title: Potentials of 3D-Web-Applications in E-Commerce - Study about the Impact of 3D-Product-Presentations

abstract: The goal of this work is to compare different versions of three-dimensional product-presentations with two-dimensional ones. Basically the usability of these technologies will be compared and other user related factors will be integrated into the test as well. These factors were determined via a literature research. In order to achieve a generalizable conclusion about 3D-web-applications in e-commerce sample products from miscellaneous product categories are chosen for the study. This paper starts with the summary of the literature research about the factors for the study. It continues by shortly introducing research methods and strategies and explaining why certain methods are selected for this kind of study. The conception of the study is described in detail in the following paragraph. With the help of the generalized results of the study, recommendations for the usage of 3D-product-presentations in practical e-commerce environments are given.


title: Multi-Modal Event Topic Model for Social Event Analysis

abstract: With the massive growth of social events in Internet, it has become more and more difficult to exactly find and organize the interesting events from massive social media data, which is useful to browse, search, and monitor social events by users or governments. To deal with this problem, we propose a novel multi-modal social event tracking and evolution framework to not only effectively capture multi-modal topics of social events, but also obtain the evolutionary trends of social events and generate effective event summary details over time. To achieve this goal, we propose a novel multi-modal event topic model (mmETM), which can effectively model social media documents, including long text with related images, and learn the correlations between textual and visual modalities to separate the visual-representative topics and non-visual-representative topics. To apply the mmETM model to social event tracking, we adopt an incremental learning strategy denoted as incremental mmETM, which can obtain informative textual and visual topics of social events over time to help understand these events and their evolutionary trends. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. Both qualitative and quantitative evaluations demonstrate that the proposed mmETM algorithm performs favorably against several state-of-the-art methods.


title: Combining manual therapy with pain neuroscience education in the treatment of chronic low back pain: A narrative review of the literature.

abstract: Teaching people with chronic low back pain (CLBP) about the neurobiology and neurophysiology of their pain is referred to as pain neuroscience education (PNE). There is growing evidence that when PNE is provided to patients with chronic musculoskeletal pain, it can result in decreased pain, pain catastrophization, disability, and improved physical performance. Because the aim of PNE is to shift the patient's focus from the tissues in the low back as the source of their pain to the brain's interpretation of inputs, many clinicians could mistakenly believe that PNE should be a "hands-off," education-only approach. An argument can be made that by providing manual therapy or exercise to address local tissue pathology, the patient's focus could be brought back to the low back tissues as the source of their problem. In this narrative literature review, we present the case for a balanced approach that combines PNE with manual therapy and exercise by considering how manual therapy can also be incorporated for interventions with patients with CLBP. We propose that as well as producing local mechanical effects, providing manual therapy within a PNE context can be seen as meeting or perhaps enhancing patient expectations, and also refreshing or sharpening body schema maps within the brain. Ideally, all of this should lead to better outcomes in patients with CLBP.


How Angular Velocity Features and Different Gyroscope Noise Types Interact and Determine Orientation Estimation Accuracy


title: AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks

abstract: Recently, several methods based on generative adversarial network (GAN) have been proposed for the task of aligning cross-domain images or learning a joint distribution of cross-domain images. One of the methods is to use conditional GAN for alignment. However, previous attempts of adopting conditional GAN do not perform as well as other methods. In this work we present an approach for improving the capability of the methods which are based on conditional GAN. We evaluate the proposed method on numerous tasks and the experimental results show that it is able to align the cross-domain images successfully in absence of paired samples. Furthermore, we also propose another model which conditions on multiple information such as domain information and label information. Conditioning on domain information and label information, we are able to conduct label propagation from the source domain to the target domain. A 2-step alternating training algorithm is proposed to learn this model.


Forecasting Social Unrest Using Activity Cascades


Digital Marketing Maturity Models: Overview and Comparison


title: Two-electrode biopotential measurements: power line interference analysis

abstract: In this paper, an analysis of power line interference in two-electrode biopotential measurement amplifiers is presented. A model of the amplifier that includes its input stage and takes into account the effects of the common mode input impedance Z/sub C/ is proposed. This approach is valid for high Z/sub C/ values, and also for some recently proposed low-Z/sub C/ strategies. It is shown that power line interference rejection becomes minimal for extreme Z/sub C/ values (null or infinite), depending on the electrode-skin impedance's unbalance /spl Delta/Z/sub E/. For low /spl Delta/Z/sub E/ values, minimal interference is achieved by a low Z/sub C/ strategy (Z/sub C/=0), while for high /spl Delta/Z/sub E/ values a very high Z/sub C/ is required. A critical /spl Delta/Z/sub E/ is defined to select the best choice, as a function of the amplifier's Common Mode Rejection Ratio (CMRR) and stray coupling capacitances. Conclusions are verified experimentally using a biopotential amplifier specially designed for this test.


title: Super-resolution from images and video

abstract: Abstract This book focuses on the super resolution of images and video. The authors’ use of the term super resolution (SR) is used to describe the process of obtaining a high resolution (HR) image, or a sequence of HR images, from a set of low resolution (LR) observations. This process has also been referred to in the literature as resolution enhancement (RE). SR has been applied primarily to spatial and temporal RE, but also to hyperspectral image enhancement. This book concentrates on motion based spatial RE, although the authors also describe motion free and hyperspectral image SR problems. Also examined is the very recent research area of SR for compression, which consists of the intentional downsampling, during pre-processing, of a video sequence to be compressed and the application of SR techniques, during post-processing, on the compressed sequence.It is clear that there is a strong interplay between the tools and techniques developed for SR and a number of other inverse problems encountered in sig...


title: Multi-script Text Extraction from Natural Scenes

abstract: Scene text extraction methodologies are usually based in classification of individual regions or patches, using a priori knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organisation through which text emerges as a perceptually significant group of atomic objects. Therefore humans are able to detect text even in languages and scripts never seen before. In this paper, we argue that the text extraction problem could be posed as the detection of meaningful groups of regions. We present a method built around a perceptual organisation framework that exploits collaboration of proximity and similarity laws to create text-group hypotheses. Experiments demonstrate that our algorithm is competitive with state of the art approaches on a standard dataset covering text in variable orientations and two languages.


title: Scalar and Tensor Holographic Artificial Impedance Surfaces

abstract: We have developed a method for controlling electromagnetic surface wave propagation and radiation from complex metallic shapes. The object is covered with an artificial impedance surface that is implemented as an array of sub-wavelength metallic patches on a grounded dielectric substrate. We pattern the effective impedance over the surface by varying the size of the metallic patches. Using a holographic technique, we design the surface to scatter a known input wave into a desired output wave. Furthermore, by varying the shape of the patches we can create anisotropic surfaces with tensor impedance properties that provide control over polarization. As an example, we demonstrate a tensor impedance surface that produces circularly polarized radiation from a linearly polarized source.


title: Towards Ontology Based Cognitive Vision

abstract: Building knowledge bases for knowledge-based vision systems is a difficult task. This paper aims at showing how an ontology composed of visual concepts can be used as a guide for describing objects from a specific domain of interest. One of the most important benefits of our approach is that the knowledge acquisition process guided by the ontology leads to a knowledge base closer to low-level vision. A visual concept ontology and a dedicated knowledge acquisition tool have been developed and are also presented. We propose a generic methodology that is not linked to any application domain. Nevertheless, an example shows how the knowledge acquisition model can be applied to the description of pollen grain images. The use of an ontology for image description is the first step towards a complete cognitive vision system that will involve a learning layer.


title: Composing graphical models with neural networks for structured representations and fast inference

abstract: We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths. Our model family augments graphical structure in latent variables with neural network observation models. For inference, we extend variational autoencoders to use graphical model approximating distributions with recognition networks that output conjugate potentials. All components of these models are learned simultaneously with a single objective, giving a scalable algorithm that leverages stochastic variational inference, natural gradients, graphical model message passing, and the reparameterization trick. We illustrate this framework with several example models and an application to mouse behavioral phenotyping.


title: Facial expressions of emotion are not culturally universal

abstract: Since Darwin’s seminal works, the universality of facial expressions of emotion has remained one of the longest standing debates in the biological and social sciences. Briefly stated, the universality hypothesis claims that all humans communicate six basic internal emotional states (happy, surprise, fear, disgust, anger, and sad) using the same facial movements by virtue of their biological and evolutionary origins [Susskind JM, et al. (2008) Nat Neurosci 11:843–850]. Here, we refute this assumed universality. Using a unique computer graphics platform that combines generative grammars [Chomsky N (1965) MIT Press, Cambridge, MA] with visual perception, we accessed the mind’s eye of 30 Western and Eastern culture individuals and reconstructed their mental representations of the six basic facial expressions of emotion. Cross-cultural comparisons of the mental representations challenge universality on two separate counts. First, whereas Westerners represent each of the six basic emotions with a distinct set of facial movements common to the group, Easterners do not. Second, Easterners represent emotional intensity with distinctive dynamic eye activity. By refuting the long-standing universality hypothesis, our data highlight the powerful influence of culture on shaping basic behaviors once considered biologically hardwired. Consequently, our data open a unique nature–nurture debate across broad fields from evolutionary psychology and social neuroscience to social networking via digital avatars.


