, respectively. After biasing the transistors of the ring mixer core at weak inversion region, the presented down-conversion and up-conversion ring mixers can operate at low LO drive power, -2 and 0 dBm, respectively, even up to 100 GHz. For MMW wireless gigabit communication, gigabit binary phase-shift keying modulation signal test is successfully performed through a direct-conversion system in this work. The presented ring mixers are suitable for 60-GHz wireless personal area network, -E-band (71-76 GHz, 81-86 GHz, and 92-95 GHz) wireless fiber, and 77-GHz anti-collision radar applications.


title: A Double-Sided Parallel-Strip Line Push–Pull Oscillator

abstract: A novel double-sided parallel-strip line (DSPSL) push-pull oscillator using two identical sub-oscillators on the opposite sides of a dielectric substrate is proposed. The two sub-oscillators, sharing a common DSPSL resonator and common ground in the middle of the substrate, generate out-of-phase fundamental signals and in-phase second harmonics. At the common DSPSL output, the second harmonics are cancelled out while the fundamental signals are well combined. By this design, an additional combiner at the output, as required by the conventional push-pull circuits, is not needed, which greatly reduces the circuit size and simplifies the design procedures of the proposed push-pull oscillator.


title: Low-Profile Dipole Antenna with Corner Cut Parasitic Patches for Bandwidth Enhancement

abstract: A broadband low-profile dipole antenna comprised of a planar radiating half-wavelength dipole element placed above four corner cut parasitic patches on a perfect electric conductor ground plane is presented in this paper. Linearly polarized radiations are generated from the excited dipole and corner cut parasitic patches. Cutting the corners of the parasitic patches significantly enhances the bandwidth of the antenna while maintaining the overall antenna structure at a low profile. These improvements in impedance bandwidth are analyzed and validated by full-wave electromagnetic simulations. The antenna design has overall dimensions of 120 mm x 120 mm x 16.3 mm (0.66λo x 0.66λo x 0.09λo at 1.66 GHz), has aλ10 dB impedance bandwidth of 1.465–1.885 GHz (25.3%), a broadside gain range of 8.47–9.12 dBi, and a radiation efficiency > 98% within the impedance bandwidth.


title: Emergency Management of Perforated Colon Cancers: How Aggressive Should We Be?

abstract: Background ::: Emergency treatment of perforated colon cancer has traditionally been linked with dismal outcomes due to the double jeopardy of a septic insult combined with a malignant disease, leaving unclear how aggressive emergency surgical procedures should be. We aimed to define short- and long-term outcomes in the current era of critical care support and oncologic advances, to provide updated data for decision making.


title: Maximum Power Point Tracking for PV system under partial shading condition via particle swarm optimization

abstract: Performance of Photovoltaic (PV) system is greatly dependent on the solar irradiation and operating temperature. Due to partial shading condition, the characteristics of a PV system considerably change and often exhibit several local maxima with one global maxima. Conventional Maximum Power Point Tracking (MPPT) techniques can easily be trapped at local maxima under partial shading. This significantly reduced the energy yield of the PV systems. In order to solve this problem, this paper proposes a Maximum Power Point tracking algorithm based on particle swarm optimization (PSO) that is capable of tracking global MPP under partial shaded conditions. The performance of proposed algorithm is evaluated by means of simulation in MATLAB Simulink. The proposed algorithm is applied to a grid connected PV system, in which a Boost (step up) DC-DC converter satisfactorily tracks the global peak.


title: Learning Universal Adversarial Perturbations with Generative Models

abstract: Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification. It was recently shown that given a dataset and classifier, there exists so called universal adversarial perturbations, a single perturbation that causes a misclassification when applied to any input. In this work, we introduce universal adversarial networks, a generative network that is capable of fooling a target classifier when it's generated output is added to a clean sample from a dataset. We show that this technique improves on known universal adversarial attacks.


