title: Computer-Assisted Synthetic Planning: The End of the Beginning

abstract: Exactly half a century has passed since the launch of the first documented research project (1965 Dendral) on computer-assisted organic synthesis. Many more programs were created in the 1970s and 1980s but the enthusiasm of these pioneering days had largely dissipated by the 2000s, and the challenge of teaching the computer how to plan organic syntheses earned itself the reputation of a "mission impossible". This is quite curious given that, in the meantime, computers have "learned" many other skills that had been considered exclusive domains of human intellect and creativity-for example, machines can nowadays play chess better than human world champions and they can compose classical music pleasant to the human ear. Although there have been no similar feats in organic synthesis, this Review argues that to concede defeat would be premature. Indeed, bringing together the combination of modern computational power and algorithms from graph/network theory, chemical rules (with full stereo- and regiochemistry) coded in appropriate formats, and the elements of quantum mechanics, the machine can finally be "taught" how to plan syntheses of non-trivial organic molecules in a matter of seconds to minutes. The Review begins with an overview of some basic theoretical concepts essential for the big-data analysis of chemical syntheses. It progresses to the problem of optimizing pathways involving known reactions. It culminates with discussion of algorithms that allow for a completely de novo and fully automated design of syntheses leading to relatively complex targets, including those that have not been made before. Of course, there are still things to be improved, but computers are finally becoming relevant and helpful to the practice of organic-synthetic planning. Paraphrasing Churchill's famous words after the Allies' first major victory over the Axis forces in Africa, it is not the end, it is not even the beginning of the end, but it is the end of the beginning for the computer-assisted synthesis planning. The machine is here to stay.


title: A high performance thermal noise-canceling transimpedance-based differential amplifier

abstract: This paper presents a design of 1.2 V thermal noise-canceling differential amplifier using 0.13 um CMOS technology. The amplifier employs a CMOS inverter-based transimpedance amplifier, and a noise-canceling circuitry. The thermal noise-canceling circuitry is used to cancel channel thermal noise of the input MOSFET. The simulation result shows the noise figure of 2.32 dB. The gain and bandwidth of the amplifier are 42.14 dB and 2 GHz respectively. The power dissipation is 13 mW.


title: Mobile Social Media and Academic Performance

abstract: Recent studies have shown that there is a negative correlation between social media and academic performance, since they can lead to behaviours that hurt students’ careers, e.g., addictedness. However, these studies either focus on smartphones and social media addictedness per se or rely on sociological surveys, which only provide approximate estimations of the phenomena. We propose to bridge this gap by (i) parametrizing social media usage and academic performance and (ii) combining smartphones and time diaries to keep track of users’ activities and their smartphone interaction. By analyzing the logs of social media apps while studying and attending lessons, and comparing them to students’ GPA, we can quantify negative and positive correlations via smartphones.


Affective Design of E-Commerce User Interfaces: How to Maximise Perceived Trustworthiness


Zsa.Descriptors: a library for real-time descriptors analysis Mikhail Malt * , Emmanuel Jourdan †


title: Specific color-pair edge detection using quaternion convolution

abstract: Color edge detection is one of the important issues for color image segmentation. A color edge approach method is presented which can detect boundaries between two specific colors. The detection procedure consists of two steps: same-hue full saturation (SHFS) conversion and quaternion mask group based edge detection. Experimental results demonstrate its capabilities on natural color images.


title: Data Visualization with Structural Control of Global Cohort and Local Data Neighborhoods

abstract: A typical objective of data visualization is to generate low-dimensional plots that maximally convey the information within the data. The visualization output should help the user not only identify the local neighborhood structure of individual samples, but also obtain a global view of the relative positioning and separation between cohorts. Here, we propose a novel visualization framework designed to satisfy these needs. By incorporating additional cohort positioning and discriminative constraints into local neighbor preservation models through the use of computed cohort prototypes, effective control over the arrangements and proximities of data cohorts can be obtained. We introduce various embedding and projection algorithms based on objective functions addressing the different visualization requirements. Their underlying models are optimized effectively using matrix manifold procedures to incorporate the problem constraints. Additionally, to facilitate large-scale applications, a matrix decomposition based model is also proposed to accelerate the computation. The improved capabilities of the new methods are demonstrated using various state-of-the-art dimensionality reduction algorithms. We present many qualitative and quantitative comparisons, on both synthetic problems and real-world tasks of complex text and image data, that show notable improvements over existing techniques.


title: Summarization of Twitter Microblogs

abstract: Owing to the sheer volume of text generated by a microblog site like Twitter, it is often difficult to fully understand what is being said about various topics. This paper presents algorithms for summarizing microblog documents. Initially, we present algorithms that produce single-document summaries but later extend them to produce summaries containing multiple documents. We evaluate the generated summaries by comparing them to both manually produced summaries and, for the multiple-post summaries, to the summarization results of some of the leading traditional summarization systems.


A Theoretical Review on IT Project Success / Failure Factors and Evaluating the Associated Risks


title: Using similes to extract basic sentiments across languages

abstract: People often use similes of pattern "as adjective as noun" to express their feelings on web medias. The adjective in the pattern is generally the salient property and strong impression of the noun entity in the speaker's mind. By querying the simile templates from search engines, we construct a large database of "noun-adjective" items in English and Chinese, which highlight the same and different basic sentiments on the same entity in the two languages. The approach is a fast and efficient way to extract people's basic sentiments and feelings across languages.


title: Foresight: Recommending Visual Insights

abstract: Current tools for exploratory data analysis (EDA) require users to manually select data attributes, statistical computations and visual encodings. This can be daunting for large-scale, complex data. We introduce Foresight, a system that helps the user rapidly discover visual insights from large high-dimensional datasets. Formally, an "insight" is a strong manifestation of a statistical property of the data, e.g., high correlation between two attributes, high skewness or concentration about the mean of a single attribute, a strong clustering of values, and so on. For each insight type, Foresight initially presents visualizations of the top k instances in the data, based on an appropriate ranking metric. The user can then look at "nearby" insights by issuing "insight queries" containing constraints on insight strengths and data attributes. Thus the user can directly explore the space of insights, rather than the space of data dimensions and visual encodings as in other visual recommender systems. Foresight also provides "global" views of insight space to help orient the user and ensure a thorough exploration process. Furthermore, Foresight facilitates interactive exploration of large datasets through fast, approximate sketching.


title: Single-stage 6.78 MHz power-amplifier design using high-voltage GaN power ICs for wireless charging applications

abstract: Traditional power amplifiers for wireless power transfer require a low voltage dc power supply generated by a power adapter. This multi-stage power conversion solution reduces the overall efficiency. A single-stage “AC-RF” power amplifier architecture using a phase-modulated full-bridge topology and high voltage GaN Power ICs is proposed to directly generate 6.78 MHz wireless power from a rectified ac source. A 50W power amplifier is built and achieves 90% ac-to-coil efficiency, which reduces overall power loss by half, comparing to existing multi-stage solutions. The operating principle of a phase-shifted full bridge power amplifier is discussed, and analytical equations are provided for the passive filter network design. A coupled ZVS-tank scheme is also proposed to improve efficiency.


title: How Does Capital Affect Bank Performance During Financial Crises

abstract: This paper empirically examines how capital affects a bank’s performance (survival and market share) and how this effect varies across banking crises, market crises, and normal times that occurred in the US over the past quarter century. We have two main results. First, capital helps small banks to increase their probability of survival and market share at all times (during banking crises, market crises, and normal times). Second, capital enhances the performance of medium and large banks primarily during banking crises. Additional tests explore channels through which capital generates these effects. Numerous robustness checks and additional tests are performed.


title: Recharging autonomous underwater vehicles from ambient wave induced motions

abstract: In this paper a novel gyroscopic system capable of recharging an autonomous underwater vehicle (AUV) using wave energy is proposed. The system, which is based on control moment gyroscope (CMG) principles, utilises the gyroscopic response of a gimballed flywheel mounted within an AUV body to generate energy from the wave induced rotational motions of the vehicle. By utilising the wave induced rotational motions of an AUV and the relative motion/torque created by a precessing gimballed flywheel promises to enable AUVs to generate energy in-situ and from a renewable source. This novel approach has several advantages. As the system is housed internally it is not exposed to the harsh underwater environment, is not susceptible to bio-fouling and does not add any hydrodynamic drag. In addition, the system can be positioned anywhere within the AUV body and the technology has the potential to be developed into an integrated energy harvesting, storage and motion control system; whereby the wave induced gyroscopic precession of the flywheel can be used to generate energy, the flywheel kinetic energy (spin) can be utilised for energy storage (similar to Kinetic Energy Recovery Systems or KERS) and motion control can be provided by precession control of the flywheel (providing a stable platform for improved monitoring/recording capabilities). In this paper a theoretical description of the system is provided including a derivation of the governing equations of motion following a momentum (Newton-Euler) approach. A numerical model is also described and simulation results for a pitching 2m AUV system are presented. The results show that the system could be used to periodically recharge an AUV remotely, enabling longer AUV deployments at sea.


