title: A stage maturity model for enterprise resource planning systems use

abstract: Enterprise Resource Planning (ERP) systems dominate the information technology landscape of many companies. Organizations are at different stages in the implementation process ranging from the initial analysis of implementation options, through completed standard implementations and to the sophisticated exploitation of ERP systems using advanced knowledge management, customer relationship management and supply chain management systems. The authors present a maturity model for ERP systems that identifies three stages and this is illustrated using case data selected from the study which is based on 24 organizations in the US and Europe. In Stage 1, organizations are managing legacy systems and starting the ERP project. In Stage 2, implementation is complete and the functionality of the ERP system is being exploited across the organization. In Stage 3, organizations have normalised the ERP system into the organization and are engaged in the process of obtaining strategic value from the system by using additional systems such as customer relationship management, knowledge management and supply chain planning. It is shown that the organizations follow an S-shaped curve, and that most companies are in the middle stage. An analysis of the implications for organizations at each stage of the maturity model is presented which will be of value to practising managers. The implications are categorised as impacts on cost, entropy, complexity, flexibility and strategic competitiveness.


title: Inductive Coherence

abstract: While probability theory is normally applied to external environments, there has been some recent interest in probabilistic modeling of the outputs of computations that are too expensive to run. Since mathematical logic is a powerful tool for reasoning about computer programs, we consider this problem from the perspective of integrating probability and logic. Recent work on assigning probabilities to mathematical statements has used the concept of coherent distributions, which satisfy logical constraints such as the probability of a sentence and its negation summing to one. Although there are algorithms which converge to a coherent probability distribution in the limit, this yields only weak guarantees about finite approximations of these distributions. In our setting, this is a significant limitation: Coherent distributions assign probability one to all statements provable in a specific logical theory, such as Peano Arithmetic, which can prove what the output of any terminating computation is; thus, a coherent distribution must assign probability one to the output of any terminating computation. To model uncertainty about computations, we propose to work with approximations to coherent distributions. We introduce inductive coherence, a strengthening of coherence that provides appropriate constraints on finite approximations, and propose an algorithm which satisfies this criterion.


title: Graph Theory Applications in Network Security

abstract: Graph theory has become a very critical component in many applications in the computing field including networking and security. Unfortunately, it is also amongst the most complex topics to understand and apply. In this paper, we review some of the key applications of graph theory in network security. We first cover some algorithmic aspects, then present network coding and its relation to routing.


title: A 2.4GHz Non-Contact Biosensor System for Continuous Monitoring of Vital-Signs

abstract: In this chapter, we present a novel Doppler-based vital signs biosensor that can monitor the respiration and heartbeat rates of a person remotely without the need of any obstructions like patches, cords, etc. We will discuss the sensor operation principle and present three generations of systems that were designed to accurately extract the respiration rate and heartbeat of subjects using the Doppler radar principle. The systems have been realized using discrete custom off the shelf (COTS) parts. The first generation of the biosensor system consisted of discrete RF components system and a bulky SRS560 amplifier and filter box. Later generations of sensors consisted of custom designed printed circuits boards (PCBs) for the Doppler transceiver and for performing the analog signal processing. The data obtained using these non-contact biosensor systems was processed and logged in real-time using a LabVIEW© Graphic User Interface (GUI). Digital signal processing extracts the vital signs by filtering, auto correlating and calculating the Discrete Fourier Transform (DFT) of the waveforms. A comparison of performance among the three different generations of sensors shows that a quadrature transceiver system using autocorrelation can extract the respiration rates and heartbeat rates most accurately. Our single PCB version of the biosensor system was found to perform as well as the system using bulky components and SRS560 box. Good data accuracy has been observed on the quadrature radar sensor system with mean detection errors for respiration rate within ~1 beat/min and for heart rate within ~3 beat/min. The continuous vital signs data measured from these portable sensors can also be wirelessly transferred to healthcare professionals to make life saving decisions and diagnosis of symptoms. In the future, our vision is that a continuous log of these vital signs info can also be used to remotely monitor and gauge the recovery of patients, and even for the prevention or prediction of severe illnesses and complications.


title: Semantic Image Inpainting with Perceptual and Contextual Losses.

abstract: In this paper, we propose a novel method for image inpainting based on a Deep Convolutional Generative Adversarial Network (DCGAN). We define a loss function consisting of two parts: (1) a contextual loss that preserves similarity between the input corrupted image and the recovered image, and (2) a perceptual loss that ensures a perceptually realistic output image. Given a corrupted image with missing values, we use back-propagation on this loss to map the corrupted image to a smaller latent space. The mapped vector is then passed through the generative model to predict the missing content. The proposed framework is evaluated on the CelebA and SVHN datasets for two challenging inpainting tasks with random 80% corruption and large blocky corruption. Experiments show that our method can successfully predict semantic information in the missing region and achieve pixel-level photorealism, which is impossible by almost all existing methods.


title: Autophagy in the Pathogenesis of Disease

abstract: Autophagy is a lysosomal degradation pathway that is essential for survival, differentiation, development, and homeostasis. Autophagy principally serves an adaptive role to protect organisms against diverse pathologies, including infections, cancer, neurodegeneration, aging, and heart disease. However, in certain experimental disease settings, the self-cannibalistic or, paradoxically, even the prosurvival functions of autophagy may be deleterious. This Review summarizes recent advances in understanding the physiological functions of autophagy and its possible roles in the causation and prevention of human diseases.


title: Recognizing new activities with limited training data

abstract: Activity recognition (AR) systems are typically built to recognize a predefined set of common activities. However, these systems need to be able to learn new activities to adapt to a user's needs. Learning new activities is especially challenging in practical scenarios when a user provides only a few annotations for training an AR model. In this work, we study the problem of recognizing new activities with a limited amount of labeled training data. Due to the shortage of labeled data, small variations of the new activity will not be detected resulting in a significant degradation of the system's recall. We propose the FE-AT (Feature-based and Attribute-based learning) approach, which leverages the relationship between existing and new activities to compensate for the shortage of the labeled data. We evaluate FE-AT on three public datasets and demonstrate that it outperforms traditional AR approaches in recognizing new activities, especially when only a few training instances are available.


title: Computational Modelling of Cholera Bacteriophage with Treatment

abstract: This paper examines the computational modelling of cholera bacteriophage with treatment. A nonlinear mathematical model for cholera bacteriophage and treatment is formulated and ana- lysed. The effective reproduction number of the nonlinear model system is calculated by next generation operator method. By using the next generation matrix approach, the disease-free equi- librium is found to be locally stable at threshold parameter less than unity and unstable at thresh- old parameter greater than unity. Globally, the disease free equilibrium point is not stable due to existence of forward bifurcation at threshold parameter equal to unity. Stability analysis and nu- merical simulations suggest that the combination of bacteriophage and treatment may contribute to lessening the severity of cholera epidemics by reducing the number of Vibrio cholerae in the en- vironment. Hence with the presence of bacteriophage virus and treatment, cholera is self-limiting in nature.


title: PREDICTION OF THE SUCCESSFULNESS OF TENNIS PLAYERS WITH MACHINE LEARNING METHODS

abstract: The aim of the study was to examine the predictability of the competitive performance of Slovene tennis players by using the most promising morphological measures and motor tests selected by automatic computer methods and by experienced tennis coaches by means of machine learning methods. The analysis included altogether 1,002 male and female tennis players who had undergone regular testing by the National Tennis Association and were positioned on the ranking list of the Slovene Tennis Association between the years 1993 and 2008. Selections of the most promising variables by means of the two automatic methods yielded similar results, whereas the selection performed on the basis of estimates made by coaches differed considerably. With regard to the analysis by means of classification methods, an accurate predictability of competitive performance for the age category younger than 16 years was observed, while the results of predictions for the age category older than 16 were poor. Among the regression methods, as opposed to the linear regression, which has yielded satisfactory results, regression trees served no useful purpose in practice. Automatic methods for identifying the most promising variables proved to be more successful than those of the coaches, which was most clearly noticeable with regard to the female tennis players and when linear regression was used.


title: The Economics of State Fragmentation - Assessing the Economic Impact of Secession

abstract: This paper provides empirical evidence that declaring independence significantly lowers per capita GDP based on a large panel of countries covering the period 1950-2013. To do so, we rely on a semi-parametric identification strategy that controls for the confounding effects of past GDP dynamics, anticipation effects, unobserved heterogeneity, model uncertainty and effect heterogeneity. Our baseline results indicate that declaring independence reduces per capita GDP by around 20\% in the long run. We subsequently propose a novel triple-difference procedure to demonstrate the stability of these results. Another methodological novelty consists of the development of a two-step estimator to shed some light on the primary channels driving our results. We find robust evidence that the adverse effects of independence increase in the extent of surface area loss, pointing to the presence of economies of scale, but that they are mitigated when newly independent states liberalize their trade regime or use their new-found political autonomy to democratize.