title: 3D solid fin model construction from 2D shapes using non-uniform rational B-spline surfaces

abstract: A computer aided design (CAD) tool has been specifically developed for rapid and easy design of solid models for surfboard and sailboard fins. This tool simplifies the lofting of advanced fin cross-sectional foils, in this instance based upon the family of standard airfoil series set by the National Advisory Committee for Aeronautics (NACA), whilst retaining a basic parametric description at each cross-section. This paper describes the way in which non-uniform rational B-spline (NURBS) surfaces are created from 2D profile splines, and are then used to generate 3D geometrical surfaces of the fins, which can be imported directly into commercial software packages for finite element stress analysis (FEA) and computational fluid dynamics (CFD). Pressure distributions, lift and drag forces are determined from a CFD flow analysis for various fins designed with this tool, and the results suggest that the incorporation of advanced foils into surfboard fins could indeed lead to increased performance over fins foiled using current standard techniques. � 2006 Elsevier Ltd. All rights reserved.


title: RIVER FLOW FORECASTING USING ARTIFICIAL NEURAL NETWORKS

abstract: Abstract River flow forecasting is required to provide basic information on a wide range of problems related to the design and operation of river systems. The availability of extended records of rainfall and other climatic data, which could be used to obtain stream flow data, initiated the practice of rainfall-runoff modelling. While conceptual or physically-based models are of importance in the understanding of hydrological processes, there are many practical situations where the main concern is with making accurate predictions at specific locations. In such situation it is preferred to implement a simple “black box” (data-driven, or machine learning) model to identify a direct mapping between the inputs and outputs without detailed consideration of the internal structure of the physical process. Artificial neural networks (ANNs) is probably the most successful machine learning technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach understanding as to the nature of the phenomena. In this study the applicability of ANNs for downstream flow forecasting in the Apure river basin (Venezuela) was investigated. Two types of ANN architectures, namely multi-layer perceptron network (MLP) and a radial basis function network (RBF) were implemented. The performances of these networks were compared with a conceptual rainfall-runoff model and they were found to be slightly better for this river flow-forecasting problem.


title: A survey of neural network accelerators

abstract: Machine-learning techniques have recently been proved to be successful in various domains, especially in emerging commercial applications. As a set of machine-learning techniques, artificial neural networks (ANNs), requiring considerable amount of computation and memory, are one of the most popular algorithms and have been applied in a broad range of applications such as speech recognition, face identification, natural language processing, ect. Conventionally, as a straightforward way, conventional CPUs and GPUs are energy-inefficient due to their excessive effort for flexibility. According to the aforementioned situation, in recent years, many researchers have proposed a number of neural network accelerators to achieve high performance and low power consumption. Thus, the main purpose of this literature is to briefly review recent related works, as well as the DianNao-family accelerators. In summary, this review can serve as a reference for hardware researchers in the area of neural networks.


title: CoLT: Coalesced Large-Reach TLBs

abstract: Translation Look aside Buffers (TLBs) are critical to system performance, particularly as applications demand larger working sets and with the adoption of virtualization. Architectural support for super pages has previously been proposed to improve TLB performance. By allocating contiguous physical pages to contiguous virtual pages, the operating system (OS) constructs super pages which need just one TLB entry rather than the hundreds required for the constituent base pages. While this greatly reduces TLB misses, these gains are often offset by the implementation difficulties of generating and managing ample contiguity for super pages. We show, however, that basic OS memory allocation mechanisms such as buddy allocators and memory compaction naturally assign contiguous physical pages to contiguous virtual pages. Our real-system experiments show that while usually insufficient for super pages, these intermediate levels of contiguity exist under various system conditions and even under high load. In response, we propose Coalesced Large-Reach TLBs (CoLT), which leverage this intermediate contiguity to coalesce multiple virtual-to-physical page translations into single TLB entries. We show that CoLT implementations eliminate 40\% to 58\% of TLB misses on average, improving performance by 14\%. Overall, we demonstrate that the OS naturally generates page allocation contiguity. CoLT exploits this contiguity to eliminate TLB misses for next-generation, big-data applications with low-overhead implementations.


