title: Gaze + pinch interaction in virtual reality

abstract: Virtual reality affords experimentation with human abilities beyond what's possible in the real world, toward novel senses of interaction. In many interactions, the eyes naturally point at objects of interest while the hands skilfully manipulate in 3D space. We explore a particular combination for virtual reality, the Gaze + Pinch interaction technique. It integrates eye gaze to select targets, and indirect freehand gestures to manipulate them. This keeps the gesture use intuitive like direct physical manipulation, but the gesture's effect can be applied to any object the user looks at --- whether located near or far. In this paper, we describe novel interaction concepts and an experimental system prototype that bring together interaction technique variants, menu interfaces, and applications into one unified virtual experience. Proof-of-concept application examples were developed and informally tested, such as 3D manipulation, scene navigation, and image zooming, illustrating a range of advanced interaction capabilities on targets at any distance, without relying on extra controller devices.


title: Advanced PROPHET Routing in Delay Tolerant Network

abstract: To solve routing jitter problem in PROPHET in delay tolerant network, advanced PROPHET routing is proposed in this paper. Average delivery predictabilities are used in advanced PROPHET to avoid routing jitter. Furthermore, we evaluate it through simulations versus PROPHET routing protocol. The experimental results show there has higher average delivery rates and shorter average delays in advanced PROPHET. Thus, it is fair to say that advanced PROPHET gives better performance than PROPHET.


title: Designing Freeform Origami Tessellations by Generalizing Resch's Patterns

abstract: In this research, we study a method to produce families of origami tessellations from given polyhedral surfaces. The resulting tessellated surfaces generalize the patterns proposed by Ron Resch and allow the construction of an origami tessellation that approximates a given surface. We will achieve these patterns by first constructing an initial configuration of the tessellated surfaces by separating each facets and inserting folded parts between them based on the local configuration. The initial configuration is then modified by solving the vertex coordinates to satisfy geometric constraints of developability, folding angle limitation, and local nonintersection. We propose a novel robust method for avoiding intersections between facets sharing vertices. Such generated polyhedral surfaces are not only applied to folding paper but also sheets of metal that does not allow 180 deg folding.


title: Ensemble Pruning using Reinforcement Learning

abstract: Multiple Classifier systems have been developed in order to improve classification accuracy using methodologies for effective classifier combination. Classical approaches use heuristics, statistical tests, or a meta-learning level in order to find out the optimal combination function. We study this problem from a Reinforcement Learning perspective. In our modeling, an agent tries to learn the best policy for selecting classifiers by exploring a state space and considering a future cumulative reward from the environment. We evaluate our approach by comparing with state-of-the-art combination methods and obtain very promising results.


title: Widely Used but also Highly Valued? Acceptance Factors and Their Perceptions in Water-Scrum-Fall Projects

abstract: Agile methodologies like Scrum propose drastic changes with respect to team hierarchies, organizational structures, planning or controlling processes. To mitigate the level of change and retain some established processes, many organizations prefer to introduce hybrid agile-traditional methodologies that combine agile with traditional development practices. Despite their importance in practice, only a few studies have examined the acceptance of such methodologies, however. In this paper, we present the results of a qualitative study that was conducted at a Swiss bank. It uses Water-Scrum-Fall, which combines Scrum with traditional practices. Based on the Diffusion of Innovations theory, we discuss several acceptance factors and investigate how they are perceived. The results indicate that, compared to traditional development methodologies, some aspects of Water-Scrum-Fall bring relative advantages and are more compatible to the way developers prefer to work. Yet, there also exist potential acceptance barriers such as a restricted individual autonomy and increased process complexity.


title: Manifold-Based Reinforcement Learning via Locally Linear Reconstruction

abstract: Feature representation is critical not only for pattern recognition tasks but also for reinforcement learning (RL) methods to solve learning control problems under uncertainties. In this paper, a manifold-based RL approach using the principle of locally linear reconstruction (LLR) is proposed for Markov decision processes with large or continuous state spaces. In the proposed approach, an LLR-based feature learning scheme is developed for value function approximation in RL, where a set of smooth feature vectors is generated by preserving the local approximation properties of neighboring points in the original state space. By using the proposed feature learning scheme, an LLR-based approximate policy iteration (API) algorithm is designed for learning control problems with large or continuous state spaces. The relationship between the value approximation error of a new data point and the estimated values of its nearest neighbors is analyzed. In order to compare different feature representation and learning approaches for RL, a comprehensive simulation and experimental study was conducted on three benchmark learning control problems. It is illustrated that under a wide range of parameter settings, the LLR-based API algorithm can obtain better learning control performance than the previous API methods with different feature representation schemes.


title: Psychoacoustic basis of sound quality evaluation and sound engineering

abstract: In R & D departments of companies, the evaluation of sounds usually is based on physical measurements. However, in "real life", the ultimate judge for sounds is the human hearing system. A customer evaluates the sound of a product by his or her hearing system, not by physical measurement tools, whatever their sophistication may be. In this situation, psychoacoustics is the scientific field of choice to bridge the gap between physical and subjective evaluations. In psychoacoustic experiments, firm relations between the physical representations of sounds and the correlated hearing sensations are established. In practical applications of psychoacoustics, two main tasks can be distinguished: First, questions of sound evaluation, usually for already existing sounds which often have to be improved and Second, questions of sound quality engineering, where for a (new or modified) product or application a suitable sound has to be "tailored". For the evaluation of sounds, basic psychoacoustic magnitudes like loudness and sharpness have proven successful, which assess volume or power and tone color of sounds, respectively. Using these descriptors, extremely different questions like the quality of piano sounds or the annoyance of snoring sounds can be assessed. In addition, psychoacoustic magnitudes related to the temporal structure of sounds like fluctuation strength or roughness can play an important part. In sound quality engineering, for each sound, the right "recipe" has to be found, how to mix the different hearing sensations, to arrive at the desired sound. For the example of warning signals, a systematic approach, incorporating elements of decision tree studies, is discussed.


title: Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources

abstract: Current recommender systems mostly do not take into account as well as they might the wealth of information available in social media, thus preventing the user from obtaining a broad and reliable overview of different opinions and ratings on a product. Furthermore, there is a lack of user control over the recommendation process-which is mostly fully automated and does not allow the user to influence the sources and mechanisms by which recommendations are produced-as well as over the presentation of recommended items. Consequently, recommendations are often not transparent to the user, are considered to be less trustworthy, or do not meet the user's situational needs. This work will investigate the theoretical foundations for user-controllable, interactive methods of recommending, will develop techniques that exploit social media data in conjunction with other sources, and will validate the research empirically in the area of e-commerce product recommendations. The methods developed are intended to be applicable in a wide range of recommending and decision support scenarios.


title: The Noisy Channel and the Braying Donkey

abstract: Several Corpus Linguistics research groups have gone beyond collation of 'raw' text, to syntactic annotation of the text. However, linguists developing these linguistic resources have used quite different wordtagging and parse-tree labelling schemes in each of these annotated corpora. This restricts the accessibility of each corpus, making it impossible for speech and handwriting researchers to collate them into a single very large training set. This is particularly problematic as there is evidence that one of these parsed corpora on its own is too small for a general statistical model of grammatical structure, but the combined size of all the above annotated corpora should deliver a much more reliable model. We are developing a set of mapping algorithms to map between the main tagsets and phrase structure grammar schemes used in the above corpora. We plan to develop a Multi-tagged Corpus and a MultiTreebank, a single text-set annotated with all the above tagging and parsing schemes. The text-set is the Spoken English Corpus: this is a half-way house between formal written text and colloquial conversational speech. However, the main deliverable to the computational linguistics research community is not the SEC-based MultiTreebank, but the mapping suite used to produce it - this can be used to combine currently-incompatible syntactic training sets into a large unified multicorpus. Our architecture combines standard statistical language modelling and a rule-base derived from linguists' analyses of tagset-mappings, in a novel yet intuitive way. Our development of the mapping algorithms aims to distinguish notational from substantive differences in the annotation schemes, and we will be able to evaluate tagging schemes in terms of how well they fit standard statistical language models such as n-pos (Markov) models.