Reinventing the IT function: the Role of IT Agility and IT Ambidexterity in Supporting Digital Business Transformation.


title: Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain

abstract: The authors describe almost entirely automated procedures for estimation of global, voxel, and cluster-level statistics to test the null hypothesis of zero neuroanatomical difference between two groups of structural magnetic resonance imaging (MRI) data. Theoretical distributions under the null hypothesis are available for (1) global tissue class volumes; (2) standardized linear model [analysis of variance (ANOVA and ANCOVA)] coefficients estimated at each voxel; and (3) an area of spatially connected clusters generated by applying an arbitrary threshold to a two-dimensional (2-D) map of normal statistics at voxel level. The authors describe novel methods for economically ascertaining probability distributions under the null hypothesis, with fewer assumptions, by permutation of the observed data. Nominal Type I error control by permutation testing is generally excellent; whereas theoretical distributions may be over conservative. Permutation has the additional advantage that it can be used to test any statistic of interest, such as the sum of suprathreshold voxel statistics in a cluster (or cluster mass), regardless of its theoretical tractability under the null hypothesis. These issues are illustrated by application to MRI data acquired from 18 adolescents with hyperkinetic disorder and 16 control subjects matched for age and gender.


title: Biogeography based optimization for Traveling Salesman Problem

abstract: Biogeography-based optimization algorithm(BBO) is a new kind of optimization algorithm based on Biogeography. It mimics the migration strategy of animals to solve the problem of optimization. A new algorithm for Traveling Salesman Problem(TSPBBO) based on BBO is presented in this paper. It is tested on classical TSP problem. The comparison results with the other kinds of optimization algorithm show that TSPBBO is a very effective algorithm for TSP combination optimization. It provides a new method for this kind of problem.


title: Home Energy Management Using Enhanced Differential Evolution and Chicken Swarm Optimization Techniques

abstract: Energy optimization is important aspect of smart gird (SG). SG integrates communication and information technology in traditional grid. In SG there is two-way communication between consumer and utility. It includes smart meter, Energy Management Controller (EMC) and smart appliances. Users can shift load from on peak hours to off peak hours by adapting Demand Side Management (DSM) strategies, which effectively reduce electricity cost. The objectives of this paper are the minimization of power consumption, electricity cost, reduction of Peak to Average Ratio (PAR) using Enhanced Differential Evolution (EDE) and Chicken Swarm Optimization (CSO) algorithms. For the calculation of cost Critical Peak Pricing (CPP) is used. The simulations result show that proposed schemes reduce electricity cost, reduce power consumption and PAR.


title: Sparse concept coding for visual analysis

abstract: We consider the problem of image representation for visual analysis. When representing images as vectors, the feature space is of very high dimensionality, which makes it difficult for applying statistical techniques for visual analysis. To tackle this problem, matrix factorization techniques, such as Singular Vector Decomposition (SVD) and Non-negative Matrix Factorization (NMF), received an increasing amount of interest in recent years. Matrix factorization is an unsupervised learning technique, which finds a basis set capturing high-level semantics in the data and learns coordinates in terms of the basis set. However, the representations obtained by them are highly dense and can not capture the intrinsic geometric structure in the data. In this paper, we propose a novel method, called Sparse Concept Coding (SCC), for image representation and analysis. Inspired from the recent developments on manifold learning and sparse coding, SCC provides a sparse representation which can capture the intrinsic geometric structure of the image space. Extensive experimental results on image clustering have shown that the proposed approach provides a better representation with respect to the semantic structure.


title: Multiview Rectification of Folded Documents

abstract: Digitally unwrapping images of paper sheets is crucial for accurate document scanning and text recognition. This paper presents a method for automatically rectifying curved or folded paper sheets from a few images captured from multiple viewpoints. Prior methods either need expensive 3D scanners or model deformable surfaces using over-simplified parametric representations. In contrast, our method uses regular images and is based on general developable surface models that can represent a wide variety of paper deformations. Our main contribution is a new robust rectification method based on ridge-aware 3D reconstruction of a paper sheet and unwrapping the reconstructed surface using properties of developable surfaces via $\ell _1$ conformal mapping. We present results on several examples including book pages, folded letters and shopping receipts.


title: Device-free human detection using WiFi signals

abstract: This paper is an initial study of device-free human detection using channel state information (CSI) in WiFi networks. After collecting the CSI data in the target area, the proposed detector contains three steps, preprocess, feature extraction, and making decisions (human presence or absence). Experiments, based on real CSI measurements, proof the concept of human detection. Results show that, using only two features, namely the missing numbers and the average variance among subcarriers, and a simple decision tree, the proposed approach achieves 74.9% detection accuracy (hitting rate), and a general 64.3% accuracy.


title: Looting the LUTs: FPGA Optimization of AES and AES-like Ciphers for Authenticated Encryption

abstract: In this paper, we investigate the efficiency of FPGA implementations of AES and AES-like ciphers, specially in the context of authenticated encryption. We consider the encryption/decryption and the authentication/verification structures of OCB-like modes (like OTR or SCT modes). Their main advantage is that they are fully parallelisable. While this feature has already been used to increase the throughput/performance of hardware implementations, it is usually overlooked while comparing different ciphers. We show how to use it with zero area overhead, leading to a very significant efficiency gain. Additionally, we show that using FPGA technology mapping instead of logic optimization, the area of both the linear and non linear parts of the round function of several AES-like primitives can be reduced, without affecting the run-time performance. We provide the implementation results of two multi-stream implementations of both the LED and AES block ciphers. The AES implementation in this paper achieves an efficiency of 38 Mbps/slice, which is the most efficient implementation in literature, to the best of our knowledge. For LED, achieves 2.5 Mbps/slice on Spartan 3 FPGA, which is 2.57x better than the previous implementation. Besides, we use our new techniques to optimize the FPGA implementation of the CAESAR candidate Deoxys-I in both the encryption only and encryption/decryption settings. Finally, we show that the efficiency gains of the proposed techniques extend to other technologies, such as ASIC, as well.


title: Synthesizing Robust Adversarial Examples

abstract: Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems. We demonstrate the existence of robust 3D adversarial objects, and we present the first algorithm for synthesizing examples that are adversarial over a chosen distribution of transformations. We synthesize two-dimensional adversarial images that are robust to noise, distortion, and affine transformation. We apply our algorithm to complex three-dimensional objects, using 3D-printing to manufacture the first physical adversarial objects. Our results demonstrate the existence of 3D adversarial objects in the physical world.


title: Improved 3D Thinning Algorithms for Skeleton Extraction

abstract: In this study, we focused on developing a novel 3D Thinning algorithm to extract one-voxel wide skeleton from various 3D objects aiming at preserving the topological information. The 3D Thinning algorithm was testified on computer-generated and real 3D reconstructed image sets acquired from TEMT and compared with other existing 3D Thinning algorithms. It is found that the algorithm has conserved medial axes and simultaneously topologies very well, demonstrating many advantages over the existing technologies. They are versatile, rigorous, efficient and rotation invariant.


title: Reduction in catheter-related infections after switching from povidone-iodine to chlorhexidine for the exit-site care of tunneled central venous catheters in children on hemodialysis

abstract: Only a few studies have investigated the optimal exit site management of tunneled central venous catheters (CVCs) in pediatric patients on chronic hemodialysis (HD). The aim of this study was to assess the efficacy of chlorhexidine solutions and a 5% povidone-iodine solution on the incidence of CVC-related infections in children on HD. The incidence of exit-site infection (ESI), tunnel infection (TI), and bloodstream infection (BSI) was assessed in two groups of tunneled CVCs. The iodopovidone group consisted of 14 CVCs used between 1 January 2011 and 30 June 2012 in 10 children, whose median age at the time of CVC placement was 11.8 years (range 1.2-19.2): 5% povidone-iodine was used for CVC exit-site care. From 1 August 2012 to 31 January 2014, 0.5% chlorhexidine gluconate/70% isopropyl alcohol was used for the exit site, and 2% chlorhexidine gluconate/70% isopropyl alcohol spray for the hub in 13 CVCs was used in 10 patients (chlorhexidine group), whose median age at the time of CVC placement was 10 years (range 1.2-19.2). Ten episodes of ESI were diagnosed in the iodopovidone group (incidence 3.4/1000 CVC days), and only one in the chlorhexidine group (incidence 0.36/1000 CVC days, P = 0.008). One TI was observed in the iodopovidone group (0.34/1000 CVC days), and none in the chlorhexidine group. The incidence of BSIs decreased from 1.7/1000 CVC days (5 cases) to 0.36/1000 CVC days (1 case, P = 0.06) after switching to chlorhexidine. Two CVCs were lost due to CVC-related infections in the iodopovidone group, whereas no CVC was lost due to infections in the chlorhexidine group. In comparison with 5% povidone-iodine, the use of chlorhexidine gluconate was associated with a reduction in the incidence of ESI, TI, and BSI in children on HD.


title: AUTOMATIC CAMERA TRACKING

abstract: The goal of automatic recovery of camera motion and scene structure from video sequences has been a staple of computer vision research for over a decade. As an area of endeavour, it has seen both steady and explosive progress over time, and now represents one of the success stories of computer vision. This task, automatic camera tracking or “matchmoving”, is the sine qua non of modern special effects, allowing the seamless insertion of computer generated objects onto live-action backgrounds (figure 2.1 shows an example). It has moved from a research problem for a small number of uncalibrated images to commercial software which can automatically track cameras through thousands of frames [1]. In addition, camera tracking is an important preprocess for many computer vision algorithms such as multiple-view shape reconstruction, novel view synthesis and autonomous vehicle navigation.