Freezing and sublimation effects on cryo-SEM imaging and microanalysis


title: Dolphin swarm algorithm

abstract: By adopting the distributed problem-solving strategy, swarm intelligence algorithms have been successfully applied to many optimization problems that are difficult to deal with using traditional methods. At present, there are many well-implemented algorithms, such as particle swarm optimization, genetic algorithm, artificial bee colony algorithm, and ant colony optimization. These algorithms have already shown favorable performances. However, with the objects becoming increasingly complex, it is becoming gradually more difficult for these algorithms to meet human’s demand in terms of accuracy and time. Designing a new algorithm to seek better solutions for optimization problems is becoming increasingly essential. Dolphins have many noteworthy biological characteristics and living habits such as echolocation, information exchanges, cooperation, and division of labor. Combining these biological characteristics and living habits with swarm intelligence and bringing them into optimization problems, we propose a brand new algorithm named the ‘dolphin swarm algorithm’ in this paper. We also provide the definitions of the algorithm and specific descriptions of the four pivotal phases in the algorithm, which are the search phase, call phase, reception phase, and predation phase. Ten benchmark functions with different properties are tested using the dolphin swarm algorithm, particle swarm optimization, genetic algorithm, and artificial bee colony algorithm. The convergence rates and benchmark function results of these four algorithms are compared to testify the effect of the dolphin swarm algorithm. The results show that in most cases, the dolphin swarm algorithm performs better. The dolphin swarm algorithm possesses some great features, such as first-slow-then-fast convergence, periodic convergence, local-optimum-free, and no specific demand on benchmark functions. Moreover, the dolphin swarm algorithm is particularly appropriate to optimization problems, with more calls of fitness functions and fewer individuals.


Home Appliance Identification for Nilm Systems Based on Deep Neural Networks


RELIGION AND HEALTH: A SYNTHESIS


title: Understanding Student Complaints in the Service Learning Pedagogy

abstract: This study draws upon student evaluations across 3 years of service learning (SL) courses at a large, urban, faith-based university identifying issues of greatest concern to students. Analysis of qualitative responses revealed perspectives on the limits, pitfalls, and barriers to successful SL student work in the community replicated over the 3 year timeframe. Over 2,200 written responses were coded and tested for reliability for each of 3 years. The top three comments SL students were most likely to express included: (1) concern about their placement in the community, for example, that sites were not prepared or that further goal-setting, communication and training by the university was necessary; (2) that the university’s choice of sites may have been ill considered; and, (3) that there were problems with time and scheduling. Ongoing assessment of students’ perspectives on their placement experience identifies practices that can enhance their learning and contributions toward building stronger communities.


title: Agile Methods and CMMI: Compatibility or Conflict?

abstract: During the last years, agile methods like eXtreme Programming have become increasingly popular. Parallel to this, more and more organizations rely on process maturity models to assess and improve their own processes or those of suppliers, since it has been getting clear that most project failures can be imputed to inconsistent, undisciplined processes. Many organizations demand CMMI compliance of projects where agile methods are employed. In this situation it is necessary to analyze the interrelations and mutual restrictions between agile methods and approaches for software process analysis and improvement. This paper analyzes to what extent the CMMI process areas can be covered by XP and where adjustments of XP have to be made. Based on this, we describe the limitations of CMMI in an agile environment and show that level 4 or 5 are not feasible under the current specifications of CMMI and XP.


title: Gametophytic and sporophytic expression of an anther-specific Arabidopsis thaliana gene.

abstract: Genomic and cDNA clones of the anther-specific APG gene from Arabidopsis thaliana and Brassica napus, which encodes a novel proline-rich protein, were isolated and characterized. Southern blotting and Northern analysis of male fertile and cytoplasmic male sterile varieties of B. napus showed that the APG gene is present as a single copy in the Arabidopsis genome, and that the B. napus APG gene is a member of a small anther-specific gene family. Analysis of developmentally staged B. napus flower buds indicated that APG transcript is confined to the anther during the period of microspore development. Reporter gene fusions established that the APG promoter directs expression in a number of cell types in anthers of transformed plants. This expression is consistent with the temporal pattern of mRNA accumulation in B. napus buds and follows a complex developmental pattern. Most significantly, the promoter is active in both sporophytic and gametophytic cell types, with activity of the transgene in each cell type being delineated by various cytological markers.


Clustering in Big Data


title: Selective carbohydrate utilization by lactobacilli and bifidobacteria

abstract: Aim - To evaluate the ability of specific carbohydrates, including commercially available products, to support the growth of representatives of two well-known groups of gut commensals, namely lactobacilli and bifidobacteria. Methods and Results - Sixty-eight bacterial strains, representing 29 human-derived lactobacilli and 39 bifidobacteria (both human- and animal-derived), were tested for their ability to metabolize 10 different carbohydrates. Analysis of growth and metabolic activity was performed using a combination of diagnostic parameters, such as final OD600, final pH, fermentation end products and growth rate. Conclusions - The data assembled in this study provide significant complementary and comparative information on the growth-promoting properties of a range of carbohydrates, while also investigating interspecies differences between lactobacilli and/or bifidobacteria with regard to their carbohydrate utilization abilities. Galacto-oligosaccharides (GOS) and lactulose were shown to support the most favourable growth characteristics, whereas relatively poor growth of lactobacilli and bifidobacteria was observed on inulin, maltodextrin and polydextrose. GOS/inulin (9 : 1) and fructo-oligosaccharides (FOS)/inulin mixtures supported mostly similar growth abilities to those obtained for GOS and FOS, respectively. Microbial consumption of GOS, as determined by high-performance anion-exchange chromatography with pulsed amperometric detection, was evident for both lactobacilli and bifidobacteria.


title: Opportunistic Environmental Sensing with Smartphones: a Critical Review of Current Literature and Applications

abstract: PURPOSE OF REVIEW ::: This review sought to summarize recent literature and applications of passive, or opportunistic, mobile sensing in the fields of exposure science in built environment settings; highlight innovative opportunistic sensing systems; and analyze their functionality, significant features, and limitations. ::: ::: ::: RECENT FINDINGS ::: Fifty-two papers related to opportunistic environmental sensing from 2009 or later were related to this review, of which 27 were included. An array of applications have emerged in environmental monitoring, employing anywhere from one to six of the phone's on-board sensors. The viability of an application is determined by several key factors: the number and quality of sensors on-board the smartphone; power and processing demand; algorithm complexity; data security; mobile network coverage; reliance on external data sources; minimum number of users required; and degree of user burden when using the application. Some factors are universal, while others are more context-specific. Future research should assess sensing applications based on these factors.


Shadow Detection in Single Still Images using TAM based Multi Step Algorithm


title: Performance Evaluation of RPL Protocol under Mobile Sybil Attacks

abstract: In Sybil attacks, a physical adversary takes multiple fabricated or stolen identities to maliciously manipulate the network. These attacks are very harmful for Internet of Things (IoT) applications. In this paper we implemented and evaluated the performance of RPL (Routing Protocol for Low-Power and Lossy Networks) routing protocol under mobile sybil attacks, namely SybM, with respect to control overhead, packet delivery and energy consumption. In SybM attacks, Sybil nodes take the advantage of their mobility and the weakness of RPL to handle identity and mobility, to flood the network with fake control messages from different locations. To counter these type of attacks we propose a trust-based intrusion detection system based on RPL.


title: How consumers’ use of brand vs attribute information evolves over time

abstract: Purpose – Prior research has identified that brands have a differential impact on consumer evaluations across various brand benefits. This paper investigates whether these effects are stable over time, or evolve in a consistent way. Design/methodology/approach – Consumer evaluations of brand benefits into overall brand and detailed attribute-specific sources through a standard confirmatory factor analysis approach have been decomposed. Two unique datasets have been analyzed; the first contains cross-sectional data from Kodak across four different consumer goods categories, and the other is a longitudinal dataset from the USA and Canada in the surface-cleaning category, covering seven brands over five years (2007-2011). Findings – A systematic evolution in brand effects has been demonstrated: a general trend is that over time and with experience, consumers rely more heavily on overall brand information to develop their evaluations. However, early in a brand’s life, or later when circumstances compel consum...


title: Probabilistic Graph-based Dependency Parsing with Convolutional Neural Network

abstract: This paper presents neural probabilistic parsing models which explore up to thirdorder graph-based parsing with maximum likelihood training criteria. Two neural network extensions are exploited for performance improvement. Firstly, a convolutional layer that absorbs the influences of all words in a sentence is used so that sentence-level information can be effectively captured. Secondly, a linear layer is added to integrate different order neural models and trained with perceptron method. The proposed parsers are evaluated on English and Chinese Penn Treebanks and obtain competitive accuracies.


