title: Hand sign language recognition for Bangla alphabet using Support Vector Machine

abstract: The sign language considered as the main language for deaf and dumb people. So, a translator is needed when a normal person wants to talk with a deaf or dumb person. In this paper, we present a framework for recognizing Bangla Sign Language (BSL) using Support Vector Machine. The Bangla hand sign alphabets for both vowels and consonants have been used to train and test the recognition system. Bangla sign alphabets are recognized by analyzing its shape and comparing its features that differentiates each sign. In proposed system, hand signs are first converted to HSV color space from RGB image. Then Gabor filters are used to acquire desired hand sign features. Since feature vector obtained using Gabor filter is in a high dimension, to reduce the dimensionality a nonlinear dimensionality reduction technique that is Kernel PCA has been used. Lastly, Support Vector Machine (SVM) is employed for classification of candidate features. The experimental results show that our proposed method outperforms the existing work on Bengali hand sign recognition.


title: Image colorization using sparse representation

abstract: Image colorization is the task to color a grayscale image with limited color cues. In this work, we present a novel method to perform image colorization using sparse representation. Our method first trains an over-complete dictionary in YUV color space. Then taking a grayscale image and a small subset of color pixels as inputs, our method colorizes overlapping image patches via sparse representation; it is achieved by seeking sparse representations of patches that are consistent with both the grayscale image and the color pixels. After that, we aggregate the colorized patches with weights to get an intermediate result. This process iterates until the image is properly colorized. Experimental results show that our method leads to high-quality colorizations with small number of given color pixels. To demonstrate one of the applications of the proposed method, we apply it to transfer the color of one image onto another to obtain a visually pleasing image.


title: Localising speech, footsteps and other sounds using resource-constrained devices

abstract: While a number of acoustic localisation systems have been proposed over the last few decades, these have typically either relied on expensive dedicated microphone arrays and workstation-class processing, or have been developed to detect a very specific type of sound in a particular scenario. However, as people live and work indoors, they generate a wide variety of sounds as they interact and move about. These human-generated sounds can be used to infer the positions of people, without requiring them to wear trackable tags. In this paper, we take a practical yet general approach to localising a number of human-generated sounds. Drawing from signal processing literature, we identify methods for resource-constrained devices in a sensor network to detect, classify and locate acoustic events such as speech, footsteps and objects being placed onto tables. We evaluate the classification and time-of-arrival estimation algorithms using a data set of human-generated sounds we captured with sensor nodes in a controlled setting. We show that despite the variety and complexity of the sounds, their localisation is feasible for sensor networks, with typical accuracies of a half metre or better. We specifically discuss the processing and networking considerations, and explore the performance trade-offs which can be made to further conserve resources.


title: Fingerprint Enhancement Using Unsupervised Hierarchical Feature Learning

abstract: We present an approach for learning low- and high-level fingerprint structures in an unsupervised manner, which we use for enhancement of fingerprint images and estimation of orientation fields, frequency images, and region masks. We incorporate the use of a convolutional deep belief network to learn features from greyscale, clean fingerprint images. We also show that reconstruction performed by the learnt network works as a suitable enhancement of the fingerprint, and hierarchical probabilistic inference is able to estimate overall fingerprint structures as well. Our approach performs better than Gabor-based enhancement and short time Fourier transform-assisted enhancement on images it was trained on. We further use information from the learnt features in first layer, which are short and oriented ridge structures, to extract the orientation field, frequency image, and region mask of input fingerprints.


title: DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks

abstract: Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations – small sensor size, compact lenses and the lack of specific hardware, – impede them to achieve the quality results of DSLR cameras. In this work we present an end-to-end deep learning approach that bridges this gap by translating ordinary photos into DSLR-quality images. We propose learning the translation function using a residual convolutional neural network that improves both color rendition and image sharpness. Since the standard mean squared loss is not well suited for measuring perceptual image quality, we introduce a composite perceptual error function that combines content, color and texture losses. The first two losses are defined analytically, while the texture loss is learned in an adversarial fashion. We also present DPED, a large-scale dataset that consists of real photos captured from three different phones and one high-end reflex camera. Our quantitative and qualitative assessments reveal that the enhanced image quality is comparable to that of DSLR-taken photos, while the methodology is generalized to any type of digital camera.


title: Improved Statistical Machine Translation Using Monolingually-Derived Paraphrases

abstract: Untranslated words still constitute a major problem for Statistical Machine Translation (SMT), and current SMT systems are limited by the quantity of parallel training texts. Augmenting the training data with paraphrases generated by pivoting through other languages alleviates this problem, especially for the so-called "low density" languages. But pivoting requires additional parallel texts. We address this problem by deriving paraphrases monolingually, using distributional semantic similarity measures, thus providing access to larger training resources, such as comparable and unrelated monolingual corpora. We present what is to our knowledge the first successful integration of a collocational approach to untranslated words with an end-to-end, state of the art SMT system demonstrating significant translation improvements in a low-resource setting.


A COMPARATIVE STUDY OF FULL ADDER USING STATIC CMOS LOGIC STYLE


title: Occupational health guidelines for the management of low back pain at work: evidence review.

abstract: There is increasing demand for evidence-based health care. Back pain is one of the most common and difficult occupational health problems, but there has been no readily available evidence base or guidance on management. There are well-established clinical guidelines for the management of low back pain, but these provide limited guidance on the occupational aspects. Occupational Health Guidelines for the Management of Low Back Pain at Work were launched by the Faculty of Occupational Medicine in March 2000. These are the first national occupational health guidelines in the UK and, as far as we are aware, the first truly evidence-linked occupational health guidelines for back pain in the world. They were based on an extensive, systematic review of the scientific literature predominantly from occupational settings or concerning occupational outcomes. The full evidence review is on the Faculty web site (www.facoccmed.ac.uk), but an abridged version is presented here to aid its dissemination.


title: Oxide nanoparticles in an Al-alloyed oxide dispersion strengthened steel: crystallographic structure and interface with ferrite matrix

abstract: AbstractOxide nanoparticles are quintessential for ensuring the extraordinary properties of oxide dispersion strengthened (ODS) steels. In this study, the crystallographic structure of oxide nanoparticles, and their interface with the ferritic steel matrix in an Al-alloyed ODS steel, i.e. PM2000, were systematically investigated by high-resolution transmission electron microscopy. The majority of oxide nanoparticles were identified to be orthorhombic YAlO3. During hot consolidation and extrusion, they develop a coherent interface and a near cuboid-on-cube orientation relationship with the ferrite matrix in the material. After annealing at 1200 °C for 1 h, however, the orientation relationship between the oxide nanoparticles and the matrix becomes arbitrary, and their interface mostly incoherent. Annealing at 1300 °C leads to considerable coarsening of oxide nanoparticles, and a new orientation relationship of pseudo-cube-on-cube between oxide nanoparticles and ferrite matrix develops. The reason for the d...


title: Cascaded Feature Network for Semantic Segmentation of RGB-D Images

abstract: Fully convolutional network (FCN) has been successfully applied in semantic segmentation of scenes represented with RGB images. Images augmented with depth channel provide more understanding of the geometric information of the scene in the image. The question is how to best exploit this additional information to improve the segmentation performance.,,In this paper, we present a neural network with multiple branches for segmenting RGB-D images. Our approach is to use the available depth to split the image into layers with common visual characteristic of objects/scenes, or common “scene-resolution”. We introduce context-aware receptive field (CaRF) which provides a better control on the relevant contextual information of the learned features. Equipped with CaRF, each branch of the network semantically segments relevant similar scene-resolution, leading to a more focused domain which is easier to learn. Furthermore, our network is cascaded with features from one branch augmenting the features of adjacent branch. We show that such cascading of features enriches the contextual information of each branch and enhances the overall performance. The accuracy that our network achieves outperforms the stateof-the-art methods on two public datasets.