title: Eutectic Gallium‐Indium (EGaIn): A Liquid Metal Alloy for the Formation of Stable Structures in Microchannels at Room Temperature

abstract: This paper describes the rheological behavior of the liquid metal eutectic gallium-indium (EGaIn) as it is injected into microfluidic channels to form stable microstructures of liquid metal. EGaIn is well- ;suited for this application because of its rheological properties at room temperature: it behaves like an elastic material until it experiences a critical surface stress, at which point it yields and flows readily. These properties allow EGaIn to fill microchannels rapidly when sufficient pressure is applied to the inlet of the channels, yet maintain structural stability within the channels once ambient pressure is restored. Experiments conducted in microfluidic channels, and in a parallel-plate rheometer, suggest that EGaIn's behavior is dictated by the properties of its surface (predominantly gallium oxide, as determined by Auger measurement s); these two experiments both yield approximately the same number for the critical surface stress required to induce EGaIn to flow (∼0 .5 N/m). This analysis–which shows that the pressure that must be exceeded for EGaIn to flow through a microchannel is inversely proportional to the critical (i.e., smallest) dimension of the channel–is useful to guide future fabrication of microfluidic channels to mold EGaIn into functional microstructures.


Integrating the SOP2 Model into the Flipped Classroom to Foster Cognitive Presence and Learning Achievements


title: Retrofit Options for Light-Frame Wood Stud Walls Subjected to Blast Loading

abstract: AbstractThis paper presents the results of an experimental program, where a total of eight retrofitted walls were subjected to blast loading, simulated by shock waves using the University of Ottawa’s shock tube. Four different external retrofit options were considered and their behavior in terms of failure mode, stiffness, and strength were documented. Nonlinear single-degree-of-freedom (SDOF) modeling was performed using material models developed specifically for each retrofit. In general, good agreement was found between the predicted level of damage and calculated displacement using the model and the measured behavior. The study shows that a significant increase in stiffness and strength can be achieved.


title: Improved email spam classification method using integrated particle swarm optimization and decision tree

abstract: E-mails have become the best way to communicate formal documents over internet among users. But many people have started sending the unwanted mails to others, also called email spam. It is found that many techniques have been proposed so far to efficient mine the emails as spam or non-spammed. In existing techniques, the use of unsupervised filtering to filter the input data set is ignored by the most of the existing researchers. The use of hybridization of data mining techniques is ignored in instruct to improve the accuracy rate further for Detection of fraudulent emails. The majority of the existing techniques are limited to various significant features of emails therefore utilising more features may provide more significant results. To handle above stated limitations a new technique is proposed in this paper. The proposed technique has integrated particle swarm optimization based on Decision Tree algorithm with unsupervised filtering to enhance the accuracy rate further. The comparative analyses have clearly pointed to better results than the available techniques.


title: Fractal-Based Description of Natural Scenes

abstract: This paper addresses the problems of 1) representing natural shapes such as mountains, trees, and clouds, and 2) computing their description from image data. To solve these problems, we must be able to relate natural surfaces to their images; this requires a good model of natural surface shapes. Fractal functions are a good choice for modeling 3-D natural surfaces because 1) many physical processes produce a fractal surface shape, 2) fractals are widely used as a graphics tool for generating natural-looking shapes, and 3) a survey of natural imagery has shown that the 3-D fractal surface model, transformed by the image formation process, furnishes an accurate description of both textured and shaded image regions. The 3-D fractal model provides a characterization of 3-D surfaces and their images for which the appropriateness of the model is verifiable. Furthermore, this characterization is stable over transformations of scale and linear transforms of intensity. The 3-D fractal model has been successfully applied to the problems of 1) texture segmentation and classification, 2) estimation of 3-D shape information, and 3) distinguishing between perceptually ``smooth'' and perceptually ``textured'' surfaces in the scene.


title: Joint Chinese Word Segmentation, POS Tagging and Parsing

abstract: In this paper, we propose a novel decoding algorithm for discriminative joint Chinese word segmentation, part-of-speech (POS) tagging, and parsing. Previous work often used a pipeline method -- Chinese word segmentation followed by POS tagging and parsing, which suffers from error propagation and is unable to leverage information in later modules for earlier components. In our approach, we train the three individual models separately during training, and incorporate them together in a unified framework during decoding. We extend the CYK parsing algorithm so that it can deal with word segmentation and POS tagging features. As far as we know, this is the first work on joint Chinese word segmentation, POS tagging and parsing. Our experimental results on Chinese Tree Bank 5 corpus show that our approach outperforms the state-of-the-art pipeline system.


title: Facial symmetry and judgements of apparent health Support for a ‘ ‘ good genes ’ ’ explanation of the attractiveness – symmetry relationship

abstract: Recent efforts are underway to develop LSI circuits that operate at power supply voltages of 1-V or lower. It is a desire that this low core voltage circuits interface to 3.3-V I/O supply. A charge pump level converter for dual power supply application is proposed using low power and high speed interface to higher I/O supply. This circuit does not consume DC power it is suitable for low power and high speed interface and can be implemented using complementary metal-oxide-semiconductor (CMOS) fabrication processes.


title: Smartphone based car-searching system for large parking lot

abstract: Find a car in a large parking lot is a challenge in our daily life. In this paper, a novel car-searching approach based on smartphone for a large parking lot is presented. In the new approach, some QR codes are set up in each area which can identify the parking spots. In addition, shortest path algorithm is used to plan the optimal car-searching path. Considering that some parkers with poor sense of direction may lose in the parking lot, a real-time navigation method is proposed for parker to search the car, which is based on the built-in sensors in smartphone and pedometer principle. The new approach is tested in a large indoor parking lot, and the experimental results show the efficiency of the proposed car-searching system.


A Concise Handbook of Respiratory Diseases


title: Cognitive Decision Making Process Supervising the Radio Dynamic Reconfiguration

abstract: Cognitive radio is a technological concept pushing for the introduction of intelligent radio operation going beyond system adaptation and reconfiguration on the basis of simple criteria and rules. Insofar, a rather limited amount of work has been published on the cognitive mechanisms that should be embedded into the communicating equipments to achieve such an intelligent behavior. Towards filling this gap, this paper presents an innovative optimization algorithm driving the decision making process supervising the cognitive radio reconfiguration. This cognitive algorithm, called RALFE for "Reason And Learn From Experience", presents interesting features since it allows to perform autonomous decision making with regard to multiple, possibly conflicting, operational objectives in the face of an uncertain environment. The proposed approach is illustrated for a case of cognitive waveform design.


title: NeuCube Neuromorphic Framework for Spatio-temporal Brain Data and Its Python Implementation

abstract: Classification and knowledge extraction from complex spatio-temporal brain data such as EEG or fMRI is a complex challenge. A novel architecture named the NeuCube has been established in prior literature to address this. A number of key points in the implementation of this framework, including modular design, extensibility, scalability, the source of the biologically inspired spatial structure, encoding, classification, and visualisation tools must be considered. A Python version of this framework that conforms to these guidelines has been implemented.


title: A 2.5-D Angularly Stable Frequency Selective Surface Using Via-Based Structure for 5G EMI Shielding

abstract: A novel broadband bandpass frequency-selective surface (FSS) designed for fifth generation (5G) EMI shielding is proposed in this paper. This new design employs the vertical vias into the 2-D periodic arrays, and such a single 2.5-D periodic layer of via-based structure is demonstrated to produce a highly stable angular response up to 60° for both TE and TM polarizations. By cascading two layers of such 2.5-D periodic arrays, the proposed FSS is able to obtain a broad passband as well as the wide out-of-band rejection. Moreover, it has a quite sharp band edge between the passband and the specified stopband. A corresponding equivalent circuit model (ECM) is further developed for better analysis of the operating principle. Finally, a prototype working at the center frequency of around 28 GHz is fabricated and measured. The main novelty of this paper is introducing the 2.5-D concept into designing a wideband FSS, and further reduce the unit size as well as improve the angular stability. Favorable agreement is achieved among the 3-D full-wave simulation, ECM and measurement. All these results demonstrate that the proposed FSS is a good candidate for 5G EMI shielding.


title: N-Bromosuccinimide (NBS) – an efficient catalyst for acetylation of wood

abstract: Abstract Solvent-free acetylation of rubberwood (Hevea brasiliensis Müll.Arg) with acetic anhydride (Ac2O) and by means of N-bromosuccinimide (NBS) as catalyst was carried out in an oil bath at 105°C and 120°C. The effect of catalyst concentration, temperature, and reaction time was studied. The extent of acetylation was measured by determining the weight percent gain (WPG), and the acetylated wood was characterized by FTIR-ATR, CP/MAS 13C NMR, and XRD spectroscopy. The presence of NBS in concentrations between 1.0% and 3.0% elevated the acetylation rate to a great extent. WPG increased from 8.1% without catalyst to 22.1% for 2% NBS concentration after 60 min reaction at 120°C. Similarly, after 60-min reaction time at 105°C, WPG with 3% NBS concentration was 19.0% compared to 7.6% with un-catalyzed reaction under the same reaction conditions. The effect of microwave heating (MWh) on the NBS-catalyzed acetylation was also investigated. A significant level of modification was achieved within a few minutes of MWh. The hydrophobic properties and dimensional stability of the acetylated wood were elevated. NBS was found to be an effective catalyst for wood acetylation.


