title: A Practical Transfer Learning Algorithm for Face Verification

abstract: Face verification involves determining whether a pair of facial images belongs to the same or different subjects. This problem can prove to be quite challenging in many important applications where labeled training data is scarce, e.g., family album photo organization software. Herein we propose a principled transfer learning approach for merging plentiful source-domain data with limited samples from some target domain of interest to create a classifier that ideally performs nearly as well as if rich target-domain data were present. Based upon a surprisingly simple generative Bayesian model, our approach combines a KL-divergence based regularizer/prior with a robust likelihood function leading to a scalable implementation via the EM algorithm. As justification for our design choices, we later use principles from convex analysis to recast our algorithm as an equivalent structured rank minimization problem leading to a number of interesting insights related to solution structure and feature-transform invariance. These insights help to both explain the effectiveness of our algorithm as well as elucidate a wide variety of related Bayesian approaches. Experimental testing with challenging datasets validate the utility of the proposed algorithm.


title: Reflective practice: a learning tool for student nurses

abstract: Reflection is a vital skill in contemporary nursing with student nurses expected to engage in reflective learning from the very beginning of the nurse educational programme. This article demonstrates the meaningful learning that resulted as a consequence of using critical reflection on practice. Gibbs' (1988) cycle aided the process highlighting the practical application of this cyclical framework to the author - a first-year student nurse. Matters concerning gender issues in nursing and professional conduct emerged from the analysis and were inherently explored. The article concludes by demonstrating the personal benefits of using Gibbs' (1988) cycle to varying situations and thus promoting its excellence as a learning tool for student nurses worldwide as a consequence.


title: Understanding Behaviors that Lead to Purchasing: A Case Study of Pinterest

abstract: Online e-commerce applications are becoming a primary vehicle for people to find, compare, and ultimately purchase products. One of the fundamental questions that arises in e-commerce is to characterize, understand, and model user long-term purchasing intent, which is important as it allows for personalized and context relevant e-commerce services. In this paper we study user activity and purchasing behavior with the goal of building models of time-varying user purchasing intent. We analyze the purchasing behavior of nearly three million Pinterest users to determine short-term and long-term signals in user behavior that indicate higher purchase intent. We find that users with long-term purchasing intent tend to save and clickthrough on more content. However, as users approach the time of purchase their activity becomes more topically focused and actions shift from saves to searches. We further find that purchase signals in online behavior can exist weeks before a purchase is made and can also be traced across different purchase categories. Finally, we synthesize these insights in predictive models of user purchasing intent. Taken together, our work identifies a set of general principles and signals that can be used to model user purchasing intent across many content discovery applications.


title: Robust Structured Nonnegative Matrix Factorization for Image Representation

abstract: Dimensionality reduction has attracted increasing attention, because high-dimensional data have arisen naturally in numerous domains in recent years. As one popular dimensionality reduction method, nonnegative matrix factorization (NMF), whose goal is to learn parts-based representations, has been widely studied and applied to various applications. In contrast to the previous approaches, this paper proposes a novel semisupervised NMF learning framework, called robust structured NMF, that learns a robust discriminative representation by leveraging the block-diagonal structure and the $\ell _{2,p}$ -norm (especially when $0 ) loss function. Specifically, the problems of noise and outliers are well addressed by the $\ell _{2,p}$ -norm ( $0 ) loss function, while the discriminative representations of both the labeled and unlabeled data are simultaneously learned by explicitly exploring the block-diagonal structure. The proposed problem is formulated as an optimization problem with a well-defined objective function solved by the proposed iterative algorithm. The convergence of the proposed optimization algorithm is analyzed both theoretically and empirically. In addition, we also discuss the relationships between the proposed method and some previous methods. Extensive experiments on both the synthetic and real-world data sets are conducted, and the experimental results demonstrate the effectiveness of the proposed method in comparison to the state-of-the-art methods.


title: Verbal and Visuospatial Short-Term and Working Memory in Children: Are They Separable?

abstract: This study explored the structure of verbal and visuospatial short-term and working memory in children between ages 4 and 11 years. Multiple tasks measuring 4 different memory components were used to capture the cognitive processes underlying working memory. Confirmatory factor analyses indicated that the processing component of working memory tasks was supported by a common resource pool, while storage aspects depend on domain-specific verbal and visuospatial resources. This model is largely stable across this developmental period, although some evidence exists that the links between the domain-specific visuospatial construct and the domain-general processing construct were higher in the 4- to- 6-year age group. The data also suggest that all working memory components are in place by 4 years of age. The term working memory refers to the capacity to store and manipulate information over brief periods of time (Baddeley & Hitch, 1974; Just & Carpenter, 1992). Extensive research over the past three decades has established that working memory is not a single store, but a memory system comprised of separable interacting components. Functioning in concert, these components provide a kind of flexible mental workspace that can be used to maintain and transform information in the course of demanding cognitive activities, and that acts as a temporary bridge between externally and internally generated mental representations. There are a range of alternative theoretical accounts of working memory capacity. Some researchers suggest that working memory consists of a domain-general aspect that coordinates information in two independent domain-specific storage components for verbal and visuospatial codes (Baddeley, 1986; see Engle, Kane, & Tuholski, 1999a, for a review). Others argue instead that working memory resources are separated into verbal and visuospatial constructs (Shah & Miyake, 1996). These studies have largely been restricted to adult


Towards Interpretable Vision Systems


title: Generalized potential of adult neural stem cells.

abstract: The differentiation potential of stem cells in tissues of the adult has been thought to be limited to cell lineages present in the organ from which they were derived, but there is evidence that some stem cells may have a broader differentiation repertoire. We show here that neural stem cells from the adult mouse brain can contribute to the formation of chimeric chick and mouse embryos and give rise to cells of all germ layers. This demonstrates that an adult neural stem cell has a very broad developmental capacity and may potentially be used to generate a variety of cell types for transplantation in different diseases.


title: Multimodal Learning Analytics and Education Data Mining: Using Computational Technologies to Measure Complex Learning Tasks

abstract: New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into students’ learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational application.


title: A Two-Stage Method for Text Line Detection in Historical Documents

abstract: This work presents a two-stage text line detection method for historical documents. Each detected text line is represented by its baseline. In a first stage, a deep neural network called ARU-Net labels pixels to belong to one of the three classes: baseline, separator and other. The separator class marks beginning and end of each text line. The ARU-Net is trainable from scratch with manageably few manually annotated example images ( ::: $$<\,50$$ ::: ). This is achieved by utilizing data augmentation strategies. The network predictions are used as input for the second stage which performs a bottom-up clustering to build baselines. The developed method is capable of handling complex layouts as well as curved and arbitrarily oriented text lines. It substantially outperforms current state-of-the-art approaches. For example, for the complex track of the cBAD: ICDAR2017 Competition on Baseline Detection the F value is increased from 0.859 to 0.922. The framework to train and run the ARU-Net is open source.


title: LinkNet: Relational Embedding for Scene Graph

abstract: Objects and their relationships are critical contents for image understanding. A scene graph provides a structured description that captures these properties of an image. However, reasoning about the relationships between objects is very challenging and only a few recent works have attempted to solve the problem of generating a scene graph from an image. In this paper, we present a novel method that improves scene graph generation by explicitly modeling inter-dependency among the entire object instances. We design a simple and effective relational embedding module that enables our model to jointly represent connections among all related objects, rather than focus on an object in isolation. Our novel method significantly benefits two main parts of the scene graph generation task: object classification and relationship classification. Using it on top of a basic Faster R-CNN, our model achieves state-of-the-art results on the Visual Genome benchmark. We further push the performance by introducing global context encoding module and geometrical layout encoding module. We validate our final model, LinkNet, through extensive ablation studies, demonstrating its efficacy in scene graph generation.


title: Teaching and Learning with ICT Tools: Issues and Challenges from Teachers’ Perceptions

abstract: In this digital era, ICT use in the classroom is important for giving students opportunities to ::: learn and apply the required 21st century skills. Hence studying the issues and challenges ::: related to ICT use in teaching and learning can assist teachers in overcoming the obstacles ::: and become successful technology users. Therefore, the main purpose of this study is to ::: analyze teachers’ perceptions of the challenges faced in using ICT tools in classrooms. A ::: quantitative research design was used to collect the data randomly from a sample of 100 ::: secondary school teachers in the state of Melaka, Malaysia. Evidence has been collected ::: through distribution of a modified‐ adopted survey questionnaire. Overall, the key issues ::: and challenges found to be significant in using ICT tools by teachers were: limited ::: accessibility and network connection, limited technical support, lack of effective training, ::: limited time and lack of teachers’ competency. Moreover, the results from independent ttest ::: show that use of ICT tools by male teachers (M =2.08, SD = .997) in the classroom is ::: higher compared to female teachers (M = 2.04, SD = .992). It is hoped that the outcome of ::: this research provides proper information and recommendation to those responsible for ::: integrating new technologies into the school teaching and learning process


title: How to increase serotonin in the human brain without drugs

abstract: For the last 4 decades, the question of how to manipulate the serotonergic system with drugs has been an important area of research in biological psychiatry, and this research has led to advances in the treatment of depression. Research on the association between various polymorphisms and depression supports the idea that serotonin plays a role, not only in the treatment of depression but also in susceptibility to depression and suicide. The research focus here has been on polymorphisms of the serotonin transporter, but other serotonin-related genes may also be involved.1–5 In the future, genetic research will make it possible to predict with increasing accuracy who is susceptible to depression. Much less attention has been given to how this information will be used for the benefit of individuals with a serotonin-related susceptibility to depression, and little evidence exists concerning strategies to prevent depression in those with such a susceptibility. Various studies have looked at early intervention in those with prodromal symptoms as well as at population strategies for preventing depression.6–11 Obviously, prevention is preferable to early intervention; moreover, although population strategies are important, they are ideally supplemented with preventive interventions that can be used over long periods of time in targeted individuals who do not yet exhibit even nonclinical symptoms. Clearly, pharmacologic approaches are not appropriate, and given the evidence for serotonin's role in the etiology and treatment of depression, nonpharmacologic methods of increasing serotonin are potential candidates to test for their ability to prevent depression.