title: Simulation of a Costa Rican Watershed: Resolution Effects and Fractals

abstract: Simulation of runoff from the Navarro watershed in Costa Rica was undertaken using the U.S. Geological Survey's Precipitation Runoff Modeling System, developed and operated within the Modular Modeling System. A geographic information system (GIS) database of watershed characteristics, and GIS-based methodology for delineating modeling response units (MRU) were utilized to enhance the spatial representation of watershed features and physical processes. Simulations were carried out using a series of MRU resolutions (from 90 m2 to 1,260 m2) in order to determine the range through which the hydrologic response could be accurately modeled. Fractal characterization provided a method to measure the relationship of the MRU patterns (based primarily on land cover) at the different resolutions. The range of self-similarity of the MRU patterns, as determined by fractal characterization, was compared to the range of resolutions through which accurate hydrologic simulations were achieved. The results from this researc...


title: Fingerprint spoof detection using minutiae-based local patches

abstract: The individuality of fingerprints is being leveraged for a plethora of day-to-day applications, ranging from unlocking a smartphone to international border security. While the primary purpose of a fingerprint recognition system is to ensure a reliable and accurate user authentication, the security of the recognition system itself can be jeopardized by spoof attacks. This study addresses the problem of developing accurate and generalizable algorithms for detecting fingerprint spoof attacks. We propose a deep convolutional neural network based approach utilizing local patches extracted around fingerprint minutiae. Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides state of the art accuracies in fingerprint spoof detection for intra-sensor, cross-material, cross-sensor, as well as cross-dataset testing scenarios. For example, the proposed approach achieves a 69% reduction in average classification error for spoof detection under both known material and cross-material scenarios on LivDet 2015 datasets.


title: Engineering Fast Route Planning Algorithms ⋆

abstract: Algorithms for route planning in transportation networks have recently undergone a rapid development, leading to methods that are up to one million times faster than Dijkstra's algorithm. We outline ideas, algorithms, implementations, and experimental methods behind this development. We also explain why the story is not over yet because dynamically changing networks, flexible objective functions, and new applications pose a lot of interesting challenges.


title: Planar Waveguide Arrays for Millimeter Wave Systems

abstract: Design of high gain and high efficiency antennas is one of the key challenges in antenna engineering and especially in millimeter wave communication systems. Various types of planar waveguide arrays with series-fed traveling wave operation have been developed in Tokyo Tech with the special focus upon efficiency enhancement as well as reduction of fabrication cost. In this review, four kinds of single layer waveguide arrays characterized with the series fed travelling wave operation are surveyed first. To cope with the bandwidth narrowing effects due to long line effects associated with the series fed operation, authors have introduced partially corporate feed embedded in the single layer waveguide. They further extended the study to cover fully corporate feed arrays with multiple layer waveguide as well; a new fabrication technique of diffusion bonding of laminated thin plates has the potential to realize the low cost mass production of multi-layer structures for the millimeter wave application. Secondly, the novel methods for loss evaluation of copper plate substrate are established for the design of post-wall waveguide arrays where dielectric loss and conductor loss is determined in wide range of millimeter wave band, by using the Whispering gallery mode resonator. This enables us to design the planar arrays with the loss taken into account. Finally, the planar arrays are now applied to two kinds of systems in the Tokyo Tech millimeter wave project; the indoor short range file-transfer systems and the outdoor communication systems for the medium range backhaul links. The latter has been field-tested in the model network built in Tokyo Tech Ookayama campus. Early stage progress of the project including unique propagation data is also reported.


title: Curbing the roofline: a scalable and flexible architecture for CNNs on FPGA

abstract: Convolutional Neural Networks (CNNs) have reached outstanding results in several complex visual recognition tasks, such as classification and scene parsing. CNNs are composed of multiple filtering layers that perform 2D convolutions over input images. The intrinsic parallelism in such a computation kernel makes it suitable to be effectively accelerated on parallel hardware. In this paper we propose a highly flexible and scalable architectural template for acceleration of CNNs on FPGA devices, based on the cooperation between a set of software cores and a parallel convolution engine that communicate via a tightly coupled L1 shared scratchpad. Our accelerator structure, tested on a Xilinx Zynq XC-Z7045 device, delivers peak performance up to 80 GMAC/s, corresponding to 100 MMAC/s for each DSP slice in the programmable fabric. Thanks to the flexible architecture, convolution operations can be scheduled in order to reduce input/output bandwidth down to 8 bytes per cycle without degrading the performance of the accelerator in most of the meaningful use-cases.


title: MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis

abstract: Android dominates the smartphone operating system market and consequently has attracted the attention of malware authors and researchers alike. Despite the considerable number of proposed malware analysis systems, comprehensive and practical malware analysis solutions are scarce and often short-lived. Systems relying on static analysis alone struggle with increasingly popular obfuscation and dynamic code loading techniques, while purely dynamic analysis systems are prone to analysis evasion. We present MARVIN, a system that combines static with dynamic analysis and which leverages machine learning techniques to assess the risk associated with unknown Android apps in the form of a malice score. MARVIN performs static and dynamic analysis, both off-device, to represent properties and behavioral aspects of an app through a rich and comprehensive feature set. In our evaluation on the largest Android malware classification data set to date, comprised of over 135,000 Android apps and 15,000 malware samples, MARVIN correctly classifies 98.24% of malicious apps with less than 0.04% false positives. We further estimate the necessary retraining interval to maintain the detection performance and demonstrate the long-term practicality of our approach.


title: Investigation of long short-term memory networks to temperature prediction for permanent magnet synchronous motors

abstract: Monitoring critical temperatures in permanent magnet synchronous motors (PMSMs) is crucial to ensure safe operation and maximum device utilization as well. In this work, the application of recurrent neural networks featuring memory blocks (LSTMs/GRUs) are investigated upon their suitability to accurate temperature time series prediction inside PMSMs or similar motor types, which is the first time in literature to the author's best knowledge. Considered motor components are stator yoke, teeth and winding as well as the rotor's permanent magnets of a highly-utilized PMSM for electric vehicle applications. Having benchmark data available, numerous neural networks are trained and optimized with the aid of the Chainer framework and particle swarm optimization is conducted for finding suitable model hyper-parameters (e.g. number of hidden neurons or layers) on a computing cluster. It is found, that the Euclidean norm performance (in the range of 1–3 K) is similar but the worst-case predication errors (in the range of 9–14 K) are significantly higher compared to established modeling techniques like lumped-parameter thermal networks (LPTNs). This initial investigation motivates future research to increase ANN-based estimation accuracy by taken other ANN topologies, training methods or hyper-parameter optimization approaches into account.


title: An analysis of latent sector errors in disk drives

abstract: The reliability measures in today's disk drive-based storage systems focus predominantly on protecting against complete disk failures. Previous disk reliability studies have analyzed empirical data in an attempt to better understand and predict disk failure rates. Yet, very little is known about the incidence of latent sector errors i.e., errors that go undetected until the corresponding disk sectors are accessed. Our study analyzes data collected from production storage systems over 32 months across 1.53 million disks (both nearline and enterprise class). We analyze factors that impact latent sector errors, observe trends, and explore their implications on the design of reliability mechanisms in storage systems. To the best of our knowledge, this is the first study of such large scale our sample size is at least anorder of magnitude larger than previously published studies and the first one to focus specifically on latent sector errors and their implications on the design and reliability of storage systems.