Knowledge Discovering using FrameNet,


title: A Simple Neural Attentive Meta-Learner

abstract: Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task. We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information. In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins.


title: Human Mobility in a Continuum Approach

abstract: Human mobility is investigated using a continuum approach that allows to calculate the probability to observe a trip to any arbitrary region, and the fluxes between any two regions. The considered description offers a general and unified framework, in which previously proposed mobility models like the gravity model, the intervening opportunities model, and the recently introduced radiation model are naturally resulting as special cases. A new form of radiation model is derived and its validity is investigated using observational data offered by commuting trips obtained from the United States census data set, and the mobility fluxes extracted from mobile phone data collected in a western European country. The new modeling paradigm offered by this description suggests that the complex topological features observed in large mobility and transportation networks may be the result of a simple stochastic process taking place on an inhomogeneous landscape.


title: Comparison of the diagnostic value of ultrasonography and standing radiography for pelvic-femoral disorders in horses.

abstract: OBJECTIVE ::: To assess agreement between ultrasonography (transcutaneous and transrectal) and standing radiography in horses with fractures in the pelvic region and disorders of the coxofemoral joint. ::: ::: ::: STUDY DESIGN ::: Case series. ::: ::: ::: ANIMALS ::: Warmblood horses (n=23) and 2 ponies. ::: ::: ::: METHODS ::: Medical records (1999-2008) of equids with pelvic or coxofemoral disorders that had pelvic radiography and ultrasonography were retrieved and results of both techniques compared. ::: ::: ::: RESULTS ::: Radiography and ultrasonography each identified equal numbers of fractures of the tuber coxa (n=4), ilial shaft (2), ischium (3), femoral neck (2), and osteoarthritis/osis of the coxofemoral joint (6). Fractures of the ilial wing (4) were only identified by ultrasonography not by standing radiography. Of 9 acetabular fractures, 3 were identified on radiographs only, 5 were identified with both modalities. One pubic fracture was identified using ultrasonography and radiography. One acetabular and 1 pubic fracture were only diagnosed on necropsy. ::: ::: ::: CONCLUSIONS ::: We found reasonable agreement (73%; 24/33) between ultrasonography and standing radiography for diagnosis of pelvic-femoral disorders. Ultrasonography was more useful for ilial wing fractures and radiography for acetabular fractures. ::: ::: ::: CLINICAL RELEVANCE ::: Ultrasonography is a rapid, safe imaging technique for detecting disorders of the pelvic region with a high diagnostic yield and is a preferred initial approach in horses with severe hindlimb lameness.


title: Suicidal Decapitation by Guillotine: Case Report and Review of the Literature

abstract: Suicidal decapitation is seldom encountered in forensic medicine practice. This study reports the analysis of a suicide committed by a 31-year-old man with a self-fabricated guillotine. The construction of the guillotine was very interesting and sophisticated. The guillotine-like blade with additional weight was placed in a large metal frame. The movement of the blade was controlled by the frame rails. The steel blade was triggered by a tensioned rubber band after releasing the safety catch. The cause of death was immediate exsanguination after complete severance of the neck. The suicide motive was most likely emotional distress after the death of his father. In medico-legal literature, there has been only one similar case of suicidal complete decapitation by a guillotine described.


title: Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals

abstract: This article primarily concerns the problem of isolated handwritten numeral recognition of major Indian scripts. The principal contributions presented here are (a) pioneering development of two databases for handwritten numerals of two most popular Indian scripts, (b) a multistage cascaded recognition scheme using wavelet based multiresolution representations and multilayer perceptron classifiers and (c) application of (b) for the recognition of mixed handwritten numerals of three Indian scripts Devanagari, Bangla and English. The present databases include respectively 22,556 and 23,392 handwritten isolated numeral samples of Devanagari and Bangla collected from real-life situations and these can be made available free of cost to researchers of other academic Institutions. In the proposed scheme, a numeral is subjected to three multilayer perceptron classifiers corresponding to three coarse-to-fine resolution levels in a cascaded manner. If rejection occurred even at the highest resolution, another multilayer perceptron is used as the final attempt to recognize the input numeral by combining the outputs of three classifiers of the previous stages. This scheme has been extended to the situation when the script of a document is not known a priori or the numerals written on a document belong to different scripts. Handwritten numerals in mixed scripts are frequently found in Indian postal mails and table-form documents.


title: MULTI-MODAL BACKGROUND SUBTRACTION USING GAUSSIAN MIXTURE MODELS

abstract: Background subtraction is a common first step in the field of video processing and it is used to reduce the effective image size in subsequent processing steps by segmenting the mostly static background from the moving or changing foreground. In this paper previous approaches towards background modeling are extended to handle videos accompanied by information gained from a novel 2D/3D camera. This camera contains a color and a PMD chip which operates on the Time-of-Flight operating principle. The background is estimated using the widely spread Gaussian mixture model in color as well as in depth and amplitude modulation. A new matching function is presented that allows for better treatment of shadows and noise and reduces block artifacts. Problems and limitations to overcome the problem of fusing high resolution color information with low resolution depth data are addressed and the approach is tested with different parameters on several scenes and the results are compared to common and widely accepted methods.


title: The role of leadership in shared mental model convergence and team performance improvement: An agent-based computational model

abstract: Abstract Research in shared mental models has immeasurably aided our understanding of effective teamwork and taskwork. However, little research has focused on the role that leaders play, if any, in influencing, developing and/or fostering shared mental models and thereby improving team performance. We developed an agent-based computational model based on McComb's theory of three-phase mental model development, where agents repeatedly share individual opinions (orientation phase), evaluate and respond to the opinions expressed by others (differentiation phase), and modify their understanding of the team based on the responses (integration phase). Leadership and team properties are represented in three experimental parameters: social network structure, heterogeneity of agents' domains of expertise, and level of their mutual interest. Participative leadership is represented by a fully connected network, while Leader–Member eXchange (LMX) is represented by a fully connected network of in-group members and several out-group members connected only to the leader. Our simulation results show that, in general, participative leadership promotes mental model convergence better than LMX. In the meantime, the team performance improvement is achieved by participative leadership only when members have both heterogeneous domains of expertise and strong mutual interest. In all other conditions, participative leadership causes the worst degradation of team performance through team development processes, while LMX is the best to minimize such team degradation. Implications and suggestions for future research are provided.


title: Grayscale Template-Matching Invariant to Rotation, Scale, Translation, Brightness and Contrast

abstract: In this paper, we consider the grayscale template-matching problem, invariant to rotation, scale, translation, brightness and contrast, without previous operations that discard grayscale information, like detection of edges, detection of interest points or segmentation/binarization of the images. The obvious "brute force" solution performs a series of conventional template matchings between the image to analyze and the template query shape rotated by every angle, translated to every position and scaled by every factor (within some specified range of scale factors). Clearly, this takes too long and thus is not practical. We propose a technique that substantially accelerates this searching, while obtaining the same result as the original brute force algorithm. In some experiments, our algorithm was 400 times faster than the brute force algorithm. Our algorithm consists of three cascaded filters. These filters successively exclude pixels that have no chance of matching the template from further processing.


title: Depression, stress and anxiety in medical students: A cross-sectional comparison between students from different semesters

abstract: OBJECTIVE ::: To compare the prevalence of anxiety, depression, and stress in medical students from all semesters of a Brazilian medical school and assess their respective associated factors. ::: ::: ::: METHOD ::: A cross-sectional study of students from the twelve semesters of a Brazilian medical school was carried out. Students filled out a questionnaire including sociodemographics, religiosity (DUREL - Duke Religion Index), and mental health (DASS-21 - Depression, Anxiety, and Stress Scale). The students were compared for mental health variables (Chi-squared/ANOVA). Linear regression models were employed to assess factors associated with DASS-21 scores. ::: ::: ::: RESULTS ::: 761 (75.4%) students answered the questionnaire; 34.6% reported depressive symptomatology, 37.2% showed anxiety symptoms, and 47.1% stress symptoms. Significant differences were found for: anxiety - ANOVA: [F = 2.536, p=0.004] between first and tenth (p=0.048) and first and eleventh (p=0.025) semesters; depression - ANOVA: [F = 2.410, p=0.006] between first and second semesters (p=0.045); and stress - ANOVA: [F = 2.968, p=0.001] between seventh and twelfth (p=0.044), tenth and twelfth (p=0.011), and eleventh and twelfth (p=0.001) semesters. The following factors were associated with (a) stress: female gender, anxiety, and depression; (b) depression: female gender, intrinsic religiosity, anxiety, and stress; and (c) anxiety: course semester, depression, and stress. ::: ::: ::: CONCLUSION ::: Our findings revealed high levels of depression, anxiety, and stress symptoms in medical students, with marked differences among course semesters. Gender and religiosity appeared to influence the mental health of the medical students.