title: An overview of non-volatile memory technology and the implication for tools and architectures

abstract: Novel nonvolatile memory technologies are gaining significant attentions from semiconductor industry in the competition of universal memory development. We used Spin-Transfer Torque Random Access Memory (STT-RAM) and Resistive Random Access Memory (R-RAM) as examples to discuss the implication of emerging nonvolatile memory for tools and architectures. Three aspects, including device and memory cell modeling, device/circuit co-design consideration and novel memory architecture, are discussed in details. The goal of these discussions is to design a high-density, low-power, high-performance nonvolatile memory with simple architecture and minimized circuit design complexity.


Detection of rare events within industrial datasets by means of data resampling and specific algorithms


title: CSVideoNet: A Recurrent Convolutional Neural Network for Compressive Sensing Video Reconstruction

abstract: In this paper, we develop a deep neural network architecture called "CSVideoNet" that can learn visual representations from random measurements for compressive sensing (CS) video reconstruction. CSVideoNet is an end-to-end trainable and non-iterative model that combines convolutional neural networks (CNNs) with a recurrent neural networks (RNN) to facilitate video reconstruction by leveraging temporal-spatial features. The proposed network can accept random measurements with a multi-level compression ratio (CR). The lightly and aggressively compressed measurements offer background information and object details, respectively. This is similar to the variable bit rate techniques widely used in conventional video coding approaches. The RNN employed by CSVideoNet can leverage temporal coherence that exists in adjacent video frames to extrapolate motion features and merge them with spatial visual features extracted by the CNNs to further enhance reconstruction quality, especially at high CRs. We test our CSVideoNet on the UCF-101 dataset. Experimental results show that CSVideoNet outperforms the existing video CS reconstruction approaches. The results demonstrate that our method can preserve relatively excellent visual details from original videos even at a 100x CR, which is difficult to realize with the reference approaches. Also, the non-iterative nature of CSVideoNet results in an decrease in runtime by three orders of magnitude over iterative reconstruction algorithms. Furthermore, CSVideoNet can enhance the CR of CS cameras beyond the limitation of conventional approaches, ensuring a reduction in bandwidth for data transmission. These benefits are especially favorable to high-frame-rate video applications.


title: Minimum cost path problem for Plug-in Hybrid Electric Vehicles

abstract: We introduce a practically important and theoretically challenging problem: finding the minimum cost path for PHEVs in a road network with refueling and charging stations. We show that this problem is NP-complete and present a mixed integer quadratically constrained formulation, a discrete approximation dynamic programming heuristic, and a shortest path heuristic as solution methodologies. Practical applications of the problem in transportation and logistics, considering specifically the long-distance trips, are discussed in detail. Through extensive computational experiments, significant insights are provided. In addition to the charging infrastructure availability, a driver’s stopping tolerance arises as another critical factor affecting the transportation costs.


title: Survey of the management of chemotherapy-induced peripheral neuropathy in Japan: Japanese Society of Medical Oncology

abstract: Background Various drugs are administered for the management of chemotherapy-induced peripheral neuropathy (CIPN) in Japan. However, there have been no surveys undertaken to identify these drugs or their frequency of prescription. Therefore, we administered a questionnaire survey to the diplomates of the Subspecialty Board of Japanese Society of Medical Oncology (JSMO) to investigate the frequency of administration of different drugs for the management of CIPN in Japan. Methods We investigated the use of vitamin B 12 , antiepileptic agents such as pregabalin, duloxetine, antidepressants other than duloxetine, non-steroidal anti-inflammatory drugs (NSAIDs), opioids and the Kampo compound goshajinkigan in a questionnaire employing a three-step scale wherein A implies routine or prophylactic administration, B implies occasional administration and C implies infrequent administration. Results Considering responses A and B together, the most frequently administered drugs for the treatment of numbness were antiepileptic drugs such as pregabalin (A+B=98.7%), vitamin B 12 (74.7%), Kampo compounds (58.7%) and duloxetine (46.8%). The most frequently prescribed drugs for pain were NSAIDs (97.7%), followed by opioids (83.1%) and finally antiepileptic drugs (82.1%). Conclusions Various drugs are frequently administered for CIPN. In addition, it was found that marked differences exist between the drugs targeted on numbness and pain.


SUBTRACTION USING RUNNING GAUSSIAN AVERAGE : A COLOR CHANNEL COMPARISON


Process of Information Systems Strategizing


title: Voice-based assessments of trustworthiness, competence, and warmth in blind and sighted adults

abstract: The study of voice perception in congenitally blind individuals allows researchers rare insight into how a lifetime of visual deprivation affects the development of voice perception. Previous studies have suggested that blind adults outperform their sighted counterparts in low-level auditory tasks testing spatial localization and pitch discrimination, as well as in verbal speech processing; however, blind persons generally show no advantage in nonverbal voice recognition or discrimination tasks. The present study is the first to examine whether visual experience influences the development of social stereotypes that are formed on the basis of nonverbal vocal characteristics (i.e., voice pitch). Groups of 27 congenitally or early-blind adults and 23 sighted controls assessed the trustworthiness, competence, and warmth of men and women speaking a series of vowels, whose voice pitches had been experimentally raised or lowered. Blind and sighted listeners judged both men’s and women’s voices with lowered pitch as being more competent and trustworthy than voices with raised pitch. In contrast, raised-pitch voices were judged as being warmer than were lowered-pitch voices, but only for women’s voices. Crucially, blind and sighted persons did not differ in their voice-based assessments of competence or warmth, or in their certainty of these assessments, whereas the association between low pitch and trustworthiness in women’s voices was weaker among blind than sighted participants. This latter result suggests that blind persons may rely less heavily on nonverbal cues to trustworthiness compared to sighted persons. Ultimately, our findings suggest that robust perceptual associations that systematically link voice pitch to the social and personal dimensions of a speaker can develop without visual input.


title: Folded cascode OTA design for wide band applications

abstract: This paper deals with design and optimization of a folded cascode operational transconductance amplifier. First, a detailed description of an optimum OTA topology is done in order to optimize MOS transistor sizing. Second, the design of folded cascode OTA, which works for frequencies that lead to a base band circuit design for RF application, is based on transistor sizing methodology. Third, folded cascode OTAs generally find several applications that are well developed. Simulation results are performed using SPICE software and BSIM3V3 model for CMOS 0.35mum process, show that the designed folded cascode OTA has a 85dB DC gain and provides a gain bandwidth product of around 332MHz


title: Benchmarking Big Data Systems: A Review

abstract: With the fast development of big data systems in recent years, a variety of open-source benchmarks have been built to evaluate and compare the workloads on these systems, and to promote their technology improvement. However, to date no comprehensive survey has been written on this topic. This paper attempts to fill the void by presenting a review of the state-of-the-art big data benchmarking efforts. The paper first gives an overview of popular open-source benchmarks from the point of view of big data systems. It then reviews the three important aspects of benchmarking - workload generation techniques, workload input data generation techniques, and metrics used to assess systems. For each aspect, the paper divides the surveyed benchmarks into different categories and describes some representative benchmarks, rather than all benchmarks listed, in each category, following the discussion of potential research directions to motivate future work in this area.


title: STEAM: Using the Arts to Train Well-Rounded and Creative Scientists

abstract: While the demand for a strong STEM workforce continues to grow, there are challenges that threaten our ability to recruit, train, and retain such a workforce in a way that is effective and sustainable and fosters innovation. One way in which we are meeting this challenge is through the use of the arts in the training of scientists. In this Perspectives article, we review the use of the arts in science education and its benefits in both K-12 and postsecondary education. We also review the use of STEAM (science, technology, engineering, arts, and mathematics) programs in science outreach and the development of professional scientists.


title: Action Recognition with Stacked Fisher Vectors

abstract: Representation of video is a vital problem in action recognition. This paper proposes Stacked Fisher Vectors (SFV), a new representation with multi-layer nested Fisher vector encoding, for action recognition. In the first layer, we densely sample large subvolumes from input videos, extract local features, and encode them using Fisher vectors (FVs). The second layer compresses the FVs of subvolumes obtained in previous layer, and then encodes them again with Fisher vectors. Compared with standard FV, SFV allows refining the representation and abstracting semantic information in a hierarchical way. Compared with recent mid-level based action representations, SFV need not to mine discriminative action parts but can preserve mid-level information through Fisher vector encoding in higher layer. We evaluate the proposed methods on three challenging datasets, namely Youtube, J-HMDB, and HMDB51. Experimental results demonstrate the effectiveness of SFV, and the combination of the traditional FV and SFV outperforms state-of-the-art methods on these datasets with a large margin.