Neural Networks Incorporating Dictionaries for Chinese Word Segmentation


title: Interactive Attention Networks for Aspect-Level Sentiment Classification

abstract: Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling their contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.


title: Attention with Intention for a Neural Network Conversation Model

abstract: In a conversation or a dialogue process, attention and intention play intrinsic roles. This paper proposes a neural network based approach that models the attention and intention processes. It essentially consists of three recurrent networks. The encoder network is a word-level model representing source side sentences. The intention network is a recurrent network that models the dynamics of the intention process. The decoder network is a recurrent network produces responses to the input from the source side. It is a language model that is dependent on the intention and has an attention mechanism to attend to particular source side words, when predicting a symbol in the response. The model is trained end-to-end without labeling data. Experiments show that this model generates natural responses to user inputs.


title: An expert system for detection of breast cancer based on association rules and neural network

abstract: This paper presents an automatic diagnosis system for detecting breast cancer based on association rules (AR) and neural network (NN). In this study, AR is used for reducing the dimension of breast cancer database and NN is used for intelligent classification. The proposed AR+NN system performance is compared with NN model. The dimension of input feature space is reduced from nine to four by using AR. In test stage, 3-fold cross validation method was applied to the Wisconsin breast cancer database to evaluate the proposed system performances. The correct classification rate of proposed system is 95.6%. This research demonstrated that the AR can be used for reducing the dimension of feature space and proposed AR+NN model can be used to obtain fast automatic diagnostic systems for other diseases.


title: Online Reservoir Adaptation by Intrinsic Plasticity for Backpropagation-Decorrelation and Echo State Learning Abstract

abstract: We propose to use a biologically motivated learning rule based on neural intrinsic plasticity to optimize reservoirs of analog neurons. This rule is based on an information maximization principle, it is local in time and space and thus computationally efficient. We show experimentally that it can drive the neurons' output activities to approximate exponential distributions. Thereby it implements sparse codes in the reservoir. Because of its incremental nature, the intrinsic plasticity learning is well suited for joint application with the online backpropagation-decorrelation or the least mean squares reservoir learning, whose performance can be strongly improved. We further show that classical echo state regression can also benefit from reservoirs, which are pre-trained on the given input signal with the implicit plasticity rule.


title: Wheeled inverted pendulum type robotic wheelchair with integrated control of seat slider and rotary link between wheels for climbing stairs

abstract: The authors describe a mechanical design and control strategy for an inverted pendulum type robotic wheelchair. The structure of the wheelchair consists of a seat slider and two rotary links between the wheels and tires on either side. In addition, the integrated control is based on a linear-quadratic regulator consisting of a gravity-center control stage and a wheel-linkage control stage for climbing stairs. The authors verified the effectiveness of the control system and the mechanical structure on a physical dynamic simulation. The authors present an overview of the kinematic structure and control strategy for climbing stairs.


title: Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud computing systems

abstract: The recent advances in cloud services technology are fueling a plethora of information technology innovation, including networking, storage, and computing. Today, various flavors have evolved of IoT, cloud computing, and so-called fog computing, a concept referring to capabilities of edge devices and users’ clients to compute, store, and exchange data among each other and with the cloud. Although the rapid pace of this evolution was not easily foreseeable, today each piece of it facilitates and enables the deployment of what we commonly refer to as a smart scenario, including smart cities, smart transportation, and smart homes. As most current cloud, fog, and network services run simultaneously in each scenario, we observe that we are at the dawn of what may be the next big step in the cloud computing and networking evolution, whereby services might be executed at the network edge, both in parallel and in a coordinated fashion, as well as supported by the unstoppable technology evolution. As edge devices become richer in functionality and smarter, embedding capacities such as storage or processing, as well as new functionalities, such as decision making, data collection, forwarding, and sharing, a real need is emerging for coordinated management of fog-to-cloud (F2C) computing systems. This article introduces a layered F2C architecture, its benefits and strengths, as well as the arising open and research challenges, making the case for the real need for their coordinated management. Our architecture, the illustrative use case presented, and a comparative performance analysis, albeit conceptual, all clearly show the way forward toward a new IoT scenario with a set of existing and unforeseen services provided on highly distributed and dynamic compute, storage, and networking resources, bringing together heterogeneous and commodity edge devices, emerging fogs, as well as conventional clouds. Introduction: The Scenario


title: Dissecting games engines: The case of Unity3D

abstract: Recent trends on how video games are played have pushed for the need to revise the game engine architecture. Indeed, game players are more mobile, using smartphones and tablets that lack CPU resources compared to PC and dedicated box. Two emerging solutions, cloud gaming and computing offload, would represent the next steps toward improving game player experience. By consequence, dissecting and analyzing game engines performances would help to better understand how to move to these new directions, which is so far missing in the literature. In this paper, we fill this gap by analyzing and evaluating one of the most popular game engine, namely Unity3D. First, we dissected the Unity3D architecture and modules. A benchmark was then used to evaluate the CPU and GPU performances of the different modules constituting Unity3D, for five representative games.


title: Is the N1c category of the new american joint committee on cancer staging system applicable to patients with rectal cancer who receive preoperative chemoradiotherapy

abstract: BACKGROUND: ::: Pericolorectal tumor deposits (TDs) are associated with adverse outcomes in patients with colorectal cancer, and the seventh edition of the American Joint Committee on Cancer (AJCC) staging system recently classified TDs without regional lymph node metastasis as category N1c. However, the definition of TDs has varied. Moreover, with the recent, widespread application of preoperative chemoradiotherapy (CRT) in rectal cancers, residual primary tumor confined to the mesorectum is not infrequent, and it is unclear whether the N1c category is appropriate for these tumors. ::: ::: METHODS: ::: To evaluate the prognostic significance of the N1c classification in patients with rectal cancers after preoperative CRT, the authors reviewed the histologic features of 136 rectal cancers that were classified previously with a tumor classification of 3 [T3], negative lymph nodes [N0], and no metastasis [M0] based on the pathologic extent of disease (ypT3N0M0). These tumors were reclassified according to the new AJCC staging system, and patient outcomes were analyzed. ::: ::: RESULTS: ::: Perirectal TDs were detected in 16 of 136 patients (11.8%). Patterns of TD included a separate nodule pattern in 6 patients (38%), a perivascular pattern in 4 patients (25%), a perineural pattern in 4 patients (25%), and a lymphatic pattern in 2 patients (12%). By using the new N1c category, 120 patients (88.2%) were classified with yp-stage IIA disease, 6 patients (4.4%) were classified with yp-stage IIIA disease, and 10 patients (7.4%) were classified with yp-stage IIIB disease. Kaplan-Meier survival analysis indicated that there were no significant differences between the TD-positive and TD-negative groups in disease-free survival (DFS) or overall survival (OS) (P = .48 for both; log-rank test). In addition, the reclassified TMN stage was not related to DFS (P = .17) or OS (P = .072). ::: ::: CONCLUSIONS: ::: The category N1c may not be appropriate for patients with rectal cancer after preoperative CRT, because the definition of ypN1c was confusing and did not have prognostic significance. Cancer 2011;. © 2011 American Cancer Society.


title: Sentimental product recommendation

abstract: This paper describes a novel approach to product recommendation that is based on opinionated product descriptions that are automatically mined from user-generated product reviews. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers. We demonstrate the benefits of this approach across a variety of Amazon product domains.


title: Pose Embeddings: A Deep Architecture for Learning to Match Human Poses

abstract: We present a method for learning an embedding that places images of humans in similar poses nearby. This embedding can be used as a direct method of comparing images based on human pose, avoiding potential challenges of estimating body joint positions. Pose embedding learning is formulated under a triplet-based distance criterion. A deep architecture is used to allow learning of a representation capable of making distinctions between different poses. Experiments on human pose matching and retrieval from video data demonstrate the potential of the method.


title: The spoofax language workbench: rules for declarative specification of languages and IDEs

abstract: Spoofax is a language workbench for efficient, agile development of textual domain-specific languages with state-of-the-art IDE support. Spoofax integrates language processing techniques for parser generation, meta-programming, and IDE development into a single environment. It uses concise, declarative specifications for languages and IDE services. In this paper we describe the architecture of Spoofax and introduce idioms for high-level specifications of language semantics using rewrite rules, showing how analyses can be reused for transformations, code generation, and editor services such as error marking, reference resolving, and content completion. The implementation of these services is supported by language-parametric editor service classes that can be dynamically loaded by the Eclipse IDE, allowing new languages to be developed and used side-by-side in the same Eclipse environment.


title: A Region Histogram of Oriented Optical Flow (RHOOF) feature for apex frame spotting in micro-expression

abstract: Micro-expressions are the momentary facial expressions that reveal genuine emotional state of people. However, the detection and recognition of micro-expression have been greatly challenging. The apex frame which indicates the most expressive state of a micro-expression will be very helpful for further research on micro-expression. But labeling the apex frame manually is very time-consuming. In this paper, we propose a novel Region Histogram of Oriented Optical Flow (RHOOF) feature to spot the apex frame automatically. First, a set of facial landmarks are detected and then 5 Regions Of Interest (ROIs) are selected from facial region based on the frequency of occurrence of action units. Finally, we extract optical flow fields frame-by-frame and compute HOOF in these ROIs. Experiments are conducted on two ideal spontaneous micro-expression databases, i.e., CASME and CASME II. Improvements of 30.77% and 19.04% are achieved respectively in CASME and CASME II when compared to the BS-RoIs.


title: Circle Planarity of Level Graphs

abstract: In this thesis we generalise the notion of level planar graphs in two directions: track planarity and radial planarity. Our main results are linear time algorithms both for the planarity test and for the computation of an embedding, and thus a drawing. Our algorithms use and generalise PQ-trees, which are a data structure for efficient planarity tests. A graph is a level graph, if it has a partition of the vertices in levels such that the vertices of each level can be placed on a horizontal line and the edges are strictly downwards. It is level planar if there are no edge crossings. Level planarity can be tested efficiently in linear time by sophisticated and complex algorithms. Level graphs exclude horizontal edges between vertices on the same level. Such edges are allowed by our track graphs. In radial level graphs the vertices of each level are placed on concentric circles and the edges are outwards. We characterise essential differences between level and radial level planar graphs, which are expressed by level non-planar biconnected components called rings. The presence of rings introduces the particular problem of the nesting of non-connected components. Further, we study forbidden subgraphs which destroy radial level planarity. The track and circle extensions are combined to form circle graphs, which allow edges along the concentric circles. Level planar graphs arise as a specialisation of directed acyclic graphs that are usually drawn by the Sugiyama algorithm, which avoids edge crossings. Applications of level or track planar drawings include for example biochemical pathways, entity relationship and UML class diagrams, or flow charts which occur for example in project management. Typical applications of radial drawings are social networks.