title: Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models

abstract: Distributions are often used to model uncertainty in many scientific datasets. To preserve the correlation among the spatially sampled grid locations in the dataset, various standard multivariate distribution models have been proposed in visualization literature. These models treat each grid location as a univariate random variable which models the uncertainty at that location. Standard multivariate distributions (both parametric and nonparametric) assume that all the univariate marginals are of the same type/family of distribution. But in reality, different grid locations show different statistical behavior which may not be modeled best by the same type of distribution. In this paper, we propose a new multivariate uncertainty modeling strategy to address the needs of uncertainty modeling in scientific datasets. Our proposed method is based on a statistically sound multivariate technique called Copula, which makes it possible to separate the process of estimating the univariate marginals and the process of modeling dependency, unlike the standard multivariate distributions. The modeling flexibility offered by our proposed method makes it possible to design distribution fields which can have different types of distribution (Gaussian, Histogram, KDE etc.) at the grid locations, while maintaining the correlation structure at the same time. Depending on the results of various standard statistical tests, we can choose an optimal distribution representation at each location, resulting in a more cost efficient modeling without significantly sacrificing on the analysis quality. To demonstrate the efficacy of our proposed modeling strategy, we extract and visualize uncertain features like isocontours and vortices in various real world datasets. We also study various modeling criterion to help users in the task of univariate model selection.


title: A Meta-Meta-Model for Seven Business Process Modeling Languages

abstract: Many different business process modelling languages (BPMLs) have been designed in recent years. In cross-organizational business processes and heterogeneous organizations where multiple BPMLs are deployed there is a need for a unified view to ease communication and foster understandability. This paper proposes a language independent abstraction of seven mainstream BPMLs' concepts, in a unified meta-meta model based on an analysis of these modelling languages. Generic concepts are identified and a unified metamodel is developed. An ontological analysis of the representational capability of this metamodel is examined in relation to the Bunge-Wand-Weber ontology and applicability of the approach is demonstrated via an Example.


title: From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records

abstract: Inferring phenotypic patterns from population-scale clinical data is a core computational task in the development of personalized medicine. One important source of data on which to conduct this type of research is patient Electronic Medical Records (EMR). However, the patient EMRs are typically sparse and noisy, which creates significant challenges if we use them directly to represent patient phenotypes. In this paper, we propose a data driven phenotyping framework called Pacifier (PAtient reCord densIFIER), where we interpret the longitudinal EMR data of each patient as a sparse matrix with a feature dimension and a time dimension, and derive more robust patient phenotypes by exploring the latent structure of those matrices. Specifically, we assume that each derived phenotype is composed of a subset of the medical features contained in original patient EMR, whose value evolves smoothly over time. We propose two formulations to achieve such goal. One is Individual Basis Approach (IBA), which assumes the phenotypes are different for every patient. The other is Shared Basis Approach (SBA), which assumes the patient population shares a common set of phenotypes. We develop an efficient optimization algorithm that is capable of resolving both problems efficiently. Finally we validate Pacifier on two real world EMR cohorts for the tasks of early prediction of Congestive Heart Failure (CHF) and End Stage Renal Disease (ESRD). Our results show that the predictive performance in both tasks can be improved significantly by the proposed algorithms (average AUC score improved from 0.689 to 0.816 on CHF, and from 0.756 to 0.838 on ESRD respectively, on diagnosis group granularity). We also illustrate some interesting phenotypes derived from our data.


Synthesis Insights from Scienti fi c Literature via Text Extraction and Machine Learning


title: Bloom Filter Based Associative Deletion

abstract: Bloom filters are widely-used powerful tools for processing set membership queries. However, they are not entirely suitable for many new applications, such as deleting one attribute value according to another attribute value for a set of data objects/items with two correlated attributes. In this paper, we introduce a concept for such an operation, called the associative deletion. To realize this operation, we propose a new Bloom filter data structure, named IABF (Improved Associative deletion Bloom Filter), which keeps the association information on the two correlated attributes of items in the given data set. Based on IABF, we present an algorithm to perform associative deletions, which can be applied to both normal data and streaming data. To further accelerate the operation, we also illustrate a hardware coprocessor implementation for a crucial component of the algorithm. Detailed theoretical analysis and experimental results demonstrate that the presented IABF technique can accurately process associative deletions with controlled false positive and negative rates.


title: Ultra-Compact and Robust Physically Unclonable Function Based on Voltage-Compensated Proportional-to-Absolute-Temperature Voltage Generators

abstract: This paper presents a technique for designing an ultra-compact and robust physically unclonable function for security-oriented applications. The circuits are based on pairs of analog circuits whose output voltage is supply voltage compensated and proportional to absolute temperature (PTAT). The difference between two outputs of a PTAT pair is digitized to produce 1 b output which is sensitive mostly only to random transistor threshold voltage variations. Fabricated in a 65 nm, the proposed 256 b PUF array takes an area of 3.07 μm ::: 2 ::: /bit, consumes 0.548 pJ/bit at a throughput of 10 Mb/s, while showing desirable robustness against temperature and supply voltage variations with 3.5% and 1.004% bit-instability across 0 to 80°C and 0.6 to 1.2 V, respectively. The unpredictability and uniqueness of the 256 b PUF output are verified by NIST randomness test and Hamming Distance between keys. As compared with the state of the art, the proposed design has an 8.3× smaller area/bit or 2× better robustness against noise and environmental variations.


title: Robust discrete optimization and network flows

abstract: We propose an approach to address data uncertainty for discrete optimization and network flow problems that allows controlling the degree of conservatism of the solution, and is computationally tractable both practically and theoretically. In particular, when both the cost coefficients and the data in the constraints of an integer programming problem are subject to uncertainty, we propose a robust integer programming problem of moderately larger size that allows controlling the degree of conservatism of the solution in terms of probabilistic bounds on constraint violation. When only the cost coefficients are subject to uncertainty and the problem is a 0−1 discrete optimization problem on n variables, then we solve the robust counterpart by solving at most n+1 instances of the original problem. Thus, the robust counterpart of a polynomially solvable 0−1 discrete optimization problem remains polynomially solvable. In particular, robust matching, spanning tree, shortest path, matroid intersection, etc. are polynomially solvable. We also show that the robust counterpart of an NP-hard α-approximable 0−1 discrete optimization problem, remains α-approximable. Finally, we propose an algorithm for robust network flows that solves the robust counterpart by solving a polynomial number of nominal minimum cost flow problems in a modified network.


title: Using word embeddings in Twitter election classification

abstract: Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to train and generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, the context window size and the dimensionality of word embeddings on the classification performance. By comparing the classification results of two word embedding models, which are trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data type should align with the Twitter classification dataset to achieve a better performance. Moreover, by evaluating the results of word embeddings models trained using various context window sizes and dimensionalities, we found that large context window and dimension sizes are preferable to improve the performance. Our experimental results also show that using word embeddings and CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings.


title: A 2-MHz 60-W Zero-Voltage-Switching Synchronous Noninverting Buck–Boost Converter With Reduced Component Values

abstract: This brief presents a high-frequency high-efficiency zero-voltage-switching (ZVS) synchronous noninverting buck–boost converter for tens-of-Watt applications. By using two pairs of small auxiliary inductors and capacitors, the proposed converter can achieve ZVS under different load currents without increasing voltage stress across power field-effect transistors. The switching power loss is thus minimized to enable high-frequency operation of the converter. The proposed converter can be also configured into the boost mode or the buck mode with ZVS operation when the input voltage changes in order to maintain high power efficiency under different input voltages. The proposed noninverting buck–boost converter has been implemented to operate at 2 MHz and deliver 60-W output. The measured peak power efficiency achieves 94.3%, and the output capacitance and its volume can be reduced by at least 14 times and 4.8 times compared with other state-of-the-art counterparts.