title: Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing

abstract: This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.


title: What Recommenders Recommend – An Analysis of Accuracy, Popularity, and Sales Diversity Effects

abstract: In academic studies, the evaluation of recommender system (RS) algorithms is often limited to offline experimental designs based on historical data sets and metrics from the fields of Machine Learning or Information Retrieval. In real-world settings, however, other business-oriented metrics such as click-through-rates, customer retention or effects on the sales spectrum might be the true evaluation criteria for RS effectiveness. In this paper, we compare different RS algorithms with respect to their tendency of focusing on certain parts of the product spectrum. Our first analysis on different data sets shows that some algorithms – while able to generate highly accurate predictions – concentrate their top 10 recommendations on a very small fraction of the product catalog or have a strong bias to recommending only relatively popular items than others. We see our work as a further step toward multiple-metric offline evaluation and to help service providers make better-informed decisions when looking for a recommendation strategy that is in line with the overall goals of the recommendation service.


title: Soft transition from probabilistic to possibilistic fuzzy clustering

abstract: In the fuzzy clustering literature, two main types of membership are usually considered: A relative type, termed probabilistic, and an absolute or possibilistic type, indicating the strength of the attribution to any cluster independent from the rest. There are works addressing the unification of the two schemes. Here, we focus on providing a model for the transition from one schema to the other, to exploit the dual information given by the two schemes, and to add flexibility for the interpretation of results. We apply an uncertainty model based on interval values to memberships in the clustering framework, obtaining a framework that we term graded possibility. We outline a basic example of graded possibilistic clustering algorithm and add some practical remarks about its implementation. The experimental demonstrations presented highlight the different properties attainable through appropriate implementation of a suitable graded possibilistic model. An interesting application is found in automated segmentation of diagnostic medical images, where the model provides an interactive visualization tool for this task


title: Modelling framework of pedestrian-footbridge interaction in vertical direction

abstract: This study presents a modelling framework of human-structure interaction in the vertical direction, which integrates the three following key issues: crowd dynamics, pedestrian-structure interaction (PSI) and inter-subject and intra-subject variability of pedestrian walking loads. The framework comprises two main models: a microscopic model of crowd dynamics and a coupled dynamic model of the PSI. The latter is composed of three sub-models: a SDOF system having the dynamic properties of the empty structure, a SDOF system for each pedestrian and a stochastic force model. Each pedestrian SDOF moves along the footbridge following the trajectory and at the velocity simulated by the crowd model and is accompanied by a stochastic walking force time-history that accounts for inter- and intra-subject variability. Performance of the suggested modelling framework is studied through simulations of the vibration responses of four virtual footbridges due to different traffic scenarios.


title: Auditory–visual stimulus pairing enhances perceptual learning in a songbird

abstract: In many oscine birds, song learning is affected by social variables, for example the behaviour of a tutor. This implies that both auditory and visual perceptual systems should be involved in the acquisition process. To examine whether and how particular visual stimuli can affect song acquisition, we tested the impact of a tutoring design in which the presentation of auditory stimuli (i.e. species-specific master songs) was paired with a well-defined nonauditory stimulus (i.e. stroboscope light flashes: Strobe regime). The subjects were male hand-reared nightingales, Luscinia megarhynchos . For controls, males were exposed to tutoring without a light stimulus (Control regime). The males’ singing recorded 9 months later showed that the Strobe regime had enhanced the acquisition of song patterns. During this treatment birds had acquired more songs than during the Control regime; the observed increase in repertoire size was from 20 to 30% in most cases. Furthermore, the copy quality of imitations acquired during the Strobe regime was better than that of imitations developed from the Control regime, and this was due to a significant increase in the number of ‘perfect’ song copies. We conclude that these effects were mediated by an intrinsic component (e.g. attention or arousal) which specifically responded to the Strobe regime. Our findings also show that mechanisms of song learning are well prepared to process information from cross-modal perception. Thus, more detailed enquiries into stimulus complexes that are usually referred to as social variables are promising.