title: Efficient Data Encoding for Convolutional Neural Network application

abstract: This article presents an approximate data encoding scheme called Significant Position Encoding (SPE). The encoding allows efficient implementation of the recall phase (forward propagation pass) of Convolutional Neural Networks (CNN)—a typical Feed-Forward Neural Network. This implementation uses only 7 bits data representation and achieves almost the same classification performance compared with the initial network: on MNIST handwriting recognition task, using this data encoding scheme losses only 0.03p in terms of recognition rate (99.27p vs. 99.3p). In terms of storage, we achieve a 12.5p gain compared with an 8 bits fixed-point implementation of the same CNN. Moreover, this data encoding allows efficient implementation of processing unit thanks to the simplicity of scalar product operation—the principal operation in a Feed-Forward Neural Network.


title: Design of Novel Teaching Episodes in Medical Education Using Emerging Experiential Digital Assets: Technology Enabled Medical Education Beyond the Gimmicky

abstract: Medical education has always been about experiential hands on training to prepare future doctors to create the necessary skillset to deal with the sensitive and immediate nature of their work. Contemporary experiential technologies such as Virtual and Augmented reality offer a realistic but consequence free test-bed in which to interact safely and cope with emotional challenges pertaining to the realistic tasks simulated through these media. This work describes the design guidelines and workflow for incorporating these novel virtual assets in medical education through serious role play learning episodes. This approach consists of the case selection, the identification of roles, information flow and narrative requirements, implementation of technological narrative tools and implementation of the learning episode. Using a specific example of case transfer through this model we describe the process, outline implementation, assessment and technological considerations. Finally rationale for this work as a counterweight to technological hype fluctuations and integration of these media into the mainstream curricula is discussed.


Live Programming by Example : Using Direct Manipulation for Live Program Synthesis


title: Corporate series feed circular spiral antenna array for UHF applications

abstract: Circular Spiral with twelve element corporateseries feed antenna array operates at the center frequency of 956MHz is designed and analyzed. Each of the single circular spiral inductor is connected after one another. The purpose of design the circular spiral corporate-series feed antenna array is to achieve high gain antenna and subsequently harvesting more RF energy. The quarter wave transformer method is used to design the corporate feed line with the same equal distributed power in each arm. The entire structure was investigated using numerical solver such as finite integration technique (FIT) method. With this setup, the results show that 19.8dB gain for antenna array can be achieved at frequency 956MHz. Therefore, the results obtained can be used as an alternative design solution for application in the UHF band in future. A good agreement between simulation and measurement results is successfully confirmed.


title: Graph-based Analysis of Semantic Drift in Espresso-like Bootstrapping Algorithms

abstract: Bootstrapping has a tendency, called semantic drift, to select instances unrelated to the seed instances as the iteration proceeds. We demonstrate the semantic drift of bootstrapping has the same root as the topic drift of Kleinberg's HITS, using a simplified graph-based reformulation of bootstrapping. We confirm that two graph-based algorithms, the von Neumann kernels and the regularized Laplacian, can reduce semantic drift in the task of word sense disambiguation (WSD) on Senseval-3 English Lexical Sample Task. Proposed algorithms achieve superior performance to Espresso and previous graph-based WSD methods, even though the proposed algorithms have less parameters and are easy to calibrate.


title: Histogram of Oriented Principal Components for Cross-View Action Recognition

abstract: Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process pointclouds for cross-view action recognition from unknown and unseen views. We propose the histogram of oriented principal components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations. At a 3D point, HOPC is computed by projecting the three scaled eigenvectors of the pointcloud within its local spatio-temporal support volume onto the vertices of a regular dodecahedron. HOPC is also used for the detection of spatio-temporal keypoints (STK) in 3D pointcloud sequences so that view-invariant STK descriptors (or Local HOPC descriptors) at these key locations only are used for action recognition. We also propose a global descriptor computed from the normalized spatio-temporal distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the performance of our proposed descriptors against nine existing techniques on two cross-view and three single-view human action recognition datasets. The experimental results show that our techniques provide significant improvement over state-of-the-art methods.


The Central American Refugee Crisis, Securitization, and the Media


title: Designing Web Usability

abstract: Preface. 1. Introduction: Why Web Usability? 2. Page Design. 3. Content Design. 4. Site Design. 5. Intranet Design. 6. Accessibility for Users with Disabilities. 7. International Use: Serving A Global Audience. 8. Future Predictions: The Only Web Constant Is Change. 9. Conclusion: Simplicity in Web Design. Recommended Readings. Index.


title: Input Convex Neural Networks

abstract: This paper presents the input convex neural network architecture. These are scalar-valued (potentially deep) neural networks with constraints on the network parameters such that the output of the network is a convex function of (some of) the inputs. The networks allow for efficient inference via optimization over some inputs to the network given others, and can be applied to settings including structured prediction, data imputation, reinforcement learning, and others. In this paper we lay the basic groundwork for these models, proposing methods for inference, optimization and learning, and analyze their representational power. We show that many existing neural network architectures can be made input-convex with only minor modification, and develop specialized optimization algorithms tailored to this setting. Finally, we highlight the performance of the methods on multi-label prediction, image completion, and reinforcement learning problems, where we show improvement over the existing state of the art in many cases.


title: Exclusive Lasso for Multi-task Feature Selection

abstract: We propose a novel group regularization which we call exclusive lasso. Unlike the group lasso regularizer that assumes covarying variables in groups, the proposed exclusive lasso regularizer models the scenario when variables in the same group compete with each other. Analysis is presented to illustrate the properties of the proposed regularizer. We present a framework of kernel based multi-task feature selection algorithm based on the proposed exclusive lasso regularizer. An efficient algorithm is derived to solve the related optimization problem. Experiments with document categorization show that our approach outperforms state-of-theart algorithms for multi-task feature selection.


title: Self-Play Monte-Carlo Tree Search in Computer Poker

abstract: © Copyright 2014. Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Self-play reinforcement learning has proved to be succ essful in many perfect information two-player games. However, research carrying over its theoretical guaranI ces and practical success to games of imperfect inform ation has been lacking. In this paper, we evaluate self- play Monte-Carlo Tree Search (MCTS) in limit Texas Hold'em and Kuhn poker. We introduce a variant of the established UCB algorithm and provide first empinc al results demonstrating its ability to find approximate Nash equilibria.


title: MEASURING ERP SYSTEM SUCCESS: A RESPECIFICATION OF THE DELONE AND MCLEAN’S IS SUCCESS MODEL

abstract: The implementation of Enterprise Resource Planning (ERP) system has grown rapidly world-wide in recent years. According to AMR Research, the globalization and centralization, and the performance management as the key drivers for continue ERP investment among the large corporations and small companies. Therefore measuring ERP systems success has been an important criterion for adopting organization. This paper presents a re-specification to DeLone and McLean’s IS success Model and empirically examines it in ERP context. A conceptual model was proposed by the comprehensive review of IS success literature that attempted to evaluate the ERP system success. Four dependent valuables (system quality, information quality, vendor/consultant’s quality, and perceived ERP benefits) were used in evaluating ERP system success. This conceptual model and relationships will be tested and validated using data gathering from small and medium enterprises (SMEs) in Malaysia. The result provides an expanded understanding of factors that measure ERP system success and suggest a ways to improve the ERP system usage. Keywords— ERP system success, Delone and McLean, SMEs in Malaysia


Harnessing the immune response to target tumors


title: Spectroscopic Characterization of Biofield Treated Metronidazole and Tinidazole

abstract: Metronidazole and tinidazole are widely used antimicrobial drugs against Gram-negative and Gram-positive anaerobic bacteria. The present study was aimed to evaluate the impact of biofield treatment on metronidazole and tinidazole using FT-IR and UV spectroscopy. The study was carried out in two groups i.e. control and treatment. Treatment groups were subjected to Mr. Trivedi’s biofield treatment while no treatment was given to control group. FT-IR spectrum of treated metronidazole showed the impact of biofield treatment on frequency of characteristic functional groups such as C=C (imidazole ring) stretching was appeared at lower frequency i.e. from 1600 cm-1 to 1553 cm-1. Likewise, NO2 asymmetric stretching and C-N symmetric stretching were appeared at higher wave number i.e. 1479 cm-1 to 1501 cm-1 and 1070 cm-1 to 1077 cm-1, respectively. FT-IR spectrum of tinidazole showed shifting in absorption peak of C-N stretching to higher wavenumber from 1002 cm-1 (control) to 1022 cm-1. The wavenumber of aromatic C=C bond (in imidazole) was shifted to lower frequency, which could be due to increases in conjugation effect. Further, increases in wavenumber of NO2 and C-N in treated sample suggested the increased force constant and bond strength as compared to control. Because of higher conjugation effect and increased bond strength, the molecule supposed to be more stable. The UV spectra of both metronidazole and tinidazole showed the similar patterns of lambda max (λmax) with respect to their control. The FT-IR results of both drugs suggest that there was an impact of biofield treatment on atomic level of metronidazole and tinidazole, as compared to control.