title: Game On! Students' Perceptions of Gamified Learning

abstract: Introduction The beginning of the 21st century has seen several macro-level trends challenge the traditional higher education model (Doyle, Buckley, & Carroll, 2013). Most notable is the arrival of a new generational group (Elam, Stratton, & Gibson, 2007; Howe & Strauss, 2000; Howe & Strauss, 2003). Referred to as "Millennials" or "Generation Y," they are widely seen as being the first "digital natives," exposed to information technology from birth. Other far-reaching changes have coincided with the arrival of this new cohort. Massification, a concept which refers to the enrolment of students beyond the levels required to repopulate academia and certain other high status professions, continues apace (Cornuel, 2007). This inevitably leads to larger and more diverse classes, with far more variance in student background and ability. University financing models are also changing (Altbach, Reisberg, & Rumbley, 2009). Higher level institutions have adapted to the changing financial environment by developing new revenue streams, including fee paying postgraduate and executive education courses, and by recruiting fee paying students from foreign jurisdictions, amplifying the negative effects of massification. One concern linked to these trends is that student engagement and motivation is declining. The symptoms are well documented. Lecturers report declining class attendance (Massingham & Herrington, 2006) coupled with difficulties in prompting interaction and discussion (Race, 2010). More pernicious problems such as plagiarism and cheating are reported to be rising (Flint, Clegg, & Macdonald, 2006). In response, educators are striving to develop innovative teaching practices to capture and retain the attention of students, particularly millennials. One approach, and the subject of growing interest, is gamification. This involves "using game-based mechanics, aesthetics and game thinking to engage people, motivate action, promote learning, and solve problems" (Kapp, 2012, p. 10). More generally, gamification is the term applied to a set of motivational triggers, such as rewards and competition, which are traditionally associated with games. While the application of gamification in education is still an emerging trend (Dicheva, Dichev, Agre, & Angelova, 2015), its proponents suggest that it can be employed to enhance student engagement and prompt learning. However, due to the dearth of empirical research in this area, little is known about how gamification impacts on students' motivation to engage and learn. For example, in what contexts does gamification have most impact on engagement? Do gamified learning activities work better in large or small classes? Does the level of specialism of the relevant degree make any difference to the effectiveness of gamification in education? What challenges or difficulties do gamified learning environments present for students? When attempting to influence behaviour, a comprehensive understanding of the target group's perceptions of the relevant issue is necessary (Gullifer & Tyson, 2010). To this end, a gamified learning intervention was deployed within two university modules taken by different student cohorts during the same semester. The first was a large group of undergraduate students while the second was a small postgraduate course. Focus groups were carried out with each cohort to develop a nuanced understanding of their perceptions of a gamified learning environment across different class sizes, level of education and subject specialism. Our paper makes three contributions. First, we assess students' perceptions of the effectiveness of gamification as a pedagogical technique. Second, we provide other educators with guidance on how gamification can be integrated into curricula. Finally, we use the data to identify outstanding issues and questions that must be fully addressed before gamification can be considered a mature pedagogical methodology. …


title: Fuzzy system reliability using intuitionistic fuzzy Weibull lifetime distribution

abstract: Present study investigates the fuzzy reliability of some systems using intuitionistic fuzzy Weibull lifetime distribution, in which the lifetime parameters are assumed to be fuzzy parameter due to uncertainty and inaccuracy of data. Expressions for fuzzy reliability, fuzzy mean time to failure, fuzzy hazard function and their α-cut have been discussed when systems follow intuitionistic fuzzy Weibull lifetime distribution. A numerical example is also taken to illustrate the methodology to calculate the fuzzy reliability characteristics of systems.


title: A New Approach Layered Architecture based Clustering for Prolong Life of Wireless Sensor Network (WSN)

abstract: Sensor nodes are the tiny particles which have to rely on limited power of energy. Sensor nodes with limited battery power are deployed to gather information in wireless environment throughout the field. Due to the small amount of energy in a node of sensor web, energy balancing among nodes is quite important. Secondly, due to lack of energy in a particular node we are also concerned to the power saving of a sensor node. We are also concerned here to prolong the life of sensor node so that it can sense the field for longer period of time. All these features are the prime necessity to introduce a new energy efficient routing algorithm in this paper.


title: Binary Spatter-Coding of Ordered K-Tuples

abstract: Information with structure is traditionally organized into records with fields. For example, a medical record consisting of name, sex, age, and weight might look like (Joe, male, 66, 77). What 77 stands for is determined by its location in the record, so that this is an example of local representation. The brain's wiring, and robustness under local damage, speak for the importance of distributed representations. The Holographic Reduced Representation (HRR) of Plate is a prime example based on real or complex vectors. This paper describes how spatter coding leads to binary HRRs, and how the fields of a record are encoded into a long binary word without fields and how they are extracted from such a word.


title: An Augmented Reality System for Astronomical Observations

abstract: Anyone who has gazed through the eyepiece of an astronomical telescope knows that, with the exception of the Moon and the planets, extra-solar astronomical objects are disappointing to observe visually. This is mainly due to their low surface brightness, but also depends on the visibility, sky brightness and telescope aperture. We propose a system which projects images of astronomical objects (with focus on nebulae and galaxies), animations and additional information directly into the eyepiece view of an astronomical telescope. As the telescope orientation is queried continuously, the projected image is adapted in real-time to the currently visible field of view. For projection, a custom-built video projection module with high contrast and low maximum luminance value was developed. With this technology visitors to public observatories have the option to experience the richness of faint astronomical objects while directly looking at them through a telescope.


title: Micro-Expression Recognition Using Color Spaces

abstract: Micro-expressions are brief involuntary facial expressions that reveal genuine emotions and, thus, help detect lies. Because of their many promising applications, they have attracted the attention of researchers from various fields. Recent research reveals that two perceptual color spaces (CIELab and CIELuv) provide useful information for expression recognition. This paper is an extended version of our International Conference on Pattern Recognition paper, in which we propose a novel color space model, tensor independent color space (TICS), to help recognize micro-expressions. In this paper, we further show that CIELab and CIELuv are also helpful in recognizing micro-expressions, and we indicate why these three color spaces achieve better performance. A micro-expression color video clip is treated as a fourth-order tensor, i.e., a four-dimension array. The first two dimensions are the spatial information, the third is the temporal information, and the fourth is the color information. We transform the fourth dimension from RGB into TICS, in which the color components are as independent as possible. The combination of dynamic texture and independent color components achieves a higher accuracy than does that of RGB. In addition, we define a set of regions of interests (ROIs) based on the facial action coding system and calculated the dynamic texture histograms for each ROI. Experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that the performances for TICS, CIELab, and CIELuv are better than those for RGB or gray.


title: A brief history of simulation revisited

abstract: In response to a request from the WSC Foundation and the WSC 2010 Program Committee, we review and slightly revise our survey of the history of simulation up to 1982, with special emphasis on some of the critical advances in the field and some of the individuals who played leading roles in those advances. Documenting the history of simulation remains a work in progress on our part, and we encourage individuals and organizations in the simulation community to bring significant historical data to our attention.


title: On the approximate realization of continuous mappings by neural networks

abstract: Abstract In this paper, we prove that any continuous mapping can be approximately realized by Rumelhart-Hinton-Williams' multilayer neural networks with at least one hidden layer whose output functions are sigmoid functions. The starting point of the proof for the one hidden layer case is an integral formula recently proposed by Irie-Miyake and from this, the general case (for any number of hidden layers) can be proved by induction. The two hidden layers case is proved also by using the Kolmogorov-Arnold-Sprecher theorem and this proof also gives non-trivial realizations.


title: Design of Electric Current Profile for Electrodynamic Tether Systems by Input Shaping Method

abstract: This paper discusses an Input Shaping method for electrodynamic tether systems. The Input Shaping method is applied to generate an input current profile to reduce the induced vibration of the electrodynamic tether system in the initial phase of the propulsion. The Lorenz force and the flexibility of the tether cause vibrations of the tether system on orbit. Two different shapers are designed to suppress the two main vibration modes. The complete shaper is designed by multiplying each of the shapers together. The flexibility of the tether is described by the lumped mass model. The robustness of the shapers is discussed by sensitivity plots. The results of numerical simulations show excellent performance of vibration suppressing by the employing Input Shaping for the electrodynamic tether system.