title: CalvinFS: consistent WAN replication and scalable metadata management for distributed file systems

abstract: Existing file systems, even the most scalable systems that store hundreds of petabytes (or more) of data across thousands of machines, store file metadata on a single server or via a shared-disk architecture in order to ensure consistency and validity of the metadata. ::: ::: This paper describes a completely different approach for the design of replicated, scalable file systems, which leverages a high-throughput distributed database system for metadata management. This results in improved scalability of the metadata layer of the file system, as file metadata can be partitioned (and replicated) across a (shared-nothing) cluster of independent servers, and operations on file metadata transformed into distributed transactions. ::: ::: In addition, our file system is able to support standard file system semantics--including fully linearizable random writes by concurrent users to arbitrary byte offsets within the same file--across wide geographic areas. Such high performance, fully consistent, geographically distributed files systems do not exist today. ::: ::: We demonstrate that our approach to file system design can scale to billions of files and handle hundreds of thousands of updates and millions of reads per second-- while maintaining consistently low read latencies. Furthermore, such a deployment can survive entire datacenter outages with only small performance hiccups and no loss of availability.


title: The Emergence of Educational Technology

abstract: This paper traces the emergence of Information Technology (IT) for educational purposes. It begins with a discourse on the relationship between culture and educational technology. A key premise is that culture is an important influence in education. Similarly, the evolution of educational technology is very much intertwined with culture. This paper traces educational development from the traditional times to modern times. Educational technology was initially viewed as technology in education, a sole focus on IT tools. Subsequently, a holistic perspective of the educational system was adopted, termed the technology of education. In this larger view, educational technology is informed by research from learning theories and other educational research. The paper elaborates on these theoretical underpinnings with some examples of educational technology before concluding with implications for practitioners and researchers.


title: Femoroacetabular impingement and classification of the cam deformity: the reference interval in normal hips

abstract: Background and purpose Most patients with femoroacetabular impingement (FAI) have a cam deformity, which may be quantified by measuring the alpha angle and anterior offset ratio (AOR). Knowledge of what constitutes a “normal” alpha angle and AOR is limited. We defined the reference intervals of these measurements from normal hips in the general population.Patients and methods 157 individuals from the general population were reviewed clinically and radiographically. 74 individuals with clinical evidence of hip disease or radiographic evidence of osteoarthritis (OA) were excluded, leaving a study group of 83 individuals (mean age 46 (22–69) years, 44 females) with normal hips. The alpha angles and AORs were measured from cross-table lateral radiographs taken in 15° internal rotation. A validation study consisting of a cadaver study and a measurement reliability study was also performed.Results The mean alpha angle was 48° in men and 47° in women. The mean AOR was 0.19, the same in men and women. Thus, sexes...


title: Multichip Self-Assembly Technology for Advanced Die-to-Wafer 3-D Integration to Precisely Align Known Good Dies in Batch Processing

abstract: An advanced die-to-wafer 3-D integration using a surface-tension-driven multichip self-assembly technology was proposed to 3-D stack a large number of known good dies (KGDs) in batch processing. The parallel self-assembly with a unique multichip pick-up tool was newly applied to die-to-wafer 3-D integration to overcome throughput and yield problems in conventional 3-D integration approaches. In addition, novel batch transfer of chips self-assembled on a carrier wafer to the corresponding target wafer was demonstrated. By using the multichip self-assembly, many KGDs can be precisely aligned and temporarily placed on a carrier wafer all at once, and then, the self-assembled KGDs can be simultaneously transferred to another target wafer in a face-to-face bonding manner at the wafer level. Average alignment accuracy was found to be approximately 400 nm when a hundred 3-mm-square chips were self-assembled on carrier wafers with small droplets of an aqueous solution. The alignment accuracy was experimentally proven to be fairly dependent on liquid surface tension as a self-assembly parameter. The liquid wettability contrast between the chip assembly areas and the surrounding areas formed on carrier wafers was another key parameter for alignment accuracy. The former and the latter areas were rendered high hydrophilic and hydrophobic. These areas, respectively, showed water contact angles less than 5° and 115°. Therefore, various sizes of chips (3 × 3 mm, 5 × 5 mm, 4 × 9 mm, and 10 × 10 mm) were self-assembled on a carrier wafer with high alignment accuracy, and further, the self-assembled chips were successfully transferred to the other faced target wafer in a batch.


title: Design of Accurate Vehicle Location System Using RFID

abstract: The simple GPS methods are unable to retrieve the real-time information of vehicle location and vehicle running state in some special circumstances such as tunnels and built-ups. Hence, the vehicle monitoring system to acquire real- time vehicle information is required. In this paper, by integrating technologies of RFID, GPS, GPRS and using LANDMARC method, an accurate vehicle location system in a variety of complex environments is proposed. The proposal is able to improve the precise vehicle location and get the mechanical information of vehicle status by the technology of wireless data communication.


title: Efficient 2D-3D Matching for Multi-Camera Visual Localization

abstract: Visual localization, i.e., determining the position and orientation of a vehicle with respect to a map, is a key problem in autonomous driving. We present a multi-camera visual inertial localization algorithm for large scale environments. To efficiently and effectively match features against a pre-built global 3D map, we propose a prioritized feature matching scheme for multi-camera systems. In contrast to existing works, designed for monocular cameras, we (1) tailor the prioritization function to the multi-camera setup and (2) run feature matching and pose estimation in parallel. This significantly accelerates the matching and pose estimation stages and allows us to dynamically adapt the matching efforts based on the surrounding environment. In addition, we show how pose priors can be integrated into the localization system to increase efficiency and robustness. Finally, we extend our algorithm by fusing the absolute pose estimates with motion estimates from a multi-camera visual inertial odometry pipeline (VIO). This results in a system that provides reliable and drift-less pose estimation. Extensive experiments show that our localization runs fast and robust under varying conditions, and that our extended algorithm enables reliable real-time pose estimation.


title: Distance Metric Learning Using Privileged Information for Face Verification and Person Re-Identification

abstract: In this paper, we propose a new approach to improve face verification and person re-identification in the RGB images by leveraging a set of RGB-D data, in which we have additional depth images in the training data captured using depth cameras such as Kinect. In particular, we extract visual features and depth features from the RGB images and depth images, respectively. As the depth features are available only in the training data, we treat the depth features as privileged information, and we formulate this task as a distance metric learning with privileged information problem. Unlike the traditional face verification and person re-identification tasks that only use visual features, we further employ the extra depth features in the training data to improve the learning of distance metric in the training process. Based on the information-theoretic metric learning (ITML) method, we propose a new formulation called ITML with privileged information (ITML+) for this task. We also present an efficient algorithm based on the cyclic projection method for solving the proposed ITML+ formulation. Extensive experiments on the challenging faces data sets EUROCOM and CurtinFaces for face verification as well as the BIWI RGBD-ID data set for person re-identification demonstrate the effectiveness of our proposed approach.


title: Impact of an augmented reality system on students' motivation for a visual art course

abstract: In this paper, the authors show that augmented reality technology has a positive impact on the motivation of middle-school students. The Instructional Materials Motivation Survey (IMMS) (Keller, 2010) based on the ARCS motivation model (Keller, 1987a) was used to gather information; it considers four motivational factors: attention, relevance, confidence, and satisfaction. Motivational factors of attention and satisfaction in an augmented-reality-based learning environment were better rated than those obtained in a slides-based learning environment. When the impact of the augmented reality system was analyzed in isolation, the attention and confidence factors were the best rated. The usability study showed that although this technology is not mature enough to be used massively in education, enthusiasm of middle-school students diminished most of the barriers found.


title: A Highly Efficient P-SSHI Rectifier for Piezoelectric Energy Harvesting

abstract: A highly efficient P-SSHI-based rectifier for piezoelectric energy harvesting is presented in this paper. The proposed rectifier utilizes the voltages at the two ends of the piezoelectric device (PD) to detect the polarity change of the current produced by the PD. The inversion process of the voltage across the PD is automatically controlled by diodes along the oscillating network. In contrast to prior works, the proposed rectifier exhibits several advantages in terms of efficiency, circuit simplicity, compatibility with commercially available PDs, and standalone operation. Experimental results show that the proposed rectifier can provide a 5.8× boost in harvested energy compared to the conventional full-wave bridge rectifier.


title: 3-D Surround View for Advanced Driver Assistance Systems

abstract: As the primary means of transportations in modern society, the automobile is developing toward the trend of intelligence, automation, and comfort. In this paper, we propose a more immersive 3-D surround view covering the automobiles around for advanced driver assistance systems. The 3-D surround view helps drivers to become aware of the driving environment and eliminates visual blind spots. The system first uses four fish-eye lenses mounted around a vehicle to capture images. Then, according to the pattern of image acquisition, camera calibration, image stitching, and scene generation, the 3-D surround driving environment is created. To achieve the real-time and easy-to-handle performance, we only use one image to finish the camera calibration through a special designed checkerboard. Furthermore, in the process of image stitching, a 3-D ship model is built to be the supporter, where texture mapping and image fusion algorithms are utilized to preserve the real texture information. The algorithms used in this system can reduce the computational complexity and improve the stitching efficiency. The fidelity of the surround view is also improved, thereby optimizing the immersion experience of the system under the premise of preserving the information of the surroundings.