title: Moderate Exercise Based on Artificial Gravity Preserves Orthostatic Tolerance and Exercise Capacity During Short-Term Head-Down Bed Rest

abstract: Numerous countermeasures have been proposed to minimize microgravity-induced physical deconditioning, but their benefits are limited. The present study aimed to investigate whether personalized aerobic exercise based on artificial gravity (AG) mitigates multisystem physical deconditioning. Fourteen men were assigned to the control group (n=6) and the countermeasure group (CM, n=8). Subjects in the CM group were exposed to AG (2 Gz at foot level) for 30 min twice daily, during which time cycling exercise of 80-95 % anaerobic threshold (AT) intensity was undertaken. Orthostatic tolerance (OT), exercise tests, and blood assays were determined before and after 4 days head-down bed rest (HDBR). Cardiac systolic function was measured every day. After HDBR, OT decreased to 50.9 % and 77.5 % of pre-HDBR values in control and CM groups, respectively. Exercise endurance, maximal oxygen consumption, and AT decreased to 96.5 %, 91.5 % and 91.8 % of pre-HDBR values, respectively, in the control group. Nevertheless, there were slight changes in the CM group. HDBR increased heart rate, sympathetic activity, and the pre-ejection period, but decreased plasma volume, parasympathetic activity and left-ventricular ejection time in the control group, whereas these effects were eliminated in the CM group. Aldosterone had no change in the control group but increased significantly in the CM group. Our study shows that 80-95 % AT aerobic exercise based on 2 Gz of AG preserves OT and exercise endurance, and affects body fluid regulation during short-term HDBR. The underlying mechanisms might involve maintained cardiac systolic function, preserved plasma volume, and improved sympathetic responses to orthostatic stress.


title: Splicing-factor alterations in cancers

abstract: Tumor-associated alterations in RNA splicing result either from mutations in splicing-regulatory elements or changes in components of the splicing machinery. This review summarizes our current understanding of the role of splicing-factor alterations in human cancers. We describe splicing-factor alterations detected in human tumors and the resulting changes in splicing, highlighting cell-type-specific similarities and differences. We review the mechanisms of splicing-factor regulation in normal and cancer cells. Finally, we summarize recent efforts to develop novel cancer therapies, based on targeting either the oncogenic splicing events or their upstream splicing regulators.


title: Broad learning system: Feature extraction based on K-means clustering algorithm

abstract: Broad Learning System [1] proposed recently demonstrates efficient and effective learning capability. This model is also proved to be suitable for incremental learning algorithms by taking the advantages of random vector flat neural networks. In this paper, a modified BLS structure based on the K-means feature extraction is developed. Compared with the original broad learning system, acceptable performance on more complicated data set, such as CIFAR-10, is achieved. Furthermore, it is proved that the proposed model in [1] is flexible and potential in various applications.


title: Temporal correlation based sparse channel estimation for TDS-OFDM in high-speed scenarios

abstract: Accurate channel estimation is essential for time domain synchronous OFDM (TDS-OFDM), which is a key enabling technology in digital terrestrial multimedia broadcasting (DTMB) standard. However, conventional channel estimation schemes for TDS-OFDM systems suffer from the obvious performance loss in high-speed scenarios. In this paper, by exploiting the temporal correlation of wireless channels, we propose a sparse channel estimation scheme to improve the channel estimation performance for TDS-OFDM systems in high-speed scenarios. Specifically, we first propose an overlap-add method of the received time-domain training sequences (TSs) to acquire the rough channel estimation, whereby the temporal correlation of wireless channels is exploited to improve the estimation performance of time-varying channels. Then, a priori information aided matching pursuit (PIA-MP) algorithm is proposed to acquire the accurate channel estimation with low complexity, whereby the priori information from the rough channel estimation is utilized to further improve the channel estimation accuracy. Simulation results demonstrate that the proposed scheme is superior to the state-of-the-art schemes in high-speed scenarios, especially under severe multipath channels with long delay spread.


title: Matrix transformer for LLC resonant converters

abstract: In this paper, a high efficiency high power density matrix transformer structure for LLC resonant converters is proposed. Matrix transformer is help to reduce leakage inductance and the AC resistance of windings. Flux cancellation method is utilized to reduce core size and loss. Synchronous Rectifier (SR) devices and output capacitors are integrated into secondary windings to eliminate termination related winding losses, via loss and leakage inductance. An 1.6 MHz 390 /12 V 1kW LLC resonant converter prototype is built to verify the proposed structure.


title: Time Dependent Information Costs, Price Controls, and Successive Government Intervention

abstract: Economists have long recognized the costliness of information. The organization of political markets from political parties (Von Ungern-Sternberg 1982) to political brand name (Nelson 1976; Telser 1976; Lott 1987a; 1987b) is thought to depend on such costs. Downs (1957) and Buchanan and Tullock (1965) argue that ideology acts as an economizing device on voter information. The inability to resell these costly investments in political brand name, however, has been found to cause entry barriers in political markets (Lott 1987a; 1989). Filmon, Romer, and Rosenthal (1982) study the problem of political markets where different people face different costs of acquiring the same information. Another approach is expressed by Kalt and Zupan (1984) who claim that these costs can explain political shirking, while Peltzman (1984), Lott (1987b), and Dougan and Munger (1989) claim that such shirking


title: Secure LSB steganography for colored images using character-color mapping

abstract: Steganography is the science of embedding the secret messages inside other medium files in a way that hides the existence of the secret message at all. Steganography can be applied to text, audio, image, and video file types. In this study, we propose a new steganography approach for digital images in which the RGB coloring model was used. The efficiency of the proposed approach has been tested and evaluated. The experimental results show that the proposed approach produce high-quality stego images that resist against visual and statistical attacks.


title: Catastrophic forgetting, rehearsal and pseudorehearsal

abstract: This paper reviews the problem of catastrophic forgetting (the loss or disruption of previously learned information when new information is learned) in neural networks, and explores rehearsal mechanisms (the retraining of some of the previously learned information as the new information is added) as a potential solution. We replicate some of the experiments described by Ratcliff (1990), including those relating to a simple 'recency' based rehearsal regime. We then develop further rehearsal regimes which are more effective than recency rehearsal. In particular, 'sweep rehearsal' is very successful at minimizing catastrophic forgetting. One possible limitation of rehearsal in general, however, is that previously learned information may not be available for retraining. We describe a solution to this problem, 'pseudorehearsal', a method which provides the advantages of rehearsal without actually requiring any access to the previously learned information (the original training population) itself. We then sugge...


title: Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors

abstract: Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with `Big Data' analytics. However, increasingly noisy, heterogeneous, and incomplete datasets as well as the need for {\em real-time} processing of streaming data pose major challenges to this end. In this context, the present paper permeates benefits from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling latent (possibly multi-way) structure from \emph{incomplete streaming} data. For low-rank matrix data, a subspace estimator is proposed based on an exponentially-weighted least-squares criterion regularized with the nuclear norm. After recasting the non-separable nuclear norm into a form amenable to online optimization, real-time algorithms with complementary strengths are developed and their convergence is established under simplifying technical assumptions. In a stationary setting, the asymptotic estimates obtained offer the well-documented performance guarantees of the {\em batch} nuclear-norm regularized estimator. Under the same unifying framework, a novel online (adaptive) algorithm is developed to obtain multi-way decompositions of \emph{low-rank tensors} with missing entries, and perform imputation as a byproduct. Simulated tests with both synthetic as well as real Internet and cardiac magnetic resonance imagery (MRI) data confirm the efficacy of the proposed algorithms, and their superior performance relative to state-of-the-art alternatives.


title: Classifying Graphs as Images with Convolutional Neural Networks

abstract: The task of graph classification is currently dominated by graph kernels, which, while powerful, scale poorly to large graphs and datasets. Convolutional Neural Networks (CNNs) offer a very appealing alternative. However, feeding graphs to CNNs is not trivial. To address this challenge, many sophisticated extensions of CNNs have recently been proposed. In this paper, we show that a classical 2D CNN architecture designed for images can also be used for graph processing in a completely off-the-shelf manner; the only prerequisite being to encode graphs as stacks of two-dimensional histograms of their node embeddings. Despite its simplicity, our method proves very competitive to state-of-the-art graph kernels, and even outperforms them by a wide margin on some datasets.


title: Effect of lonidamine on the utilization of 14C-labeled glucose by human astrocytoma cells.

abstract: The effect of lonidamine (LND), 1-(2,4-dichlorobenzyl)-1H-indazol-3 carboxylic acid, on the utilization of carbon from 14C-labeled glucose by cell cultures of the permanent strain LI derived from a human glioblastoma multiforme (astrocytoma) has been investigated. The results may be summarized as follows. Aerobic glycolysis is the main energy-yielding process as shown by the fact that the greatest part of glucose carbon atoms is incorporated into lactate. Nevertheless, the amount of glucose converted accounts for only 63% of the lactate produced, indicating the presence of an elevated endogenous aerobic glycolysis. The amount of glucose carbon atoms incorporated into CO2, lipids, nucleic acid, and supporting structures is low. LND decreased the incorporation of 14C activity in all the above mentioned isolated compounds because of its ability to inhibit glucose phosphorylation. Consequently, there is a lower concentration of glucose-6-phosphate which, in turn, affects the rate of formation of several metabolites in glycolytic and pentose phosphate pathways. Experiments with [1-14C]-2-deoxy-D-glucose further substantiate the idea of glucose phosphorylation as a main target of LND and strongly suggest the presence of a mitochondrially bound hexokinase. The higher inhibition of glucose phosphorylation in exponentially growing cells indicates a further shift of the enzyme toward mitochondria-bound form and confirms the importance of the energy status of the cell in eliciting the response to LND. The reduced capacity of LND-treated cells to synthetize ATP and glucose-6-phosphate reflects the decreased synthesis of proteins and nucleic acids, which affects cell growth and duplication.