title: Regularized Deep Learning for Face Recognition With Weight Variations

abstract: Body weight variations are an integral part of a person’s aging process. However, the lack of association between the age and the weight of an individual makes it challenging to model these variations for automatic face recognition. In this paper, we propose a regularizer-based approach to learn weight invariant facial representations using two different deep learning architectures, namely, sparse-stacked denoising autoencoders and deep Boltzmann machines. We incorporate a body-weight aware regularization parameter in the loss function of these architectures to help learn weight-aware features. The experiments performed on the extended WIT database show that the introduction of weight aware regularization improves the identification accuracy of the architectures both with and without dropout.


title: Imbalanced text classification: A term weighting approach

abstract: The natural distribution of textual data used in text classification is often imbalanced. Categories with fewer examples are under-represented and their classifiers often perform far below satisfactory. We tackle this problem using a simple probability based term weighting scheme to better distinguish documents in minor categories. This new scheme directly utilizes two critical information ratios, i.e. relevance indicators. Such relevance indicators are nicely supported by probability estimates which embody the category membership. Our experimental study using both Support Vector Machines and Naive Bayes classifiers and extensive comparison with other classic weighting schemes over two benchmarking data sets, including Reuters-21578, shows significant improvement for minor categories, while the performance for major categories are not jeopardized. Our approach has suggested a simple and effective solution to boost the performance of text classification over skewed data sets.


title: What is ERP

abstract: Though enterprise resource planning (ERP) has gained some prominence in the information systems (IS) literature over the past few years and is a significant phenomenon in practice, through (a) historical analysis, (b) meta-analysis of representative IS literature, and (c) a survey of academic experts, we reveal dissenting views on the phenomenon. Given this diversity of perspectives, it is unlikely that at this stage a broadly agreed definition of ERP can be achieved. We thus seek to increase awareness of the issues and stimulate further discussion, with the ultimate aim being to: (1) aid communication amongst researchers and between researchers and practitioners; (2) inform development of teaching materials on ERP and related concepts in university curricula and in commercial education and training; and (3) aid communication amongst clients, consultants and vendors. Increased transparency of the ERP-concept within IS may also benefit other aligned fields of knowledge.


SSD as SQLite Engine


title: Calculating Medium and Image Gamut Boundaries for Gamut Mapping

abstract: The Segment Maxima Method for calculating gamut boundary descriptors of both colour reproduction media and colour images is introduced. Methods for determining the gamut boundary along a given line of mapping used by gamut mapping algorithms are then described, whereby these methods use the Gamut Boundary Descriptor obtained using the Segment Maxima Method. Throughout the article, the focus is both on colour reproduction media and colour images as well as on the suitability of the methods for use in gamut mapping. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 394–401, 2000


title: Compact planar log-periodic dipole array based yagi-uda antenna

abstract: In this paper, a compact planar Yagi-Uda antenna based on two elements log-periodic dipole array has been designed to achieve an end fire directional characteristics over a broad range of frequencies. To feed the driven elements of log-periodic dipole array, a tapered microstrip balun is designed, which converts unbalanced coaxial feed to balanced microstrip line feed. The prototype of antenna has been fabricated on a low cost glass epoxy FR4 substrate with overall size of the antenna is 0.41λ o × 0.54λ o at center frequency (84mm × 111mm). The measured results have been compared with simulated results. Measured results show a bandwidth of approximately 41.4% centered at 1.45 GHz for VSWR < 2. Maximum gain is 7.2 dBi with nearly 1 dB variations over the bandwidth. Peak front to back ratio is 28 dB with better than 15 dB has been achieved over the bandwidth.


title: Noise level estimation using weak textured patches of a single noisy image

abstract: A patch-based noise level estimation algorithm is proposed in this paper, with patches generated from a single noisy image. One can easily estimate the noise level from image patches using principal component analysis (PCA) if the image comprises only weak textured patches. The challenge for patch-based noise level estimation is how to select weak textured patches from a noisy image. As described in this paper, we propose a novel algorithm to select weak textured patches from a single noisy image based on the gradients of the patches and their statistics. Then we estimate the noise level from the selected weak textured patches using PCA. We demonstrate experimentally that the proposed noise level estimation algorithm outperforms the state-of-the-art algorithm.


Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules


title: Bias Factor and Therapeutic Window Correlate to Predict Safer Opioid Analgesics

abstract: Summary Biased agonism has been proposed as a means to separate desirable and adverse drug responses downstream of G protein-coupled receptor (GPCR) targets. Herein, we describe structural features of a series of mu-opioid-receptor (MOR)-selective agonists that preferentially activate receptors to couple to G proteins or to recruit βarrestin proteins. By comparing relative bias for MOR-mediated signaling in each pathway, we demonstrate a strong correlation between the respiratory suppression/antinociception therapeutic window in a series of compounds spanning a wide range of signaling bias. We find that βarrestin-biased compounds, such as fentanyl, are more likely to induce respiratory suppression at weak analgesic doses, while G protein signaling bias broadens the therapeutic window, allowing for antinociception in the absence of respiratory suppression.


title: Principles of remote attestation

abstract: Remote attestation is the activity of making a claim about properties of a target by supplying evidence to an appraiser over a network. We identify five central principles to guide development of attestation systems. We argue that (i) attestation must be able to deliver temporally fresh evidence; (ii) comprehensive information about the target should be accessible; (iii) the target, or its owner, should be able to constrain disclosure of information about the target; (iv) attestation claims should have explicit semantics to allow decisions to be derived from several claims; and (v) the underlying attestation mechanism must be trustworthy. We illustrate how to acquire evidence from a running system, and how to transport it via protocols to remote appraisers. We propose an architecture for attestation guided by these principles. Virtualized platforms, which are increasingly well supported on stock hardware, provide a natural basis for our attestation architecture.


title: Hiring Teams from Rivals: Theory and Evidence on the Evolving Relationships in the Corporate Legal Market

abstract: How are relationships between clients and law firms in the corporate legal market evolving, and why? Drawing on interview and survey data from 166 chief legal officers of SP (2) formally limited in duration and subject to renegotiation, these relationships nevertheless continue to be enduring; and (3) clients focus not only on law firm platforms and lead partners, but also on teams and departments within the preferred providers, allocating work to these subunits at rival firms over time, and following “star” lawyers from firm to firm more often if they move as part of a team. The combination of long-term relationships and subunit rivalry provides law firms in these relationships with steady aggregate work flows and allows companies to keep cost pressure on firms while preserving relationship-specific capital, quality assurance, and soft forms of legal capacity insurance - that is, a soft guarantee that their law firms will stand ready to provide legal services when and as needed by their clients. Apart from their descriptive value, our findings have normative implications for law firms, corporate departments, law firms, and law schools.


title: Generating Pseudo-ground Truth for Predicting New Concepts in Social Streams

abstract: The manual curation of knowledge bases is a bottleneck in fast paced domains where new concepts constantly emerge. Identification of nascent concepts is important for improving early entity linking, content interpretation, and recommendation of new content in real-time applications. We present an unsupervised method for generating pseudo-ground truth for training a named entity recognizer to specifically identify entities that will become concepts in a knowledge base in the setting of social streams. We show that our method is able to deal with missing labels, justifying the use of pseudo-ground truth generation in this task. Finally, we show how our method significantly outperforms a lexical-matching baseline, by leveraging strategies for sampling pseudo-ground truth based on entity confidence scores and textual quality of input documents.