title: Learning of closed-loop motion control

abstract: Learning motion control as a unified process of designing the reference trajectory and the controller is one of the most challenging problems in robotics. The complexity of the problem prevents most of the existing optimization algorithms from giving satisfactory results. While model-based algorithms like iterative linear-quadratic-Gaussian (iLQG) can be used to design a suitable controller for the motion control, their performance is strongly limited by the model accuracy. An inaccurate model may lead to degraded performance of the controller on the physical system. Although using machine learning approaches to learn the motion control on real systems have been proven to be effective, their performance depends on good initialization. To address these issues, this paper introduces a two-step algorithm which combines the proven performance of a model-based controller with a model-free method for compensating for model inaccuracy. The first step optimizes the problem using iLQG. Then, in the second step this controller is used to initialize the policy for our PI2-01 reinforcement learning algorithm. This algorithm is a derivation of the PI2 algorithm enabling more stable and faster convergence. The performance of this method is demonstrated both in simulation and experimental results.


title: Integration of nanoscale memristor synapses in neuromorphic computing architectures

abstract: Conventional neuro-computing architectures and artificial neural networks have often been developed with no or loose connections to neuroscience. As a consequence, they have largely ignored key features of biological neural processing systems, such as their extremely low-power consumption features or their ability to carry out robust and efficient computation using massively parallel arrays of limited precision, highly variable, and unreliable components. Recent developments in nano-technologies are making available extremely compact and low power, but also variable and unreliable solid-state devices that can potentially extend the offerings of availing CMOS technologies. In particular, memristors are regarded as a promising solution for modeling key features of biological synapses due to their nanoscale dimensions, their capacity to store multiple bits of information per element and the low energy required to write distinct states. In this paper, we first review the neuro- and neuromorphic computing approaches that can best exploit the properties of memristor and scale devices, and then propose a novel hybrid memristor-CMOS neuromorphic circuit which represents a radical departure from conventional neuro-computing approaches, as it uses memristors to directly emulate the biophysics and temporal dynamics of real synapses. We point out the differences between the use of memristors in conventional neuro-computing architectures and the hybrid memristor-CMOS circuit proposed, and argue how this circuit represents an ideal building block for implementing brain-inspired probabilistic computing paradigms that are robust to variability and fault tolerant by design.


title: Sentiment analysis using sentence minimization with natural language generation (NLG)

abstract: The analysis of feeling is used to define the attitude of a writer in relation to a subject or the appropriate global polarity of a document. The proposed work is to provide a platform in order to visualize the relative analysis of feedback for some particular product. In doing so, instead of the basic truthful information, the analysis will be done based on comments and comments developed from various sources. In this approach, the analysis of feeling at the document level will be carried out taking into account all aspects in the same way using natural language processing techniques. The present unsupervised method is used for sentence minimization that relies on a Stanford-type dependency for extracting information elements and compressed sentences are generated via a Natural language generation engine (NLG). An automatic evaluation of the same is done and F-scores of about 87.51 is achieved.


title: CNT-based sensor arrays for local strain measurements in soft pneumatic actuators

abstract: Soft robotics is a recent trend in engineering that seeks to create machines that are soft, compliant, and capable of withstanding damage, wear and high stress. Soft pneumatic actuators (SPAs) are a key element of soft robots, and their elastomeric substrate enables generation of sophisticated motion with simple controls. Although several methods for fabrication, material selection, and structure design have been investigated for the construction of SPAs, limited attention has been paid to the integration of distributed sensors for performing localized measurement. Carbon nanotubes (CNTs) are molecular-scale tubes of carbon atoms with remarkable mechanical and electronic properties, showing potential application in sensing devices. In this paper, we present the design, fabrication, and testing of a novel type of CNT-based sensor array combined with silver nanowires for measuring localized strain along the bottom layer of a SPA. Simulation and experimentation have been performed in order to analyze the soft actuator deformation during bending. The results demonstrate the promise of the proposed SPA with integrated strain sensing, which lays groundwork for a myriad of applications in grasping, manipulation, and bio-inspired locomotion.


title: Best First Over-Sampling for Multilabel Classification

abstract: Learning from imbalanced multilabel data is a challenging task. It has attracted considerable attention recently. In this paper we propose a MultiLabel Best First Over-sampling (ML-BFO) to improve the performance of multilabel classification algorithms, based on imbalance minimization and Wilson's ENN rule. Our experimental results show that ML-BFO not only duplicates fewer samples but also reduces the imbalance level much more than two state-of-the-art multilabel sampling methods, i.e., an over-sampling method LP-ROS and an under-sampling method MLeNN. Besides, ML-BFO significantly improves the performance of multilabel classification algorithms, and performs much better than LP-ROS and MLeNN.


title: A New Dimension of Health Care: Systematic Review of the Uses, Benefits, and Limitations of Social Media for Health Communication

abstract: BACKGROUND ::: There is currently a lack of information about the uses, benefits, and limitations of social media for health communication among the general public, patients, and health professionals from primary research. ::: ::: ::: OBJECTIVE ::: To review the current published literature to identify the uses, benefits, and limitations of social media for health communication among the general public, patients, and health professionals, and identify current gaps in the literature to provide recommendations for future health communication research. ::: ::: ::: METHODS ::: This paper is a review using a systematic approach. A systematic search of the literature was conducted using nine electronic databases and manual searches to locate peer-reviewed studies published between January 2002 and February 2012. ::: ::: ::: RESULTS ::: The search identified 98 original research studies that included the uses, benefits, and/or limitations of social media for health communication among the general public, patients, and health professionals. The methodological quality of the studies assessed using the Downs and Black instrument was low; this was mainly due to the fact that the vast majority of the studies in this review included limited methodologies and was mainly exploratory and descriptive in nature. Seven main uses of social media for health communication were identified, including focusing on increasing interactions with others, and facilitating, sharing, and obtaining health messages. The six key overarching benefits were identified as (1) increased interactions with others, (2) more available, shared, and tailored information, (3) increased accessibility and widening access to health information, (4) peer/social/emotional support, (5) public health surveillance, and (6) potential to influence health policy. Twelve limitations were identified, primarily consisting of quality concerns and lack of reliability, confidentiality, and privacy. ::: ::: ::: CONCLUSIONS ::: Social media brings a new dimension to health care as it offers a medium to be used by the public, patients, and health professionals to communicate about health issues with the possibility of potentially improving health outcomes. Social media is a powerful tool, which offers collaboration between users and is a social interaction mechanism for a range of individuals. Although there are several benefits to the use of social media for health communication, the information exchanged needs to be monitored for quality and reliability, and the users' confidentiality and privacy need to be maintained. Eight gaps in the literature and key recommendations for future health communication research were provided. Examples of these recommendations include the need to determine the relative effectiveness of different types of social media for health communication using randomized control trials and to explore potential mechanisms for monitoring and enhancing the quality and reliability of health communication using social media. Further robust and comprehensive evaluation and review, using a range of methodologies, are required to establish whether social media improves health communication practice both in the short and long terms.


title: Driving-Style-Based Codesign Optimization of an Automated Electric Vehicle: A Cyber-Physical System Approach

abstract: This paper studies the codesign optimization approach to determine how to optimally adapt automatic control of an intelligent electric vehicle to driving styles. A cyber-physical system (CPS)-based framework is proposed for codesign optimization of the plant and controller parameters for an automated electric vehicle, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. Driving style recognition algorithm is developed using unsupervised machine learning and validated via vehicle experiments. Adaptive control algorithms are designed for three driving styles with different protocol selections. Performance exploration method is presented. Parameter optimizations are implemented based on the defined objective functions. Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance. The results validate the feasibility and effectiveness of the proposed CPS-based codesign optimization approach.


title: Efficiently Compiling Efficient Query Plans for Modern Hardware

abstract: As main memory grows, query performance is more and more determined by the raw CPU costs of query processing itself. The classical iterator style query processing technique is very simple and exible, but shows poor performance on modern CPUs due to lack of locality and frequent instruction mispredictions. Several techniques like batch oriented processing or vectorized tuple processing have been proposed in the past to improve this situation, but even these techniques are frequently out-performed by hand-written execution plans. ::: ::: In this work we present a novel compilation strategy that translates a query into compact and efficient machine code using the LLVM compiler framework. By aiming at good code and data locality and predictable branch layout the resulting code frequently rivals the performance of hand-written C++ code. We integrated these techniques into the HyPer main memory database system and show that this results in excellent query performance while requiring only modest compilation time.