title: Set Variability and Failure Induced by Complementary Switching in Bipolar RRAM

abstract: The resistive switching memory (RRAM) offers fast switching, low-voltage operation, and scalable device area. However, reliability and variability issues must be understood, particularly in the low-current operation regime. This letter addresses set-state variability and presents a new set failure phenomenon in RRAM, leading to a high-resistance tail in the set-state distribution. The set failure is due to complementary switching of the RRAM, causing an increase of resistance soon after the set transition. The dependence of set failure on the programing current is explained by the increasing voltage stress across the RRAM device causing filament disconnection.


title: Delay minimization for relay-based cooperative data exchange with network coding

abstract: We study the Relay-based Cooperative Data Exchange (RCDE) problem, where initially each client has access to a subset of a set of $n$ original packets, referred to as their side information, and wants to retrieve all other original packets via cooperation. Unlike traditional Cooperative Data Exchange (CDE), in our proposed relay-based model, clients can only cooperate via a relay. The data exchange is completed over two phases, namely Uploading Phase and Downloading Phase. In the Uploading Phase, the clients will encode the original packets and transmit the coded packets to the relay. In the Downloading Phase, the relay will reencode the received packets and multicast the reencoded packets, each to a subgroup of clients. The coded packets in the two phases are carefully selected so that each client can retrieve all $n$ original packets with minimum total transmission delay, based on its initial side information and on the coded packets it receives from the relay. In addition, we assume that the bandwidths between the relay and different clients are different, and that the upload/download bandwidths between the relay and the same client are also different. We establish a coding scheme that has the minimum total delay and show that it can be found in polynomial time, for sufficiently large underlying fields. We also design a heuristic algorithm that has a low complexity with binary field size. Our simulations show that the performance of the binary solution is very close to that of the optimal solution. All coding schemes considered in this work are scalar.


abstract: A new method of Meandered Variable Pitch angle Printed Quadrifilar Helix Antenna (MVPQHA) is described. This type of antenna helps in reducing the size of the Printed Quadrifilar Helix Antenna (PQHA) and makes the bandwidth of the antenna broader. The new type of antenna is compared with other existing types of Printed Quadrifilar helix antenna in terms of size, return loss and bandwidth.


title: A full Ka-band waveguide-based spatial power-combining amplifier using e-plane anti-phase probes

abstract: A full Ka-band solid-state power amplifier has been presented and designed using a waveguide-based spatial power-combining technique based on E-plane anti-phase probes. The power divider/combiner employed in this design provides not only a high power-combining efficiency over a full waveguide bandwidth, but also efficient heat sinking for the active power devices. A simple design approach for the power divider/combiner has been developed. The measured insertion loss of the power divider/combiner is less than 0.25dB across the 26-40GHz band. The measured output saturated power of the solidstate amplifier is higher than 27.5dbm over the entire Ka-band. The obtained power-combining efficiency is from 83% to 90% in this frequency band. These results indicate that the proposed power-combining scheme is suitable for implementation of low cost, broadband power amplifiers operating at millimeter wave frequency.


title: INTERSPEECH 2007 Effects of Quiz-style Information Presentation on User Understanding

abstract: This paper proposes quiz-style information presentation for interactive systems as a means to improve user understanding in educational tasks. Since the nature of quizzes can highly motivate users to stay voluntarily engaged in the interaction and keeptheir attention on receiving information, it is expectedthat information presented as quizzes can be better understood by users. To verify the effectiveness of the approach, we implemented read-out and quiz systems and performed comparison experiments using human subjects. In the task of memorizing biographical facts, the results showed that user understanding for the quiz system was significantly better than that for the read-out system, and that the subjects were more willing to use the quiz system despite the long duration of the quizzes. This indicates that quiz-style information presentation promotes engagement in the interaction with the system, leading to the improved user understanding. Index Terms: information presentation, quiz, user understanding


title: Spanish nurses’ attitudes towards research and perceived barriers and facilitators of research utilisation: a comparative survey of nurses with and without experience as principal investigators

abstract: Aims and objective. To examine attitudes towards research and perceived barriers and facilitators of research utilisation in clinical practice in a broad cross-section of Spanish nurses. ::: ::: Background. Nurses’ attitudes towards research are critical in determining whether study findings are used to improve practice. ::: ::: Design. Cross-sectional comparative survey in Hospitals, Primary Care Centres and University-affiliated schools of nursing. ::: ::: Methods. Surveys were completed by 917 nurses: 69 who received funding from the Spanish national agency (1998–2004) and a nationally representative sample of 848 nurses who did not have the same research experience (the Comparison group). Two instruments (BARRIERS and Attitudes towards nursing research) were translated and culturally adapted for use in Spain. A descriptive analysis of demographic and practice characteristics was performed. Total scale scores, as well as subscale scores, were computed and compared across the two groups using one-way analysis of variance (anova) and multivariate analysis of variance (manova) with post hoc tests. Pearson product-moment correlation coefficients were computed between the total tool scores and subscales measuring barriers and attitudes in both groups. ::: ::: Results. The investigators differed from other nurses on several demographic and work characteristics (more males, older age and more likely to work a fixed day shift schedule). On the whole, investigators showed more favourable attitudes but perceived several elements as posing greater barriers to research utilisation than the Comparison groups. Across all respondents, issues related to the quality of research were rated as the greatest barriers to research utilisation, followed by organisational barriers, barriers involving the communication of findings and finally, those related to nurses’ values, awareness and skills. ::: ::: Conclusions. Very similar profiles of perceptions and attitudes regarding research were found in these samples of Spanish nurses relative to those from other countries in earlier reports. Nurses who had experience conducting research demonstrated more favourable research-related attitudes and perceived barriers differently than those without such experience. ::: ::: Relevance to clinical practice. Understanding different organisational and experience perspectives is important to identify challenges and opportunities to ensure research utilisation in clinical practice.


title: Cubature Kalman Filters

abstract: In this paper, we present a new nonlinear filter for high-dimensional state estimation, which we have named the cubature Kalman filter (CKF). The heart of the CKF is a spherical-radial cubature rule, which makes it possible to numerically compute multivariate moment integrals encountered in the nonlinear Bayesian filter. Specifically, we derive a third-degree spherical-radial cubature rule that provides a set of cubature points scaling linearly with the state-vector dimension. The CKF may therefore provide a systematic solution for high-dimensional nonlinear filtering problems. The paper also includes the derivation of a square-root version of the CKF for improved numerical stability. The CKF is tested experimentally in two nonlinear state estimation problems. In the first problem, the proposed cubature rule is used to compute the second-order statistics of a nonlinearly transformed Gaussian random variable. The second problem addresses the use of the CKF for tracking a maneuvering aircraft. The results of both experiments demonstrate the improved performance of the CKF over conventional nonlinear filters.


title: The differential effect of the teacher-student interpersonal relationship on student outcomes for students with different ethnic backgrounds

abstract: Background ::: The differential effectiveness of schools and teachers receives a growing interest, but few studies focused on the relevance of student ethnicity for this effectiveness and only a small number of these studies investigated teaching in terms of the teacher–student interpersonal relationship. Furthermore, the methodology employed often restricted researchers to investigating direct effects between variables across large samples of students. ::: ::: Aims ::: This study uses causal modelling to investigate associations between student background characteristics, students' perceptions of the teacher–student interpersonal relationship, and student outcomes, across and within several population subgroups in Dutch secondary multi-ethnic classes. ::: ::: Methods and sample ::: Multi-group structural equation modelling was used to investigate causal paths between variables in four ethnic groups: Dutch (N = 387), Turkish first- and second-generation immigrant students (N = 267), Moroccan first and second generation (N = 364), and Surinamese second-generation students (N = 101). ::: ::: Results ::: Different structural paths were necessary to explain associations between variables in the different (sub) groups. Different amounts of variance in student attitudes could be explained by these variables. ::: ::: Conclusions ::: The teacher–student interpersonal relationship is more important for students with a non-Dutch background than for students with a Dutch background. Results suggest that the teacher–student relationship is more important for second generation than for first-generation immigrant students. Multi-group causal model analyses can provide a better, more differentiated picture of the associations between student background variables, teacher behaviour, and student outcomes than do more traditional types of analyses.