title: Fast Recognition of Remixed Music Audio

abstract: We present an efficient algorithm for automatically detecting remixes of pop songs in large commercial collections. Remixes are closely related as commercial products but they are not closely related in their audio spectral content because of the nature of the remixing process. Therefore spectral modelling approaches to audio similarity fail to recognize them. We propose a new approach - that chops songs into small chunks called audio shingles - to recognize remixed songs. We model the distribution of pair-wise distances between shingles by two independent processes - one corresponding to remix content and the other corresponding to non-remix content in a database. A nearest neighbour algorithm groups songs if they share shingles drawn from the remix process. Our results show 1) log-chromagram shingles separate remixed from non-remixed content with 75%-75% precision-recall performance, cepstral coefficient features do not separate the two distributions adequately 2) increasing the observations from the remix distribution increases the separability. Efficient implementation follows from the separability of the distributions using locality sensitive hashing (LSH) which speeds up automatic grouping of remixes by between one to two orders of magnitude in a 2018-song test set.


title: Design and Experimental Study of 4H-SiC Trenched Junction Barrier Schottky Diodes

abstract: This paper presents the design, fabrication, and experimental analysis of 1200 V 4H-SiC trenched junction barrier Schottky (TJBS) diodes. Design considerations and device performances of the TJBS devices are compared with those of the conventional planar JBS diodes via numerical simulation, analytical modeling, and experiments. It was found that, for conventional planar JBS diodes, due to its limited P+ implantation depth, there is a tight tradeoff between forward ON-resistance and blocking voltage. This does not only put stringent requirement on obtaining a narrow photolithography line width ( \(\sim 1.5\) \(\mu \) m), but also makes the device design window narrow. The TJBS diodes can substantially alleviate such tradeoff and obtain a larger design window that enables good reverse blocking and forward conduction capabilities at the same time. As a result, this structure demands less restriction on line width control (2.2–3.2 \(\mu \) m) of the fabrication process and hence can improve the device yield.


title: Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry

abstract: The goal of this paper is to show how co-word analysis techniques can be used to study interactions between academic and technological research. It is based upon a systematic content analysis of publications in the polymer science field over a period of 15 years. The results concern a.) the evolution of research in different subject areas and the patterns of their interaction; b.) a description of subject area “life cycles”; c.) an analysis of ”research trajectories” given factors of stability and change in a research network; d.) the need to use both science push and technology pull theories to explain the interaction dynamics of a research field. The co-word techniques developed in this paper should help to build a bridge between research in scientometrics and work underway to better understand the economics of innovation.


title: R EPORTS Forecasting Agriculturally Driven Global Environmental Change

abstract: During the next 50 years, which is likely to be the final period of rapid agricultural expansion, demand for food by a wealthier and 50% larger global population will be a major driver of global environmental change. Should past dependences of the global environmental impacts of agriculture on human population and consumption continue, 10(9) hectares of natural ecosystems would be converted to agriculture by 2050. This would be accompanied by 2.4- to 2.7-fold increases in nitrogen- and phosphorus-driven eutrophication of terrestrial, freshwater, and near-shore marine ecosystems, and comparable increases in pesticide use. This eutrophication and habitat destruction would cause unprecedented ecosystem simplification, loss of ecosystem services, and species extinctions. Significant scientific advances and regulatory, technological, and policy changes are needed to control the environmental impacts of agricultural expansion.


title: Low-Frequency Power Decoupling in Single-Phase Applications: A Comprehensive Overview

abstract: This paper presents a literature overview of all techniques proposed until the submission of this paper in terms of mitigating power oscillation in single-phase applications. This pulsating energy is the major factor for increasing the size of passive components and power losses in the converter and can be responsible for losses or malfunctioning of the dc sources. Reduction of power ripple at twice the fundamental frequency is one of the key elements to increase power converter density without lack of dc stiffness. Pulsation reduction is achieved by incorporating control techniques or auxiliary circuitries with energy storage capability in reactive elements to avoid this oscillating power to propagate through the converter, creating what is called as single-phase power decoupling. The topologies are divided as: rectifiers, inverters, and bidirectional. Among them, it is possible to classify as isolated and nonisolated converters. The energy storage method may be classify as: capacitive and inductive. For the power decoupling technique, it is convenient to divide as control and topology. The power decoupling technique may be implemented as series or parallel with respect to the ac, dc or link side. This paper represents the best reference on this topic.


title: Demographic Factors and Playing Variables in Online Computer Gaming

abstract: Despite the growing popularity of online game playing, there has been no primary survey of its players. Therefore, an online questionnaire survey was used to examine basic demographic factors of online computer game players who played the popular online game Everquest (i.e., gender, age, marital status, nationality, education level, occupation). The survey also examined playing frequency (i.e., amount of time spent playing the game a week), playing history (i.e., how long they had been playing the game, who they played the game with, whether they had ever gender swapped their game character), the favorite and least favorite aspects of playing the game, and what they sacrifice (if anything) to play the game. Results showed that 81% of online game players were male, and that the mean age of players was 27.9 years of age. For many players, the social aspects of the game were the most important factor in playing. A small minority of players appear to play excessively (over 80 h a week), and results suggest th...


title: Improved Computation for Levenberg–Marquardt Training

abstract: The improved computation presented in this paper is aimed to optimize the neural networks learning process using Levenberg-Marquardt (LM) algorithm. Quasi-Hessian matrix and gradient vector are computed directly, without Jacobian matrix multiplication and storage. The memory limitation problem for LM training is solved. Considering the symmetry of quasi-Hessian matrix, only elements in its upper/lower triangular array need to be calculated. Therefore, training speed is improved significantly, not only because of the smaller array stored in memory, but also the reduced operations in quasi-Hessian matrix calculation. The improved memory and time efficiencies are especially true for large sized patterns training.


title: Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning

abstract: This article introduces a new non-linear dictionary learning method for histograms in the probability simplex. The method leverages optimal transport theory, in the sense that our aim is to reconstruct histograms using so called displacement interpolations (a.k.a. Wasserstein barycenters) between dictionary atoms; such atoms are themselves synthetic histograms in the probability simplex. Our method simultaneously estimates such atoms, and, for each datapoint, the vector of weights that can optimally reconstruct it as an optimal transport barycenter of such atoms. Our method is computationally tractable thanks to the addition of an entropic regularization to the usual optimal transportation problem, leading to an approximation scheme that is efficient, parallel and simple to differentiate. Both atoms and weights are learned using a gradient-based descent method. Gradients are obtained by automatic differentiation of the generalized Sinkhorn iterations that yield barycenters with entropic smoothing. Because of its formulation relying on Wasserstein barycenters instead of the usual matrix product between dictionary and codes, our method allows for non-linear relationships between atoms and the reconstruction of input data. We illustrate its application in several different image processing settings.


title: Cross-domain Semantic Parsing via Paraphrasing

abstract: Existing studies on semantic parsing mainly focus on the in-domain setting. We formulate cross-domain semantic parsing as a domain adaptation problem: train a semantic parser on some source domains and then adapt it to the target domain. Due to the diversity of logical forms in different domains, this problem presents unique and intriguing challenges. By converting logical forms into canonical utterances in natural language, we reduce semantic parsing to paraphrasing, and develop an attentive sequence-to-sequence paraphrase model that is general and flexible to adapt to different domains. We discover two problems, small micro variance and large macro variance, of pre-trained word embeddings that hinder their direct use in neural networks, and propose standardization techniques as a remedy. On the popular Overnight dataset, which contains eight domains, we show that both cross-domain training and standardized pre-trained word embeddings can bring significant improvement.


title: Reactive avoidance using embedded stereo vision for MAV flight

abstract: High speed, low latency obstacle avoidance is essential for enabling Micro Aerial Vehicles (MAVs) to function in cluttered and dynamic environments. While other systems exist that do high-level mapping and 3D path planning for obstacle avoidance, most of these systems require high-powered CPUs on-board or off-board control from a ground station.