title: Refining the Nasal Dorsum with Free Diced Cartilage

abstract: Refining the nasal dorsum has become a major challenge in modern rhinoplasty as irregularities of the nasal dorsum account for a significant number of revision surgeries. In our department, free diced cartilage is now routinely applied for smoothening of the nasal dorsum. In this retrospective study, the outcomes with regard to irregularities or contour deficits of the nasal dorsum of 431 rhinoplasty cases operated by a single surgeon between July 2013 and June 2015, using free diced cartilage, are compared with 327 cases operated by the same surgeon between January 2007 and December 2008, before the introduction of the free diced cartilage technique. A decrease in early revision surgeries (i.e., revision within the 2-year period evaluated) due to dorsal irregularities or contour deficits is seen. Being a quick, easy, and highly cost-effective procedure, we feel that free diced cartilage is currently the ideal technique for refinements of the nasal dorsum.


title: Map Reduce for big data processing based on traffic aware partition and aggregation

abstract: Big data refers to data sets whose volume is 500+ terabytes of data per day. The velocity makes it difficult to capture, manage, process and analyze 2 million records per day. Another characteristics of big data is variability which makes it difficult to identify the reason for losses in i.e., images, audio, video, sensor data and log files etc., Hadoop can be used to analyze this huge amount of data using Hadoop an approximate early result for executing the job partially becomes available for the user even before completion of job which reduce the response time. In Layers 3 Traffic aware clustering programming model is used for processing big data which includes the data processing function map by sort and reducing techniques. The implementation of the layers three traffic aware clustering method will be on the top of Hadoop which is partitioned into HDFS fixed sized blocks and generates intermediate output as a collection of pairs. The conventional hash function method is used for partitioning intermediate data among reduced task but it is not traffic efficient. In this paper to reduce network traffic cost, a Map Reduce task is done by designing data partition and aggregator that can reduce task merged traffic from multiple map tasks. The proposed algorithm is more efficient to reduce response time and the simulation results have showed proposal can reduce network traffic.


title: Factors of collaborative working: A framework for a collaboration model

abstract: Abstract The ability of organisations to support collaborative working environments is of increasing importance as they move towards more distributed ways of working. Despite the attention collaboration has received from a number of disparate fields, there is a lack of a unified understanding of the component factors of collaboration. As part of our work on a European Integrated Project, CoSpaces, collaboration and collaborative working and the factors which define it were examined through the literature and new empirical work with a number of partner user companies in the aerospace, automotive and construction sectors. This was to support development of a descriptive human factors model of collaboration – the CoSpaces Collaborative Working Model (CCWM). We identified seven main categories of factors involved in collaboration: Context, Support, Tasks, Interaction Processes, Teams, Individuals, and Overarching Factors, and summarised these in a framework which forms a basis for the model. We discuss supporting evidence for the factors which emerged from our fieldwork with user partners, and use of the model in activities such as collaboration readiness profiling.


title: Learning Bayesian Network Equivalence Classes with Ant Colony Optimization

abstract: Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACO-E, to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed. Secondly, moves can be given in terms of indices that are not based on construction graph nodes. The results of testing show that ACO-E performs better than a greedy search and other state-of-the-art and metaheuristic algorithms whilst searching in the space of equivalence classes.


title: MrKIP: Rootkit Recognition With Kernel Function Invocation Pattern *

abstract: Existing mechanisms tracing user-level activities such as system calls and APIs can be circumvented by the kernel-level rootkits. In this paper, a novel system, MrKIP, is proposed to recognize rootkits based on their kernel-level activities. Our scheme semiautomatically generates suitable locations for analysts to implement checkpoints, which are used to profile kernel-space activities. Then, collected rootkits are executed in an emulator with these checkpoints for behavior profiling. The collected behaviors are clustered and used for model construction. The constructed model can be used to recognize new variants of rootkit families. Our scheme differs from conventional tracers due to its ability to cover kernel-space malware and the whole-system scope. In addition, monitoring at the kernel level raises high barrier for malware to evade, since all tasks are eventually executed through the basic kernel functions.


title: Modeling Dormant Fruit Trees for Agricultural Automation

abstract: Dormant pruning of fruit trees is one of the most costly and labor-intensive activities in specialty crop production. We present a system that solves the first step in the process of automated pruning: accurately measuring and modeling the fruit trees. Our system employs a laser sensor to collect observations of fruit trees from multiple perspectives, and it uses these observations to measure parameters needed for pruning. A split-and-merge clustering algorithm divides the collected data into three sets of points: trunk candidates, junction point candidates, and branches. The trunk candidates and junction point candidates are then further refined by a robust fitting algorithm that models as cylinders each segment of the trunk and primary branches. In this work, we focus on measuring the diameters of the primary branches and the trunk, which are important factors in dormant pruning and can be obtained directly from the cylindrical models. We show that the results are qualitatively satisfactory using synthetic and real data. Our experiments with three synthetic and three real apple trees of two different varieties showed that the system is able to identify the primary branches with an average accuracy of 98% and estimate their diameters with an average error of 0.6 cm. Although the current implementation of the system is too slow for large-scale practical applications (it can measure approximately two trees per hour), our study shows that the proposed approach may serve as a fundamental building block of robotic pruners in the near future.


title: Classification with incomplete data using dirichlet process priors

abstract: A non-parametric hierarchical Bayesian framework is developed for designing a classifier, based on a mixture of simple (linear) classifiers. Each simple classifier is termed a local "expert", and the number of experts and their construction are manifested via a Dirichlet process formulation. The simple form of the "experts" allows analytical handling of incomplete data. The model is extended to allow simultaneous design of classifiers on multiple data sets, termed multi-task learning, with this also performed non-parametrically via the Dirichlet process. Fast inference is performed using variational Bayesian (VB) analysis, and example results are presented for several data sets. We also perform inference via Gibbs sampling, to which we compare the VB results.


title: Waste collection multi objective model with real time traceability data.

abstract: Waste collection is a highly visible municipal service that involves large expenditures and difficult operational problems, plus it is expensive to operate in terms of investment costs (i.e. vehicles fleet), operational costs (i.e. fuel, maintenances) and environmental costs (i.e. emissions, noise and traffic congestions). Modern traceability devices, like volumetric sensors, identification RFID (Radio Frequency Identification) systems, GPRS (General Packet Radio Service) and GPS (Global Positioning System) technology, permit to obtain data in real time, which is fundamental to implement an efficient and innovative waste collection routing model. The basic idea is that knowing the real time data of each vehicle and the real time replenishment level at each bin makes it possible to decide, in function of the waste generation pattern, what bin should be emptied and what should not, optimizing different aspects like the total covered distance, the necessary number of vehicles and the environmental impact. This paper describes a framework about the traceability technology available in the optimization of solid waste collection, and introduces an innovative vehicle routing model integrated with the real time traceability data, starting the application in an Italian city of about 100,000 inhabitants. The model is tested and validated using simulation and an economical feasibility study is reported at the end of the paper.


title: A new machine learning paradigm for terrain reconstruction

abstract: Terrain models that permit multiresolution access are essential for model predictive control of unmanned aerial vehicles in low-level flights. The authors present the extreme learning machine (ELM), a recently proposed learning paradigm, as a mechanism for learning the stored digital elevation information to allow multiresolution access. We give results of simulations designed to compare the performance of our approach with two other approaches for multiresolution access, namely: 1) linear interpolation on Delaunay triangles of the sampled terrain data points and 2) terrain learning using support vector machines (SVMs). The results show that to achieve the same mean square error during access, the memory needed in our approach is significantly lower. Additionally, the offline training time for the ELM network is much less than that for the SVM


title: Bearingless slice motor concepts without permanent magnets in the rotor

abstract: Bearingless permanent magnet motors are very compact and favorable for high speed designs. Especially the bearingless slice motor (using a permanent magnet excited rotor disc) features a mechanically simple and very cost effective fully magnetically levitated system by stabilizing some degrees of freedom by permanent magnetic reluctance forces.


title: A Gate Drive With Power Over Fiber-Based Isolated Power Supply and Comprehensive Protection Functions for 15-kV SiC MOSFET

abstract: This paper presents a 15kV silicon carbide (SiC) MOSFET gate drive, which features high common-mode (CM) noise immunity, small size, light weight, and robust yet flexible protection functions. To enhance the gate-drive power reliability, a power over fiberbased isolated power supply is designed to replace the traditional design based on isolation transformer. It delivers the gate-drive power by laser light via optical fiber over a long distance (>1 m), so a high isolation voltage (>20 kV) is achieved, and the circuit size and weight are reduced. More importantly, it eliminates the parasitic CM capacitance coupling the power stage and control stage, and thus eradicates the control signal distortion caused by high dv/dt in switching transients of the high-voltage SiC devices. In addition, the gate-drive circuit design integrates comprehensive protection functions, including the overcurrent protection, undervoltage/overvoltage lockout, active miller clamping, soft turn off, and fault report. The overcurrent protection responds within 400 ns. The experimental results from a 15kV double-pulse tester are presented to validate the design.


title: Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework

abstract: The use of high-volume quantitative radiomics features extracted from multi-parametric magnetic resonance imaging (MP-MRI) is gaining attraction for the autodetection of prostate tumors, since it provides a plethora of mineable data, which can be used for both detection and prognosis of prostate cancer. While current voxel-resolution radiomics-driven prostate tumor detection approaches utilize quantitative radiomics features associated with individual voxels on an independent basis, the incorporation of additional information regarding the spatial and radiomics feature relationships between voxels has significant potential for achieving a more reliable detection performance. Motivated by this, we present a novel approach for automatic prostate cancer detection using a radiomics-driven conditional random field (RD-CRF) framework. In addition to the high-throughput extraction and utilization of a comprehensive set of voxel-level quantitative radiomics features, the proposed RD-CRF framework leverages inter-voxel spatial and radiomics feature relationships to ensure that the autodetected tumor candidates exhibit interconnected tissue characteristics reflective of cancerous tumors. We evaluated the performance of the proposed framework using clinical prostate MP-MRI data of 20 patients, and the results of RD-CRF framework demonstrated a clear improvement with respect to the state-of-the-art in quantitative radiomics for automatic voxel-resolution prostate cancer detection.