title: A Study of Visual Studio Usage in Practice

abstract: Integrated Development Environments (IDEs) provide a convenient standalone solution that supports developers during various phases of software development. In order to provide better support for developers within such IDEs, we need to understand how much time developers spend using various parts of a given IDE and how often they use available assistance tools. To infer useful conclusions, such information should be gathered for different types of IDEs for different languages. In this paper, we instrument the previously unexplored Visual Studio IDE and track the interactions of developers at an industry partner's software-development department. As a result, we capture interactions for more than 6300 hours of work time, from between 27 and 84 professional C# developers. Our work reports how much time professional developers spend on activities such as code editing and execution or navigation, as well as how often they use assistance tools provided by the IDE. We compare our findings to those of prior studies involving other IDEs and discuss the implications of the commonalities and differences for research on (integrated) developer-assistance tools.


title: A Maturity Model for Scaling Agile Development

abstract: Although the agile software development approaches have gained wide acceptance in practice, the concerns regarding the scalability and integration of agile approaches in traditional system development organizations have remained. The difficulty of adopting agile practices increases when there is a need to scale these practices. Scaled Agile Framework (Safe) has emerged as a solution to address some of these concerns. Despite few encouraging results of Safe adoption, case studies indicate several challenges of Safe adoption. Currently, there is a lack of a well-structured gradual approach for implementing and establishing Safe. Before and during Safe adoption, organizations require a uniform model for assessing the current state and progress, and for establishing a roadmap for the initiative. To address this need, we developed a maturity model for adopting agile and Safe practices. Taking an existing agile maturity model as the basis, we extended the model with practices that are key to scaling agile practices for the Safe. The model is developed and refined using a Delphi study. Subsequently, a case study was conducted in a large organization where the model was applied to assess the maturity level of the organization in adopting Safe.


title: Uplink NOMA in Large-Scale Systems: Coverage and Physical Layer Security

abstract: In this paper, the physical layer security of uplink non-orthogonal multiple access (NOMA) in large-scale networks is analyzed. An stochastic geometry approach is applied to derive new analytical expressions for the coverage probability and the effective secrecy throughput (EST) of the kth NOMA user, which may use a fixed or an adaptive transmission rate. We consider a protected zone around the legitimate terminals to establish an eavesdropper-exclusion area. We assume that the channel state information associated with eavesdroppers is not available at the base station. The impact of an imperfect successive interference cancellation is also taken into account in this work. Our analysis allows an easy selection of the wiretap code rates that maximizes the EST. In addition, our framework also allows an optimum selection of other system parameters like the transmit power or the eavesdropper-exclusion radius.


Parallel programming with NVIDIA CUDA


title: Design and evaluation of incremental data structures and algorithms for dynamic query interfaces

abstract: A dynamic query interface (DQI) is a database access mechanism that provides continuous real-time feedback to the user during query formulation. Previous work shows that DQIs are elegant and powerful interfaces to small databases. Unfortunately, when applied to large databases, previous DQI algorithms slow to a crawl. We present a new incremental approach to DQI algorithms and display updates that work well with large databases, both in theory and in practice.


title: The Scalability of Trustless Trust

abstract: Permission-less blockchains can realise trustless trust, albeit at the cost of limiting the complexity of computation tasks. To explain the implications for scalability, we have implemented a trust model for smart contracts, described as agents in an open multi-agent system. Agent intentions are not necessarily known and autonomous agents have to be able to make decisions under risk. The ramifications of these general conditions for scalability are analysed for Ethereum and then generalised to other current and future platforms. Finally, mechanisms from the trust model are applied to a verifiable computation algorithm and implemented in the Ethereum blockchain. We show in experiments that the algorithm needs at most six semi-honest verifiers to detect false submission.


Demonstration of interactive dialog teaching for learning a practical end-to-end dialog manager


title: Face Recognition Based on PCA and LDA Combination Feature Extraction

abstract: A whole face recognition system was proposed in the paper based on PCA and LDA combination feature extraction. Normalization was used to eliminate the redundant information interference. Principal Component Analysis (PCA) was used for feature extraction and dimension reduction. Linear Discriminate Analysis (LDA) was used to further improve the separability of samples in the subspace and extract LDA features. Nearest Neighbor Classifier (NNC) was adopted for face recognition. Face recognition rate was improved in our experiments on ORL face database with our approach.


title: Teaching Computer Architecture and Organization using Simulation and FPGAs

abstract: Computer architecture is often taught by using software to design and simulate hardware modules and then using individual components to implement them. Our aim in this paper is to share our teaching experience of this subject in a way to enhance student learning outcome by developing projects for the computer architecture lab to help students better understand the theoretical concepts of the subject and to gain hands-on type of experience and apply that for more realistic projects. As a result, we have noticed that students show better understanding of the subject over the last few semesters. We present in this work an ALU computer module design exercise as we used it in our computer architecture course in the Department of Computer Science and Engineering at Qatar University. The approach can be well adopted for a first course in digital logic design, computer organization, and/or computer architecture. In specific, we designed and implemented an 8-bit arithmetic and logic unit, which performs 14 different arithmetic and logic operations. We did the design, simulation, and FPGA-based implementation of the proposed ALU module using QUARTUS II design software and Altera DE2 FPGA Board.


title: Semantic Wordification of Document Collections

abstract: Word clouds have become one of the most widely accepted visual resources for document analysis and visualization, motivating the development of several methods for building layouts of keywords extracted from textual data. Existing methods are effective to demonstrate content, but are not capable of preserving semantic relationships among keywords while still linking the word cloud to the underlying document groups that generated them. Such representation is highly desirable for exploratory analysis of document collections. In this paper we present a novel approach to build document clouds, named ProjCloud that aim at solving both semantical layouts and linking with document sets. ProjCloud generates a semantically consistent layout from a set of documents. Through a multidimensional projection, it is possible to visualize the neighborhood relationship between highly related documents and their corresponding word clouds simultaneously. Additionally, we propose a new algorithm for building word clouds inside polygons, which employs spectral sorting to maintain the semantic relationship among words. The effectiveness and flexibility of our methodology is confirmed when comparisons are made to existing methods. The technique automatically constructs projection based layouts the user may choose to examine in the form of the point clouds or corresponding word clouds, allowing a high degree of control over the exploratory process. © 2012 Wiley Periodicals, Inc.


title: Short-term traffic forecasting using self-adjusting k-nearest neighbours

abstract: Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbour (kNN) method is widely used for short-term traffic forecasting. However, the self-adjustment of kNN parameters has been a problem due to dynamic traffic characteristics. This study proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training for the parameters. A real-world dataset with more than one year traffic records is used to conduct experiments. The results show that the DP-kNN can perform better than the manually adjusted kNN and other benchmarking methods in terms of accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement.


title: Attention-based LSTM-CNNs for uncertainty identification on Chinese social media texts

abstract: Uncertainty identification is an important semantic processing task, which is crucial to the quality of information in terms of factuality in many techniques, e.g. topic detection, question answering. Especially in social media, the texts are written informally which are widely used in many applications, so the factuality has become a premier concern. However, existing approaches that still rely on lexical cues suffer greatly from the casual or word-of-mouth peculiarity of social media, in which the cue phrases are often expressed in sub-standard form or even omitted from sentences. To tackle these problems, this paper proposes the attention-based LSTM-CNNs for the uncertainty identification on social media texts, named ALUNI. ALUNI incorporates attention-based LSTM networks to represent the semantics of words, and convolutional neural networks to capture the most important semantics of uncertainty for identification. Experiments are conducted on both Chinese Weibo and news datasets, and 78.19% and 73.95% of F1-measure scores are achieved with 11% and 3% improvement over the baseline, respectively.


title: Probiotics, gut microbiota, and their influence on host health and disease

abstract: The gastrointestinal tract of mammals hosts a high and diverse number of different microorganisms, known as intestinal microbiota. Many probiotics were originally isolated from the gastrointestinal tract, and they were defined by the Food and Agriculture Organization of the United Nations (FAO)/WHO as “live microorganisms which when administered in adequate amounts confer a health benefit on the host.” Probiotics exert their beneficial effects on the host through four main mechanisms: interference with potential pathogens, improvement of barrier function, immunomodulation and production of neurotransmitters, and their host targets vary from the resident microbiota to cellular components of the gut–brain axis. However, in spite of the wide array of beneficial mechanisms deployed by probiotic bacteria, relatively few effects have been supported by clinical data. In this regard, different probiotic strains have been effective in antibiotic-associated diarrhea or inflammatory bowel disease for instance. The aim of this review was to compile the molecular mechanisms underlying the beneficial effects of probiotics, mainly through their interaction with the intestinal microbiota and with the intestinal mucosa. The specific benefits discussed in this paper include among others those elicited directly through dietary modulation of the human gut microbiota.


title: Extracting Topics Based on Word2Vec and Improved Jaccard Similarity Coefficient

abstract: To extract key topics from news articles, this paper researches into a new method to discover an efficient way to construct text vectors and improve the efficiency and accuracy of document clustering based on Word2Vec model. This paper proposes a novel algorithm, which combines Jaccard similarity coefficient and inverse dimension frequency to calculate the importance degree between each dimension in text vector and the corresponding document. Text vectors is constructed based on the importance degree and improve the accuracy of text cluster and key topics extraction. The algorithm is also implemented on MapReduce and the efficiency is improved.