Serbo-Croatian Split Vocatives: Class Change via Lexicalization


title: Modeling and Simulation Investigation of Sensitivity of Symmetric Split Gate Junctionless FET for Biosensing Application

abstract: In this paper, an analytical model has been developed for a split gate junctionless (JL) MOSFET, which works as a bio-transistor to detect the analytes (biomolecules), such as protein, DNA, enzyme, cell, and so on, using the dielectric-modulation technique. We have also studied the neutralization of charged analytes when charge reduction due to drying out. The analytical model has been investigated by solving 2-D Poisson’s equation. For the validation of analytical results, a split gate JL MOSFET has been virtually fabricated through sprocess and the electrostatic properties of device simulated by sdevice tools of the “Sentaurus” device simulator. For the bio-species immobilization, an underlap region also known as open cavity is formed in the device through etching oxide material and gate material from the middle of the channel. The electrostatic properties of device, such as surface potential, threshold voltage, drain current, and sensitivity, get affected by the immobilization of analytes at the SiO2 layer (which acts as an adhesion layer in the cavity region). The change in the threshold voltage and transfer characteristics of the device is pondered as the sensing metric to detect the bio-targets under dry environment.


title: Decoupling and Control of Real and Reactive Power in Grid-Connected Photovoltaic Power System

abstract: The paper presents a detailed modeling and simulation of different control schemes of the real and reactive power flows in a three-phase voltage source inverter (VSI) interfacing a photovoltaic (PV) generation system to the power grid. Synchronisation of the inverter and grid AC waveforms is achieved using a phase-locked-loop (PLL) circuit. An effective decoupling strategy based on proportional-integral (PI) controllers is designed to eliminate the interaction between the two current components. Finally, the influence of the grid disturbances on the PV system and the influence of the solar energy intermittency on the power grid have been tested. The overall model is implemented in Matlab and Simulink/SimPowerSystems toolboxes. Simulations results with the PV system operating with real irradiance data will be presented to demonstrate the performance of the proposed decoupling and control strategies under different conditions of the power grid. Keywords-Photovoltaic; boost converter; VSC; MPPT


title: Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey

abstract: In order to understand the formation and subsequent evolution of galaxies one must first distinguish between the two main morphological classes of massive systems: spirals and early-type systems. This paper introduces a project, Galaxy Zoo, which provides visual morphological classifications for nearly one million galaxies, extracted from the Sloan Digital Sky Survey (SDSS). This achievement was made possible by inviting the general public to visually inspect and classify these galaxies via the internet. The project has obtained more than 4 × 107 individual classifications made by ∼105 participants. We discuss the motivation and strategy for this project, and detail how the classifications were performed and processed. We find that Galaxy Zoo results are consistent with those for subsets of SDSS galaxies classified by professional astronomers, thus demonstrating that our data provide a robust morphological catalogue. Obtaining morphologies by direct visual inspection avoids introducing biases associated with proxies for morphology such as colour, concentration or structural parameters. In addition, this catalogue can be used to directly compare SDSS morphologies with older data sets. The colour–magnitude diagrams for each morphological class are shown, and we illustrate how these distributions differ from those inferred using colour alone as a proxy for morphology.


Learning to predict closed questions on stack overflow


Chapter 12 DATA MINING IN SOCIAL MEDIA


title: Park Here! a smart parking system based on smartphones' embedded sensors and short range Communication Technologies

abstract: Nowadays, the convergence of Internet of Things (IoT) networking and mobile applications is favoring the deployment of novel and advanced smart parking systems through which users can be informed in real-time about the presence of vacant parking spots close to their destinations. In this paper, we provide an example of such opportunity, by describing Park Here!, a novel mobile application that aims at mitigating the overhead caused by parking spot seeking operations in urban areas. Our solution targets common city environments, where no per-spot sensors are available, and there is no remote service allowing the reservation in-advance of a parking spot. For this scenario, we propose a novel algorithm for the automatic detection of parking actions performed by the user, through the analysis of smartphone embedded sensors' (accelerometer/gyroscope), and of the Bluetooth connectivity. Once a parking event has been detected, an adaptive strategy allows disseminating the information over the target scenario, using a combination of Internet connection to a remote server, and Device-to-Device (D2D) connections over WiFi Direct links. Preliminary experiments demonstrate the accuracy of the proposed algorithm in correctly identifying parking events in an automatic way, and hence in notifying information to other potentially interested users.


title: Cell death by necrosis: towards a molecular definition.

abstract: Necrosis has been defined as a type of cell death that lacks the features of apoptosis and autophagy, and is usually considered to be uncontrolled. Recent research suggests, however, that its occurrence and course might be tightly regulated. After signaling- or damage-induced lesions, necrosis can include signs of controlled processes such as mitochondrial dysfunction, enhanced generation of reactive oxygen species, ATP depletion, proteolysis by calpains and cathepsins, and early plasma membrane rupture. In addition, the inhibition of specific proteins involved in regulating apoptosis or autophagy can change the type of cell death to necrosis. Because necrosis is prominent in ischemia, trauma and possibly some forms of neurodegeneration, further biochemical comprehension and molecular definition of this process could have important clinical implications.


title: PaperSpeckle: microscopic fingerprinting of paper

abstract: Paper forgery is among the leading causes of corruption in many developing regions. In this paper, we introduce PaperSpeckle, a robust system that leverages the natural randomness property present in paper to generate a fingerprint for any piece of paper. Our goal in developing PaperSpeckle is to build a low-cost paper based authentication mechanism for applications in rural regions such as microfinance, healthcare, land ownership records, supply chain services and education which heavily rely on paper based records. Unlike prior paper fingerprinting techniques that have extracted fingerprints based on the fiber structure of paper, PaperSpeckle uses the texture speckle pattern, a random bright/dark region formation at the microscopic level when light falls on to the paper, to extract a unique fingerprint to identify paper. In PaperSpeckle, we show how to extract a "repeatable" texture speckle pattern of a microscopic region of a paper using low-cost machinery involving paper, pen and a cheap microscope. Using extensive testing on different types of paper, we show that PaperSpeckle can produce a robust repeatable fingerprint even if paper is damaged due to crumpling, printing or scribbling, soaking in water or aging with time.


title: Hekaton: SQL server's memory-optimized OLTP engine

abstract: Hekaton is a new database engine optimized for memory resident data and OLTP workloads. Hekaton is fully integrated into SQL Server; it is not a separate system. To take advantage of Hekaton, a user simply declares a table memory optimized. Hekaton tables are fully transactional and durable and accessed using T-SQL in the same way as regular SQL Server tables. A query can reference both Hekaton tables and regular tables and a transaction can update data in both types of tables. T-SQL stored procedures that reference only Hekaton tables can be compiled into machine code for further performance improvements. The engine is designed for high con-currency. To achieve this it uses only latch-free data structures and a new optimistic, multiversion concurrency control technique. This paper gives an overview of the design of the Hekaton engine and reports some experimental results.


title: Ruler Based Automatic C-Arm Image Stitching Without Overlapping Constraint

abstract: In this paper, we propose a new method for stitching multiple fluoroscopic images taken by a C-arm instrument. We employ an X-ray radiolucent ruler with numbered graduations while acquiring the images, and the image stitching is based on detecting and matching ruler parts in the images to the corresponding parts of a virtual ruler. To achieve this goal, we first detect the regular spaced graduations on the ruler and the numbers. After graduation labeling, for each image, we have the location and the associated number for every graduation on the ruler. Then, we initialize the panoramic X-ray image with the virtual ruler, and we “paste” each image by aligning the detected ruler part on the original image, to the corresponding part of the virtual ruler on the panoramic image. Our method is based on ruler matching but without the requirement of matching similar feature points in pairwise images, and thus, we do not necessarily require overlap between the images. We tested our method on eight different datasets of X-ray images, including long bones and a complete spine. Qualitative and quantitative experiments show that our method achieves good results.


title: Manifold Gaussian Processes for regression

abstract: Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness assumptions on the structure of the function to be modeled. To model complex and non-differentiable functions, these smoothness assumptions are often too restrictive. One way to alleviate this limitation is to find a different representation of the data by introducing a feature space. This feature space is often learned in an unsupervised way, which might lead to data representations that are not useful for the overall regression task. In this paper, we propose Manifold Gaussian Processes, a novel supervised method that jointly learns a transformation of the data into a feature space and a GP regression from the feature space to observed space. The Manifold GP is a full GP and allows to learn data representations, which are useful for the overall regression task. As a proof-of-concept, we evaluate our approach on complex non-smooth functions where standard GPs perform poorly, such as step functions and robotics tasks with contacts.


title: SnapPaste: an interactive technique for easy mesh composition

abstract: Editing and manipulation of existing 3D geometric objects are a means to extend their repertoire and promote their availability. Traditionally, tools to compose or manipulate objects defined by 3D meshes are in the realm of artists and experts. In this paper, we introduce a simple and effective user interface for easy composition of 3D mesh-parts for non-professionals. Our technique borrows from the cut-and-paste paradigm where a user can cut parts out of existing objects and paste them onto others to create new designs. To assist the user attach objects to each other in a quick and simple manner, many applications in computer graphics support the notion of “snapping”. Similarly, our tool allows the user to loosely drag one mesh part onto another with an overlap, and lets the system snap them together in a graceful manner. Snapping is accomplished using our Soft-ICP algorithm which replaces the global transformation in the ICP algorithm with a set of point-wise locally supported transformations. The technique enhances registration with a set of rigid to elastic transformations that account for simultaneous global positioning and local blending of the objects. For completeness of our framework, we present an additional simple mesh-cutting tool, adapting the graph-cut algorithm to meshes.