On the semantics of self-unpacking malware code


title: Combining Classification and Clustering for Tweet Sentiment Analysis

abstract: The goal of sentiment analysis is to determine opinions, emotions, and attitudes presented in source material. In tweet sentiment analysis, opinions in messages can be typically categorized as positive or negative. To classify them, researchers have been using traditional classifiers like Naive Bayes, Maximum Entropy, and Support Vector Machines (SVM). In this paper, we show that a SVM classifier combined with a cluster ensemble can offer better classification accuracies than a stand-alone SVM. In our study, we employed an algorithm, named C3E-SL, capable to combine classifier and cluster ensembles. This algorithm can refine tweet classifications from additional information provided by clusterers, assuming that similar instances from the same clusters are more likely to share the same class label. The resulting classifier has shown to be competitive with the best results found so far in the literature, thereby suggesting that the studied approach is promising for tweet sentiment classification.


title: Designing ScratchJr: support for early childhood learning through computer programming

abstract: ScratchJr is a graphical programming language based on Scratch and redesigned for the unique developmental and learning needs of children in kindergarten to second grade. The creation of ScratchJr addresses the relative lack of powerful technologies for digital creation and computer programming in early childhood education. ScratchJr will provide software for children to create interactive, animated stories as well as curricula and online resources to support adoption by educators. This paper describes the goals and challenges of creating a developmentally appropriate programming tool for children ages 5-7 and presents the path from guiding principles and studies with young children to current ScratchJr designs and plans for future work.


title: © 2008 Science Publications A Shuffle Image-Encryption Algorithm

abstract: Problem statement: Image encryption needs to be secure by resisting statistical attacks and other types of attacks. Approach: The new algorithm, call it the Shuffle Encryption Algorithm (SEA), applies nonlinear s-box byte substitution. Then, it performed a shuffling operation partially dependent on the input data and uses the given key. Results: SEA was implemented and tested with different data, mainly consisting of images. Results confirmed its security, shown through statistical analysis using histograms, correlation and covariance. Conclusion: New algorithm was suited for encrypting images and other types of data.


title: Spatio-Temporal GrabCut human segmentation for face and pose recovery

abstract: In this paper, we present a full-automatic Spatio-Temporal GrabCut human segmentation methodology. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model for seed initialization. Spatial information is included by means of Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, human segmentation is combined with Shape and Active Appearance Models to perform full face and pose recovery. Results over public data sets as well as proper human action base show a robust segmentation and recovery of both face and pose using the presented methodology.


title: Hardware support for non-photorealistic rendering

abstract: Special features such as ridges, valleys and silhouettes, of a polygonal scene are usually displayed by explicitly identifying and then rendering `edges' for the corresponding geometry. The candidate edges are identified using the connectivity information, which requires preprocessing of the data. We present a non-obvious but surprisingly simple to implement technique to render such features without connectivity information or preprocessing. At the hardware level, based only on the vertices of a given flat polygon, we introduce new polygons, with appropriate color, shape and orientation, so that they eventually appear as special features.


title: Named Entity Recognition from Unstructured Handwritten Document Images

abstract: Named entity recognition is an important topic in the field of natural language processing, whereas in document image processing, such recognition is quite challenging without employing any linguistic knowledge. In this paper we propose an approach to detect named entities (NEs) directly from offline handwritten unstructured document images without explicit character/word recognition, and with very little aid from natural language and script rules. At the preprocessing stage, the document image is binarized, and then the text is segmented into words. The slant/skew/baseline corrections of the words are also performed. After preprocessing, the words are sent for NE recognition. We analyze the structural and positional characteristics of NEs and extract some relevant features from the word image. Then the BLSTM neural network is used for NE recognition. Our system also contains a post-processing stage to reduce the true NE rejection rate. The proposed approach produces encouraging results on both historical and modern document images, including those from an Australian archive, which are reported here for the very first time.


title: Attributed Social Network Embedding

abstract: Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks. These rich attribute information of social actors reveal the homophily effect, exerting huge impacts on the formation of social networks. In this paper, we explore the rich evidence source of attributes in social networks to improve network embedding. We propose a generic Social Network Embedding framework (SNE), which learns representations for social actors (i.e., nodes) by preserving both the structural proximity and attribute proximity. While the structural proximity captures the global network structure, the attribute proximity accounts for the homophily effect. To justify our proposal, we conduct extensive experiments on four real-world social networks. Compared to the state-of-the-art network embedding approaches, SNE can learn more informative representations, achieving substantial gains on the tasks of link prediction and node classification. Specifically, SNE significantly outperforms node2vec with an 8.2% relative improvement on the link prediction task, and a 12.7% gain on the node classification task.


title: Hamiltonian Variational Auto-Encoder

abstract: Variational Auto-Encoders (VAEs) have become very popular techniques to perform inference and learning in latent variable models as they allow us to leverage the rich representational power of neural networks to obtain flexible approximations of the posterior of latent variables as well as tight evidence lower bounds (ELBOs). Combined with stochastic variational inference, this provides a methodology scaling to large datasets. However, for this methodology to be practically efficient, it is necessary to obtain low-variance unbiased estimators of the ELBO and its gradients with respect to the parameters of interest. While the use of Markov chain Monte Carlo (MCMC) techniques such as Hamiltonian Monte Carlo (HMC) has been previously suggested to achieve this [23, 26], the proposed methods require specifying reverse kernels which have a large impact on performance. Additionally, the resulting unbiased estimator of the ELBO for most MCMC kernels is typically not amenable to the reparameterization trick. We show here how to optimally select reverse kernels in this setting and, by building upon Hamiltonian Importance Sampling (HIS) [17], we obtain a scheme that provides low-variance unbiased estimators of the ELBO and its gradients using the reparameterization trick. This allows us to develop a Hamiltonian Variational Auto-Encoder (HVAE). This method can be reinterpreted as a target-informed normalizing flow [20] which, within our context, only requires a few evaluations of the gradient of the sampled likelihood and trivial Jacobian calculations at each iteration.


title: Self-Oscillating Resonant Converter With Contactless Power Transfer and Integrated Current Sensing Transformer

abstract: In this paper, an integrated transformer is proposed to provide current sensing in the primary side and passive current phase detection for a self-oscillating contactless resonant converter (SOCRC). The proposed transformer integrates power transfer and current sensing. By using the secondary current phase of a series/series-compensated resonant converter in the control of the inverter, better output controllability, dynamic response, and self-adaptability can be achieved simultaneously. The integrated current sensing winding, being a crucial part of SOCRC, is analyzed in detail. In order to make the output voltage insensitive to the clearance and misalignment conditions, the requirement of the new integrated transformer is given. Experimental results of an 80-W SOCRC prototype verify the analytical results.


title: π-ADL: an Architecture Description Language based on the higher-order typed π-calculus for specifying dynamic and mobile software architectures

abstract: A key aspect of the design of any software system is its architecture. An architecture description, from a runtime perspective, should provide a formal specification of the architecture in terms of components and connectors and how they are composed together. Further, a dynamic or mobile architecture description must provide a specification of how the architecture of the software system can change at runtime. Enabling specification of dynamic and mobile architectures is a large challenge for an Architecture Description Language (ADL). This article describes π-ADL, a novel ADL that has been designed in the ArchWare European Project to address specification of dynamic and mobile architectures. It is a formal, well-founded theoretically language based on the higher-order typed π-calculus. While most ADLs focus on describing software architectures from a structural viewpoint, π-ADL focuses on formally describing architectures encompassing both the structural and behavioural viewpoints. The π-ADL design principles, concepts and notation are presented. How π-ADL can be used for specifying static, dynamic and mobile architectures is illustrated through case studies. The π-ADL toolset is outlined.