title: Calcium hydroxylapatite: A review on safety and complications

abstract: BACKGROUND ::: Radiesse® , or calcium hydroxylapatite (CaHA), is a biodegradable, biostimulatory soft tissue filler suitable for deeper folds and wrinkles. In the literature, good results have been documented with the use of CaHA and patient satisfaction scores are high. This study reviews the current literature on safety and complications of CaHA. ::: ::: ::: METHODS ::: A literature search in MEDLINE/PubMed electronic database was conducted. A total of 21 articles were included and screened for reports of adverse events (AEs). ::: ::: ::: RESULTS ::: Twenty-one peer-reviewed articles, published between 2004 and 2015, were included. A total of 5081 treatments with CaHA were performed on 2779 patients. A total of 173 (3%) AEs were reported. The assessed types of AEs consisted of nodules (n=166, 96%), persistent inflammation/swelling (n=4, 2%), persistent erythema (n=2, 1%), and overcorrection (n=1, 1%). ::: ::: ::: CONCLUSION ::: Based on the results in this study, CaHA appears to have a good safety profile. Nodules are by far the most common AE. Of the reported nodules, 49% occurred in "dynamic" areas currently known for having a higher tendency for nodules. Several treatment approaches exist for managing CaHA nodules; however, in most cases, CaHA nodules are not visible and resolve without intervention.


title: Secu Wear: An Open Source, Multi-component Hardware/Software Platform for Exploring Wearable Security

abstract: Wearables are the next big development in the mobile internet of things. Operating in a body area network around a smartphone user they serve a variety of commercial, medical, and personal uses. Whether used for fitness tracking, mobile health monitoring, or as remote controllers, wearable devices can include sensors that collect a variety of data and actuators that provide hap tic feedback and unique user interfaces for controlling software and hardware. Wearables are typically wireless and use Bluetooth LE (low energy) to transmit data to a waiting smartphone app. Frequently, apps forward this data onward to online web servers for tracking. Security and privacy concerns abound when wearables capture sensitive data or provide critical functionality. This paper develops a platform, called SecuWear, for conducting wearable security research, collecting data, and identifying vulnerabilities in hardware and software. SecuWear combines open source technologies to enable researchers to rapidly prototype security vulnerability test cases, evaluate them on actual hardware, and analyze the results to understand how best to mitigate problems. The paper includes two types of evaluation in the form of a comparative analysis and empirical study. The results reveal how several passive observation attacks present themselves in wearable applications and how the SecuWear platform can capture the necessary information needed to identify and combat such attacks.


title: FFT-Based BP Decoding of General LDPC Codes Over Abelian Groups

abstract: We introduce a wide class of low-density parity-check (LDPC) codes, large enough to include LDPC codes over finite fields, rings, or groups, as well as some nonlinear codes. A belief-propagation decoding procedure with the same complexity as for the decoding of LDPC codes over finite fields is also presented. Moreover, an encoding procedure is developed


Analysis of the affecting factors on aircraft takeoff and landing ground path length


title: Gay and Bisexual Identity Development Among Female-to-Male Transsexuals in North America: Emergence of a Transgender Sexuality

abstract: We studied a North American sample of female-to-male (FtM) transsexuals sexually attracted to men, aiming to understand their identity and sexuality in the context of a culture of transgender empowerment. Sex-reassigned FtM transsexuals, 18 years or older and attracted to men, were recruited via an FtM community conference and listserv. Participants (N = 25) responded to open-ended questions about identity development, sexual behavior, and social support. Data were analyzed by content analysis. Scores for sexual identity, self esteem, sexual functioning, and psychological adjustment were compared to those of a comparison group (N = 76 nontransgender gay and bisexual men). Of the 25 FtMs, 15 (60%) identified as gay, 8 (32%) as bisexual, and 2 (8%) as queer. All were comfortable with their gender identity and sexual orientation. The FtM group was more bisexual than the nontransgender gay and bisexual controls. No significant group differences were found in self esteem, sexual satisfaction, or psychological adjustment. For some FtMs, sexual attractions and experiences with men affirmed their gender identity; for others, self-acceptance of a transgender identity facilitated actualization of their attractions toward men. Most were “out” as transgender among friends and family, but not on the job or within the gay community. Disclosure and acceptance of their homosexuality was limited. The sexual identity of gay and bisexual FtMs appears to mirror the developmental process for nontransgender homosexual men and women in several ways; however, participants also had experiences unique to being both transgender and gay/bisexual. This signals the emergence of a transgender sexuality.


title: Succinct suffix arrays based on run-length encoding

abstract: A succinet full-text self-index is a data structure built on a text T = t1t2...tn, which takes little space (ideally close to that of the compressed text), permits efficient search for the occurrences of a pattern P = p1p2...pm in T, and is able to reproduce any text substring, so the self-index replaces the text.Several remarkable self-indexes have been developed in recent years. Many of those take space proportional to nH0 or nHk bits, where Hk is the kth order empirical entropy of T. The time to count how many times does P occur in T ranges from O(m) to O(m log n).In this paper we present a new self-index, called RLFM index for "run-length FM-index", that counts the occurrences of P in T in O(m) time when the alphabet size is σ = O(polylog(n)). The RLFM index requires nHklogσ + O(n) bits of space, for any k ≤ αlogσn and constant 0 < α < 1. Previous indexes that achieve O(m) counting time either require more than nH0 bits of space or require that σ = O(1). We also show that the RLFM index can be enhanced to locate occurrences in the text and display text substrings in time independent of σ.In addition, we prove a close relationship between the kth order entropy of the text and some regularities that show up in their suffix arrays and in the Burrows-Wheeler transform of T. This relationship is of independent interest and permits bounding the space occupancy of the RLFM index, as well as that of other existing compressed indexes.Finally, we present some practical considerations in order to implement the RLFM index. We empirically compare our index against the best existing implementations and show that it is practical and competitive against those. In passing, we obtain a competitive implementation of an existing theoretical proposal that can be seen as a simplified RLFM index, and explore other practical ideas such as Huffman-shaped wavelet trees.


title: Effect of perspective taking on the cognitive representation of persons : A merging of self and other

abstract: Two experiments examined the possibility that perspective taking leads observers to create cognitive representations of others that substantially overlap with the observers' own self-representations. In Experiment I observers receiving role-taking instructions were more likely to ascribe traits to a novel target that they (observers) had earlier indicated were self-descriptive. This pattern was most pronounced, however, for positively valenced traits. In Experiment 2 some participants received role-taking instructions but were also given a distracting memory task. In the absence of this task, role taking again produced greater overlap-primarily for positive traits-between self- and target representations. In the presence of the memory task, the degree of self-target overlap was significantly reduced for all traits, regardless of valence. Possible explanations for these findings are discussed.


title: A guided clothoid spline

abstract: Abstract A method is described whereby a G 2 planar curve consisting of clothoid (Cornu spiral) segments, circular arcs and straight line segments is constructed. The curve passes through given points, is expressed parametrically, and has the attractive feature that the arc length and curvature are piecewise linear functions of the parameter. The given points through which the curve passes must be restricted somewhat so that a unique clothoid exists between each pair of points.


title: On bayesian upper confidence bounds for bandit problems

abstract: Stochastic bandit problems have been analyzed from two dierent perspectives: a frequentist view, where the parameter is a deterministic unknown quantity, and a Bayesian approach, where the parameter is drawn from a prior distribution. We show in this paper that methods derived from this second perspective prove optimal when evaluated using the frequentist cumulated regret as a measure of performance. We give a general formulation for a class of Bayesian index policies that rely on quantiles of the posterior distribution. For binary bandits, we prove that the corresponding algorithm, termed BayesUCB, satisfies finite-time regret bounds that imply its asymptotic optimality. More generally, Bayes-UCB appears as an unifying framework for several variants of the UCB algorithm addressing dierent


title: Position-Indexed Formulations for Kidney Exchange

abstract: A kidney exchange is an organized barter market where patients in need of a kidney swap willing but incompatible donors. Determining an optimal set of exchanges is theoretically and empirically hard. Traditionally, exchanges took place in cycles, with each participating patient-donor pair both giving and receiving a kidney. The recent introduction of chains, where a donor without a paired patient triggers a sequence of donations without requiring a kidney in return, increased the efficacy of fielded kidney exchanges---while also dramatically raising the empirical computational hardness of clearing the market in practice. While chains can be quite long, unbounded-length chains are not desirable: planned donations can fail before transplant for a variety of reasons, and the failure of a single donation causes the rest of that chain to fail, so parallel shorter chains are better in practice. In this paper, we address the tractable clearing of kidney exchanges with short cycles and chains that are long but bounded. This corresponds to the practice at most modern fielded kidney exchanges. We introduce three new integer programming formulations, two of which are compact. Furthermore, one of these models has a linear programming relaxation that is exactly as tight as the previous tightest formulation (which was not compact) for instances in which each donor has a paired patient. On real data from the UNOS nationwide exchange in the United States and the NLDKSS nationwide exchange in the United Kingdom, as well as on generated realistic large-scale data, we show that our new models are competitive with all existing solvers---in many cases outperforming all other solvers by orders of magnitude. Finally, we note that our position-indexed chain-edge formulation can be modified in a straightforward way to take post-match edge failure into account, under the restriction that edges have equal probabilities of failure. Post-match edge failure is a primary source of inefficiency in presently-fielded kidney exchanges. We show how to implement such failure-aware matching in our model, and also extend the state-of-the-art general branch-and-price-based non-compact formulation for the failure-aware problem to run its pricing problem in polynomial time.