title: Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation

abstract: Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extend. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE) to provide not only better but also scalable brightness preservation. BBHE separates the input image's histogram into two based on its mean before equalizing them independently. While the separation is done only once in BBHE, this paper proposes to perform the separation recursively; separate each new histogram further based on their respective mean. It is analyzed mathematically that the output image's mean brightness will converge to the input image's mean brightness as the number of recursive mean separation increases. Besides, the recursive nature of RMSHE also allows scalable brightness preservation, which is very useful in consumer electronics. Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), have been properly enhanced by RMSHE.


title: Live intrinsic video

abstract: Intrinsic video decomposition refers to the fundamentally ambiguous task of separating a video stream into its constituent layers, in particular reflectance and shading layers. Such a decomposition is the basis for a variety of video manipulation applications, such as realistic recoloring or retexturing of objects. We present a novel variational approach to tackle this underconstrained inverse problem at real-time frame rates, which enables on-line processing of live video footage. The problem of finding the intrinsic decomposition is formulated as a mixed variational e2-ep-optimization problem based on an objective function that is specifically tailored for fast optimization. To this end, we propose a novel combination of sophisticated local spatial and global spatio-temporal priors resulting in temporally coherent decompositions at real-time frame rates without the need for explicit correspondence search. We tackle the resulting high-dimensional, non-convex optimization problem via a novel data-parallel iteratively reweighted least squares solver that runs on commodity graphics hardware. Real-time performance is obtained by combining a local-global solution strategy with hierarchical coarse-to-fine optimization. Compelling real-time augmented reality applications, such as recoloring, material editing and retexturing, are demonstrated in a live setup. Our qualitative and quantitative evaluation shows that we obtain high-quality real-time decompositions even for challenging sequences. Our method is able to outperform state-of-the-art approaches in terms of runtime and result quality -- even without user guidance such as scribbles.


title: Filters in topology optimization

abstract: In this article, a modified (‘filtered’) version of the minimum compliance topology optimization problem is studied. The direct dependence of the material properties on its pointwise density is replaced by a regularization of the density field by the mean of a convolution operator. In this setting it is possible to establish the existence of solutions. Moreover, convergence of an approximation by means of finite elements can be obtained. This is illustrated through some numerical experiments. The ‘filtering’ technique is also shown to cope with two important numerical problems in topology optimization, checkerboards and mesh dependent designs. Copyright © 2001 John Wiley & Sons, Ltd.


title: The role of synergy in using enterprise architecture for business transformation

abstract: Enterprise architecture (EA) provides an integrated representation of an organization's current and desirable future business capabilities, processes, systems, data and IT infrastructure. EA can interact with and enhance other organizational capabilities, including business transformation capabilities. Despite significant interest and investment in EA, there is little understanding of how EA can augment other organizational capabilities. In this research in progress paper, we focus on the role of EA in augmenting a firm's business transformation capability. We conceptualize a synergistic relationship between EA and a firm's business transformation capability and the emergent EA-enabled business transformation capability. We propose a research model that uses synergy and EA-enabled business transformation capability to explain how transformation outcomes and organizational benefits can be enhanced using EA. We argue that EA capability can lead to the exploitation of existing resources by sharing and reuse of assets and exploration of new capabilities by reconfiguring and integrating resources. At an organizational level, EA can increase flexibility, agility and business-IT alignment. The model forms the basis for planned mixed method empirical work combining case studies and a survey.


title: HIT-ARM I high speed dexterous robot arm

abstract: A dexterous, light weight arm with high precision, high speed manoeuvrability has been built for playing ping-pong. It is based on the modular design concept. The 6 DOFs robot arm consists of two shoulder joints, two elbow joints and one wrist joint. Brushless DC motors and harmonic reducer with a large central hole are applied on the shoulder joints and the elbow joints. The wrist joints are characterized by a bevel gear differential mechanism and driven by two flat DC motors, through a harmonic reducer and timing belt arrangement. Each modular joint contains a joint torque sensor, a joint position sensor, a motor position sensor, a current sensor and a temperature sensor. In each joint, there is a Field Programmable Gate Array(FPGA) for communication and control of BLDC motors. The core of the arm controller is a Intel Core II processor with 2.0 GHz. A PCI board based on FPGA is used for the communication between the arm controller and joint electrical units. Experiment results show that the dexterous arm can play table tennis successfully.


title: Trolling on Tinder® (and other dating apps): Examining the role of the Dark Tetrad and impulsivity

abstract: Abstract No longer conceptualised as only for the “desperate”, online dating offers many benefits over face-to-face dating. Accompanying the benefits of online dating is the potential for new, distinct forms of antisocial behaviour online, such as trolling. The current study (N = 357) sought to explore the antisocial behaviour of trolling on Location-Based Real-Time Dating applications (i.e., LBRTD apps) in an online sample of Australians sourced from the community. Specifically, we examined the role of participant's sex and of the personality traits of narcissism, Machiavellianism, psychopathy, sadism, and impulsivity in predicting perpetration of trolling behaviours on LBRTD apps. Although there were no sex differences, the traits of psychopathy, sadism, and dysfunctional impulsivity were significantly associated with trolling behaviours. Subsequent moderation analysis revealed that dysfunctional impulsivity predicts perpetration of trolling, but only if the individual has medium or high levels of trait psychopathy. Results of the current study aid in further conceptualising the personality of the Internet “troll”. Future research should further explore antisocial online behaviours, such as other hostile behaviour that occurs on LBRTD apps.


title: Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-tagging

abstract: Music auto-tagging is often handled in a similar manner to image classification by regarding the two-dimensional audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstraction. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pretrained convolutional networks separately and aggregate them altogether giving a long audio clip. Finally, we put them into fully connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms the previous state-of-the-art methods on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.


title: Enhancing multi-label classification by modeling dependencies among labels

abstract: Abstract In this paper, we propose a novel framework for multi-label classification, which directly models the dependencies among labels using a Bayesian network. Each node of the Bayesian network represents a label, and the links and conditional probabilities capture the probabilistic dependencies among multiple labels. We employ our Bayesian network structure learning method, which guarantees to find the global optimum structure, independent of the initial structure. After structure learning, maximum likelihood estimation is used to learn the conditional probabilities among nodes. Any current multi-label classifier can be employed to obtain the measurements of labels. Then, using the learned Bayesian network, the true labels are inferred by combining the relationship among labels with the labels׳ estimates obtained from a current multi-labeling method. We further extend the proposed multi-label classification method to deal with incomplete label assignments. Structural Expectation-Maximization algorithm is adopted for both structure and parameter learning. Experimental results on two benchmark multi-label databases show that our approach can effectively capture the co-occurrent and the mutual exclusive relation among labels. The relation modeled by our approach is more flexible than the pairwise or fixed subset labels captured by current multi-label learning methods. Thus, our approach improves the performance over current multi-label classifiers. Furthermore, our approach demonstrates its robustness to incomplete multi-label classification.


title: Revisiting the NSGA-II crowding-distance computation

abstract: This paper improves upon the reference NSGA-II procedure by removing an instability in its crowding distance operator. This instability stems from the cases where two or more individuals on a Pareto front share identical fitnesses. In those cases, the instability causes their crowding distance to either become null, or to depend on the individual's position within the Pareto front sequence. Experiments conducted on nine different benchmark problems show that, by computing the crowding distance on unique fitnesses instead of individuals, both the convergence and diversity of NSGA-II can be significantly improved.


title: Brain mechanisms of proactive interference in working memory

abstract: It has long been known that storage of information in working memory suffers as a function of proactive inter- ference. Here we review the results of experiments using approaches from cognitive neuroscience to reveal a pattern of brain activity that is a signature of proactive interference. Many of these results derive from a single paradigm that requires one to resolve interference from a previous experi- mental trial. The importance of activation in left inferior fron- tal cortex is shown repeatedly using this task and other tasks. We review a number of models that might account for the behavioral and imaging findings about proactive interfer- ence, raising questions about the adequacy of these models. © 2005 Published by Elsevier Ltd on behalf of IBRO.


title: Textual Summarisation of Flowcharts in Patent Drawings for CLEF-IP 2012.