title: Defective Lipolysis Persists in Hearts of Rats With Heymann Nephritis in the Absence of Nephrotic Plasma

abstract: Catabolism of trig lyceride-rich lipoproteins, including chylomicrons (CM), is reduced in the nephrotic syndrome. It has been suggested that hyperlipidemia per se might lead to reduced CM catabolism by saturating catabolic sites. Evidence also implicates disordered high-density lipoprotein function as reducing the activity of lipoprotein lipase (LPL), the final effector of CM lipolysis. To establish whether CM lipolysis would be abnormal in the absence of either abnormal rat lipoproteins or hyperlipidemia, we measured CM lipolysis by isolated perfused hearts of rats with passive Heymann nephritis. We found that lipolysis was significantly reduced by 30% at 30 minutes (246 ± 40 μmol v 164 ± 10 μmol fatty acid released/hr, P P v 210.6 ± 25.9 μmol free fatty acid released/hr/g heart, P v 19.4 ± 11.7 μmol free fatty acid released/hr/heart, P


title: Tool-supported requirements prioritization: Comparing the AHP and CBRank methods

abstract: Requirements prioritization aims at identifying the most important requirements for a software system, a crucial step when planning for system releases and deciding which requirements to implement in each release. Several prioritization methods and supporting tools have been proposed so far. How to evaluate their properties, with the aim of supporting the selection of the most appropriate method for a specific project, is considered a relevant question. In this paper, we present an empirical study aiming at evaluating two state-of-the art tool-supported requirements prioritization methods, AHP and CBRank. We focus on three measures: the ease of use, the time-consumption and the accuracy. The experiment has been conducted with 23 experienced subjects on a set of 20 requirements from a real project. Results indicate that for the first two characteristics CBRank overcomes AHP, while for the accuracy AHP performs better than CBRank, even if the resulting ranks from the two methods are very similar. The majority of the users found CBRank the ''overall best'' method.


title: Metaphor corpus annotated for source - target domain mappings

abstract: Besides making our thoughts more vivid and filling our communication with richer imagery, metaphor also plays an important structural role in our cognition. Although there is a consensus in the linguistics and NLP research communities that the phenomenon of metaphor is not restricted to similarity-based extensions of meanings of isolated words, but rather involves reconceptualization of a whole area of experience (target domain) in terms of another (source domain), there still has been no proposal for a comprehensive procedure for annotation of cross-domain mappings. However, a corpus annotated for conceptual mappings could provide a new starting point for both linguistic and cognitive experiments. The annotation scheme we present in this paper is a step towards filling this gap. We test our procedure in an experimental setting involving multiple annotators and estimate their agreement on the task. The associated corpus annotated for source target domain mappings will be publicly available.


title: Smartphones An Emerging Tool for Social Scientists

abstract: Recent developments in mobile technologies have produced a new kind of device: a programmable mobile phone, the smartphone. In this article, the authors argue that the technological and social char...