title: Working-memory capacity protects model-based learning from stress

abstract: Accounts of decision-making have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental advances suggest that this classic distinction between habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning, called model-free and model-based learning. Popular neurocomputational accounts of reward processing emphasize the involvement of the dopaminergic system in model-free learning and prefrontal, central executive–dependent control systems in model-based choice. Here we hypothesized that the hypothalamic-pituitary-adrenal (HPA) axis stress response—believed to have detrimental effects on prefrontal cortex function—should selectively attenuate model-based contributions to behavior. To test this, we paired an acute stressor with a sequential decision-making task that affords distinguishing the relative contributions of the two learning strategies. We assessed baseline working-memory (WM) capacity and used salivary cortisol levels to measure HPA axis stress response. We found that stress response attenuates the contribution of model-based, but not model-free, contributions to behavior. Moreover, stress-induced behavioral changes were modulated by individual WM capacity, such that low-WM-capacity individuals were more susceptible to detrimental stress effects than high-WM-capacity individuals. These results enrich existing accounts of the interplay between acute stress, working memory, and prefrontal function and suggest that executive function may be protective against the deleterious effects of acute stress.


title: Novel Flexible Wearable Sensor Materials and Signal Processing for Vital Sign and Human Activity Monitoring

abstract: Advances in flexible electronic materials and smart textile, along with broad availability of smart phones, cloud and wireless systems have empowered the wearable technologies for significant impact on future of digital and personalized healthcare as well as consumer electronics. However, challenges related to lack of accuracy, reliability, high power consumption, rigid or bulky form factor and difficulty in interpretation of data have limited their wide-scale application in these potential areas. As an important solution to these challenges, we present latest advances in novel flexible electronic materials and sensors that enable comfortable and conformable body interaction and potential for invisible integration within daily apparel. Advances in novel flexible materials and sensors are described for wearable monitoring of human vital signs including, body temperature, respiratory rate and heart rate, muscle movements and activity. We then present advances in signal processing focusing on motion and noise artifact removal, data mining and aspects of sensor fusion relevant to future clinical applications of wearable technology.


title: Deep Similarity-Based Batch Mode Active Learning with Exploration-Exploitation

abstract: Active learning aims to reduce manual labeling efforts by proactively selecting the most informative unlabeled instances to query. In real-world scenarios, it's often more practical to query a batch of instances rather than a single one at each iteration. To achieve this we need to keep not only the informativeness of the instances but also their diversity. Many heuristic methods have been proposed to tackle batch mode active learning problems, however, they suffer from two limitations which if addressed would significantly improve the query strategy. Firstly, the similarity amongst instances is simply calculated using the feature vectors rather than being jointly learned with the classification model. This weakens the accuracy of the diversity measurement. Secondly, these methods usually exploit the decision boundary by querying the data points close to it. However, this can be inefficient when the labeled set is too small to reveal the true boundary. In this paper, we address both limitations by proposing a deep neural network based algorithm. In the training phase, a pairwise deep network is not only trained to perform classification, but also to project data points into another space, where the similarity can be more precisely measured. In the query selection phase, the learner selects a set of instances that are maximally uncertain and minimally redundant (exploitation), as well as are most diverse from the labeled instances (exploration). We evaluate the effectiveness of the proposed method on a variety of classification tasks: MNIST classification, opinion polarity detection, and heart failure prediction. Our method outperforms the baselines with both higher classification accuracy and faster convergence rate.


title: ALLMAPS: robust scaffold ordering based on multiple maps

abstract: The ordering and orientation of genomic scaffolds to reconstruct chromosomes is an essential step during de novo genome assembly. Because this process utilizes various mapping techniques that each provides an independent line of evidence, a combination of multiple maps can improve the accuracy of the resulting chromosomal assemblies. We present ALLMAPS, a method capable of computing a scaffold ordering that maximizes colinearity across a collection of maps. ALLMAPS is robust against common mapping errors, and generates sequences that are maximally concordant with the input maps. ALLMAPS is a useful tool in building high-quality genome assemblies. ALLMAPS is available at: https://github.com/tanghaibao/jcvi/wiki/ALLMAPS.


Teaching Learning Practices in Schools under Shiksha Vikash Samiti, Odisha: Perception of Major Stakeholders.


title: Efficient Content Distribution in DOOH Advertising Networks Exploiting Urban Geo-Social Connectivity

abstract: Digital out-of-home (DOOH) advertising networks, comprised of pervasive distributed digital signages (screens), are rapidly growing. It is reported that more than 70% of DOOH revenue comes from local ads, while it is especially challenging to decide when and where to deliver the most suitable ad due to the spatio-temporal dynamics of human mobility and preferences. Understanding urban geo-social connectivity in terms of people movement would greatly benefit ad content distribution, and could potentially be utilized by a large number of mobile applications and geo-social services. However, existing DOOH ad distribution systems are designed to target individuals, which might not be the best choice in public spaces, and do not consider the preferences of "cohort of users". In this paper, we propose an alternative approach to target cohort of users extracting urban geo-social connectivity through large-scale mobile network data and existing geo-social service data. We construct a dynamic urban geo-social connectivity graph, and formulate the problem of distributing ads for maximum exposure to the "right" users under a constrained budget. Hence, we propose a heuristic algorithm. Simulation results show that our system targeting "cohort of users" achieves a maximum 300% improvement compared to naive distributing method in displaying ads to the "right people" when user preferences are completely known, while a minimum of 25% improvement when the knowledge of user preferences is limited.


title: The Influence of Passive Haptic Feedback and Difference Interaction Metaphors on Presence and Task Performance

abstract: This paper explores the influence of passive haptic feedback on presence and task performance using two important interaction metaphors. We compared direct interaction with the user's hand with interaction using a stylus. Twenty-four participants performed a simple selection task consisting of pressing buttons while playing a memory game, with haptic feedback and interaction metaphor as the independent variables. We measured task performance by computing errors and time between button presses. We measured presence with questionnaires and through a new method based on users' involuntary movements. Our results suggest that passive haptic feedback improves both presence and task performance. However, small but significant differences related to the interaction metaphor were only apparent when haptic feedback was not provided.


title: Towards quantifying surgical suturing skill with force, motion and image sensor data

abstract: This work contributes towards the vision of a suture platform that is able to objectively quantify suturing skill by integrating data from multiple sensing modalities. A first step towards such a platform is the synchronization of data from multiple sensor streams and perhaps even multiple systems. We present the design of a novel suture platform with force and motion sensors as well as video capture for recording hand motion. Software methods to synchronize these data, as well as a Graphical User Interface (GUI) that extracts suture cycle data is created. Results indicate that sensor data was successfully synchronized using time-offset calculation. Sensor data was extracted for individual suture cycles for all participants including average force, peak force, average acceleration, peak acceleration and time to complete suture. Our results indicate the viability of the system to synchronize sensor data, enabling its use to provide objective feedback to trainees regarding their suturing skills.


title: An effective approach for classification of advanced malware with high accuracy

abstract: Combating malware is very important for software/systems security, but to prevent the software/systems from the advanced malware, viz. metamorphic malware is a challenging task, as it changes the structure/code after each infection. Therefore in this paper, we present a novel approach to detect the advanced malware with high accuracy by analyzing the occurrence of opcodes (features) by grouping the executables. These groups are made on the basis of our earlier studies [1] that the difference between the sizes of any two malware generated by popular advanced malware kits viz. PS-MPC, G2 and NGVCK are within 5 KB. On the basis of obtained promising features, we studied the performance of thirteen classifiers using N-fold cross-validation available in machine learning tool WEKA. Among these thirteen classifiers we studied in-depth top five classifiers (Random forest, LMT, NBT, J48 and FT) and obtain more than 96.28% accuracy for the detection of unknown malware, which is better than the maximum detection accuracy (95.9%) reported by Santos et al (2013). In these top five classifiers, our approach obtained a detection accuracy of 97.95% by the Random forest.


title: Distribution fields for tracking

abstract: Visual tracking of general objects often relies on the assumption that gradient descent of the alignment function will reach the global optimum. A common technique to smooth the objective function is to blur the image. However, blurring the image destroys image information, which can cause the target to be lost. To address this problem we introduce a method for building an image descriptor using distribution fields (DFs), a representation that allows smoothing the objective function without destroying information about pixel values. We present experimental evidence on the superiority of the width of the basin of attraction around the global optimum of DFs over other descriptors. DFs also allow the representation of uncertainty about the tracked object. This helps in disregarding outliers during tracking (like occlusions or small misalignments) without modeling them explicitly. Finally, this provides a convenient way to aggregate the observations of the object through time and maintain an updated model. We present a simple tracking algorithm that uses DFs and obtains state-of-the-art results on standard benchmarks.