abstract: The CLEF-IP 2012 track included the Flowchart Recognition task, an image-based task where the goal was to process binary images of flowcharts taken from patent draw- ings to produce summaries containing information about their structure. The textual summaries include information about the flowchart title, the box-node shapes, the con- necting edge types, text describing flowchart content and the structural relationships between nodes and edges. An algorithm designed for this task and characterised by the following method steps is presented: * Text-graphic segmentation based on connected-component clustering; * Line segment bridging with an adaptive, oriented filter; * Box shape classification using a stretch-invariant transform to extract features based on shape-specific symmetry; * Text object recognition using a noisy channel model to enhance the results of a commercial OCR package. Performance evaluation results for the CLEF-IP 2012 Flowchart Recognition task are not yet available so the performance of the algorithm has been measured by com- paring algorithm output with object-level ground-truth values. An average F-score was calculated by combining node classification and edge detection (ignoring edge di- rectivity). Using this measure, a third of all drawings were recognized without error (average F-score=1.00) and 75% show an F-score of 0.78 or better. The most impor- tant failure modes of the algorithm have been identified as text-graphic segmentation, line-segment bridging and edge directivity classification. The text object recognition module of the algorithm has been independently eval- uated. Two different state-of-the-art OCR software packages were compared and a post-correction method was applied to their output. Post-correction yields an im- provement of 9% in OCR accuracy and a 26% reduction in the word error rate.


title: ConvNets-Based Action Recognition from Depth Maps through Virtual Cameras and Pseudocoloring

abstract: In this paper, we propose to adopt ConvNets to recognize human actions from depth maps on relatively small datasets based on Depth Motion Maps (DMMs). In particular, three strategies are developed to effectively leverage the capability of ConvNets in mining discriminative features for recognition. Firstly, different viewpoints are mimicked by rotating virtual cameras around subject represented by the 3D points of the captured depth maps. This not only synthesizes more data from the captured ones, but also makes the trained ConvNets view-tolerant. Secondly, DMMs are constructed and further enhanced for recognition by encoding them into Pseudo-RGB images, turning the spatial-temporal motion patterns into textures and edges. Lastly, through transferring learning the models originally trained over ImageNet for image classification, the three ConvNets are trained independently on the color-coded DMMs constructed in three orthogonal planes. The proposed algorithm was extensively evaluated on MSRAction3D, MSRAction3DExt and UTKinect-Action datasets and achieved the state-of-the-art results on these datasets.


title: Shuffle phase optimization in spark

abstract: Data is continuously increasing in volume, variety and velocity and is becoming a precious and an irreplaceable asset. The insights drawn from Data analysis are used to transform healthcare, Engineering of products, Cyber Security, National intelligence and business. Data is becoming extremely large that, it is becoming difficult to process using traditional data storage systems, because of their centralized data processing behavior. Big Data tools like Hadoop and Apache Spark use MapReduce as programming paradigm which has decentralized data processing behavior. Spark performs in-memory cluster computing which makes it 100x times faster than Hadoop. This paper proposes to identify various inefficiencies in the current design of shuffle phase in Spark. Shuffle phase is all-to-all communication mechanism and can potentially introduce network, disk, memory and CPU scheduling overheads. A simple strategy to optimize shuffle performance is by using NIO buffers and large buffer read and writes during shuffling which would result in better performance by reducing the number of disk read and writes when partition size in spill is small. Data from input channel is buffered into large buffer by performing a single read using NIO buffer. Then, data from buffer is put into the output channel for shuffling. This reduces the number of disk operations as well as multiple copies of data between JVM and native memory. This method also reduces the CPU scheduling overheads by providing non-blocking modes for threads provided by NIO buffer.


title: Detecting Emotional Contagion in Massive Social Networks

abstract: Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony.


title: An extended online purchase intention model for middle-aged online users

abstract: This study provides an extended online purchase intention model.We conducted this study with middle-aged online users.The findings enrich the prior online purchase models and the TAM.The results illustrate the impact of habitual online usage.We may finish the first study to examine attitude and habitual online usage together. This article focuses on examining the determinants and mediators of the purchase intention of non-online purchasers between ages 31 and 60 who mostly have strong purchasing power. It proposes a new online purchase intention model by integrating the technology acceptance model with additional determinants and adding habitual online usage as a new mediator. Based on a sample of more than 300 middle-aged non-online purchasers, beyond some situationally-specific predictor variables, online purchasing attitude and habitual online usage are key mediators. Personal awareness of security only affects habitual online usage, indicating a concern of middle-aged users. Habitual online usage is a more influential mediator than attitude for enhancing the perceptual online purchase readiness. Once a strong habit forms, behavioral intention is guided by habit not attitude. This model illustrates the role of habitual online usage related to purchase intentions. The findings have implications for researchers, online marketers and retailers with targets in the middle-aged market segment, assisting them to understand the target group's characteristics and formulate better online strategies.


title: High level pre-synthesis optimization steps using hierarchical conditional dependency graphs

abstract: Taking advantage of existing High-Level Synthesis (HLS) tools is a possibility that merits consideration. Existing HLS technology took many years to develop and has reached a stage where complex combinations of optimization trade-off's can be handled. At this stage, developing a whole new tool chain based on a different internal representation seems to be quite impractical. Nevertheless it is always possible to add some high level optimizing step which permits to improve the performance of existing tools. In this context we propose high level pre-synthesis optimization using our HCDGs (Hierarchical Conditional Dependency Graph) as internal representation. Combination of pre-synthesis and powerful HCDG representation is a principal novelty of our approach. Experimental results using as target synthesis tool the SYNOPSYS behavioral compiler, show significant amelioration of the synthesis results.


title: Efficient circular arc interpolation based on active tolerance control

abstract: In this paper, we present an efficient sub-optimal algorithm for fitting smooth planar parametric curves by G1 arc splines. To fit a parametric curve by an arc spline within a prescribed tolerance, we first sample a set of points and tangents on the curve adaptively as well as with enough density, so that an interpolation biarc spline curve can be with any desired high accuracy. Then, we construct new biarc curves interpolating local triarc spirals explicitly based on the control of permitted tolerances. To reduce the segment number of fitting arc spline as much as possible, we replace the corresponding parts of the spline by the new biarc curves and compute active tolerances for new interpolation steps. By applying the local biarc curve interpolation procedure recursively and sequentially, the result circular arcs with no radius extreme are minimax-like approximation to the original curve while the arcs with radius extreme approximate the curve parts with curvature extreme well too, and we obtain a near optimal fitting arc spline in the end. Even more, the fitting arc spline has the same end points and end tangents with the original curve, and the arcs will be jointed smoothly if the original curve is composed of several smooth connected pieces. The algorithm is easy to be implemented and generally applicable to circular arc interpolation problem of all kinds of smooth parametric curves. The method can be used in wide fields such as geometric modeling, tool path generation for NC machining and robot path planning, etc. Several numerical examples are given to show the effectiveness and efficiency of the method.


title: Textile linear polarization reconfigurable ring slot antenna for 5.8 GHz band

abstract: In this paper, a textile linear polarization reconfigurable antenna for 5.8 GHz band is proposed. It radiates linearly polarized wave which can be controlled in the range of angles ± 30° with respect to the longitudinal axis of the antenna with maximum radiation in the perpendicular direction to the antenna plane. Measured results prove that the proposed antenna achieves at least impedance bandwidth of 11.2 % (angle 0°) for the reflection coefficient less than -10 dB and according simulation achieved the simulated gain between 6.5–7.5 dBi in the perpendicular direction to the antenna plane.


title: Stable Levitation of a Passive Magnetic Bearing

abstract: A design for a passive magnetic bearing system that can stably levitate a rotor in all directions is described. The bearing system consists of levitation magnets coupled with a Halbach array stabilizer, which induces currents in stabilization coils, in order to overcome the inherent instability of a system composed only of permanent magnets. The levitation magnet system consists of two pairs of annular ring magnets which provide an upward magnetic levitation force to counteract the downward gravitational force of the rotor. The Halbach array stabilizer consists of two stabilization coils shifted in angular position with respect to one another and centered in the vertical direction between two rotating Halbach arrays. Magnetic fields from permanent magnets are calculated using superposition of fields due to patches of magnetization charge at surfaces where the magnetization is discontinuous. Induced currents in the stabilization coils are calculated by computing the time derivative of the magnetic flux through those coils. Magnetic forces on the rotor are computed using a superposition of forces on each patch of magnetization charge. The entire magnetic bearing system, consisting of both the levitation magnets and the Halbach array stabilizer, is stable to both vertical and lateral displacements. Results are compared with a simpler straightened approximation of the Halbach array stabilizer.