title: Radiesse®: a novel rejuvenation treatment for the upper arms

abstract: Nonsurgical esthetic improvement of the upper arms is a desirable goal for many individuals. Radiesse(®) (calcium hydroxylapatite) is an effective dermal filler for a number of indications because of its volumizing effect and the ability to stimulate neocollagenesis. No studies have reported on its safety and effectiveness for the treatment of the upper arm. In a prospective, open-label study, 30 subjects seeking improvement in the esthetic appearance of their upper arms received injections with Radiesse(®) (1.5 mL/arm) at two separate visits, 1 month apart. Subjects returned for a follow-up visit 4 months after the second treatment. The primary endpoint was the degree of overall subject and evaluator (investigators and study nurses) satisfaction assessed using the 5-point Global Satisfaction Scale (ranging from "1" very dissatisfied to "5" very satisfied). Secondary endpoints included assessments of skin quality (flaccidity and volume distribution) using a new Visual Analog Scale for upper arms and overall assessment of treatment using the Global Aesthetic Improvement Scale. All (100%) of both subjects and evaluators were "satisfied" or "very satisfied" with treatment. The mean Global Satisfaction Score for investigators and study nurses was 4.60 and for subjects 4.53 (satisfied to very satisfied). Assessments of flaccidity and volume improved significantly compared with baseline at the post-treatment visit and also between visits. Compared with baseline, 77% of subjects were rated as considerably improved (good or great improvement) by the investigator and study nurse; 73% of subjects rated themselves as considerably improved and 43% of these rated a "great improvement." All stated they would repeat the treatment and recommend it to others. No adverse events were reported. Radiesse(®) is an effective minimally invasive treatment option for improving upper arm contours and was associated with a 100% satisfaction rate for both subjects and evaluators.


title: Improving UWB-Based Localization in IoT Scenarios with Statistical Models of Distance Error

abstract: Interest in the Internet of Things (IoT) is rapidly increasing, as the number of connected devices is exponentially growing. One of the application scenarios envisaged for IoT technologies involves indoor localization and context awareness. In this paper, we focus on a localization approach that relies on a particular type of communication technology, namely Ultra Wide Band (UWB). UWB technology is an attractive choice for indoor localization, owing to its high accuracy. Since localization algorithms typically rely on estimated inter-node distances, the goal of this paper is to evaluate the improvement brought by a simple (linear) statistical model of the distance error. On the basis of an extensive experimental measurement campaign, we propose a general analytical framework, based on a Least Square (LS) method, to derive a novel statistical model for the range estimation error between a pair of UWB nodes. The proposed statistical model is then applied to improve the performance of a few illustrative localization algorithms in various realistic scenarios. The obtained experimental results show that the use of the proposed statistical model improves the accuracy of the considered localization algorithms with a reduction of the localization error up to 66%.


title: Semantic audio-visual data fusion for automatic emotion recognition

abstract: The paper describes a novel technique for the recognition of emotions from multimodal data. We focus on the recognition of the six prototypic emotions. The results from the facial expression recognition and from the emotion recognition from speech are combined using a bi-modal multimodal semantic data fusion model that determines the most probable emotion of the subject. Two types of models based on geometric face features for facial expression recognition are being used, depending on the presence or absence of speech. In our approach we define an algorithm that is robust to changes of face shape that occur during regular speech. The influence of phoneme generation on the face shape during speech is removed by using features that are only related to the eyes and the eyebrows. The paper includes results from testing the presented models.


title: Randomization in privacy preserving data mining

abstract: Suppose there are many clients, each having some personal information, and one server, which is interested only in aggregate, statistically significant, properties of this information. The clients can protect privacy of their data by perturbing it with a randomization algorithm and then submitting the randomized version. The randomization algorithm is chosen so that aggregate properties of the data can be recovered with sufficient precision, while individual entries are significantly distorted. How much distortion is needed to protect privacy can be determined using a privacy measure. Several possible privacy measures are known; finding the best measure is an open question. This paper presents some methods and results in randomization for numerical and categorical data, and discusses the issue of measuring privacy.


title: On using norm estimators for event-triggered control with dynamic output feedback

abstract: For feedback stabilization of a control system using dynamic output feedback, we consider the problem of finding two different sequences of time instants at which the sampled outputs (respectively, control inputs) must be sent to the controller (resp. the plant). Instead of static inequalities, the states of so-called norm estimators are used to determine sampling instants. Using the tools from Lyapunov theory for hybrid systems and stability of cascaded nonlinear systems, it is shown that the closed loop system is globally asymptotically stable. Additional assumptions are required on the controller and system dynamics to guarantee that the proposed sampling routines do not lead to an accumulation of sampling times over a finite interval.