Neuroanatomical characterisation of Alzheimer ’ s disease using deep learning


title: Super Fast Event Recognition in Internet Videos

abstract: Techniques for recognizing high-level events in consumer videos on the Internet have many applications. Systems that produced state-of-the-art recognition performance usually contain modules requiring extensive computation, such as the extraction of the temporal motion trajectories, which cannot be deployed on large-scale datasets. In this paper, we provide a comprehensive study on efficient methods in this area and identify technical options for super fast event recognition in Internet videos. We start from analyzing a multimodal baseline that has produced good performance on popular benchmarks, by systematically evaluating each component in terms of both computational cost and contribution to recognition accuracy. After that, we identify alternative features, classifiers, and fusion strategies that can all be efficiently computed. In addition, we also provide a study on the following interesting question: for event recognition in Internet videos, what is the minimum number of visual and audio frames needed to obtain a comparable accuracy to that of using all the frames? Results on two rigorously designed datasets indicate that similar results can be maintained by using only a small portion of the visual frames. We also find that, different from the visual frames, the soundtracks contain little redundant information and thus sampling is always harmful. Integrating all the findings, our suggested recognition system is 2,350-fold faster than a baseline approach with even higher recognition accuracies. It recognizes 20 classes on a 120-second video sequence in just 1.78 seconds, using a regular desktop computer.


title: A Comprehensive Review of Axial-Flux Permanent-Magnet Machines

abstract: This paper presents a state-of-the-art review of axial-flux permanent-magnet (AFPM) machines in the aspects of construction, features, electromagnetic and thermal modeling, simulation, analysis, design, materials, and manufacturing. Some key references on the above-mentioned aspects pertaining to the machine are discussed briefly. Particular emphasis is given on the design and performance analysis of AFPM machines. A comparison among different permanent magnet machines is also provided. Thus, this paper makes a bridge between the currently used permanent magnet machines in industry and the recent developments of AFPM machines.


title: Wisdom of crowds for robust gene network inference

abstract: Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.


title: Exploring Chinese users' acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory

abstract: Instant messaging (IM) is a popular Internet application around the world. In China, the competition in the IM market is very intense and there are over 10 IM products available. We examine the intrinsic and extrinsic motivations that affect Chinese users' acceptance of IM based on the theory of planned behavior (TPB), the technology acceptance model (TAM), and the flow theory. Results demonstrate that users' perceived usefulness and perceived enjoyment significantly influence their attitude towards using IM, which in turn impacts their behavioral intention. Furthermore, perceived usefulness, users' concentration, and two components of the theory of planned behavior (TPB): subjective norm and perceived behavioral control, also have significant impact on the behavioral intention. Users' intention determines their actual usage behavior.


title: Analysis and Improvement on the Structure of Variable Reluctance Resolvers

abstract: Resolvers are often used in inverter-driven permanent magnet synchronous motors as the rotor position sensors. This paper analyzes a variable-reluctance (VR) resolver structure capable of generating absolute position signals on the basis of a review of conventional multipole VR resolvers. The analyzed VR resolver is made by varying rotor-pole shape according to a certain waveform, which can be determined by solving an inverse electromagnetic field problem. Finally, the paper proposes several procedures, based on the samples tested and an error analysis, to improve the accuracy of the VR resolver.


title: Applying HCI principles to AR systems design

abstract: To date most Augmented Reality (AR) design guidelines that can be found in the literature are rather narrow suggestions derived from specific problems by researchers. Applying general HCI principles to AR systems has only been partially explored. In this paper we investigate how such general guidelines may relate to the emerging domain of AR application design. CR


title: Simple and Knowledge-intensive Generative Model for Named Entity Recognition

abstract: Almost all of the existing work on Named Entity Recognition (NER) consists of the following pipeline stages – part-of-speech tagging, segmentation, and named entity type classification. The requirement of hand-labeled training data on these stages makes it very expensive to extend to different domains and entity classes. Even with a large amount of hand-labeled data, existing techniques for NER on informal text, such as social media, perform poorly due to a lack of reliable capitalization, irregular sentence structure and a wide range of vocabulary. In this paper, we address the lack of hand-labeled training data by taking advantage of weak super vision signals. We present our approach in two parts. First, we propose a novel generative model that combines the ideas from Hidden Markov Model (HMM) and n-gram language models into what we call an N-gram Language Markov Model (NLMM). Second, we utilize large-scale weak supervision signals from sources such as Wikipedia titles and the corresponding click counts to estimate parameters in NLMM. Our model is simple and can be implemented without the use of Expectation Maximization or other expensive iterative training techniques. Even with this simple model, our approach to NER on informal text outperforms existing systems trained on formal English and matches state-of-the-art NER systems trained on hand-labeled Twitter messages. Because our model does not require hand-labeled data, we can adapt our system to other domains and named entity classes very easily. We demonstrate the flexibility of our approach by successfully applying it to the different domain of extracting food dishes from restaurant reviews with very little extra work.


title: Watset: Automatic Induction of Synsets from a Graph of Synonyms

abstract: This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources.


title: IGTree: Using Trees for Compression and Classification in Lazy Learning Algorithms

abstract: We describe the IGTree learning algorithm, which compresses an instance base into a tree structure. The concept of information gain is used as a heuristic function for performing this compression. IGTree produces trees that, compared to other lazy learning approaches, reduce storage requirements and the time required to compute classifications. Furthermore, we obtained similar or better generalization accuracy with IGTree when trained on two complex linguistic tasks, viz. letter–phoneme transliteration and part-of-speech-tagging, when compared to alternative lazy learning and decision tree approaches (viz., IB1, information-gain-weighted IB1, and C4.5). A third experiment, with the task of word hyphenation, demonstrates that when the mutual differences in information gain of features is too small, IGTree as well as information-gain-weighted IB1 perform worse than IB1. These results indicate that IGTree is a useful algorithm for problems characterized by the availability of a large number of training instances described by symbolic features with sufficiently differing information gain values.


title: Emotional Intelligence: Painting Different Paths for Low-Anxious and High-Anxious Psychopathic Variants

abstract: Psychopathic individuals may be disaggregated into low-anxious (emotionally stable “primary psychopaths”) and high-anxious (emotionally disturbed “secondary psychopaths”) variants that may differ in their capacity for adaptive behavior. In turn, the skills encompassed by emotional intelligence (EI) predict social and business success. Based on a sample of 188 male undergraduates, we evaluate the performance of low-anxious psychopathic, high-anxious psychopathic, and low psychopathic comparison groups on a measure of EI. High-anxious psychopaths manifested significantly lower EI than the other two groups, particularly with respect to managing emotions and facilitating thoughts. In contrast, low-anxious psychopaths manifested intact EI, with skill in facilitating thoughts. High-anxious (but not low anxious) psychopaths were more likely than low psychopathic comparisons to manifest violence. These results are consistent with the notion that primary psychopaths have greater capacity to attain success in traditional society than secondary psychopaths, and invite a direct test of this hypothesis in future research.


title: Individualized patient-centered lifestyle recommendations : An expert system for communicating patient specific cardiovascular risk information and prioritizing lifestyle options

abstract: Graphical abstractDisplay Omitted Highlights? The expert system is constructed by supervised learning approach and optimization. ? The system predicts 10-year CVD risks based on cardiovascular risk information. ? The lifestyle option with the lowest CVD risk varies based on individuals. ? The system prioritizes lifestyle options based risks, limitations, and preferences. We propose a proof-of-concept machine-learning expert system that learned knowledge of lifestyle and the associated 10-year cardiovascular disease (CVD) risks from individual-level data (i.e., Atherosclerosis Risk in Communities Study, ARIC). The expert system prioritizes lifestyle options and identifies the one that maximally reduce an individual's 10-year CVD risk by (1) using the knowledge learned from the ARIC data and (2) communicating for patient-specific cardiovascular risk information and personal limitations and preferences (as defined by variables used in this study). As a result, the optimal lifestyle is not only prioritized based on an individual's characteristics but is also relevant to personal circumstances.We also explored probable uses and tested the system in several examples using real-world scenarios and patient preferences. For example, the system identifies the most effective lifestyle activities as the starting point for an individual's behavior change, shows different levels of BMI changes and the associated CVD risk reductions to encourage weight loss, identifies whether weight loss or smoking cessation is the most urgent change for a diabetes patient, etc. Answers to the questions noted above vary based on an individual's characteristics. Our validation results from clinical trial simulations, which compared original with the optimal lifestyle using an independent dataset, show that the optimal individualized patient-centered lifestyle consistently reduced 10-year CVD risks.


title: Efficient discovery of time series motifs with large length range in million scale time series

abstract: Detecting repeated variable-length patterns, also called variable-length motifs, has received a great amount of attention in recent years. Current state-of-the-art algorithm utilizes fixed-length motif discovery algorithm as a subroutine to enumerate variable-length motifs. As a result, it may take hours or days to execute when enumeration range is large. In this work, we introduce an approximate algorithm called HierarchIcal based Motif Enumeration (HIME) to detect variable-length motifs with a large enumeration range in million-scale time series. We show in the experiments that the scalability of the proposed algorithm is significantly better than that of the state-of-theart algorithm. Moreover, the motif length range detected by HIME is considerably larger than previous sequence-matching based approximate variable-length motif discovery approach. We demonstrate that HIME can efficiently detect meaningful variable-length motifs in long, real world time series.