Character-based Bidirectional LSTM-CRF with words and characters for Japanese Named Entity Recognition


title: Facial landmark localization by enhanced convolutional neural network

abstract: Facial landmark localization is important to many facial recognition and analysis tasks, such as face attributes analysis, head pose estimation, 3D face modeling, and facial expression analysis. In this paper, we propose a new approach to localizing landmarks in facial image by deep convolutional neural network (DCNN). We make two enhancements on the CNN to adapt it to the feature localization task as follows. First, we replace the commonly used max pooling by depth-wise convolution to obtain better localization performance. Second, we define a response map for each facial points as a 2D probability map indicating the presence likelihood, and train our model with a KL divergence loss. To obtain robust localization results, our approach first takes the expectations of the response maps of enhanced CNN and then applies auto-encoder model to the global shape vector, which is effective to rectify the outlier points by the prior global landmark configurations. The proposed ECNN method achieves 5.32% mean error on the experiments on the 300-W dataset, which is comparable to the state-of-the-art performance on this standard benchmark, showing the effectiveness of our methods.


title: An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases

abstract: The Smith Waterman algorithm for sequence alignment is one of the main tools of bioinformatics. It is used for sequence similarity searches and alignment of similar sequences. The high end Graphical Processing Unit (GPU), used for processing graphics on desktop computers, deliver computational capabilities exceeding those of CPUs by an order of magnitude. Recently these capabilities became accessible for general purpose computations thanks to CUDA programming environment on Nvidia GPUs and ATI Stream Computing environment on ATI GPUs. Here we present an efficient implementation of the Smith Waterman algorithm on the Nvidia GPU. The algorithm achieves more than 3.5 times higher per core performance than previously published implementation of the Smith Waterman algorithm on GPU, reaching more than 70% of theoretical hardware performance. The differences between current and earlier approaches are described showing the example for writing efficient code on GPU.


title: Design & development of 2×2 microstrip patch antenna array in SAR for satellite application

abstract: A Novel S-band Antenna for Synthetic aperture radar for Satellite application has been designed, fabricated, and validated. The objective of this project is to design an antenna working at 2.4GHz frequency for SAR. The features of microstrip antenna are small in size, light in weight, low cost, compatible to rigid surface, microstrip antennas are low profile antennas. In the first, we have designed a single patch Microstrip Antenna. And we have simulated its results and related graphical representations. After that we have designed a 2×1 square patch microstrip antenna array and after that 2×2 square patch microstrip antenna array. In this project gain of 11.4db is achieved with 2×2 patch antenna array In this project, the antenna structure is designed, simulated and validated using HFSS 15.0 software.


title: Autonomous human---robot proxemics: socially aware navigation based on interaction potential

abstract: To enable situated human---robot interaction (HRI), an autonomous robot must both understand and control proxemics--the social use of space--to employ natural communication mechanisms analogous to those used by humans. This work presents a computational framework of proxemics based on data-driven probabilistic models of how social signals (speech and gesture) are produced (by a human) and perceived (by a robot). The framework and models were implemented as autonomous proxemic behavior systems for sociable robots, including: (1) a sampling-based method for robot proxemic goal state estimation with respect to human---robot distance and orientation parameters, (2) a reactive proxemic controller for goal state realization, and (3) a cost-based trajectory planner for maximizing automated robot speech and gesture recognition rates along a path to the goal state. Evaluation results indicate that the goal state estimation and realization significantly improve upon past work in human---robot proxemics with respect to "interaction potential"--predicted automated speech and gesture recognition rates as the robot enters into and engages in face-to-face social encounters with a human user--illustrating their efficacy to support richer robot perception and autonomy in HRI.


title: A Survey on Attacks and Countermeasures in

abstract: Security is an essential service for wired and wireless network communications. The success of mobile ad hoc network (MANET) will depend on people ’s confidence in its security. However, the characteristics of MANET pose both challenges and opportunities in achieving security goals, such as confidentiality, authentication, integrity, availability, access control, and non-repudiation. We provide a survey of attacks and countermeasures in MANET in this chapter. The countermeasures are features or functions that reduce or eliminate security vulnerabilities and attacks. First, we give an overview of attacks according to the protocol layers, and to security attributes and mechanisms. Then we present preventive approaches following the order of the layered protocol layers. We also put forward an overview of MANET intrusion detection systems (IDS), which are reactive approaches to thwart attacks and used as a second line of defense.


title: Comparative analysis of 8 X 8 Bit Vedic and Booth Multiplier

abstract: Speed and power consumption are one of the most important parameters to judge the performance of a computational method. In this paper, we compare two algorithms for 8 Bit multiplication namely Vedic Multiplication Algorithm and Booth algorithm. This paper aims in bringing to the fore the differences in compilation speeds and the chip area consumption of the two methodologies. The programming language used is Verilog and the synthesis has been done on Xilinx 14.5.


title: Sampling-50 years after Shannon

abstract: This paper presents an account of the current state of sampling, 50 years after Shannon's formulation of the sampling theorem. The emphasis is on regular sampling, where the grid is uniform. This topic has benefitted from a strong research revival during the past few years, thanks in part to the mathematical connections that were made with wavelet theory. To introduce the reader to the modern, Hilbert-space formulation, we reinterpret Shannon's sampling procedure as an orthogonal projection onto the subspace of band-limited functions. We then extend the standard sampling paradigm for a presentation of functions in the more general class of "shift-in-variant" function spaces, including splines and wavelets. Practically, this allows for simpler-and possibly more realistic-interpolation models, which can be used in conjunction with a much wider class of (anti-aliasing) prefilters that are not necessarily ideal low-pass. We summarize and discuss the results available for the determination of the approximation error and of the sampling rate when the input of the system is essentially arbitrary; e.g., nonbandlimited. We also review variations of sampling that can be understood from the same unifying perspective. These include wavelets, multiwavelets, Papoulis generalized sampling, finite elements, and frames. Irregular sampling and radial basis functions are briefly mentioned.


title: Automated Essay Scoring by Maximizing Human-Machine Agreement

abstract: Previous approaches for automated essay scoring (AES) learn a rating model by minimizing either the classification, regression, or pairwise classification loss, depending on the learning algorithm used. In this paper, we argue that the current AES systems can be further improved by taking into account the agreement between human and machine raters. To this end, we propose a rankbased approach that utilizes listwise learning to rank algorithms for learning a rating model, where the agreement between the human and machine raters is directly incorporated into the loss function. Various linguistic and statistical features are utilized to facilitate the learning algorithms. Experiments on the publicly available English essay dataset, Automated Student Assessment Prize (ASAP), show that our proposed approach outperforms the state-of-the-art algorithms, and achieves performance comparable to professional human raters, which suggests the effectiveness of our proposed method for automated essay scoring.


title: Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach

abstract: This paper presents a real-time, object-independent grasp synthesis method which can be used for closed-loop grasping. Our proposed Generative Grasping Convolutional Neural Network (GG-CNN) predicts the quality and pose of grasps at every pixel. This one-to-one mapping from a depth image overcomes limitations of current deep-learning grasping techniques by avoiding discrete sampling of grasp candidates and long computation times. Additionally, our GG-CNN is orders of magnitude smaller while detecting stable grasps with equivalent performance to current state-of-the-art techniques. The light-weight and single-pass generative nature of our GG-CNN allows for closed-loop control at up to 50Hz, enabling accurate grasping in non-static environments where objects move and in the presence of robot control inaccuracies. In our real-world tests, we achieve an 83% grasp success rate on a set of previously unseen objects with adversarial geometry and 88% on a set of household objects that are moved during the grasp attempt. We also achieve 81% accuracy when grasping in dynamic clutter.


title: Neural Architecture Search with Reinforcement Learning

abstract: Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214.


title: The relationship of age to ten dimensions of job performance.

abstract: Previous reviews of the literature on the relationship between age and job performance have largely focused on core task performance but have paid much less attention to other job behaviors that also contribute to productivity. The current study provides an expanded meta-analysis on the relationship between age and job performance that includes 10 dimensions of job performance: core task performance, creativity, performance in training programs, organizational citizenship behaviors, safety performance, general counterproductive work behaviors, workplace aggression, on-the-job substance use, tardiness, and absenteeism. Results show that although age was largely unrelated to core task performance, creativity, and performance in training programs, it demonstrated stronger relationships with the other 7 performance dimensions. Results also highlight that the relationships of age with core task performance and with counterproductive work behaviors are curvilinear in nature and that several sample characteristics and data collection characteristics moderate age-performance relationships. The article concludes with a discussion of key research design issues that may further knowledge about the age-performance relationship in the future.


title: Is Adult ADHD a Childhood-Onset Neurodevelopmental Disorder? Evidence From a Four-Decade Longitudinal Cohort Study

abstract: Objective:Despite a prevailing assumption that adult ADHD is a childhood-onset neurodevelopmental disorder, no prospective longitudinal study has described the childhoods of the adult ADHD population. The authors report follow-back analyses of ADHD cases diagnosed in adulthood, alongside follow-forward analyses of ADHD cases diagnosed in childhood, in one cohort.Method:Participants belonged to a representative birth cohort of 1,037 individuals born in Dunedin, New Zealand, in 1972 and 1973 and followed to age 38, with 95% retention. Symptoms of ADHD, associated clinical features, comorbid disorders, neuropsychological deficits, genome-wide association study-derived polygenic risk, and life impairment indicators were assessed. Data sources were participants, parents, teachers, informants, neuropsychological test results, and administrative records. Adult ADHD diagnoses used DSM-5 criteria, apart from onset age and cross-setting corroboration, which were study outcome measures.Results:As expected, childhood...