title: Continuously Differentiable Exponential Linear Units

abstract: Exponential Linear Units (ELUs) are a useful rectifier for constructing deep learning architectures, as they may speed up and otherwise improve learning by virtue of not have vanishing gradients and by having mean activations near zero. However, the ELU activation as parametrized in [1] is not continuously differentiable with respect to its input when the shape parameter alpha is not equal to 1. We present an alternative parametrization which is C1 continuous for all values of alpha, making the rectifier easier to reason about and making alpha easier to tune. This alternative parametrization has several other useful properties that the original parametrization of ELU does not: 1) its derivative with respect to x is bounded, 2) it contains both the linear transfer function and ReLU as special cases, and 3) it is scale-similar with respect to alpha.


title: Collostructions: Investigating the interaction of words and constructions

abstract: This paper introduces an extension of collocational analysis that takes into account grammatical structure and is specifically geared to investigating the interaction of lexemes and the grammatical constructions associated with them. The method is framed in a construction-based approach to language, i.e. it assumes that grammar consists of signs (form-meaning pairs) and is thus not fundamentally different from the lexicon. The method is applied to linguistic expressions at various levels of abstraction (words, semi-fixed phrases, argument structures, tense, aspect and mood). The method has two main applications: first, to increase the adequacy of grammatical description by providing an objective way of identifying the meaning of a grammatical construction and determining the degree to which particular slots in it prefer or are restricted to a particular set of lexemes; second, to provide data for linguistic theory-building.


title: A state-of-art survey on TQM applications using MCDM techniques

abstract: Article history: Received October 2, 2012 Received in Revised Format March 12, 2013 Accepted March 18, 2013 Available online March 2


title: Assessing and Scaffolding Collaborative Learning in Online Discussions

abstract: In this paper we present two computational approaches that can be used characterize and measure online threaded discussions and demonstrate that they can objectively validate student-reported differences in collaborative learning between tutor-scaffolded and non-scaffolded discussion activities. The first approach, thread profiling, is used to characterize user interactions that tend to broaden and deepen discussions, and gives insight into how tutors participate in discussions. The second approach, which uses a natural language discourse processor, is used to compare the rhetoric of tutors and students, and shows that tutors consistently use more attributions, elaborations, and enablements to scaffold discussions. To test these ideas we processed twenty-four online activities, constituting over one thousand message posts, during a course at the British Open University. These computational methods and findings have application in virtual tutoring systems and the automated assessment of discussions.


title: Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks

abstract: Scientific collaboration and endorsement are well-established research topics which utilize three kinds of methods: survey/questionnaire, bibliometrics, and complex network analysis. This paper combines topic modeling and path-finding algorithms to determine whether productive authors tend to collaborate with or cite researchers with the same or different interests, and whether highly cited authors tend to collaborate with or cite each other. Taking information retrieval as a test field, the results show that productive authors tend to directly coauthor with and closely cite colleagues sharing the same research interests; they do not generally collaborate directly with colleagues having different research topics, but instead directly or indirectly cite them; and highly cited authors do not generally coauthor with each other, but closely cite each other.


EFFICIENCY ANALYSIS OF QUALITY THRESHOLD CLUSTERING ALGORITHMS


title: Physical Layer Network Coding

abstract: A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11). This paper shows that the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC) scheme to coordinate transmissions among nodes. In contrast to straightforward network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM) waves for equivalent coding operation. And in doing so, PNC can potentially achieve 100% and 50% throughput increases compared with traditional transmission and straightforward network coding, respectively, in multi-hop networks. More specifically, the information-theoretic capacity of PNC is almost double that of traditional transmission in the SNR region of practical interest (higher than 0dB). We believe this is a first paper that ventures into EM-wave-based network coding at the physical layer and demonstrates its potential for boosting network capacity.


title: APTR is a prognostic marker in cirrhotic patients with portal hypertension during TIPS procedure.

abstract: Portal hypertension is a major cause of mortality and morbidity in cirrhotic patients. In this study, we aimed to analyze the clinical characteristics of Alu-mediated p21 transcriptional regulator (APTR) during transjugular intrahepatic portosystemic shunt (TIPS) procedure. Portal and hepatic venous blood was drawn from 84 patients with liver cirrhosis and portal hypertension before and after TIPS treatment. Then, we detected biochemical, hemodynamic parameters and APTR expression before and after TIPS treatment. Indeed, TIPS treatment could markedly ameliorate the serum blood urea nitrogen (BUN) level and portal vein hemodynamics in cirrhotic patients. We found that portal venous levels of APTR was significantly decreased after TIPS treatment and its aberrant expression levels were positively correlated with Model for End Stage Liver Disease (MELD), portal hepatic venous pressure gradient (PHPG) in patients. Higher APTR expression in portal vein was associated with poor prognosis. APTR level in portal vein was an independent predictors of mortality. Our data indicated that APTR may serve as a novel biomarker for cirrhotic patients with portal hypertension before and after receiving TIPS.


title: Gender Dysphoria in Adults

abstract: Gender dysphoria (GD), a term that denotes persistent discomfort with one's biologic sex or assigned gender, replaced the diagnosis of gender identity disorder in the Diagnostic and Statistical Manual of Mental Disorders in 2013. Subtypes of GD in adults, defined by sexual orientation and age of onset, have been described; these display different developmental trajectories and prognoses. Prevalence studies conclude that fewer than 1 in 10,000 adult natal males and 1 in 30,000 adult natal females experience GD, but such estimates vary widely. GD in adults is associated with an elevated prevalence of comorbid psychopathology, especially mood disorders, anxiety disorders, and suicidality. Causal mechanisms in GD are incompletely understood, but genetic, neurodevelopmental, and psychosocial factors probably all contribute. Treatment of GD in adults, although largely standardized, is likely to evolve in response to the increasing diversity of persons seeking treatment, demands for greater client autonomy, and ...


MARWind : Mobile Augmented Reality Wind Farm Visualization


EVOLUTION OF THE METHODS FOR ESTIMATION BIODIVERSITY IN REINDEER (Rangifer tarandus) (review)


title: Accelerated Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Nonconvex Optimization

abstract: We propose an accelerated stochastic block coordinate descent algorithm for nonconvex optimization under sparsity constraint in the high dimensional regime. The core of our algorithm is leveraging both stochastic partial gradient and full partial gradient restricted to each coordinate block to accelerate the convergence. We prove that the algorithm converges to the unknown true parameter at a linear rate, up to the statistical error of the underlying model. Experiments on both synthetic and real datasets backup our theory.


title: MeT: workload aware elasticity for NoSQL

abstract: NoSQL databases manage the bulk of data produced by modern Web applications such as social networks. This stems from their ability to partition and spread data to all available nodes, allowing NoSQL systems to scale. Unfortunately, current solutions' scale out is oblivious to the underlying data access patterns, resulting in both highly skewed load across nodes and suboptimal node configurations. In this paper, we first show that judicious placement of HBase partitions taking into account data access patterns can improve overall throughput by 35%. Next, we go beyond current state of the art elastic systems limited to uninformed replica addition and removal by: i) reconfiguring existing replicas according to access patterns and ii) adding replicas specifically configured to the expected access pattern. MeT is a prototype for a Cloud-enabled framework that can be used alone or in conjunction with OpenStack for the automatic and heterogeneous reconfiguration of a HBase deployment. Our evaluation, conducted using the YCSB workload generator and a TPC-C workload, shows that MeT is able to i) autonomously achieve the performance of a manual configured cluster and ii) quickly reconfigure the cluster according to unpredicted workload changes.


title: Service Function Chaining: Creating a Service Plane via Network Service Headers

abstract: As service chaining matures, interoperability is a must. Network service headers provide the required data-plane information needed to construct topologically independent service paths as well as to pass opaque metadata between classification and service functions.


SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor


title: Learning Scalable Deep Kernels with Recurrent Structure

abstract: Many applications in speech, robotics, finance, and biology deal with sequential data, where ordering matters and recurrent structures are common. However, this structure cannot be easily captured by standard kernel functions. To model such structure, we propose expressive closed-form kernel functions for Gaussian processes. The resulting model, GP-LSTM, fully encapsulates the inductive biases of long short-term memory (LSTM) recurrent networks, while retaining the non-parametric probabilistic advantages of Gaussian processes. We learn the properties of the proposed kernels by optimizing the Gaussian process marginal likelihood using a new provably convergent semi-stochastic gradient procedure and exploit the structure of these kernels for scalable training and prediction. This approach provides a practical representation for Bayesian LSTMs. We demonstrate state-of-the-art performance on several benchmarks, and thoroughly investigate a consequential autonomous driving application, where the predictive uncertainties provided by GP-LSTM are uniquely valuable.


title: Translating the viewing position in single equirectangular panoramic images

abstract: Equirectangular panoramas are popular tools for achieving 360 degree immersed viewing experiences. A panorama captures a scene from one point and panoramic viewers allow the user to control the viewing direction, but the viewer is not allowed to move around. This study proposes a strategy for transforming equirectangular panoramic images with the effect of moving freely in three dimensions. The strategy assumes that the panorama is an enclosed space comprising a flat ground and flat vertical walls. A equirectuangular Hough transform is proposed for detecting the boundaries of the respective planes. The panoramic image is then decomposed into the respective planes, the viewing point is translated and a new panoramic image based on the new viewing position is composed. Preliminary proof of concept test shows that the strategy allows free translation within simple panoramic images.


title: Context-aware sensors

abstract: Wireless sensor networks typically consist of a large number of sensor nodes embedded in a physical space. Such sensors are low-power devices that are primarily used for monitoring several physical phenomena, potentially in remote harsh environments. Spatial and temporal dependencies between the readings at these nodes highly exist in such scenarios. Statistical contextual information encodes these spatio-temporal dependencies. It enables the sensors to locally predict their current readings based on their own past readings and the current readings of their neighbors. In this paper, we introduce context-aware sensors. Specifically, we propose a technique for modeling and learning statistical contextual information in sensor networks. Our approach is based on Bayesian classifiers; we map the problem of learning and utilizing contextual information to the problem of learning the parameters of a Bayes classifier, and then making inferences, respectively. We propose a scalable and energy-efficient procedure for online learning of these parameters in-network, in a distributed fashion. We discuss applications of our approach in discovering outliers and detection of faulty sensors, approximation of missing values, and in-network sampling. We experimentally analyze our approach in two applications, tracking and monitoring.


title: Illuminant-Based Transformed Spaces for Image Forensics

abstract: In this paper, we explore transformed spaces, represented by image illuminant maps, to propose a methodology for selecting complementary forms of characterizing visual properties for an effective and automated detection of image forgeries. We combine statistical telltales provided by different image descriptors that explore color, shape, and texture features. We focus on detecting image forgeries containing people and present a method for locating the forgery, specifically, the face of a person in an image. Experiments performed on three different open-access data sets show the potential of the proposed method for pinpointing image forgeries containing people. In the two first data sets (DSO-1 and DSI-1), the proposed method achieved a classification accuracy of 94% and 84%, respectively, a remarkable improvement when compared with the state-of-the-art methods. Finally, when evaluating the third data set comprising questioned images downloaded from the Internet, we also present a detailed analysis of target images.


title: Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD

abstract: Attention deficit hypo-activity disorder (ADHD) comprises a deficit in behavioral inhibition. A theoretical model is constructed that links inhibition to 4 executive neuropsychological functions that appear to depend on it for their effective execution: (a) working memory, (b) self-regulation of affect-motivation-arousal, (c) internalization of speech, and (d) reconstitution (behavioral analysis and synthesis). Extended to ADHD, the model predicts that ADHD should be associated with secondary impairments in these 4 executive abilities and the motor control they afford. The author reviews evidence for each of these domains of functioning and finds it to be strongest for deficits in behavioral inhibition, working memory, regulation of motivation, and motor control in those with ADHD. Although the model is promising as a potential theory of self-control and ADHD, far more research is required to evaluate its merits and the many predictions it makes about ADHD.


title: Real-Time Human Ambulation, Activity, and Physiological Monitoring: Taxonomy of Issues, Techniques, Applications, Challenges and Limitations

abstract: Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions.


title: Acute toxicity and synergistic and antagonistic effects of the aromatic compounds of some essential oils against Culex quinquefasciatus Say larvae

abstract: The efficacy of 30 aromatic compounds and their mutual binary combinations was assessed for acute toxicity against the larvae Culex quinquefasciatus. Based on comparison of the lethal doses, thymol and p-cymene were selected as the most effective (LD50 = 18 and 21 mg L(-1), respectively, and LD90 = 25 and 30 mg L(-1), respectively). Although the LD50 for terpinolene and trans-anethole was also estimated at 21 mg L(-1), their LD90 was significantly higher compared to the substances above (245 and 34 mg L(-1), respectively). In total, 435 binary combinations were tested, of which 249 combinations showed a significant synergistic effect, while 74 combinations showed a significant antagonistic effect on mortality. Only nine substances were identified as being able to create a synergistic effect with more than 20 substances: limonene, trans-anethole, 4-allylanisole, carvacrol, isoeugenol, menthone, carvone, borneol, and camphor. The highest synergistic effect on larval mortality was achieved for the combinations: eugenol and isoeugenol, carvone and carvacrol, carvone and 4-allylanisole, carvone and α-terpineol, carvone and menthone, limonene and trans-anethole, limonene and menthone, α-pinene and menthone, β-citronellol and menthone, carvacrol and 4-allylanisole, carvacrol and terpineol, α-terpinene and trans-anethole, camphor and menthone, camphene and menthone, and 4-allylanisole and menthone. Significant differences between achieved mortality and the mutual mixing ratio were found for the five selected binary mixtures that had shown the most significant synergistic effect in the previous tests. The mixture of limonene and trans-anethole showed the highest mortality, with the mixing ratio 1:1; the mixture of eugenol and isoeugenol caused 90.2% mortality, with the mixing ratio 1:3. One hundred percent mortality was achieved if carvacrol was contained in a mixture with carvone in a ratio >2. After a comparison of all our results, based on our experiments, we can choose two pairs that caused mortality higher than 90% in concentrations lower than 20 mg L(-1): limonene and trans-anethole (with the mixing ratio 1:1), and carvone and carvacrol (with the mixing ratio 1:2-3). The information gained can thus be used in the development of new botanical insecticides based on essential oils (EOs) and particularly in the creation of formulations.


title: A Few Prolific Liars: Variation in the Prevalence of Lying

abstract: It has been commonplace in the deception literature to assert the pervasive nature of deception in communication practices. Previous studies of lie prevalence find that lying is unusual compared to honest communication. Recent research, and reanalysis of previous studies reporting the frequency of lies, shows that most people are honest most of the time and the majority of lies are told by a few prolific liars. The current article reports a statistical method for distinguishing prolific liars from everyday liars and provides a test of the few prolific liars finding by examining lying behavior in the United Kingdom. Participants (N = 2,980) were surveyed and asked to report on how often they told both little white lies and big important lies. Not surprisingly, white lies were more common than big lies. Results support and refine previous findings about the distinction between everyday and prolific liars, and implications for theory are discussed.


title: Attention-Based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions

abstract: Many studies have been conducted so far on image restoration, the problem of restoring a clean image from its distorted version. There are many different types of distortion affecting image quality. Previous studies have focused on single types of distortion, proposing methods for removing them. However, image quality degrades due to multiple factors in the real world. Thus, depending on applications, e.g., vision for autonomous cars or surveillance cameras, we need to be able to deal with multiple combined distortions with unknown mixture ratios. For this purpose, we propose a simple yet effective layer architecture of neural networks. It performs multiple operations in parallel, which are weighted by an attention mechanism to enable selection of proper operations depending on the input. The layer can be stacked to form a deep network, which is differentiable and thus can be trained in an end-to-end fashion by gradient descent. The experimental results show that the proposed method works better than previous methods by a good margin on tasks of restoring images with multiple combined distortions.


title: Prediction of Students' Academic Success Using Data Mining Methods

abstract: Success is very important for all of us. Most people wants prosperity, reputation, and richness that can only be achieved with the success. A society that wants to be successful should pay attention to their new generation because they are the future of the world. If we want to invest to our future, we must contribute to success of our new generations. Therefore, in this study, the academic performance of the students that belong to different levels of education like primary, secondary, and high school levels is tried to be determined by applying various classification methods such as Multilayer Perceptron (MLP), Random Forest (RF), Naïve Bayes (NB), Decision Tree (J48), and Voting classifiers. It is also observed which characteristics are more related to the improvement of academic performance of the students. Features like absence of student, parent's school satisfaction, raising hands on class, and parent who is responsible for the student can affect the success of the student. A comparison is made with other study that previously worked on the same data set. As a result, better classification accuracy is achieved. We observe the best classification accuracy as 80.6% by Voting classifier, while the previous study has the highest accuracy as 79.1 % by applying Artificial Neural Network (ANN) classifier. Also, in our study, Apriori algorithm is applied to detect relationships between features.


title: Efficient and Robust Machine Learning for Real-World Systems

abstract: While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. On top of this, it is crucial to treat uncertainty in a consistent manner in all but the simplest applications of machine learning systems. In particular, a desideratum for any real-world system is to be robust in the presence of outliers and corrupted data, as well as being `aware' of its limits, i.e.\ the system should maintain and provide an uncertainty estimate over its own predictions. These complex demands are among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology into every day's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. First we provide a comprehensive review of resource-efficiency in deep neural networks with focus on techniques for model size reduction, compression and reduced precision. These techniques can be applied during training or as post-processing and are widely used to reduce both computational complexity and memory footprint. As most (practical) neural networks are limited in their ways to treat uncertainty, we contrast them with probabilistic graphical models, which readily serve these desiderata by means of probabilistic inference. In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems.