title: Visualization of Medical Data Based on EHR Standards

abstract: Background: To organize an efficient interaction between a doctor and an EHR the data has to be presented in the most convenient way. Medical data presentation methods and models must be flexible in order to cover the needs of the users with different backgrounds and requirements. Most visualization methods are doctor oriented, however, there are indications that the involvement of patients can optimize healthcare. Objectives: The research aims at specifying the state of the art of medical data visualization. The paper analyzes a number of projects and defines requirements for a generic ISO 13606 based data visualization method. In order to do so it starts with a systematic search for studies on EHR user interfaces. Methods: In order to identify best practices visualization methods were evaluated according to the following criteria: limits of application, customizability, re-usability. The visualization methods were compared by using specified criteria. Results: The review showed that the analyzed projects can contribute knowledge to the development of a generic visualization method. However, none of them proposed a model that meets all the necessary criteria for a re-usable standard based visualization method. The shortcomings were mostly related to the structure of current medical concept specifications. Conclusion: The analysis showed that medical data visualization methods use hardcoded GUI, which gives little flexibility. So medical data visualization has to turn from a hardcoded user interface to generic methods. This requires a great effort because current standards are not suitable for organizing the management of visualization data. This contradiction between a generic method and a flexible and user-friendly data layout has to be overcome.


title: On the Algorithmic Implementation of Stochastic Discrimination

abstract: Stochastic discrimination is a general methodology for constructing classifiers appropriate for pattern recognition. It is based on combining arbitrary numbers of very weak components, which are usually generated by some pseudorandom process, and it has the property that the very complex and accurate classifiers produced in this way retain the ability, characteristic of their weak component pieces, to generalize to new data. In fact, it is often observed, in practice, that classifier performance on test sets continues to rise as more weak components are added, even after performance on training sets seems to have reached a maximum. This is predicted by the underlying theory, for even though the formal error rate on the training set may have reached a minimum, more sophisticated measures intrinsic to this method indicate that classifier performance on both training and test sets continues to improve as complexity increases. We begin with a review of the method of stochastic discrimination as applied to pattern recognition. Through a progression of examples keyed to various theoretical issues, we discuss considerations involved with its algorithmic implementation. We then take such an algorithmic implementation and compare its performance, on a large set of standardized pattern recognition problems from the University of California Irvine, and Statlog collections, to many other techniques reported on in the literature, including boosting and bagging. In doing these studies, we compare our results to those reported in the literature by the various authors for the other methods, using the same data and study paradigms used by them. Included in the paper is an outline of the underlying mathematical theory of stochastic discrimination and a remark concerning boosting, which provides a theoretical justification for properties of that method observed in practice, including its ability to generalize.


title: Robust C-Loss Kernel Classifiers

abstract: The correntropy-induced loss (C-loss) function has the nice property of being robust to outliers. In this paper, we study the C-loss kernel classifier with the Tikhonov regularization term, which is used to avoid overfitting. After using the half-quadratic optimization algorithm, which converges much faster than the gradient optimization algorithm, we find out that the resulting C-loss kernel classifier is equivalent to an iterative weighted least square support vector machine (LS-SVM). This relationship helps explain the robustness of iterative weighted LS-SVM from the correntropy and density estimation perspectives. On the large-scale data sets which have low-rank Gram matrices, we suggest to use incomplete Cholesky decomposition to speed up the training process. Moreover, we use the representer theorem to improve the sparseness of the resulting C-loss kernel classifier. Experimental results confirm that our methods are more robust to outliers than the existing common classifiers.


title: The Emergence of Spectral Universality in Deep Networks

abstract: Recent work has shown that tight concentration of the entire spectrum of singular values of a deep network's input-output Jacobian around one at initialization can speed up learning by orders of magnitude. Therefore, to guide important design choices, it is important to build a full theoretical understanding of the spectra of Jacobians at initialization. To this end, we leverage powerful tools from free probability theory to provide a detailed analytic understanding of how a deep network's Jacobian spectrum depends on various hyperparameters including the nonlinearity, the weight and bias distributions, and the depth. For a variety of nonlinearities, our work reveals the emergence of new universal limiting spectral distributions that remain concentrated around one even as the depth goes to infinity.


title: Squamosal Suture Synostosis: Incidence, Associations, and Implications for Treatment

abstract: Squamosal suture craniosynostosis is thought to be a relatively rare entity. In the authors' experience, it is underreported in imaging examinations and the existing literature. The authors sought to determine the incidence of squamosal synostosis, whether it is increasing in frequency, and its relationship with synostosis of the major calvarial sutures.Patients undergoing computed tomography imaging for suspected craniosynostosis over a 15-year period were reviewed by a plastic surgeon and pediatric neuroradiologist. Patients with synostosis of the squamosal sutures were identified and involvement of additional sutures, gender, and the presence of a known syndromic diagnosis were recorded. Patients greater than 4 years of age or those with prior craniofacial surgery were excluded.One hundred twenty-five patients met inclusion criteria, 26 of whom had squamosal suture synostosis (26/125, 20.8%). Squamosal synostosis was found in isolation in 3 patients (3/26, 11.5%), with 1 additional major suture in 10 patients (10/26, 38.5%), and ≥2 major sutures in 13 patients (13/26, 50%). Squamosal synostosis was more common in patients with a syndromic diagnosis (11/26 syndromic, 15/99 nonsyndromic, P < 0.001). Eleven of 26 patients with squamosal synostosis were identified in the radiology report (42.3%).Craniosynostosis of the squamosal suture is much more common than previously reported and can contribute to abnormal head shape in isolation, or in combination with major sutures. Squamosal suture synostosis is underdiagnosed clinically and radiologically, although insufficient evidence exists to determine if its true incidence is increasing.


title: Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization

abstract: In this work we propose the use of a modified version of the correlation coefficient as a performance criterion for the image alignment problem. The proposed modification has the desirable characteristic of being invariant with respect to photometric distortions. Since the resulting similarity measure is a nonlinear function of the warp parameters, we develop two iterative schemes for its maximization, one based on the forward additive approach and the second on the inverse compositional method. As it is customary in iterative optimization, in each iteration the nonlinear objective function is approximated by an alternative expression for which the corresponding optimization is simple. In our case we propose an efficient approximation that leads to a closed form solution (per iteration) which is of low computational complexity, the latter property being particularly strong in our inverse version. The proposed schemes are tested against the forward additive Lucas-Kanade and the simultaneous inverse compositional algorithm through simulations. Under noisy conditions and photometric distortions our forward version achieves more accurate alignments and exhibits faster convergence whereas our inverse version has similar performance as the simultaneous inverse compositional algorithm but at a lower computational complexity.


title: People detection and tracking from aerial thermal views

abstract: Detection and tracking of people in visible-light images has been subject to extensive research in the past decades with applications ranging from surveillance to searchand-rescue. Following the growing availability of thermal cameras and the distinctive thermal signature of humans, research effort has been focusing on developing people detection and tracking methodologies applicable to this sensing modality. However, a plethora of challenges arise on the transition from visible-light to thermal images, especially with the recent trend of employing thermal cameras onboard aerial platforms (e.g. in search-and-rescue research) capturing oblique views of the scenery. This paper presents a new, publicly available dataset of annotated thermal image sequences, posing a multitude of challenges for people detection and tracking. Moreover, we propose a new particle filter based framework for tracking people in aerial thermal images. Finally, we evaluate the performance of this pipeline on our dataset, incorporating a selection of relevant, state-of-the-art methods and present a comprehensive discussion of the merits spawning from our study.


title: Blocking Objectionable Web Content by Leveraging Multiple Information Sources ABSTRACT

abstract: The World Wide Web has now become a humongous archive of various contents. The inordinate amount of information found on the web presents a challenge to deliver right information to the right users. On one hand, the abundant information is freely accessible to all web denizens; on the other hand, much of such information may be irrelevant or even deleterious to some users. For example, some control and filtering mechanisms are desired to prevent inappropriate or offensive materials such as pornographic websites from reaching children. Ways of accessing websites are termed as Access Scenarios. An Access Scenario can include using search engines (e.g., image search that has very little textual content), URL redirection to some websites, or directly typing (porn) website URLs. In this paper we propose a framework to analyze a website from several different aspects or information sources, and generate a classification model aiming to accurately classify such content irrespective of access scenarios. Extensive experiments are performed to evaluate the resulting system, which illustrates the promise of the proposed approach.


title: On crowdsourced interactive live streaming: a Twitch.tv-based measurement study

abstract: Empowered by today's rich tools for media generation and collaborative production, the multimedia service paradigm is shifting from the conventional single source, to multi-source, to many sources, and now toward crowdsource. Such crowdsourced live streaming platforms as Twitch.tv allow general users to broadcast their content to massive viewers, thereby greatly expanding the content and user bases. The resources available for these non-professional broadcasters however are limited and unstable, which potentially impair the streaming quality and viewers' experience. The diverse live interactions among the broadcasters and viewers can further aggravate the problem. In this paper, we present an initial investigation on the modern crowdsourced live streaming systems. Taking Twitch as a representative, we outline their inside architecture using both crawled data and captured traffic of local broadcasters/viewers. Closely examining the access data collected in a two-month period, we reveal that the view patterns are determined by both events and broadcasters' sources. Our measurements explore the unique source- and event-driven views, showing that the current delay strategy on the viewer's side substantially impacts the viewers' interactive experience, and there is significant disparity between the long broadcast latency and the short live messaging latency. On the broadcaster's side, the dynamic uploading capacity is a critical challenge, which noticeably affects the smoothness of live streaming for viewers.


title: An RTP/RTCP based approach for multimedia group and inter-stream synchronization

abstract: Most multimedia group and inter-stream synchronization techniques define or use proprietary protocols with new control messages. Many multimedia applications have been developed using RTP/RTCP as the standard for transmission of multimedia streams over IP networks. Instead of defining a new protocol, we propose the use of RTP/RTCP to provide synchronization. We take advantage of the feedback capabilities provided by RTCP and the ability to extend the protocol by extending and creating RTCP messages containing synchronization information. We have implemented our proposal and tested it in our University WAN. Our experiments have shown that network load resulting from synchronization is minimized and that asynchronies are within acceptable limits for multimedia applications.