title: Stylized augmented reality for improved immersion

abstract: The ultimate goal of augmented reality is to provide the user with a view of the surroundings enriched by virtual objects. Practically all augmented reality systems rely on standard real-time rendering methods for generating the images of virtual scene elements. Although such conventional computer graphics algorithms are fast, they often fail to produce sufficiently realistic renderings. The use of simple lighting and shading methods, as well as the lack of knowledge about actual lighting conditions in the real surroundings, cause virtual objects to appear artificial. In this paper, we propose an entirely novel approach for generating augmented reality images in video see-through systems. Our method is based on the idea of applying stylization techniques for reducing the visual realism of both the camera image and the virtual graphical objects. A special painterly image filter is applied to the camera video stream. The virtual scene elements are generated using a non-photorealistic rendering method. Since both the camera image and the virtual objects are stylized in a corresponding "cartoon-like" or "sketch-like" way, they appear very similar. As a result, the graphical objects seem to be an actual part of the real surroundings. We describe both the new painterly filter for the camera image and the non-photorealistic rendering method for virtual scene elements, which has been adapted for this purpose. Both are fast enough for generating augmented reality images in real-time and are highly customizable. The results obtained using our method, are very promising and show that it improves immersion in augmented reality.


Towards a Comprehensive Picture of the Great Firewall's DNS Censorship


title: Deep learning: an overview and main paradigms

abstract: In the present paper, we examine and analyze main paradigms of learning of multilayer neural networks starting with a single layer perceptron and ending with deep neural networks, which are considered regarded as a breakthrough in the field of the intelligent data processing. The baselessness of some ideas about the capacity of multilayer neural networks is shown and transition to deep neural networks is justified. We discuss the principal learning models of deep neural networks based on the restricted Boltzmann machine (RBM), an autoassociative approach and a stochastic gradient method with a Rectified Linear Unit (ReLU) activation function of neural elements.


title: Suburban Drug Dealing: A Case Study in Ambivalent Economics

abstract: Abstract ::: Purpose ::: This chapter contrasts two “careers in dope” (Waldorf, 1973), one a Hispanic crack dealer and the other a White trafficker of powder cocaine. The first dealer worked openly on the street, in the urban style; the latter dealt indoors, exclusively through networks of kin and friends, the only way to sell drugs in the suburbs. This chapter seeks to establish “suburban” drug sales as a particular modality, with dynamics specific to its context. ::: ::: ::: Methodology/approach ::: Two in-depth case cases are examined. They are drawn from a larger set of oral interviews that explore the life histories of drug dealers, with an emphasis on how they sold marijuana and cocaine, and how and why they quit selling. ::: ::: ::: Findings ::: First, the suburban style of drug sales has much to do with the mitigated risks White people face as dealers. Second, suburban dealing illuminates the limits of conventional economic theory to explain drug dealing universally. ::: ::: ::: Originality/value ::: Because suburban drug deals happen among friends and kin relations they are never anonymous. Making sense of economic transactions among intimates raises a number issues fundamental to economic anthropology: the ambivalence of gifts in socialeconomic relationships, and more generally the integration of economic phenomena in social dynamics.


Predicting Student Retention from Behavior in an Online Orientation Course


title: Joint offloading and resource allocation for computation and communication in mobile cloud with computing access point

abstract: We consider a general multi-user mobile cloud computing system with a computing access point (CAP), where each mobile user has multiple independent tasks that may be processed locally, at the CAP, or at a remote cloud server. The CAP serves both as the network access gateway and a computation service provider to the mobile users. We aim to jointly optimize the offloading decisions of all users' tasks as well as the allocation of computation and communication resources, to minimize the overall cost of energy, computation, and delay for all users. This problem is NP-hard in general. We propose an efficient three-step algorithm comprising of semidefinite relaxation (SDR), alternating optimization (AO), and sequential tuning (ST). It is shown to always compute a locally optimal solution, and give nearly optimal performance under a wide range of parameter settings. Through evaluating the performance of different combinations of the three components of this SDR-AO-ST algorithm, we provide insights into their roles and contributions in the overall solution. We further compare the performance of SDR-AO-ST against a lower bound to the minimum cost, purely local processing, purely cloud processing, and hybrid local-cloud processing without using the CAP. Our numerical results demonstrate the effectiveness of the proposed algorithm in the joint management of computation and communication resources in mobile cloud computing systems with a CAP.


title: Stable Affine Frames on Isophotes

abstract: We propose a new affine-covariant feature, the stable affine frame (SAF). SAFs lie on the boundary of extremal regions, i.e. on isophotes. Instead of requiring the whole isophote to be stable with respect to intensity perturbation as in maximally stable extremal regions (MSERs), stability is required only locally, for the primitives constituting the three-point frames. The primitives are extracted by an affine invariant process that exploits properties of bitangents and algebraic moments. Thus, instead of using closed stable isophotes, i.e. MSERs, and detecting affine frames on them, SAFs are sought even on some unstable extremal regions. We show experimentally on standard datasets that SAFs have repeatability comparable to the best affine covariant detectors tested in the state-of-the-art report (Mikolajczyk et al., 2005) and consistently produce a significantly higher number of features per image. Moreover, the features cover images more evenly than MSERs, which facilitates robustness to occlusion.


title: Speech-Based Visual Question Answering

abstract: This paper introduces speech-based visual question answering (VQA), the task of generating an answer given an image and a spoken question. Two methods are studied: an end-to-end, deep neural network that directly uses audio waveforms as input versus a pipelined approach that performs ASR (Automatic Speech Recognition) on the question, followed by text-based visual question answering. Furthermore, we investigate the robustness of both methods by injecting various levels of noise into the spoken question and find both methods to be tolerate noise at similar levels.


title: Malayalam word sense disambiguation

abstract: This paper presents an outline of our work to develop a word sense disambiguation system in Malayalam. Word sense disambiguation (WSD) is a linguistically based mechanism for automatically defining the correct sense of a word in the context. WSD is a long standing problem in computational linguistics. A particular word may have different meanings in different contexts. For human beings, it is easy to extract the correct meaning by analyzing the sentences. In the area of natural language processing, we are trying to simulate all of these human capabilities with a computer system. In many natural language processing tasks such as machine translation, information retrieval etc., Word Sense Disambiguation plays an important role to improve the quality of systems.


title: A K-band microwave measuring system for the analysis of tree stems

abstract: The internal structure of growing trees and freshly cut logs can be characterized in real time by analysing the transmission and reflection of Kuor K-band microwave energy injected with a horizontal polarization towards the material. Information about the moisture content, material bends, number and location of knots and sections of spoiled wood e.g. due to insects can be gathered in real time. Most sensitive test parameters are attenuation, group delay and the rotation of a linearly polarized wavefront. A simultaneous recording of reflection reduces errors caused by non-significant surface deformations. The spatial resolution, humidity equalization and noise immunity can be improved by applying a wideband frequency modulation. Commercial building blocks supplemented with a special antenna arrangement give possibilities also for the rough harvester environment.


title: Autophagy: Mitochondria encaged

abstract: Myosin VI is recruited to ubiquitylated mitochondria and drives their encapsulation into actin cages to sequester them for mitophagy.


title: Volumetric spatial transformer network for object recognition

abstract: Understanding 3D environments is a vital element of modern computer vision research due to paramount relevance in many vision systems, spanning a wide field of application scenarios from self-driving cars to autonomous robots [Qi et al. 2016]. At the present time, object recognition mainly employs two methods: volumetric CNNs [Wu Z 2015] and multi-view CNNs [Xu et al. 2015] [Xu et al. 2016]. In this paper, we propose a volumetric spatial transformer network for object recognition. It fills the gap between 3D CNN and 2D CNN for the first time, and provides an end-to-end training fashion. Given a 3D shape, the network can automatically select the best view that maximizes the accuracy of object recognition.


title: Aspects of Replayability and Software Engineering: Towards a Methodology of Developing Games

abstract: One application of software engineering is the vast and widely popular video game entertainment industry. Success of a video game product depends on how well the player base receives it. Of research towards understanding factors of success behind releasing a video game, we are interested in studying a factor known as Replayability. Towards a software engineering oriented game design methodology, we collect player opinions on Replayability via surveys and provide methods to analyze the data. We believe these results can help game designers to more successfully produce entertaining games with longer lasting appeal by utilizing our software engineering techniques.


title: A set of ethical principles for design science research in information systems

abstract: Over the past decade, design science research (DSR) has re-emerged as an important research paradigm in the field of information systems. However, the approaches currently recommended for conducting design science research do not include an ethical component. Thus, the objective of this paper is to initiate a debate about the need for ethical principles for DSR in Information Systems (IS). To launch this debate, we suggest that a set of ethical principles for DSR in IS must be created. Although the interpretation and application of these principles might not always be straightforward, our argument is that all DSR practitioners in IS should devote at least some time to consider ethical principles.