title: Online Learning with Regularized Kernel for One-class Classification

abstract: This paper presents an online learning with regularized kernel based one-class extreme learning machine (ELM) classifier and is referred as online RK-OC-ELM. The baseline kernel hyperplane model considers whole data in a single chunk with regularized ELM approach for offline learning in case of one-class classification (OCC). Further, the basic hyper plane model is adapted in an online fashion from stream of training samples in this paper. Two frameworks viz., boundary and reconstruction are presented to detect the target class in online RKOC-ELM. Boundary framework based one-class classifier consists of single node output architecture and classifier endeavors to approximate all data to any real number. However, one-class classifier based on reconstruction framework is an autoencoder architecture, where output nodes are identical to input nodes and classifier endeavor to reconstruct input layer at the output layer. Both these frameworks employ regularized kernel ELM based online learning and consistency based model selection has been employed to select learning algorithm parameters. The performance of online RK-OC-ELM has been evaluated on standard benchmark datasets as well as on artificial datasets and the results are compared with existing state-of-the art one-class classifiers. The results indicate that the online learning one-class classifier is slightly better or same as batch learning based approaches. As, base classifier used for the proposed classifiers are based on the ELM, hence, proposed classifiers would also inherit the benefit of the base classifier i.e. it will perform faster computation compared to traditional autoencoder based one-class classifier.


title: Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering

abstract: While question answering (QA) with neural network, i.e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge for developing and evaluating neural QA system. To alleviate this problem, we propose a large scale human annotated real-world QA dataset WebQA with more than 42k questions and 556k evidences. As existing neural QA methods resolve QA either as sequence generation or classification/ranking problem, they face challenges of expensive softmax computation, unseen answers handling or separate candidate answer generation component. In this work, we cast neural QA as a sequence labeling problem and propose an end-to-end sequence labeling model, which overcomes all the above challenges. Experimental results on WebQA show that our model outperforms the baselines significantly with an F1 score of 74.69% with word-based input, and the performance drops only 3.72 F1 points with more challenging character-based input.


title: Characterizing hypervisor vulnerabilities in cloud computing servers

abstract: The rise of the Cloud Computing paradigm has led to security concerns, taking into account that resources are shared and mediated by a Hypervisor which may be targeted by rogue guest VMs and remote attackers. In order to better define the threats to which a cloud server's Hypervisor is exposed, we conducted a thorough analysis of the codebase of two popular open-source Hypervisors, Xen and KVM, followed by an extensive study of the vulnerability reports associated with them. Based on our findings, we propose a characterization of Hypervisor Vulnerabilities comprised of three dimensions: the trigger source (i.e. where the attacker is located), the attack vector (i.e. the Hypervisor functionality that enables the security breach), and the attack target (i.e. the runtime domain that is compromised). This can be used to understand potential paths different attacks can take, and which vulnerabilities enable them. Moreover, most common paths can be discovered to learn where the defenses should be focused, or conversely, least common paths can be used to find yet-unexplored ways attackers may use to get into the system.


title: Optimal design of a compact 99.3% efficient single-phase PFC rectifier

abstract: Due to rising energy costs the efficiency of power electronics converter systems is of higher and higher importance, especially for applications with continuous operation as e.g. server power supplies. For designing such supplies numerous parameters like the switching frequency and the characteristic values and the geometry of magnetic components must be determined. In this paper, an optimisation procedure, which automatically determines the parameter values of a single-phase bridgeless PFC rectifier for maximum efficiency is presented. There, continuous and discontinuous operation mode, as well as a concept for magnetic integration of the CM and DM filter inductors is included. For verifying the considerations, a prototype with 99.3% efficiency and a power density of 1.35kW/dm3 is presented.