title: A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems

abstract: Stereo vision is an important feature that enables machine vision systems to perceive their environment in 3D. While machine vision has spawned a variety of software algorithms to solve the stereo-correspondence problem, their implementation and integration in small, fast, and efficient hardware vision systems remains a difficult challenge. Recent advances made in neuromorphic engineering offer a possible solution to this problem, with the use of a new class of event-based vision sensors and neural processing devices inspired by the organizing principles of the brain. Here we propose a radically novel model that solves the stereo-correspondence problem with a spiking neural network that can be directly implemented with massively parallel, compact, low-latency and low-power neuromorphic engineering devices. We validate the model with experimental results, highlighting features that are in agreement with both computational neuroscience stereo vision theories and experimental findings. We demonstrate its features with a prototype neuromorphic hardware system and provide testable predictions on the role of spike-based representations and temporal dynamics in biological stereo vision processing systems.


title: Information diffusion among users on Facebook fan pages over time: Its impact on movie box office

abstract: In this work, we investigate the impact of social influence of a Facebook fan page on movie box offices. We aim to enhance the accuracy of predicting the box office by leveraging the social influence among users in the fan page. We develop the Global Influence Model to compute the user influence and predict the engagements between the fan page and users. In addition, we propose the Linear Box Office Revenue Prediction Model to bridge the gap between Facebook fan pages and the box offices by utilizing the social influence and some statistics obtained from Facebook fan pages. By considering the social influence, the accuracy of forecasting box offices for movies can be improved significantly.


title: Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble

abstract: This short paper reports the method and the evaluation results of Casio and Shinshu University joint team for the ISBI Challenge 2017 - Skin Lesion Analysis Towards Melanoma Detection - Part 3: Lesion Classification hosted by ISIC. Our online validation score was 0.958 with melanoma classifier AUC 0.924 and seborrheic keratosis classifier AUC 0.993.


title: Comparison of IEEE 802.11 and IEEE 802.15.4 for Future Green Multichannel Multi-radio Wireless Sensor Networks

abstract: Multi-channel MAC protocols have recently obtained considerable attention in wireless network ing research because they promise to increase capacity of wirele ss networks significantly by exploiting multiple frequency band s. In this paper, we do a comparison between IEEE 802.11 and IEEE 802.15.4 and investigate the performance between both using simulations conducted in NS2. This investigation will allow us to determine the feasibility in having IEEE 802.11 being considered as a future medium for wireless sensor networks operating in a multichannel environment at high data rate with streaming data t hat would be a challenge for IEEE 802.15.4. More so IEEE 802.15.4 will be facing severe challenge to operate in the 2.4GHz frequency band when the IEEE 802.11n becomes popular, operating within the same frequency band. We demonstrate through simulations that IEEE 802.11 perform better with high data rate, streaming constant bit rate and at longer range comparing to 802.15.4 which operates better with small data size at much shorter range. The outcome from this paper will be valuable for our future work in designing a multich annel MAC protocol for contention-based 802.11 WSN.


title: A fast Heuristic for the Train Scheduling Problem

abstract: Abstract The train scheduling problem is an integer programming problem known to be NP hard. In practice such a problem is often required to be solved in real time, hence a quick heuristic that allows a good feasible solution to be obtained in a predetermined and finite number of steps is most desired. We propose an algorithm which is based on local optimality criteria in the event of a potential crossing conflict. The suboptimal but feasible solution can be obtained very quickly in polynomial time. The model can also be generalized to cater for the possibility of overtaking when the trains have different speed. We also furnish a complexity analysis to show the NP-completeness of the problem. Simulation results for two non-trivial examples are presented to demonstrate the effectiveness of the proposed algorithm.


title: Parasitic Currents in Stray Paths of Some Topologies of YASA AFPM Machines: Trend With Machine Size

abstract: In some topologies of yokeless and segmented armature (YASA) axial flux permanent-magnet (AFPM) machines, the particular frame structure required to fasten floating teeth can originate stray paths where parasitic currents may circulate. This paper investigates how the amplitude of these currents and the related additional Joule losses depend on the machine size. Reference is made to wind energy generators, rated from some tens of kilowatts up to a few megawatts. The developed model is mainly based on wind turbine basic data of vendor data sheets, and on the quantities evaluated and tested in two YASA AFPM prototypes, rated 50 and 200 kW.


abstract: Background: Migration of dental implants into the maxillary sinus is an uncommon, but increasingly reported complication. Implant migration may result from initial lack of primary stability, intrasinusal and nasal pressure changes, autoimmune reaction to the implant or incorrect distribution of occlusal forces. This retrospective study aims at analyzing the factors that may influence implant migration into the maxillary sinus cavity. Material and methods: Fourteen patients presenting a total 15 implants that migrated into the maxillary sinus were recruited. Diagnosis of this complication was based on imaging techniques, such as cone beam computerized tomography scan and panoramic radiography. Clinical data were recorded in all cases and processed for statistical analysis. Results: ABH was below 6 mm in the majority of cases. However, almost 50% of the patients did not receive any site preparation treatment prior to implant insertion. Five patients (33.3%) were treated by osteotome techniques, but only one of them had bone grafting. Therefore, 73.3% of sites did not receive any biomaterial to increase available bone height. The most common complication-associated factors found on this study were related to implant design (cylindrical), implant dimension (diameter), implant restoration/rehabilitation method (partial removable denture), site-specific anatomy (initial residual bone height between 5 and 6.9 mm), demographics (age), and biomaterials. Conclusion: Patient selection and proper treatment planning, as well as the application of the appropriate sinus augmentation technique, are critical aspects that should be controlled to minimize the risk of implant migration into the maxillary sinus cavity. [Correction added after online publication August 17 2011: The Conclusion was revised to provide better clarity to the reader.] Occlusal rehabilitation of the edentulous posterior maxilla with implant-supported restorations represents a unique clinical challenge. Posterior upper maxilla bone is typically soft, due to its thin or non-existing cortical and very spongiotic trabeculae, possibly compromising implants′ primary stability and, therefore, consecutively its implant failure (Adell et al. 1990; Misch 1990a). To offset this biomechanical disadvantage different therapeutic strategies have been developed. These include, but are not limited to, special drilling protocols, modified implant designs, and the use of bone condensers (e.g. osteotome-based implant placement). On the other hand, tooth loss typically triggers a cascade of events that ultimately leads to alveolar bone resorption (Schropp et al. 2003). Resorptive processes are particularly dramatic in the posterior maxilla, resulting in marked vertical bone deficiency that may contraindicate conventional implant placement. Various therapeutic alternatives have been proposed to overcome this limitation. Sinus floor elevation, also known as sinus augmentation, is regarded as a predictable procedure for implant site development in this region. Since it was first described (Boyne & James 1980), this technique has proven its efficacy and reliability in a variety of clinical scenarios using different grafting materials, and modifications of the original surgical protocol (Wallace & Froum 2003; Pjetursson et al. 2008; Tan et al. 2008). A number of alternatives Date: Accepted 11 June 2011 To cite this article: Galindo-Moreno P, Padial-Molina M, Avila G, Rios HF, Hernandez-Cortes P, Wang H-L. Complications associated with implant migration into the maxillary sinus cavity. Clin. Oral Impl. Res. 23, 2012, 1152–1160 doi: 10.1111/j.1600-0501.2011.02278.x 1152 © 2011 John Wiley & Sons A/S to ridge augmentation procedures, such as tilted implants, zygomatic implants, pterygoid implants, short implants (<10 mm), restorations in cantilever or even graftless sinus floor elevation have been described as suitable methods to restore posterior occlusal function with implant-supported prostheses (Thor et al. 2007). An increasing debate exists in the scientific community regarding the treatment of choice to obtain satisfactory outcomes with minimal trauma and to shorten the total treatment time. However, selection of an inadequate treatment option may derive into serious complications, such as implant migration inside the sinus cavity. Since the first case was described (Regev et al. 1995), other authors have depicted the occurrence of this adverse event into the maxillary and other paranasal sinuses. Most reports have included a limited number of implants (Regev et al. 1995; Iida et al. 2000; Raghoebar & Vissink 2003; Nakamura et al. 2004; Galindo et al. 2005; Varol et al. 2006; Guler & Delilbasi 2007; Kim et al. 2007; Kitamura 2007; Lubbe et al. 2008; Flanagan 2009; Borgonovo et al. 2010; Kluppel et al. 2010; Ramotar et al. 2010; Scarano et al. 2010; Tsodoulos et al. 2010), with only a couple that include a slightly larger series of cases (Chiapasco et al. 2009; Ridaura-Ruiz et al. 2009). Various treatment modalities have been employed to deal with this complication, from a conservative approach (i.e. leave the migrated implant untreated under monitoring) to endoscopic transnasal procedures or a conventional Cadwell-Luc technique. Different theories have been proposed and aimed at explaining the mechanism by which implant migrations occurs. Some of the proposed primary factors involved in this complication include changes in the intrasinusal and nasal pressures (Galindo et al. 2005), autoimmune reaction to the implant or incorrect distribution of occlusal forces (Regev et al. 1995). Nevertheless, it is important to consider that inadequate treatment to rehabilitate edentulous segments of the posterior maxilla (e.g. absence of implant site development) may be the underlying cause of implant migration in many instances (Chiapasco et al. 2009). This retrospective study aimed at identifying the factors that may contribute to the occurrence of implant migration into the maxillary sinus cavity. In addition, we evaluated the pathology derived from these adverse events and proposed different therapeutic approaches to resolve these complications. Materials and methods Study population Migrated implants from patients who suffered dental implant displacement into the maxillary sinus were included in this retrospective study. Migrations took place at different stages of the treatment sequence and maintenance, between the years 2005 and 2010. Patients were treated in a private practice setting (P.G.-M.). Institutional Review Board from the University of Michigan issued an exemption to this study because of the use of collected existing data in such a manner that subjects cannot be identified, directly or through identifiers linked to the subjects (HUM0048824). All of the patients were informed about their clinical circumstances, and everyone who underwent corrective surgery signed an informed consent. Data collection Diagnosis of the migration was assessed based on imaging techniques, such as cone beam computerized tomography (CBCT) and panoramic radiography (PR). Radiographic diagnosis was complemented with a clinical examination in all patients. Standardized digital panoramic radiographs (Kodak ACR-2000; Eastman Kodak Company, Rochester, NY, USA) were obtained at the diagnosis appointment, prior to surgery when it was realized. Specialized software (Dent-A-View v1.0; DigiDent, DIT, Nesher, Israel) was used to make linear measurements. Information recorded included patient’s age and gender, smoking habits (smoker/nonsmoker), initial implant location, implant diameter and length, implant macroand micro design features (i.e. implant design and type of surface), sinus augmentation status (presence or absence), grafting material used, available bone height at the time of implant placement (ABH), type of prosthesis, pathology derived from the migration and type of therapy indicated to resolve the complication. Data analysis Statistical data analysis was aimed firstly at describing the main features of the distribution of the measures: central tendency and data dispersion for scalar variables, and relative frequencies for categorical ones. Only 14 implants were considered for the statistical analyses, considering just one implant per patient. Binomial and chi-square randomization-based tests were used for the analysis of proportions. Kendall Tau-b was used for determining the significance of associations between ordinal and scale variables, and Cramer V was used for pairs of nominal variables. Secondly, we studied how patient factors (smoking habits, alveolar height) and implant features (implant design, diameter, length) affect implant migration into sinus cavity. Finally, backward logistic regression (P out = 0.10, 20 interactions) was used to explore whether presence or absence of related complications can be postdicted. Presence/absence of complications served as the dependent and age, gender, smoking habits, alveolar height, implant diameter, implant length, and biomaterial served as predictors. Analyses were performed using SPSS for Windows (PASW 18.0; SPSS Inc., Chicago, IL, USA).