title: Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling

abstract: Event extraction from texts aims to detect structured information such as what has happened, to whom, where and when. Event extraction and visualization are typically considered as two different tasks. In this paper, we propose a novel approach based on probabilistic modelling to jointly extract and visualize events from tweets where both tasks benefit from each other. We model each event as a joint distribution over named entities, a date, a location and event-related keywords. Moreover, both tweets and event instances are associated with coordinates in the visualization space. The manifold assumption that the intrinsic geometry of tweets is a low-rank, non-linear manifold within the high-dimensional space is incorporated into the learning framework using a regularization. Experimental results show that the proposed approach can effectively deal with both event extraction and visualization and performs remarkably better than both the state-of-the-art event extraction method and a pipeline approach for event extraction and visualization.


title: A multidimensional approach for detecting irony

abstract: Irony is a pervasive aspect of many online texts, one made all the more difficult by the absence of face-to-face contact and vocal intonation. As our media increasingly become more social, the problem of irony detection will become even more pressing. We describe here a set of textual features for recognizing irony at a linguistic level, especially in short texts created via social media such as Twitter postings or "tweets". Our experiments concern four freely available data sets that were retrieved from Twitter using content words (e.g. "Toyota") and user-generated tags (e.g. "#irony"). We construct a new model of irony detection that is assessed along two dimensions: representativeness and relevance. Initial results are largely positive, and provide valuable insights into the figurative issues facing tasks such as sentiment analysis, assessment of online reputations, or decision making.


Design and Analysis of a Soft Pneumatic Actuator with Origami Shell Reinforcement


title: Development and comparison of a quantitative TaqMan-MGB real-time PCR assay to three other methods of quantifying vaccinia virions.

abstract: Abstract Plaque assays are a widely used method to quantify stocks of viruses. Although this method is well established for titrating viral stocks, it is time consuming and can take several days to complete. In this study, the creation and validation of a quantitative real-time PCR (qPCR) assay for enumerating virions of vaccinia virus is reported. PCR primers and a minor groove-binding probe were designed to hybridize to the DNA polymerase gene (E9L) from a number of different orthopoxviruses. The number of viral genomes determined using qPCR was approximately similar to results obtained using OD260 measurements and a direct count of fluorescent virions by microscopy indicating that all three methods are comparable in their ability to quantify virions from a purified stock. In addition, this report describes methodologies to harvest and quantify, using the qPCR assay, three of the four types of vaccinia virions produced during morphogenesis: intracellular mature virions, cell-associated enveloped virions, and extracellular enveloped virions. Using these procedures a particle to plaque forming unit of 61:1, 14:1 and 6:1 was calculated for IMV, CEV and EEV, respectively. These results show that qPCR can be used as a fast and accurate assay to quantify stocks of vaccinia virus over several orders of magnitude from both purified and unpurified stocks and should be applicable to other members of the orthopoxvirus genera.


Sustaining the Momentum: Archival Analysis of Enterprise Resource Planning Systems (2006–2012)


title: Large-scale short-term urban taxi demand forecasting using deep learning

abstract: The world has seen in recent years great successes in applying deep learning (DL) for many application domains. Though powerful, DL is not easy to be used well. In this invited paper, we study an urban taxi demand forecast problem using DL, and we show a number of key insights in modeling a domain problem as a suitable DL task. We also conduct a systematic comparison of two recent deep neural networks (DNNs) for taxi demand prediction, i.s., the ST-ResNet and FLC-Net, on New York city taxi record dataset. Our experimental results show DNNs indeed outperform most traditional machine learning techniques, but such superior results can only be achieved with proper design of the right DNN architecture, where domain knowledge plays a key role.


title: On language ‘ utility ’ : processing complexity and communicative efficiency

abstract: Functionalist typologists have long argued that pressures associated with language usage influence the distribution of grammatical properties across the world's languages. Specifically, grammatical properties may be observed more often across languages because they improve a language's utility or decrease its complexity. While this approach to the study of typology offers the potential of explaining grammatical patterns in terms of general principles rather than domain-specific constraints, the notions of utility and complexity are more often grounded in intuition than empirical findings. A suitable empirical foundation might be found in the terms of processing preferences: in that case, psycholinguistic measures of complexity are then expected correlate with typological patterns. We summarize half a century of psycholinguistic work on ‘processing complexity’ in an attempt to make this work accessible to a broader audience: What makes something hard to process for comprehenders, and what determines speakers' preferences in production? We also briefly discuss recently emerging approaches that link preferences in production to communicative efficiency. These approaches can be seen as providing well-defined measures of utility. With these psycholinguistic findings in mind, it is possible to investigate the extent to which language usage is reflected in typological patterns. We close with a summary of paradigms that allow the link between language usage and typology to be studied empirically. WIREs Cogni Sci 2011 2 323–335 DOI: 10.1002/wcs.126 ::: ::: ::: ::: For further resources related to this article, please visit the WIREs website


title: Al-Quran themes classification using ontology

abstract: Knowledge representations of Al-Quran involved the classification of contents for understanding the readers.In information ::: technology environment, the representation and classification approaches must be understood by human and computer in order to ensure the correct semantics of Al-Quran knowledge.Current approaches used conventional methods such as taxonomy, hierarchy or tree structure, which only provides a concept definition without linked to another possibility of knowledge ::: explanation.This research aims to explore the approach to represent and classify the Al-Quran knowledge by using ontology.The ontology model for Al-Quran is developed according to the themes of Al-Quran knowledge as described in Syammil Al-Quran Miracle the Reference.The ontology approach has shown that the Al-Quran knowledge can be classified and presented to the readers systematically. Moreover, the ontology structure that representing the relationships among the theme concepts in Al-Quran was reviewed and validated by the domain experts in Al-Quran.


title: Social Networking Information Disclosure and Continuance Intention: A Disconnect

abstract: This paper tests a privacy calculus model for Facebook users. The model posits that both the costs and benefits related to privacy will influence users' information disclosure and their usage continuance intention. In a sample of business college students, we find that the privacy calculus model is not well-supported. Instead, two factors (privacy concern and information sensitivity) that are costs to controlling access to personal information predict information disclosure. Technology trusting beliefs also predict information disclosure, but in the opposite direction as that proposed. We also find that while enjoyment and perceived usefulness predict Facebook usage continuance intention, information sensitivity and privacy concern do not. Thus, the predictors of continuance intention are completely different from the predictors of information disclosure. This means users display a clear disconnect between their reasons to disclose on Facebook and their reasons to continue to use Facebook.


title: The uncertain case of credit card fraud detection

abstract: Uncertainty is inherent in many real-time event-driven applications. Credit card fraud detection is a typical uncertain domain, where potential fraud incidents must be detected in real time and tagged before the transaction has been accepted or denied. We present extensions to the IBM Proactive Technology Online (PROTON) open source tool to cope with uncertainty. The inclusion of uncertainty aspects impacts all levels of the architecture and logic of an event processing engine. The extensions implemented in PROTON include the addition of new built-in attributes and functions, support for new types of operands, and support for event processing patterns to cope with all these. The new capabilities were implemented as building blocks and basic primitives in the complex event processing programmatic language. This enables implementation of event-driven applications possessing uncertainty aspects from different domains in a generic manner. A first application was devised in the domain of credit card fraud detection. Our preliminary results are encouraging, showing potential benefits that stem from incorporating uncertainty aspects to the domain of credit card fraud detection.


title: A comprehensive assessment of solar photovoltaic technologies: Literature review

abstract: In the modern era renewable energy generation technologies have broad societal impact and need to be assessed considering multiple perspectives including: social, technological, economic, environmental, and political (STEEP). Assessment of energy technologies is a complex process because it is based on multiple criteria and multiple decision makers, and usually requires analytical models. The purpose of this paper is to review the literature to determine the assessment methods used and the perspectives covered. As a result of the literature analysis a list of perspectives addressed by each research study is compiled and the gaps in the research are identified. The focus of this paper is solar photovoltaic technologies.


title: Recurrent Attention Models for Depth-Based Person Identification

abstract: We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark. Our approach leverages unique 4D spatio-temporal signatures to address the identification problem across days. Formulated as a reinforcement learning task, our model is based on a combination of convolutional and recurrent neural networks with the goal of identifying small, discriminative regions indicative of human identity. We demonstrate that our model produces state-of-the-art results on several published datasets given only depth images. We further study the robustness of our model towards viewpoint, appearance, and volumetric changes. Finally, we share insights gleaned from interpretable 2D, 3D, and 4D visualizations of our model's spatio-temporal attention.


title: Psychopathic disorders and criminal responsibility in the USA

abstract: The insanity defense in the United States is available to provide a legal excuse for those whose criminal acts were due to serious mental illness. To an underappreciated extent, the evolution of the insanity defense is integrally related to the evolution of conceptions of psychopathic disorders and their relevance, or determined lack thereof, to the insanity defense. As legal and mental health professionals discuss the ideal insanity defense and whether psychopathic disorders should be qualifying or disqualifying conditions, any practical outcome of such discussions must take into account the politics of the insanity defense. When this is done, it becomes apparent that the insanity defense itself, even for serious, psychotic mental disorders, is withering under relentless attack and must be advanced and defended without diluting its salient importance by including psychopathic disorders.