title: Predicting political affiliation of posts on Facebook

abstract: Recently, social media such as Facebook has been more popular. Receiving information from Facebook and generating or spreading information on Facebook every day has become a general lifestyle. This new information-exchanging platform contains a lot of meaningful messages including users' emotions and preferences. Using messages on Facebook or in general social media to predict the election result and political affiliation has been a trend. In Taiwan, for example, almost every politician tries to have public opinion polls by using social media; almost every politician has his or her own fan page on Facebook, and so do the parties. We make an effort to predict to what party, DPP or KMT, two major parties in Taiwan, a post would be related or affiliated. We design features and models for the prediction, and we evaluate as well as compare them with the data collected from several political fan pages on Facebook. The results show that we can obtain accuracy higher than 90% when the text and interaction features are used with a nearest neighbor classifier.


title: A Case of Fetus in Fetu

abstract: Fetus in fetu is a rare developmental aberration, characterized by encasement of partially developed monozygotic, diamniotic, and monochorionic fetus into the normally developing host. A 4-month-old boy presented with abdominal mass. Radiological investigations gave the suspicion of fetus in fetu. At surgery a fetus enclosed in an amnion like membrane at upper retroperitoneal location was found and excised. The patient is doing well after the operation.


title: HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition

abstract: In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). To achieve more effective accomplishment of the coarse-to-fine tasks for hierarchical visual recognition, multiple sets of deep features are first extracted from the different layers of deep convolutional neural networks (deep CNNs). A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, and it can provide a good environment for identifying the inter-related learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can achieve the global optimum easily and obtain more discriminative node classifiers for distinguishing the visually-similar atomic object classes (in the same group) effectively. Our HD-MTL algorithm can control the inter-level error propagation effectively by using an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on both the accuracy rates and the computational efficiency for large-scale visual recognition.


title: Path Loss Simulation of an Infrared Optical Wireless System for Aircrafts

abstract: In this paper, the infrared optical wireless path loss inside an aircraft cabin is estimated. To this end, a Monte Carlo ray-tracing (MCRT) simulation is performed in a geometric computer-aided design (CAD) cabin model. Position, azimuth (AZ), elevation (EL) and field of view (FOV) properties of transmitters and receivers are defined. Key path loss parameters such as the path loss exponent and the standard deviation of the shadowing component are determined for particular line-of-sight (LOS) and non-line-of-sight (NLOS) cases. A LOS path loss exponent of 1.92 and a shadowing standard deviation of 0.81dB are obtained. In NLOS conditions, however, the path loss exponent varies depending on the nature of the particular NLOS case considered. The presented scenarios yield NLOS path loss exponent values of 2.26 and 1.28, and shadowing standard deviation values of 1.27dB and 0.7dB, respectively.


title: The Nature of Suffering and the Nature of Opportunity at the End of Life

abstract: Encountering a patient who is suffering in the midst of terminal illness is an all-too-common occurrence for clinicians who care for the elderly. This article explores the personal experience of suffering in the context of life-limiting illness. The concept of personhood is used to illuminate the nature of suffering. Clinical observation documents that some persons experience a subjectively heightened sense of well-being as they die. The concept of personhood and the model of life-long human development is applied to the explication of this apparent paradox, enabling an understanding of the nature of opportunity at the end of life.


title: On GPS Tracking of Mobile Devices

abstract: Tracking living beings and devices using the Global Positioning System (GPS) has become prevalent and indispensable over the last few years. Applications range from military and national security to personal safety and comfort. This paper reviews some of the recent application areas and discusses the benefits and issues of GPS tracking. It then presents a software architecture of a general tracking system to locate lost and misplaced devices (and those that carry such devices).


title: Modelling and controller design for a non-inverting buck-boost chopper

abstract: Due to its simplicity, low voltage stress, high reliability, low switch and inductor losses, and small inductor size, the non-inverting buck-boost chopper has found a lot of attentions in applications where it is necessary to step-up or step-down the DC voltage. In this paper, a successful switching strategy for this converter is reported and small signal averaged state-space model is obtained, which lets decide about the control strategy and analyse the stability and performance of the closed loop control system. Appropriate control requirements have been defined and the closed loop performance under several control strategies has been investigated through extensive simulations.


A Deeper Look at FFT and Winograd Convolutions


title: SparkClouds: Visualizing Trends in Tag Clouds

abstract: Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations-multiple line graphs and stacked bar charts-as well as Parallel Tag Clouds. Results show that SparkClouds' ability to show trends compares favourably to the alternative visualizations.


title: Cultural Issues in Disclosures of Child Sexual Abuse.

abstract: Cultural norms affect the likelihood that child sexual abuse will be discovered by an adult or disclosed by a child. Cultural norms also affect whether abused children's families will report child sexual abuse to authorities. This article explores the ways ethnic and religious culture affect child sexual abuse disclosure and reporting, both in the United States and internationally. Guidelines for culturally sensitive child abuse interviewing are provided to facilitate disclosures of abuse from culturally diverse children in formal settings.


title: Kripke modelling and verification of temporal specifications of a multiple UAV system

abstract: A verifiable multiple UAV system cooperatively monitoring a road network is presented in this paper. The focus is on formal modelling and verification which can guarantee correctness of concurrent reactive systems such as multi-UAV systems. Kripke modelling is used to formally model the distributed cooperative control strategy, and to verify correctness of the specifications. Desirable properties of the mission such as liveness are specified in Computation Tree Logic (CTL). Model checking technique is used to exhaustively explore the state space to verify whether the system behaviour, modelled by Kripke model, satisfies the specifications. Violation of a specification is analysed by means of the counter-example generated by SMV model checking tool.


title: Text Segmentation as a Supervised Learning Task

abstract: Text segmentation, the task of dividing a document into contiguous segments based on its semantic structure, is a longstanding challenge in language understanding. Previous work on text segmentation focused on unsupervised methods such as clustering or graph search, due to the paucity in labeled data. In this work, we formulate text segmentation as a supervised learning problem, and present a large new dataset for text segmentation that is automatically extracted and labeled from Wikipedia. Moreover, we develop a segmentation model based on this dataset and show that it generalizes well to unseen natural text.


title: Judgment error in pie chart variations

abstract: Pie charts and their variants are prevalent in business settings and many other uses, even if they are not popular with the academic community. In a recent study, we found that contrary to general belief, there is no clear evidence that these charts are read based on the central angle. Instead, area and arc length appear to be at least equally important. ::: ::: In this paper, we build on that study to test several pie chart variations that are popular in information graphics: exploded pie chart, pie with larger slice, elliptical pie, and square pie (in addition to a regular pie chart used as the baseline). We find that even variants that do not distort central angle cause greater error than regular pie charts. Charts that distort the shape show the highest error. Many of our predictions based on the previous study's results are borne out by this study's findings.


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title: Feature Selective Networks for Object Detection

abstract: Objects for detection usually have distinct characteristics in different sub-regions and different aspect ratios. However, in prevalent two-stage object detection methods, Region-of-Interest (RoI) features are extracted by RoI pooling with little emphasis on these translation-variant feature components. We present feature selective networks to reform the feature representations of RoIs by exploiting their disparities among sub-regions and aspect ratios. Our network produces the sub-region attention bank and aspect ratio attention bank for the whole image. The RoI-based sub-region attention map and aspect ratio attention map are selectively pooled from the banks, and then used to refine the original RoI features for RoI classification. Equipped with a light-weight detection subnetwork, our network gets a consistent boost in detection performance based on general ConvNet backbones (ResNet-101, GoogLeNet and VGG-16). Without bells and whistles, our detectors equipped with ResNet-101 achieve more than 3% mAP improvement compared to counterparts on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO datasets.


title: Improved binary particle swarm optimization using catfish effect for feature selection

abstract: The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acceptable classification accuracy. Feature selection is a preprocessing technique with great importance in the fields of data analysis and information retrieval processing, pattern classification, and data mining applications. This paper presents a novel optimization algorithm called catfish binary particle swarm optimization (CatfishBPSO), in which the so-called catfish effect is applied to improve the performance of binary particle swarm optimization (BPSO). This effect is the result of the introduction of new particles into the search space (''catfish particles''), which replace particles with the worst fitness by the initialized at extreme points of the search space when the fitness of the global best particle has not improved for a number of consecutive iterations. In this study, the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) was used to evaluate the quality of the solutions. CatfishBPSO was applied and compared to 10 classification problems taken from the literature. Experimental results show that CatfishBPSO simplifies the feature selection process effectively, and either obtains higher classification accuracy or uses fewer features than other feature selection methods.