title: First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations

abstract: In this work we study the use of 3D hand poses to recognize first-person dynamic hand actions interacting with 3D objects. Towards this goal, we collected RGB-D video sequences comprised of more than 100K frames of 45 daily hand action categories, involving 26 different objects in several hand configurations. To obtain hand pose annotations, we used our own mo-cap system that automatically infers the 3D location of each of the 21 joints of a hand model via 6 magnetic sensors and inverse kinematics. Additionally, we recorded the 6D object poses and provide 3D object models for a subset of hand-object interaction sequences. To the best of our knowledge, this is the first benchmark that enables the study of first-person hand actions with the use of 3D hand poses. We present an extensive experimental evaluation of RGB-D and pose-based action recognition by 18 baselines/state-of-the-art approaches. The impact of using appearance features, poses, and their combinations are measured, and the different training/testing protocols are evaluated. Finally, we assess how ready the 3D hand pose estimation field is when hands are severely occluded by objects in egocentric views and its influence on action recognition. From the results, we see clear benefits of using hand pose as a cue for action recognition compared to other data modalities. Our dataset and experiments can be of interest to communities of 3D hand pose estimation, 6D object pose, and robotics as well as action recognition.


title: Body-swapping Experiment with an Android: Investigation of the Relationship Between Agency and a Sense of Ownership toward a different Body

abstract: The experiment described in this paper is performed within a system that provides a human with the possibility and capability to be physically immersed in the body of an android robot, Geminoid HI-2. The participant, through the swapped body of Geminoid HI-2, is able to see his/her own body in front of themselves like a reflection in a mirror -- then they can touch their own body by the hands of the Geminoid HI-2. We are investigating how the sense of body-ownership is generated during visual and tactile tests and how the presence of agency impacts one's body-ownership.


title: Object turning for barcode search

abstract: In this paper, a flexible manipulation system used in a catalogue sale business is presented. One application of such a system is the object turning operation for the barcode search. The regrasping procedures are studied in detail to its significance in object turning. This work focuses on regrasping tasks with several different grippers.


title: Motivation and Creativity: Effects of Motivational Orientation on Creative Writers

abstract: This study directly tested the hypothesis that intrinsic motivation is conducive to creativity and extrinsic motivation is detrimental. Chosen because they identified themselves as actively involved in creative writing, 72 young adults participated in individual laboratory sessions where they were asked to write 2 brief poems. Before writing the second poem, subjects in an intrinsic orientation condition completed a questionnaire that focused on intrinsic reasons for being involved in writing. Subjects in an extrinsic orientation condition completed a questionnaire that focused on extrinsic reasons. Those in a control condition were not given a questionnaire on reasons for writing. Although there were no initial differences between conditions on prior involvement in writing or on creativity of the first poems written, there were significant differences in the creativity of the poems written after the experimental manipulations. Poems written under an extrinsic orientation were significantly less creative than those written in the other two conditions. Implications for social-psychological and individual-difference conceptions of creativity are discussed.


title: Investigation of magnesium-zinc-calcium alloys and bone marrow derived mesenchymal stem cell response in direct culture.

abstract: Abstract Crystalline Mg–Zn–Ca ternary alloys have recently attracted significant interest for biomedical implant applications due to their promising biocompatibility, bioactivity, biodegradability and mechanical properties. The objective of this study was to characterize as-cast Mg–xZn–0.5Ca (x = 0.5, 1.0, 2.0, 4.0 wt.%) alloys, and determine the adhesion and morphology of bone marrow derived mesenchymal stem cells (BMSCs) at the interface with the Mg–xZn–0.5Ca alloys. The direct culture method (i.e. seeding cells directly onto the surface of the sample) was established in this study to probe the highly dynamic cell–substrate interface and thus to elucidate the mechanisms of BMSC responses to dynamic alloy degradation. The results showed that the BMSC adhesion density on these alloys was similar to the cell-only positive control and the BMSC morphology appeared more anisotropic on the rapidly degrading alloy surfaces in comparison with the cell-only positive control. Importantly, neither culture media supplemented with up to 27.6 mM Mg 2+ ions nor media intentionally adjusted up to alkaline pH 9 induced any detectable adverse effects on BMSC responses. We speculated that degradation-induced dynamic surface topography played an important role in modulating cell morphology at the interface. This study presents a clinically relevant in vitro model for screening bioresorbable alloys, and provides useful design guidelines for determining the degradation rate of implants made of Mg–Zn–Ca alloys.


title: Evolutionary Game Design

abstract: It is easy to create new combinatorial games but more difficult to predict those that will interest human players. We examine the concept of game quality, its automated measurement through self-play simulations, and its use in the evolutionary search for new high-quality games. A general game system called Ludi is described and experiments conducted to test its ability to synthesize and evaluate new games. Results demonstrate the validity of the approach through the automated creation of novel, interesting, and publishable games.


title: Pixels and Parchment: The Application of RTI and Infrared Imaging to the Dead Sea Scrolls.

abstract: The Dead Sea Scrolls, one of the most important archaeological discoveries of the twentieth century, are in constant need of conservation and restoration. An ongoing research project being carried out at the paper conservation laboratory of the Israel Museum Jerusalem is applying the combined potentials of Reflectance Transmission and Infrared imaging to deteriorating fragments of one of the most badly damaged Scrolls. Early tests show that both infrared imaging and RTI imaging have a lot to offer the conservation process, the one offering surface penetration information and the other surface texture analysis. However, it is the fusion of the two technologies together which holds the promise of a unique insight into the “surface beneath the surface”. This technique, while showing great promise, is still in its early stages of testing and definitive conclusions are as of yet not possible.


title: BodyBeat: a mobile system for sensing non-speech body sounds

abstract: In this paper, we propose BodyBeat, a novel mobile sensing system for capturing and recognizing a diverse range of non-speech body sounds in real-life scenarios. Non-speech body sounds, such as sounds of food intake, breath, laughter, and cough contain invaluable information about our dietary behavior, respiratory physiology, and affect. The BodyBeat mobile sensing system consists of a custom-built piezoelectric microphone and a distributed computational framework that utilizes an ARM microcontroller and an Android smartphone. The custom-built microphone is designed to capture subtle body vibrations directly from the body surface without being perturbed by external sounds. The microphone is attached to a 3D printed neckpiece with a suspension mechanism. The ARM embedded system and the Android smartphone process the acoustic signal from the microphone and identify non-speech body sounds. We have extensively evaluated the BodyBeat mobile sensing system. Our results show that BodyBeat outperforms other existing solutions in capturing and recognizing different types of important non-speech body sounds.


title: Using the Delphi method

abstract: The reliability and validity of the selected research method are subjects to which every researcher is bound to address himself when representing the findings and conclusions of his/her work. In this study we will discuss the reliability, validity and philosophical aspects of the Delphi method, first with a small literature review and then by representing two different surveys conducted using the Delphi method. The point of view in our report is the usability of Delphi in collecting qualitative data for software engineering research. The most significant features of the Delphi method are its recursion and the possibility to get immediate feedback and evaluate one's own answer. Although there are many forms of Delphi techniques, these features exist in one form or the other in all Delphi variations. In Delphi-based surveys, the minimum number of participants is smaller and participants are not selected at random but because of their particular expertise. Among the traditional research methods this is seen to cause the risk of bias and endangering both reliability and validity. In the Delphi method, the recursion produced by three or more rounds, the expertise and - in most cases - anonymity of participants, and the opportunity to evaluate and argue one's own answer after having seen the other opinions and arguments are thought to guarantee the quality of well-planned and well-conducted research.


title: Teaching a lay theory before college narrows achievement gaps at scale

abstract: Previous experiments have shown that college students benefit when they understand that challenges in the transition to college are common and improvable and, thus, that early struggles need not portend a permanent lack of belonging or potential. Could such an approach—called a lay theory intervention—be effective before college matriculation? Could this strategy reduce a portion of racial, ethnic, and socioeconomic achievement gaps for entire institutions? Three double-blind experiments tested this possibility. Ninety percent of first-year college students from three institutions were randomly assigned to complete single-session, online lay theory or control materials before matriculation (n > 9,500). The lay theory interventions raised first-year full-time college enrollment among students from socially and economically disadvantaged backgrounds exiting a high-performing charter high school network or entering a public flagship university (experiments 1 and 2) and, at a selective private university, raised disadvantaged students’ cumulative first-year grade point average (experiment 3). These gains correspond to 31–40% reductions of the raw (unadjusted) institutional achievement gaps between students from disadvantaged and nondisadvantaged backgrounds at those institutions. Further, follow-up surveys suggest that the interventions improved disadvantaged students’ overall college experiences, promoting use of student support services and the development of friendship networks and mentor relationships. This research therefore provides a basis for further tests of the generalizability of preparatory lay theories interventions and of their potential to reduce social inequality and improve other major life transitions.


Active Sentiment Domain Adaptation


title: A multidisciplinary approach towards computational thinking for science majors

abstract: This paper describes the development and initial evaluation of a new course ``Introduction to Computational Thinking'' taken by science majors to fulfill a college computing requirement. The course was developed by computer science faculty in collaboration with science faculty and it focuses on the role of computing and computational principles in scientific inquiry. It uses Python and Python libraries to teach computational thinking via basic programming concepts, data management concepts, simulation, and visualization. Problems with a computational aspect are drawn from different scientific disciplines and are complemented with lectures from faculty in those areas. Our initial evaluation indicates that the problem-driven approach focused on scientific discovery and computational principles increases the student's interest in computing.