title: Transmission grating velocimeter for particles electric mobility measurements

abstract: A novel optical velocimeter has been designed in order to measure with a high resolution electrophoretic mobility distributions of suspended particles. It uses a transmission grating on which the dark‐field image of the particles is focused. A spectral analysis of the transmitted time‐modulated light intensity allows the determination of the velocity spectrum. The principle, a description of the apparatus, and some results are given. The velocity resolution is significantly better than with the laser Doppler techniques for different reasons which are discussed.


title: A collaborative representation based projections method for feature extraction

abstract: In graph embedding based methods, we usually need to manually choose the nearest neighbors and then compute the edge weights using the nearest neighbors via L2 norm (e.g. LLE). It is difficult and unstable to manually choose the nearest neighbors in high dimensional space. So how to automatically construct a graph is very important. In this paper, first, we give a L2-graph like L1-graph. L2-graph calculates the edge weights using the total samples, avoiding manually choosing the nearest neighbors; second, a L2-graph based feature extraction method is presented, called collaborative representation based projections (CRP). Like SPP, CRP aims to preserve the collaborative representation based reconstruction relationship of data. CRP utilizes a L2 norm graph to characterize the local compactness information. CRP maximizes the ratio between the total separability information and the local compactness information to seek the optimal projection matrix. CRP is much faster than SPP since CRP calculates the objective function with L2 norm while SPP calculate the objective function with L1 norm. Experimental results on FERET, AR, Yale face databases and the PolyU finger-knuckle-print database demonstrate that CRP works well in feature extraction and leads to a good recognition performance. We give a L2 norm graph based on collaborative representation.We propose a collaborative representation based projections (CRP) for feature extraction.CRP is a Rayleigh quotient form and can be calculated via generalized eigenvalue decomposition.


title: Sudokus and Gröbner bases: not only a Divertimento

abstract: Sudoku is a logic-based placement puzzle. We recall how to translate this puzzle into a 9-colouring problem which is equivalent to a (big) algebraic system of polynomial equations. We study how far Grobner bases techniques can be used to treat these systems produced by Sudokus. This general purpose tool can not. be considered as a good solver, but we show that it can be useful to provide information on systems that are -in spite of their origin- - hard to solve.


title: A bayesian approach to filtering junk E-mail, in: Learning for Text Categorization

abstract: In addressing the growing problem of junk E-mail on the Internet, we examine methods for the automated construction of filters to eliminate such unwanted messages from a user’s mail stream. By casting this problem in a decision theoretic framework, we are able to make use of probabilistic learning methods in conjunction with a notion of differential misclassification cost to produce filters Which are especially appropriate for the nuances of this task. While this may appear, at first, to be a straight-forward text classification problem, we show that by considering domain-specific features of this problem in addition to the raw text of E-mail messages, we can produce much more accurate filters. Finally, we show the efficacy of such filters in a real world usage scenario, arguing that this technology is mature enough for deployment.


title: BIM: Enabling Sustainability and Asset Management through Knowledge Management

abstract: Building Information Modeling (BIM) is the use of virtual building information models to develop building design solutions and design documentation and to analyse construction processes. Recent advances in IT have enabled advanced knowledge management, which in turn facilitates sustainability and improves asset management in the civil construction industry. There are several important qualifiers and some disadvantages of the current suite of technologies. This paper outlines the benefits, enablers, and barriers associated with BIM and makes suggestions about how these issues may be addressed. The paper highlights the advantages of BIM, particularly the increased utility and speed, enhanced fault finding in all construction phases, and enhanced collaborations and visualisation of data. The paper additionally identifies a range of issues concerning the implementation of BIM as follows: IP, liability, risks, and contracts and the authenticity of users. Implementing BIM requires investment in new technology, skills training, and development of new ways of collaboration and Trade Practices concerns. However, when these challenges are overcome, BIM as a new information technology promises a new level of collaborative engineering knowledge management, designed to facilitate sustainability and asset management issues in design, construction, asset management practices, and eventually decommissioning for the civil engineering industry.


title: Predictors and Outcomes of Postpartum Mothers' Perceptions of Readiness for Discharge after Birth

abstract: ABSTRACT Objective To identify predictors and outcomes of postpartum mothers' perceptions of their readiness for hospital discharge. Design A correlational design with path analyses was used to explore predictive relationships among transition theory-related variables. Setting Midwestern tertiary perinatal center. Participants One hundred and forty-one mixed-parity postpartum mothers who had experienced vaginal birth or Cesarean delivery of normal healthy infants. Methods Before hospital discharge, patients completed questionnaires about sociodemographic characteristics, hospitalization factors, quality of discharge teaching, and readiness for discharge. Three weeks postdischarge, mothers were contacted by telephone to collect coping difficulty and health care utilization data. Main Outcome Measures Readiness for Hospital Discharge Scale, Post-Discharge Coping Difficulty Scale, Utilization of postdischarge services. Results Quality of discharge teaching, specifically the relative difference in the amount of informational content needed and received and the skills of nurses in delivering discharge teaching, explained 38% of the variance in postpartum mothers' perceptions of discharge readiness. Readiness for discharge scores explained 22% of the variance in postdischarge coping difficulty scores. Nurses' skills in delivery of discharge teaching, coping difficulty, patient characteristics, and birth hospitalization factors were predictive of utilization of family support and postdischarge health care services. Conclusion A trajectory of influence was evident in the sequential relationships of quality of discharge teaching, readiness for discharge, postdischarge coping, and utilization of family support and health care services. Transitions theory provided a useful framework for conceptualizing and investigating the transition home after childbirth.


title: Bimodal Modelling of Source Code and Natural Language

abstract: We consider the problem of building probabilistic models that jointly model short natural language utterances and source code snippets. The aim is to bring together recent work on statistical modelling of source code and work on bimodal models of images and natural language. The resulting models are useful for a variety of tasks that involve natural language and source code. We demonstrate their performance on two retrieval tasks: retrieving source code snippets given a natural language query, and retrieving natural language descriptions given a source code query (i.e., source code captioning). Experiments show there to be promise in this direction, and that modelling the structure of source code improves performance.


title: Fuzzy line graphs

abstract: Abstract The notion of a fuzzy line graph of a fuzzy graph is introduced. We give a necessary and sufficient condition for a fuzzy graph to be isomorphic to its corresponding fuzzy line graph. We examine when an isomorphism between two fuzzy graphs follows from an isomorphism of their corresponding fuzzy line graphs. We give a necessary and sufficient condition for a fuzzy graph to be the fuzzy line graph of some fuzzy graph.


title: Synthetic aperture radar interferometry

abstract: Synthetic aperture radar interferometry is an imaging technique for measuring the topography of a surface, its changes over time, and other changes in the detailed characteristic of the surface. By exploiting the phase of the coherent radar signal, interferometry has transformed radar remote sensing from a largely interpretive science to a quantitative tool, with applications in cartography, geodesy, land cover characterization, and natural hazards. This paper reviews the techniques of interferometry, systems and limitations, and applications in a rapidly growing area of science and engineering.


title: Predicting Delivery Capability in Iterative Software Development

abstract: Iterative software development has become widely practiced in industry. Since modern software projects require fast, incremental delivery for every iteration of software development, it is essential to monitor the execution of an iteration, and foresee a capability to deliver quality products as the iteration progresses. This paper presents a novel, data-driven approach to providing automated support for project managers and other decision makers in predicting delivery capability for an ongoing iteration. Our approach leverages a history of project iterations and associated issues, and in particular, we extract characteristics of previous iterations and their issues in the form of features. In addition, our approach characterizes an iteration using a novel combination of techniques including feature aggregation statistics, automatic feature learning using the Bag-of-Words approach, and graph-based complexity measures. An extensive evaluation of the technique on five large open source projects demonstrates that our predictive models outperform three common baseline methods in Normalized Mean Absolute Error and are highly accurate in predicting the outcome of an ongoing iteration.