title: Heat transfer characteristics of impingement spray cooling system for electronic test cards

abstract: A closed-loop impingement spray cooling system which is capable of cooling a 1kW 6U electronic test card has been developed. The heated surface area is 13.5 × 15.1 cm2. R134a was used as working fluid in a modified refrigeration closed-loop cycle. Four vapor assisted nozzles are implemented to generate spray to cover a large ratio of the heated area of the card. The effects of mass flow rate and spray saturation temperature on thermal performance are investigated experimentally in this study. Experimental results are promising with a stable average temperature of around 25 C being maintained on the heated surface with small temperature variation on the heated surface at suitable operating conditions. It is found that cooling performance has been improved with increasing of mass flow rate and spray saturation temperature.


title: Smartphone Malware and Its Propagation Modeling: A Survey

abstract: Smartphones are pervasively used in society, and have been both the target and victim of malware writers. Motivated by the significant threat that presents to legitimate users, we survey the current smartphone malware status and their propagation models. The content of this paper is presented in two parts. In the first part, we review the short history of mobile malware evolution since 2004, and then list the classes of mobile malware and their infection vectors. At the end of the first part, we enumerate the possible damage caused by smartphone malware. In the second part, we focus on smartphone malware propagation modeling. In order to understand the propagation behavior of smartphone malware, we recall generic epidemic models as a foundation for further exploration. We then extensively survey the smartphone malware propagation models. At the end of this paper, we highlight issues of the current smartphone malware propagation models and discuss possible future trends based on our understanding of this topic.


title: Integration technologies for a medium voltage modular multi-level converter with hot swap capability

abstract: Recent development in modular converter topologies have raised the discussion about exploiting the inherent redundancy of such converters in order to maximize their availability. One such solution involves the replacement of power electronic building blocks (PEBBs) during operation, a procedure also referred to as ‘hot swap’. Hot swap is considered as state-of-the-art in today's IT systems and low voltage power supplies. At medium voltage however, hot swap is getting more demanding, mainly because of the higher voltages and because of the often series connected PEBBs. In this paper we therefore present dedicated integration technologies addressing the required pluggability and insulation of PEBBs, and therefore enabling hot swap at medium voltage. The technologies developed cover auxiliary power supply, cooling, communication and insulation, and are demonstrated under real conditions in a full scale medium voltage concept converter. Hot swap was successfully demonstrated at a DC-link voltage of 3.4 kV, an output current of 460 Arms and an output power of 0.55 MW.


title: A DDS-based distributed simulation for anti-air missile systems

abstract: This paper introduces the development of a distributed air-defense engagement simulation model based on data distribution service (DDS). To design and develop effectively, system developers need a high-resolution engagement simulation including complex engineering-level models and operational scenario models. Increasing the resolution of the model results in the growing model's complexity which requires greater resources than that of a single computer. We tried to build a distributed engagement model using AddSIM-DDS which combines the Advanced distributed simulation environment (AddSIM) and DDS. We describe an air-defense scenario, overall structure of the model, and simulation construction on distributed nodes. We also define several DDS topic types for interoperation. Finally, we provide the results that show the validity and effectiveness of the DDS-based distributed simulation.


title: Delayed ettringite formation (DEF) in mortars of white cement

abstract: Abstract In this study white cement CEM I-52.5 and white limestone cement CEM II-LL, A and B, with 15% and 25% limestone substitution, were studied. The way delayed ettringite forms, due to exposure to high temperatures (50 °C) and external sulphate attacks, was examined in the mortar samples. The mortars were immersed at 50 °C for 180 days in: (a) a saturated Ca(OH) 2 solution and (b) a 5% Na 2 SO 4 solution. During the experiment’s duration, the mortar samples were being observed visually on a regular basis while their expansion was estimated on a weekly basis by measuring the change of length with a micrometer. At the end of the experiment, the mortar samples’ compressive strength was determined and the deterioration products were identified through means of X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM-EDAX), Thermogravimetry (TG) and Infra-Red Spectroscopy (FT-IR). Concluding it is evident that the amount of ettringite is proportional to the C 3 A content of cement. Sulphates amount in cement is the controlling factor for heat induced ettringite formation since when they are consumed the reaction stops. On the other hand in the case of external sulphate attack another important controlling factor is the compressive strength of the cement; the higher compressive strength the lower the risk of expansion. Finally, in the case of external sulphate attack, limestone, when added to cement, was proved to enhance the durability against sulphates attack when compared to a cement of the same class.


title: Boosting Algorithms for Parallel and Distributed Learning

abstract: The growing amount of available information and its distributed and heterogeneous nature has a major impact on the field of data mining. In this paper, we propose a framework for parallel and distributed boosting algorithms intended for efficient integrating specialized classifiers learned over very large, distributed and possibly heterogeneous databases that cannot fit into main computer memory. Boosting is a popular technique for constructing highly accurate classifier ensembles, where the classifiers are trained serially, with the weights on the training instances adaptively set according to the performance of previous classifiers. Our parallel boosting algorithm is designed for tightly coupled shared memory systems with a small number of processors, with an objective of achieving the maximal prediction accuracy in fewer iterations than boosting on a single processor. After all processors learn classifiers in parallel at each boosting round, they are combined according to the confidence of their prediction. Our distributed boosting algorithm is proposed primarily for learning from several disjoint data sites when the data cannot be merged together, although it can also be used for parallel learning where a massive data set is partitioned into several disjoint subsets for a more efficient analysis. At each boosting round, the proposed method combines classifiers from all sites and creates a classifier ensemble on each site. The final classifier is constructed as an ensemble of all classifier ensembles built on disjoint data sets. The new proposed methods applied to several data sets have shown that parallel boosting can achieve the same or even better prediction accuracy considerably faster than the standard sequential boosting. Results from the experiments also indicate that distributed boosting has comparable or slightly improved classification accuracy over standard boosting, while requiring much less memory and computational time since it uses smaller data sets.