Ordinary everyday memories: Some of the things of which selves are made


title: A Deep-Learning-Based Geological Parameterization for History Matching Complex Models

abstract: A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN–PCA method is inspired by recent developments in computer vision using deep learning. CNN–PCA can be viewed as a generalization of an existing optimization-based PCA (O-PCA) method. Both CNN–PCA and O-PCA entail post-processing a PCA model to better honor complex geological features. In CNN–PCA, rather than use a histogram-based regularization as in O-PCA, a new regularization involving a set of metrics for multipoint statistics is introduced. The metrics are based on summary statistics of the nonlinear filter responses of geological models to a pre-trained deep CNN. In addition, in the CNN–PCA formulation presented here, a convolutional neural network is trained as an explicit transform function that can post-process PCA models quickly. CNN–PCA is shown to provide both unconditional and conditional realizations that honor the geological features present in reference SGeMS geostatistical realizations for a binary channelized system. Flow statistics obtained through simulation of random CNN–PCA models closely match results for random SGeMS models for a demanding case in which O-PCA models lead to significant discrepancies. Results for history matching are also presented. In this assessment CNN–PCA is applied with derivative-free optimization, and a subspace randomized maximum likelihood method is used to provide multiple posterior models. Data assimilation and significant uncertainty reduction are achieved for existing wells, and physically reasonable predictions are also obtained for new wells. Finally, the CNN–PCA method is extended to a more complex nonstationary bimodal deltaic fan system, and is shown to provide high-quality realizations for this challenging example.


title: An indoor autonomous guided vehicle based on electromagnetic tracking and positioning ibeacon

abstract: In order to further improve the efficiency of indoor unmanned transport, a low-power and efficient navigation and positioning system is designed. The system combines the advantages of the detecting method of electromagnetic signals and the indoor positioning technology based on Bluetooth Low Energy (BTLE). By analyzing the magnetic field model, common inductance coils are used as a sensor to detect the magnetic field, and the differential steering mechanism of the motor is controlled by the tracking characteristics of the dual-inductive electromagnetic sensor. For GPS can not provide satisfactory indoor positioning results, on the basis of the electromagnetic tracking system combined with the indoor positioning technology based on spatial information mode, a stable and independent indoor positioning system is finally realized. And with the predetermined electromagnetic track crossing point, the dead reckoning accumulated error for signpost is revised. The experimental results show that the system can achieve stable operation, and the system design is reasonable and basically meets the design goals and users' needs.


title: Guided Proceduralization: Optimizing Geometry Processing and Grammar Extraction for Architectural Models

abstract: We describe a guided proceduralization framework that optimizes geometry processing on architectural input models to extract target grammars. We aim to provide efficient artistic workflows by creating procedural representations from existing 3D models, where the procedural expressiveness is controlled by the user. Architectural reconstruction and modeling tasks have been handled as either time consuming manual processes or procedural generation with difficult control and artistic influence. We bridge the gap between creation and generation by converting existing manually modeled architecture to procedurally editable parametrized models, and carrying the guidance to procedural domain by letting the user define the target procedural representation. Additionally, we propose various applications of such procedural representations, including guided completion of point cloud models, controllable 3D city modeling, and other benefits of procedural modeling.


title: RANSAC-SVM for large-scale datasets

abstract: Support Vector Machines (SVMs), though accurate, are still difficult to solve large-scale applications, due to the computational and storage requirement. To relieve this problem, we propose RANSAC-SVM method, which trains a number of small SVMs for randomly selected subsets of training set, while tuning their parameters to fit SVMs to whole training set. RANSAC-SVM achieves good generalization performance, which close to the Bayesian estimation, with small subset of the training samples, and outperforms the full SVM solution in some condition.


title: Model-Driven Data Acquisition in Sensor Networks

abstract: Declarative queries are proving to be an attractive paradigm for ineracting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this paper, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques.


title: An Active Learning Approach for Improving the Accuracy of Automated Domain Model Extraction

abstract: Domain models are a useful vehicle for making the interpretation and elaboration of natural-language requirements more precise. Advances in natural-language processing (NLP) have made it possible to automatically extract from requirements most of the information that is relevant to domain model construction. However, alongside the relevant information, NLP extracts from requirements a significant amount of information that is superfluous (not relevant to the domain model). Our objective in this article is to develop automated assistance for filtering the superfluous information extracted by NLP during domain model extraction. To this end, we devise an active-learning-based approach that iteratively learns from analysts’ feedback over the relevance and superfluousness of the extracted domain model elements and uses this feedback to provide recommendations for filtering superfluous elements. We empirically evaluate our approach over three industrial case studies. Our results indicate that, once trained, our approach automatically detects an average of ≈ 45% of the superfluous elements with a precision of ≈ 96%. Since precision is very high, the automatic recommendations made by our approach are trustworthy. Consequently, analysts can dispose of a considerable fraction – nearly half – of the superfluous elements with minimal manual work. The results are particularly promising, as they should be considered in light of the non-negligible subjectivity that is inherently tied to the notion of relevance.


title: Decreasingly naive Bayes: Aggregating n-dependence estimators

abstract: Averaged n-Dependence Estimators (AnDE) is an approach to probabilistic classification learning that learns without search. It utilizes a single parameter that transforms the approach between a low-variance high-bias learner (Naive Bayes) and a high-variance low-bias learner with Bayes optimal asymptotic error. It extends the underlying strategy of Averaged One-Dependence Estimators (AODE), which relaxes the Naive Bayes independence assumption while retaining many of Naive Bayes’ desirable computational and theoretical properties. AnDE further relaxes the independence assumption by generalizing AODE to higher-levels of dependence. Extensive experimental evaluation shows that the bias-variance trade-off for Averaged 2-Dependence Estimators results in strong predictive accuracy over a wide range of data sets. It has training time linear with respect to the number of examples, supports incremental learning, handles directly missing values, and is robust in the face of noise. Beyond the practical utility of its lower-order variants, AnDE is of interest in that it demonstrates that it is possible to create low-bias high-variance generative learners and suggests strategies for developing even more powerful classifiers.


title: Can 3D Pose be Learned from 2D Projections Alone?

abstract: 3D pose estimation from a single image is a challenging task in computer vision. We present a weakly supervised approach to estimate 3D pose points, given only 2D pose landmarks. Our method does not require correspondences between 2D and 3D points to build explicit 3D priors. We utilize an adversarial framework to impose a prior on the 3D structure, learned solely from their random 2D projections. Given a set of 2D pose landmarks, the generator network hypothesizes their depths to obtain a 3D skeleton. We propose a novel Random Projection layer, which randomly projects the generated 3D skeleton and sends the resulting 2D pose to the discriminator. The discriminator improves by discriminating between the generated poses and pose samples from a real distribution of 2D poses. Training does not require correspondence between the 2D inputs to either the generator or the discriminator. We apply our approach to the task of 3D human pose estimation. Results on Human3.6M dataset demonstrates that our approach outperforms many previous supervised and weakly supervised approaches.


title: Dead-Time Compensation of Inverters Considering Snubber and Parasitic Capacitance

abstract: In power electronics drive systems, dead time is used to prevent shoot-through over power devices. However, dead time can lead to distortion of ac output voltages and currents. Various compensation methods have been proposed to overcome this drawback. However, most strategies assume the power switches as ideal switches and the transition from ON to OFF or vice versa is infinitely fast. In this paper, effects of inverter snubber and parasitic capacitance to the switching instants are investigated when doing dead-time compensation. A new dead-time compensation method is presented with the capacitance being considered. It is verified that the compensation becomes more accurate and effective for the specific application after the modification. It is also shown that the proposed compensation can make the inverter system stable and robust. This proposed approach is validated by the experimental results.


title: Conspicuous consumption and household indebtedness

abstract: Using a novel, large dataset of consumer transactions in Singapore, we study how conspicuous consumption affects household indebtedness. The coexistence of private housing (condominiums) and subsidized public housing (HDB) allows us to identify conspicuous consumers. Conditional on income and other socioeconomic characteristics, those who choose to reside in condominiums – considered a status good in Singapore – are likely to be more conspicuous than their counterparts living in HDB units. We find that condominium residents spend considerably more (by 25%) on conspicuous goods but not differently on inconspicuous goods. Compared with their matched HDB counterparts, these consumers with higher conspicuous motivation carry 7% more credit card debt and 108% more delinquent credit card debt. Our results suggest that status-seeking-induced conspicuous consumption is an important determinant of household indebtedness.