Classification of Trash for Recyclability Status


title: Location aware sensor routing protocol for mobile wireless sensor networks

abstract: Location aware sensor routing (LASeR) protocol is a novel solution to the challenges of routing in mobile wireless sensor networks (MWSNs). It addresses the high reliability and low latency requirements of emerging applications. The protocol uses location information to maintain a gradient field even in highly mobile environments, whilst reducing the routing overhead. This allows the protocol to utilise a blind forwarding technique to propagate packets towards the sink. The protocol inherently utilises multiple paths simultaneously to create route diversity and increase its robustness. LASeR is designed for use in a high variety of MWSN applications with autonomous land, sea or air vehicles. Analytical expressions are derived and evaluated against the simulations. Extensive modelling and simulation of the proposed routing protocol has shown it to be highly adaptable and robust. It is compared with the recent MWSN proactive highly ambulatory sensor routing protocol, the high performance mobility adaptive cross-layer routing protocol, as well as ad-hoc on-demand distance vector and optimised link state routing. Protocols are evaluated on packet delivery ratio, end-to-end delay, overhead, throughput and energy consumption. The results highlight both the high performance of LASeR in various challenging environments and its superiority over the state-of-the-art.


title: Exploring Miner Evolution in Bitcoin Network

abstract: In recent years, Bitcoin, a peer-to-peer network based crypto digital currency, has attracted a lot of attentions from the media, the academia, and the general public. A user in Bitcoin network can create Bitcoins by packing and verifying new transactions in the network using their computation power. Driven by the price surge of Bitcoin, users are increasingly investing on expensive specialized hardware for Bitcoin mining. To obtain steady payouts, users also pool their computation resources to conduct pool mining. In this paper, we study the evolution of Bitcoin miners by analyzing the complete transaction blockchain. We characterize how the productivity, computation power and transaction activity of miners evolve over time. We also conduct an in-depth study on the largest mining pool F2Pool. We show how it grows over time and how computation power is distributed among its miners. Finally, we build a simple economic model to explain the evolution of Bitcoin miners.


title: REAL TIME HAND TRACKING AND 3 D GESTURE RECOGNITION FOR INTERACTIVE INTERFACES USING HMM

abstract: An improved coolant distribution system for a rotating non-consumable electrode wheel in an arc melting furnace. A rotating tubular drive shaft suspends the electrode wheel above the melt surface in an obliquely inclined orientation whereby successive portions of the wheel periphery are brought into arc-forming proximity to the melt surface as the wheel is rotated. A non rotating nozzle is positioned within the electrode wheel with its discharge orifice in heat dissipating proximity to the interior surface of the wheel at the portion of the wheel periphery in arc-forming proximity to the melt at any given instant.


title: Autonomous Cloud Federation for High-Throughput Queries over Voluminous Datasets

abstract: The breadth and depth of information being generated and stored continues to grow rapidly, causing an information explosion. Observational devices and remote sensing equipment are no exception here, giving researchers new avenues for detecting and predicting phenomena at a global scale. To cope with increasing storage loads, hybrid clouds offer an elastic solution that also satisfies processing and budgetary needs. In this article, the authors describe their algorithms and system design for dealing with voluminous datasets in a hybrid cloud setting. Their distributed storage framework autonomously tunes in-memory data structures and query parameters to ensure efficient retrievals and minimize resource consumption. To circumvent processing hotspots, they predict changes in incoming traffic and federate their query resolution structures to the public cloud for processing. They demonstrate their framework's efficacy on a real-world, petabyte dataset consisting of more than 20 billion files.


title: High voltage nanosecond pulser operating at 100 kHz pulse repetition frequency

abstract: Eagle Harbor Technologies (EHT) is developing high voltage nanosecond pulsers capable of driving nonlinear transmission lines for high power microwave production and generating non-equilibrium plasmas. Other applications for high voltage nanosecond pulsers include plasma medicine, material science, enhanced combustion, drag reduction, surface and water decontamination and Electromagnetic device testing. The EHT nanosecond pulser technology is capable of producing high voltage (up to 60 kV) pulses (width 20–500 ns) with fast rise times (< 20 ns) at high pulse repetition frequency (adjustable up to 100 kHz) for CW operation. EHT nanosecond pulsers allow the user to independently control the output voltage, pulse width, and pulse repetition frequency, enabling researchers to explore non-equilibrium plasmas or high power microwave sources over a wide range of parameters.


title: Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system

abstract: It is true that the"best"neural network is not necessarily the one with the most"brain-like"behavior. Understanding biological intelligence, however, is a fundamental goal for several distinct disciplines. Translating our understanding of intelligence to machines is a fundamental problem in robotics. Propelled by new advancements in Neuroscience, we developed a spiking neural network (SNN) that draws from mounting experimental evidence that a number of individual neurons is associated with spatial navigation. By following the brain's structure, our model assumes no initial all-to-all connectivity, which could inhibit its translation to a neuromorphic hardware, and learns an uncharted territory by mapping its identified components into a limited number of neural representations, through spike-timing dependent plasticity (STDP). In our ongoing effort to employ a bioinspired SNN-controlled robot to real-world spatial mapping applications, we demonstrate here how an SNN may robustly control an autonomous robot in mapping and exploring an unknown environment, while compensating for its own intrinsic hardware imperfections, such as partial or total loss of visual input.


title: Dynamic Multimodal Instance Segmentation guided by natural language queries

abstract: We address the problem of segmenting an object given a natural language expression that describes it. Current techniques tackle this task by either (\textit{i}) directly or recursively merging linguistic and visual information in the channel dimension and then performing convolutions; or by (\textit{ii}) mapping the expression to a space in which it can be thought of as a filter, whose response is directly related to the presence of the object at a given spatial coordinate in the image, so that a convolution can be applied to look for the object. We propose a novel method that integrates these two insights in order to fully exploit the recursive nature of language. Additionally, during the upsampling process, we take advantage of the intermediate information generated when downsampling the image, so that detailed segmentations can be obtained. We compare our method against the state-of-the-art approaches in four standard datasets, in which it surpasses all previous methods in six of eight of the splits for this task.


title: Mobile application development: web vs. native

abstract: Web apps are cheaper to develop and deploy than native apps, but can they match the native user experience?


title: Pay Equity: Legislative and Legal Developments

abstract: This report discusses pay equity litigation, including Wal-Mart Stores v. Dukes, a case in which the Supreme Court rejected class action status for current and former female Wal-Mart employees who allege that the company has engaged in pay discrimination.


Classification and Comparison of Routing Protocols in Wireless Sensor Networks


title: Electromagnetic detection of HIV DNA in the blood of AIDS patients treated by antiretroviral therapy

abstract: Electromagnetic signals of low frequency have been shown to be durably produced in aqueous dilutions of the Human Imunodeficiency Virus DNA. In vivo, HIV DNA signals are detected only in patients previously treated by antiretroviral therapy and having no detectable viral RNA copies in their blood. We suggest that the treatment of AIDS patients pushes the virus towards a new mode of replication implying only DNA, thus forming a reservoir insensitive to retroviral inhibitors. Implications for new approaches aimed at eradicating HIV infection are discussed.


title: Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs

abstract: Knowledge graphs are large, useful, but incomplete knowledge repositories. They encode knowledge through entities and relations which define each other through the connective structure of the graph. This has inspired methods for the joint embedding of entities and relations in continuous low-dimensional vector spaces, that can be used to induce new edges in the graph, i.e., link prediction in knowledge graphs. Learning these representations relies on contrasting positive instances with negative ones. Knowledge graphs include only positive relation instances, leaving the door open for a variety of methods for selecting negative examples. In this paper we present an empirical study on the impact of negative sampling on the learned embeddings, assessed through the task of link prediction. We use state-of-the-art knowledge graph embeddings -- \rescal , TransE, DistMult and ComplEX -- and evaluate on benchmark datasets -- FB15k and WN18. We compare well known methods for negative sampling and additionally propose embedding based sampling methods. We note a marked difference in the impact of these sampling methods on the two datasets, with the "traditional" corrupting positives method leading to best results on WN18, while embedding based methods benefiting the task on FB15k.