Student Performance Analysis Using Data Mining Technique


title: On dynamic data-driven selection of sensor streams

abstract: Sensor nodes have limited local storage, computational power, and battery life, as a result of which it is desirable to minimize the storage, processing and communication from these nodes during data collection. The problem is further magnified by the large volumes of data collected. In real applications, sensor streams are often highly correlated with one another or may have other kinds of functional dependencies. For example, a group of sound sensors in a given geographical proximity may pick almost the same set of signals. Clearly, since there are considerable functional dependencies between different sensors, there are huge redundancies in the data collected by sensors. These redundancies may also change as the data evolve over time. In this paper, we discuss real time algorithms for reducing the volume of the data collected in sensor networks. The broad idea is to determine the functional dependencies between sensor streams efficiently in real time, and actively collect the data only from a minimal set of sensors. The remaining sensors collect the data passively at low sampling rates in order to detect any changing trends in the underlying data. We present real time algorithms in order to minimize the power consumption in reducing the data collected and show that the resulting data retains almost the same amount of information at a much lower cost.


title: A Survey on Intrabody Communications for Body Area Network Applications

abstract: The rapid increase in healthcare demand has seen novel developments in health monitoring technologies, such as the body area networks (BAN) paradigm. BAN technology envisions a network of continuously operating sensors, which measure critical physical and physiological parameters e.g., mobility, heart rate, and glucose levels. Wireless connectivity in BAN technology is key to its success as it grants portability and flexibility to the user. While radio frequency (RF) wireless technology has been successfully deployed in most BAN implementations, they consume a lot of battery power, are susceptible to electromagnetic interference and have security issues. Intrabody communication (IBC) is an alternative wireless communication technology which uses the human body as the signal propagation medium. IBC has characteristics that could naturally address the issues with RF for BAN technology. This survey examines the on-going research in this area and highlights IBC core fundamentals, current mathematical models of the human body, IBC transceiver designs, and the remaining research challenges to be addressed. IBC has exciting prospects for making BAN technologies more practical in the future.


title: Digital motherhood: how does technology help new mothers?

abstract: New mothers can experience social exclusion, particularly during the early weeks when infants are solely dependent on their mothers. We used ethnographic methods to investigate whether technology plays a role in supporting new mothers. Our research identified two core themes: (1) the need to improve confidence as a mother; and (2) the need to be more than \'18just' a mother. We reflect on these findings both in terms of those interested in designing applications and services for motherhood and also the wider CHI community.


title: Tooth labeling in cone-beam CT using deep convolutional neural network for forensic identification

abstract: In large disasters, dental record plays an important role in forensic identification. However, filing dental charts for corpses is not an easy task for general dentists. Moreover, it is laborious and time-consuming work in cases of large scale disasters. We have been investigating a tooth labeling method on dental cone-beam CT images for the purpose of automatic filing of dental charts. In our method, individual tooth in CT images are detected and classified into seven tooth types using deep convolutional neural network. We employed the fully convolutional network using AlexNet architecture for detecting each tooth and applied our previous method using regular AlexNet for classifying the detected teeth into 7 tooth types. From 52 CT volumes obtained by two imaging systems, five images each were randomly selected as test data, and the remaining 42 cases were used as training data. The result showed the tooth detection accuracy of 77.4% with the average false detection of 5.8 per image. The result indicates the potential utility of the proposed method for automatic recording of dental information.


title: Cloud computing and emerging IT platforms: Vision, hype, and reality for . . .

abstract: With the significant advances in Information and Communications Technology (ICT) over the last half century, there is an increasingly perceived vision that computing will one day be the 5th utility (after water, electricity, gas, and telephony). This computing utility, like all other four existing utilities, will provide the basic level of computing service that is considered essential to meet the everyday needs of the general community. To deliver this vision, a number of computing paradigms have been proposed, of which the latest one is known as Cloud computing. Hence, in this paper, we define Cloud computing and provide the architecture for creating Clouds with market-oriented resource allocation by leveraging technologies such as Virtual Machines (VMs). We also provide insights on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain Service Level Agreement (SLA)-oriented resource allocation. In addition, we reveal our early thoughts on interconnecting Clouds for dynamically creating global Cloud exchanges and markets. Then, we present some representative Cloud platforms, especially those developed in industries, along with our current work towards realizing market-oriented resource allocation of Clouds as realized in Aneka enterprise Cloud technology. Furthermore, we highlight the difference between High Performance Computing (HPC) workload and Internet-based services workload. We also describe a meta-negotiation infrastructure to establish global Cloud exchanges and markets, and illustrate a case study of harnessing 'Storage Clouds' for high performance content delivery. Finally, we conclude with the need for convergence of competing IT paradigms to deliver our 21st century vision.


title: Curriculum learning of multiple tasks

abstract: Sharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data. However, in a realistic scenario of multi-task learning not all tasks are equally related to each other, hence it could be advantageous to transfer information only between the most related tasks. In this work we propose an approach that processes multiple tasks in a sequence with sharing between subsequent tasks instead of solving all tasks jointly. Subsequently, we address the question of curriculum learning of tasks, i.e. finding the best order of tasks to be learned. Our approach is based on a generalization bound criterion for choosing the task order that optimizes the average expected classification performance over all tasks. Our experimental results show that learning multiple related tasks sequentially can be more effective than learning them jointly, the order in which tasks are being solved affects the overall performance, and that our model is able to automatically discover a favourable order of tasks.


title: Malware characteristics and threats on the internet ecosystem

abstract: Malware encyclopedias now play a vital role in disseminating information about security threats. Coupled with categorization and generalization capabilities, such encyclopedias might help better defend against both isolated and clustered specimens.In this paper, we present Malware Evaluator, a classification framework that treats malware categorization as a supervised learning task, builds learning models with both support vector machines and decision trees and finally, visualizes classifications with self-organizing maps. Malware Evaluator refrains from using readily available taxonomic features to produce species classifications. Instead, we generate attributes of malware strains via a tokenization process and select the attributes used according to their projected information gain. We also deploy word stemming and stopword removal techniques to reduce dimensions of the feature space. In contrast to existing approaches, Malware Evaluator defines its taxonomic features based on the behavior of species throughout their life-cycle, allowing it to discover properties that previously might have gone unobserved. The learning and generalization capabilities of the framework also help detect and categorize zero-day attacks. Our prototype helps establish that malicious strains improve their penetration rate through multiple propagation channels as well as compact code footprints; moreover, they attempt to evade detection by resorting to code polymorphism and information encryption. Malware Evaluator also reveals that breeds in the categories of Trojan, Infector, Backdoor, and Worm significantly contribute to the malware population and impose critical risks on the Internet ecosystem.


Software Vulnerability Analysis


title: Concurrent HER2 vaccination and inhibition of kinase activity: safety and immunogenicity.

abstract: Evaluation of: Hamilton E, Blackwell K, Hobeika AC et al. Phase I clinical trial of HER2-specific immunotherapy with concomitant HER2 kinase inhibition. J. Transl. Med. 10, 28 (2012). Passive immunotherapy with the monoclonal antibody trastuzumab and tyrosine kinase activity inhibition with lapatinib are HER2-targeted therapies used in the clinic for the treatment of HER2-overexpressing breast cancers. Unfortunately, the therapeutic efficacy of both these therapies is abolished by primary and acquired tumor resistance. Active immunotherapy against HER2, which, thanks to trastuzumab, is a clinically validated tumor-associated antigen, might provide an alternative therapeutic strategy for HER2-overexpressing breast cancers. This Phase I study of HER2 immunotherapy with concomitant lapatinib treatment in 12 patients with metastatic breast cancer resistant to trastuzumab demonstrates the feasibility and safety of concurrent vaccination against HER2 and inhibition of HER1 and HER2 kinases. However, it is incon...


title: Going digital: understanding paper and photo documentation practices in early childhood education

abstract: Documentation of development is a critical aspect of the work of early childhood education teachers. Through field observations and interviews, we detail the process and tools used in documenting development for children age three months to five-years-old at one school. Teachers use paper forms and printed photos to record and analyze observations of development. The evolving final product is a paper portfolio of development. This practice relies heavily on the teacher's ability to objectively observe children in situ, create a record of the activity, and make salient the link between evidence and developmental milestones. We describe current paper and photo documentation practices in light of an online record keeping system that will be introduced at this school within the next year. The present analysis contributes to a growing literature on the role of paper and digital media in documentation.


title: Third Party Auditing (TPA) for Data Storage Security in Cloud with RC5 Algorithm

abstract: Cloud computing is an internet based computing which enables sharing of services. Cloud computing allows users to use applications without installation any application and access their personal files and application at any computer with internet or intranet access. Many users place their data in the cloud, so correctness of data and security is a prime concern.Cloud Computing is technology for next generation Information and Software enabled work that is capable of changing the software working environment. It is interconnecting the large-scalecomputing resources to effectively integrate, and to computing resources as a service to users. To ensure the correctness of data, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the data stored in the cloud., the auditing process should bring in no new vulnerabilities towards user data privacy, and introduce no additional online burden to user. In this paper, we propose a secure cloud storage system supporting privacy-preserving public auditing. We further extend our result to enable the TPA to perform audits for multiple users simultaneously and efficiently with RC5 Encryption Algorithm. This shows the proposed scheme is highly efficient and data modification attack, and even server colluding attacks. Here Work is focuses on RC5 Encryption Algorithm for stored data in cloud. Resulted encrypted method is secure and easy to use.


title: CCIoT-CMfg: Cloud Computing and Internet of Things-Based Cloud Manufacturing Service System

abstract: Recently, Internet of Things (IoT) and cloud computing (CC) have been widely studied and applied in many fields, as they can provide a new method for intelligent perception and connection from M2M (including man-to-man, man-to-machine, and machine-to-machine), and on-demand use and efficient sharing of resources, respectively. In order to realize the full sharing, free circulation, on-demand use, and optimal allocation of various manufacturing resources and capabilities, the applications of the technologies of IoT and CC in manufacturing are investigated in this paper first. Then, a CC- and IoT-based cloud manufacturing (CMfg) service system (i.e., CCIoT-CMfg) and its architecture are proposed, and the relationship among CMfg, IoT, and CC is analyzed. The technology system for realizing the CCIoT-CMfg is established. Finally, the advantages, challenges, and future works for the application and implementation of CCIoT-CMfg are discussed.