title: Design, modeling, and control of pneumatic artificial muscles with integrated soft sensing

abstract: Presented are techniques for designing, modeling, and control of reliable pneumatic artificial muscle actuators with integrated low profile sensors for position feedback. The sensor is fabricated through a three-dimensional manufacturing process based on a modified lathe approach for controlling viscous and viscoelastic materials as well as on direct writing of liquid metal. Next, a new precision pneumatic muscle design and its integration with the sensor is illustrated. A theoretical model and experimental characterization of the muscle-sensor package are presented with high correlation and repeatability. Finally, a position feedback sliding mode controller is implemented with a position error of <0.9% of maximum muscle contraction.


title: Approaches to Identifying Synthetic Lethal Interactions in Cancer

abstract: © 2015, Yale Journal of Biology and Medicine Inc. All rights reserved. Targeting synthetic lethal interactions is a promising new therapeutic approach to exploit specific changes that occur within cancer cells. Multiple approaches to investigate these interactions have been developed and successfully implemented, including chemical, siRNA, shRNA, and CRISPR library screens. Genome-wide computational approaches, such as DAISY, also have been successful in predicting synthetic lethal interactions from both cancer cell lines and patient samples. Each approach has its advantages and disadvantages that need to be considered depending on the cancer type and its molecular alterations. This review discusses these approaches and examines case studies that highlight their use.


Curanderismo: A Picture of Mexican-American Folk Healing


title: Audio Based Bird Species Identification using Deep Learning Techniques

abstract: Reference EPFL-CONF-229232 URL: http://ceur-ws.org/Vol-1609/16090547.pdf Record created on 2017-06-21, modified on 2017-07-11


title: Mining fuzzy association rules

abstract: In his paper, we introduce a novel technique, called F-APACS, for mining jkzy association rules. &istlng algorithms involve discretizing the domains of quantitative attrilmtes into intervals so as to discover quantitative association rules. i%ese intervals may not be concise and meaning@ enough for human experts to easily obtain nontrivial knowledge from those rules discovered. Instead of using intervals, F-APACS employs linguistic terms to represent the revealed regularities and exceptions. The linguistic representation is especially usefil when those rules discovered are presented to human experts for examination. The definition of linguistic terms is based onset theory and hence we call the rides having these terms fuzzy association rules. The use of fq techniques makes F-APACS resilient to noises such as inaccuracies in physical measurements of real-life entities and missing values in the databases. Furthermore, F-APACS employs adjusted difference analysis which has the advantage that it does not require any user-supplied thresholds which are often hard to determine. The fact that F-APACS is able to mine fiuy association rules which utilize linguistic representation and that it uses an objective yet meanhg@ confidence measure to determine the interestingness of a rule makes it vety effective at the discovery of rules from a real-life transactional database of a PBX system provided by a telecommunication corporation


title: Porous Cu3Sn Formation in Cu-Sn IMC-Based Micro-Joints

abstract: Microbumps are widely used to interconnect stacked chips with through-Si-vias (TSVs) in vertical direction to enable 3D IC integration. Several recent studies reported [1, 2] that a unique porous void formation was found in Cu-Sn microbump particularly in Cu3Sn layer. This porous Cu3Sn formation was only reported in microbump which has constrained joint thickness (


title: User mobility from the view of cellular data networks

abstract: Understanding the user mobility is essential to resource optimization and algorithm evaluation in mobile networks, such as network planning, content distribution, and evaluation of hand-over mechanisms. Existing human mobility models focus on extracting mobility patterns from Call Detail Records (CDRs) or WiFi traces. While the former only captures movements during phone calls, the latter does not provide direct answers to mobility of cellular network users in a large scale. In this paper, we take the first step to investigate if the mobility properties derived from cellular data traffic is different from the previous findings using other data source, especially the commonly used CDR based approach. We present a comprehensive characterization of the mobility patterns from the cellular data networks' perspective, using a set of systematic methods. We find that the data network records can provide finer granularity of location and movement information. Three different temporal movement patterns are identified. Furthermore, we propose a new method for predicting future application usage given the mobility patterns and show promising results.