title: A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm

abstract: Neural networks play an increasingly important role in the field of machine learning and are included in many applications in society. Unfortunately, neural networks suffer from adversarial samples generated to attack them. However, most of the generation approaches either assume that the attacker has full knowledge of the neural network model or are limited by the type of attacked model. In this paper, we propose a new approach that generates a black-box attack to neural networks based on the swarm evolutionary algorithm. Benefiting from the improvements in the technology and theoretical characteristics of evolutionary algorithms, our approach has the advantages of effectiveness, black-box attack, generality, and randomness. Our experimental results show that both the MNIST images and the CIFAR-10 images can be perturbed to successful generate a black-box attack with 100\% probability on average. In addition, the proposed attack, which is successful on distilled neural networks with almost 100\% probability, is resistant to defensive distillation. The experimental results also indicate that the robustness of the artificial intelligence algorithm is related to the complexity of the model and the data set. In addition, we find that the adversarial samples to some extent reproduce the characteristics of the sample data learned by the neural network model.


title: Hybridization effects of the different geographic populationof Chlamys farreri III.The yearlong(1~2 years old) growth and development of Chinese population and Russian population and their reciprocal crosses

abstract: On the basis of the research of juvenile(2,3,4 months) and medium-term(6,8,10,12 months) growth and survival of three populationof twodifferentgeographic areas inChlamys farrerifrom Russia and China and their F_(1)hybrids derived from Chinese cultural population(CC)♀×Russian wild population(RW)♂andF_(1)′ hybridsderived fromChinese wild population(CW)♀×Russian wild population(RW)♂,the study of the later period(12,14,16,19,21,24months)growth and development of Chlamys farreri was carried out.The fourdeterminedresults indicated that there existeddifferent extent heterosis(3.16%～37.18%) for growth in F_(1)hybrids andF_(1)′ hybrids,the former had a stronger heterosisand it was abetter cross combination,the uptrendamong traits were wetweight being greater than shell width being greater than shell length being greater than shell height,Chinese cultural population could berecognized as excellent parent,andthe growth rate(the daily increment) had reached a peak at 16months(November),it was a good time for harvestofChlamys farreri.


title: Multi-criteria service recommendation based on user criteria preferences

abstract: Research in recommender systems is now starting to recognise the importance of multiple selection criteria to improve the recommendation output. In this paper, we present a novel approach to multi-criteria recommendation, based on the idea of clustering users in "preference lattices" (partial orders) according to their criteria preferences. We assume that some selection criteria for an item (product or a service) will dominate the overall ranking, and that these dominant criteria will be different for different users. Following this assumption, we cluster users based on their criteria preferences, creating a "preference lattice". The recommendation output for a user is then based on ratings by other users from the same or close clusters. Having introduced the general approach of clustering, we proceed to formulate three alternative recommendation methods instantiating the approach: (a) using the aggregation function of the criteria, (b) using the overall item ratings, and (c) combining clustering with collaborative filtering. We then evaluate the accuracy of the three methods using a set of experiments on a service ranking dataset, and compare them with a conventional collaborative filtering approach extended to cover multiple criteria. The results indicate that our third method, which combines clustering and extended collaborative filtering, produces the highest accuracy.


title: RAPID TARGET DETECTION IN HIGH RESOLUTION REMOTE SENSING IMAGES USING YOLO MODEL

abstract: Abstract. Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.


title: Fingerprint template protection schemes: A literature review

abstract: The fingerprint is the most widely used technology for identification or authentication systems, which can be known as fingerprint authentication systems (FAS).In addition to providing security, the fingerprint is also easy to use, very reliable and has a high accuracy for identity recognition. FAS is still exposed to security attacks because fingerprint information is unencrypted.Therefore, fingerprint information requires protection known as fingerprint template protection (FTP).This paper aims to provide an organized literature on FTP.Three research questions were formulated to guide the literature analysis.First, this ::: analysis focuses on the types of FTP schemes; second, the metrics used for evaluating the FTP schemes; and finally, the common datasets used for evaluating the FTP schemes. The latest information and references are analysed and classified based on FTP methods and publication year to obtain information related to the development and application of FTP.This study mainly surveyed 62 documents reported on FTP schemes between the year 2000 and 2017.The results of this survey can be a source of reference for other researchers in finding literature relevant to the FTP.


title: Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware

abstract: In recent years the field of neuromorphic low-power systems gained significant momentum, spurring brain-inspired hardware systems which operate on principles that are fundamentally different from standard digital computers and thereby consume orders of magnitude less power. However, their wider use is still hindered by the lack of algorithms that can harness the strengths of such architectures. While neuromorphic adaptations of representation learning algorithms are now emerging, the efficient processing of temporal sequences or variable length-inputs remains difficult, partly due to challenges in representing and configuring the dynamics of spiking neural networks. Recurrent neural networks (RNN) are widely used in machine learning to solve a variety of sequence learning tasks. In this work we present a train-and-constrain methodology that enables the mapping of machine learned (Elman) RNNs on a substrate of spiking neurons, while being compatible with the capabilities of current and near-future neuromorphic systems. This “train-and-constrain” method consists of first training RNNs using backpropagation through time, then discretizing the weights and finally converting them to spiking RNNs by matching the responses of artificial neurons with those of the spiking neurons. We demonstrate our approach by mapping a natural language processing task (question classification), where we demonstrate the entire mapping process of the recurrent layer of the network on IBM's Neurosynaptic System TrueNorth, a spike-based digital neuromorphic hardware architecture. TrueNorth imposes specific constraints on connectivity, neural and synaptic parameters. To satisfy these constraints, it was necessary to discretize the synaptic weights to 16 levels, discretize the neural activities to 16 levels, and to limit fan-in to 64 inputs. Surprisingly, we find that short synaptic delays are sufficient to implement the dynamic (temporal) aspect of the RNN in the question classification task. Furthermore we observed that the discretization of the neural activities is beneficial to our train-and-constrain approach. The hardware-constrained model achieved 74% accuracy in question classification while using less than 0.025% of the cores on one TrueNorth chip, resulting in an estimated power consumption of ≈ 17µW.


title: How Common is Men's Self-Reported Sexual Interest in Prepubescent Children?

abstract: Consistent evidence exists for sexual interest in children in nonclinical/nonforensic male populations. However, prevalences for community men's self-reported sexual interest in children have been based on indiscriminate definitions including postpubescent individuals, age-restricted samples, and/or small convenience samples. The present research assessed men's self-reported sexual interest in children (including child prostitution and child sex tourism) on the community level and examined the link between strictly defined sexual fantasies and behaviors involving prepubescent children. In an online survey of 8,718 German men, 4.1% reported sexual fantasies involving prepubescent children, 3.2% reported sexual offending against prepubescent children, and 0.1% reported a pedophilic sexual preference. Sexual fantasies involving prepubescent children were positively related to sexual offending against prepubescent children. Sexual interest in children was associated with subjectively perceived need for therap...


title: Predicting the Popularity of Online News Based on Multivariate Analysis

abstract: An increasing number of online news triggers wide academic concern for the prediction of news popularity, which is affected by users' behaviors and not easy to predict. However, existing methods that predict the popularity of online news after publication are not timely enough, and predicting before publication lacks discriminatory features. This paper explores the variables which may affect news popularity and presents a novel methodology to predict the popularity of online news before publication. Through the observation of news, we first find that grammatical construction of titles can affect news popularity, and experiments show that this feature can improve R^2 statistics of the prediction model by 6.62% exactly. Besides, we improve traditional category and author features by using logarithmic conversion to views first and calculating a score of these features instead of stuffing them into learning models directly. Using these features and two other features, we finally predict news popularity in two aspects: whether the news will be popular and how many views the news ultimately attract.


title: Comparison of methods for current measurement in WBG systems

abstract: One of the biggest challenges faced in the characterization of wide-band gap (WBG) semiconductors is the accurate measurement of the current through the device. Most measuring techniques available do not have the necessary bandwidth to capture the high-frequency current content in such systems. Many of the solutions that do have the necessary bandwidth introduce a “choke-point” into the system by requiring the DC bus to pass through the small sensor aperture, which is not ideal due to the attendant increase in the bus inductance. Many designers use a coaxial shunt resistor when performing WBG device characterization due to the high bandwidth of this approach. In this study, an alternative method was considered using an array of parallel SMT resistors as a custom shunt. This method was evaluated against other measuring techniques in respect to accuracy and found to be in good agreement. This solution was found to have a lower parasitic inductance than competing methods, while reducing the cost of the components by two orders of magnitude. In addition, this paper demonstrates that a limited bandwidth (20 MHz) current transformer (CT) can be used with reasonable results when evaluating SiC multichip power modules (MCPMs); this is not the case with discrete SiC devices, which require a high-bandwidth current shunt for accurate measurement.