title: A timeline visualization system for road traffic big data

abstract: The rapid converging of big data and IoT (Internet of Things) technologies provides more opportunities in the area of road traffic applications. In this paper, we discuss a timeline visualization tool which enables us to better understand of traffic behaviors from road traffic big data.


title: Good Friends, Bad News - Affect and Virality in Twitter

abstract: The link between affect, defined as the capacity for sentimental arousal on the part of a message, and virality, defined as the probability that it be sent along, is of significant theoretical and practical importance, e.g. for viral marketing. The basic measure of virality in Twitter is the probability of retweet and we are interested in which dimensions of the content of a tweet leads to retweeting. We hypothesize that negative news content is more likely to be retweeted, while for non-news tweets positive sentiments support virality. To test the hypothesis we analyze three corpora: A complete sample of tweets about the COP15 climate summit, a random sample of tweets, and a general text corpus including news. The latter allows us to train a classifier that can distinguish tweets that carry news and non-news information. We present evidence that negative sentiment enhances virality in the news segment, but not in the non-news segment. Our findings may be summarized ‘If you want to be cited: Sweet talk your friends or serve bad news to the public’.


title: 10.1177/0894439304271540 SOCIAL Barak / SEXUAL SCIENCE HARASSMENT COMPUTER REVIEW ON THE INTERNET Sexual Harassment on the Internet

abstract: Sexual harassment offline is a well-known, highly prevalent, extensively investigated, and intensively treated social problem. An accepted model classifies sexual harassment behaviors into the categories of gender harassment, unwanted sexual attention, and sexual coercion. Theory and research show that sexual harassment behavior occurs as a product of person × situation characteristics and has substantial personal and organizational costs. This article reviews the available information on sexual harassment in cyberspace, equates this phenomenon with what has been learned about sexual harassment offline, points to specific characteristics of online culture and technology that reinforce the behavior, and proposes ways of promoting prevention.


title: What's in a name: the taxonomic status of human head and body lice.

abstract: Human head lice (Anoplura: Pediculidae: Pediculus) are pandemic, parasitizing countless school children worldwide due to the evolution of insecticide resistance, and human body (clothing) lice are responsible for the deaths of millions as a result of vectoring several deadly bacterial pathogens. Despite the obvious impact these lice have had on their human hosts, it is unclear whether head and body lice represent two morphological forms of a single species or two distinct species. To assess the taxonomic status of head and body lice, we provide a synthesis of publicly available molecular data in GenBank, and we compare phylogenetic and population genetic methods using the most diverse geographic and molecular sampling presently available. Our analyses find reticulated networks, gene flow, and a lack of reciprocal monophyly, all of which indicate that head and body lice do not represent genetically distinct evolutionary units. Based on these findings, as well as inconsistencies of morphological, behavioral, and ecological variability between head and body lice, we contend that no known species concept would recognize these louse morphotypes as separate species. We recommend recognizing head and body lice as morphotypes of a single species, Pediculus humanus, until compelling new data and analyses (preferably analyses of fast evolving nuclear markers in a coalescent framework) indicate otherwise.


title: Robot Enhanced Therapy for Children with Autism (DREAM): A Social Model of Autism

abstract: The development of social robots for children with autism has been a growth field for the past 15 years. This article reviews studies in robots and autism as a neurodevelopmental disorder that impacts socialcommunication development, and the ways social robots could help children with autism develop social skills. Drawing on ethics research from the EU-funded Development of Robot-Enhanced Therapy for Children with Autism (DREAM) project (framework 7), this paper explores how ethics evolves and developed in this European project.


title: High resolution partial fingerprint alignment using pore – valley descriptors

abstract: This paper discusses the alignment of high resolution partial fingerprints, which is a crucial step in partial fingerprint recognition. The previously developed fingerprint alignment methods, including minutia-based and non-minutia feature based ones, are unsuitable for partial fingerprints because small fingerprint fragments often do not have enough features required by these methods. In this paper, we propose a new approach to aligning high resolution partial fingerprints based on pores, a type of fingerprint fine ridge features that are abundant on even small fingerprint areas. Pores are first extracted from the fingerprint images by using a difference of Gaussian filtering approach. After pore detection, a novel pore-valley descriptor (PVD) is proposed to characterize pores based on their locations and orientations, as well as the ridge orientation fields and valley structures around them. A PVD-based coarse-to-fine pore matching algorithm is then developed to locate pore correspondences. Once the corresponding pores are determined, the alignment transformation between two partial fingerprints can be estimated. The proposed method is compared with representative minutia based and orientation field based methods using the established high resolution partial fingerprint dataset and two fingerprint matchers. The experimental results show that the PVD-based method can more accurately locate corresponding feature points, estimate the alignment transformations, and hence significantly improve the accuracy of high resolution partial fingerprint recognition.


title: The Effect of Probiotic Yogurt on Blood Glucose and cardiovascular Biomarkers in Patients with Type II Diabetes: A Randomized Controlled Trial

abstract: Background: Given the high prevalence of type II diabetes and its complications, the evidence regarding the beneficial effects of probiotic yogurt on some cardiovascular biomarkers in diabetic patients is worthy of investigation. Aim: To investigate the effect of probiotic yogurt on blood glucose level and cardiovascular biomarkers in patients with type II diabetes. Method:This randomized, clinical trial was conducted on 90 patients with type II diabetes who visited the 5 Azar diabetes clinic in Gorgan, Iran, in 2014. The intervention group consumed three 100 g packages of probiotic yogurt per day for four weeks, while the control group used an equal amount of plain yogurt. Dietary intake, as well as anthropometric and biochemical parameters were measured before and after the trial. To analyze the data, independent t-test, paired t-test, and analysis of covariance were performed, using SPSS version 18. Results: The mean ages of the intervention and control groups were 50.49Â±10.92 and 50.13Â±9.20 years, respectively. In the intervention group, paired t-test showed significant differences between mean levels of blood glucose, cholesterol, low-density lipoprotein (LDL), triglycerides, diastolic blood pressure, and glycated hemoglobin before and after four weeks of daily intake of probiotic yogurt (P 0.05). At the end of trial, the independent t-test showed a significant difference between the two groups in terms of mean levels of blood glucose, LDL, triglycerides, blood pressure, and glycated hemoglobin (P<0.05).Implications for Practice: Consumption of probiotic yogurt showed beneficial effects on blood glucose, glycated hemoglobin, blood pressure, and serum lipid levels in the intervention group. However, it had no significant effect on cholesterol, high-density lipoprotein, and C-reactive protein as compared with the control group. The authors recommend further longitudinal studies to draw a definitive conclusion in this regard. Overall, consumption of probiotic yoghurt can be recommended as an adjunctive therapy for type II diabetic patients.


title: Grasping Force Control for a Robotic Hand by Slip Detection Using Developed Micro Laser Doppler Velocimeter

abstract: The purpose of this paper is to show the feasibility of grasping force control by feeding back signals of the developed micro-laser Doppler velocimeter (μ-LDV) and by discriminating whether a grasped object is slipping or not. LDV is well known as a high response surface velocity sensor which can measure various surfaces-such as metal, paper, film, and so on-thus suggesting the potential application of LDV as a slip sensor for grasping various objects. However, the use of LDV as a slip sensor has not yet been reported because the size of LDVs is too large to be installed on a robotic fingertip. We have solved the size problem and enabled the performance of a feasibility test with a few-millimeter-scale LDV referred to as micro-LDV (μ-LDV) by modifying the design which was adopted from MEMS (microelectromechanical systems) fabrication process. In this paper, by applying our developed μ-LDV as a slip sensor, we have successfully demonstrated grasping force control with three target objects-aluminum block, wood block, and white acrylic block-considering that various objects made of these materials can be found in homes and factories, without grasping force feedback. We provide proofs that LDV is a new promising candidate slip sensor for grasping force control to execute target grasping.


title: Application-specific slicing for MVNO and traffic characterization [Invited]

abstract: In this paper, we apply the concept of a software-defined data plane to defining new services for mobile virtual network operators (MVNOs) to empower them with the capability of tailoring fine-grained subscription plans that can meet users' demands. For this purpose, we have recently proposed the concept of application-and/ or device-specific slicing that classifies application-and/or device-specific traffic into slices and introduced various applications of our proposed system [Optical Fiber Communication Conf. (OFC), 2016, paper Th3I.4]. This paper reports the prototype implementation of the proposal into a real MVNO connecting customized smartphones so we can identify applications/devices from the given traffic with 100% accuracy. We first characterize the traffic patterns of popular applications to build a detailed understanding of how network resources are utilized for real users and applications and what their demands on different network resources are. Then, we classify traffic according to the devices and find that the flow characteristics of LTE RAN slices are very similar to those of 3G. We discover that most of the bandwidth is consumed by those flows with the duration of several hundreds of seconds, and the flow size is larger than 5 Mbytes in both LTE and 3G MVNO networks.