title: Adaptive support-weight approach for correspondence search

abstract: We present a new window-based method for correspondence search using varying support-weights. We adjust the support-weights of the pixels in a given support window based on color similarity and geometric proximity to reduce the image ambiguity. Our method outperforms other local methods on standard stereo benchmarks.


title: Building text classifiers using positive and unlabeled examples

abstract: We study the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. We first introduce some new methods for the two steps, and perform a comprehensive evaluation of all possible combinations of methods of the two steps. We then propose a more principled approach to solving the problem based on a biased formulation of SVM, and show experimentally that it is more accurate than the existing techniques.


title: Genetically programmed learning classifier system description and results

abstract: An agent population can be evolved in a complex environment to perform various tasks and optimize its job performance using Learning Classifier System (LCS) technology. Due to the complexity and knowledge content of some real-world systems, having the ability to use genetic programming, GP, to represent the LCS rules provides a great benefit. Methods have been created to extend LCS theory into operation across the power-set of GP-enabled rule content. This system uses a full bucket-brigade system for GP-LCS learning. Using GP in the LCS system allows the functions and terminals of the actual problem environment to be used internally directly in the rule set, enabling more direct interpretation of the operation of the LCS system. The system was designed and built, and underwent a year of independent testing at an advanced technology research laboratory. This paper describes the top-level operation of the system, and includes some of the results of the testing effort, and performance figures.


title: On Provably Safe Obstacle Avoidance for Autonomous Robotic Ground Vehicles

abstract: Nowadays, robots interact more frequently with a dynamic environment outside limited manufacturing sites and in close proximity with humans. Thus, safety of motion and obstacle avoidance are vital safety features of such robots. We formally study two safety properties of avoiding both stationary and moving obstacles: (i) passive safety, which ensures that no collisions can happen while the robot moves, and (ii) the stronger passive friendly safety in which the robot further maintains sufficient maneuvering distance for obstacles to avoid collision as well. We use hybrid system models and theorem proving techniques that describe and formally verify the robot’s discrete control decisions along with its continuous, physical motion. Moreover, we formally prove that safety can still be guaranteed despite location and actuator uncertainty.


title: Automatic ARIMA time series modeling for adaptive I/O prefetching

abstract: Inadequate I/O performance remains a major challenge in using high-end computing systems effectively. To address this problem, we present TsModeler, an automatic time series modeling and prediction framework for adaptive I/O prefetching that uses ARIMA time series models to predict the temporal patterns of I/O requests. These online pattern analysis techniques and cutoff indicators for autocorrelation patterns enable multistep online predictions suitable for multiblock prefetching. This work also combines time series predictions with spatial Markov model predictions to determine when, what, and how many blocks to prefetch. Experimental results show reductions in execution time compared to the standard Linux file system across various hardware configurations.


title: Biomass for energy in developing countries

abstract: Biomass, like fossil fuels, can provide cooking and heating energy, electricity, chemicals and liquid fuels. Today about 14% of the worldwide primary energy supply is provided by biomass resources — equivalent to 1000 million tons oil each year. Most of this biomass use occurs in rural areas of developing countries where half the world's population lives. For example Kenya derives about 75%, India 50%, China 33% and Brazil 25% of their total energy from biomass, A number of industrialized countries also derive a considerable amount of energy from biomass, such as Finland 18%, Ireland 16%, Sweden 9% and USA 3%. World ependiture on biomass programmes exceeds S 2 bn/yr; several natioal biomass energy programmes are discussed. Biomass resources and conversion technologies are described, as are the factors necessary for successful regional implementation of biomass energy schemes.


title: The GNOME Project : a Case Study of Open Source , Global Software Development Research Section

abstract: Many successful free/open source software (FOSS) projects start with the premise that their contributors are rarely colocated, and as a consequence, these projects are cases of global software development (GSD). This article describes how the GNOME Project, a large FOSS project, has tried to overcome the disadvantages of GSD. The main goal of GNOME is to create a GUI desktop for Unix systems, and encompasses close to two million lines of code. More than 500 individuals (distributed across the world) have contributed to the project. This article also describes the software development methods and practices used by the members of the project, and its organizational structure. The article ends by proposing a list of practices that could benefit other global software development projects, both FOSS and commercial. Copyright © 2004 John Wiley & Sons, Ltd.


title: Dynamics of Consumer Demand for New Durable Goods

abstract: This paper specifies and estimates a dynamic model of consumer preferences for new durable goods with persistent heterogeneous consumer tastes, rational expectations about future products and repeat purchases over time. Most new consumer durable goods, particularly consumer electronics, are characterized by relatively high initial prices followed by rapid declines in prices and improvements in quality. The evolving nature of product attributes suggests the importance of modeling dynamics in estimating consumer preferences. We estimate the model on the digital camcorder industry using a panel data set on prices, sales and characteristics. We find that dynamics are a very important determinant of consumer preferences and that estimated coefficients are more plausible than with traditional static models. We use the estimates to investigate the value of new consumer goods and intertemporal elasticities of demand.


title: An item response theory analysis of self-report measures of adult attachment.

abstract: Self-report measures of adult attachment are typically scored in ways (e.g., averaging or summing items) that can lead to erroneous inferences about important theoretical issues, such as the degree of continuity in attachment security and the differential stability of insecure attachment patterns. To determine whether existing attachment scales suffer from scaling problems, the authors conducted an item response theory (IRT) analysis of 4 commonly used self-report inventories: Experiences in Close Relationships scales (K. A. Brennan, C. L. Clark, & P. R. Shaver, 1998), Adult Attachment Scales (N. L. Collins & S. J. Read, 1990), Relationship Styles Questionnaire (D. W. Griffin & K. Bartholomew, 1994) and J. Simpson's (1990) attachment scales. Data from 1,085 individuals were analyzed using F. Samejima's (1969) graded response model. The authors' findings indicate that commonly used attachment scales can be improved in a number of important ways. Accordingly, the authors show how IRT techniques can be used to develop new attachment scales with desirable psychometric properties.


title: Brain Tumor Segmentation on MR Image Using K-Means and Fuzzy-Possibilistic Clustering

abstract: Automated tumor segmentation and estimation from the magnetic resonance imaging (MRI) is a very crucial task from medical point of view due to high varieties of tumor tissues. The advantage of using the MR images is to provide the anatomical structure of the brain that plays a significant role during automated brain tumor detection. In this work, a method for brain tumor segmentation from MR images is proposed which is based on fuzzy-possibilistic C-means (FPCM) and shape based topological properties to identify the exact tumor region. A patch based K-means method is also implemented for skull stripping (brain tissue extraction) as a preprocessing step. Experimental results show that the proposed method has achieved better performance based on volume metrics than previous state-of-the-art algorithms with respect to ground truth (manual segmentation) on MRI standard benchmark datasets.


title: A Survey on Deep Learning: Algorithms, Techniques, and Applications

abstract: The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.


Child and Adolescent Behavior Inventory (CABI): Standardization for Age 6-17 Years and First Clinical Application


Effectiveness of teaching facial anatomy through cadaver dissection on aesthetic physicians’ knowledge


The healthy human microbiome


title: Modelling physiological deterioration in post-operative patient vital-sign data

abstract: Patients who undergo upper-gastrointestinal surgery have a high incidence of post-operative complications, often requiring admission to the intensive care unit several days after surgery. A dataset comprising observational vital-sign data from 171 post-operative patients taking part in a two-phase clinical trial at the Oxford Cancer Centre, was used to explore the trajectory of patients' vital-sign changes during their stay in the post-operative ward using both univariate and multivariate analyses. A model of normality based vital-sign data from patients who had a "normal" recovery was constructed using a kernel density estimate, and tested with "abnormal" data from patients who deteriorated sufficiently to be re-admitted to the intensive care unit. The vital-sign distributions from "normal" patients were found to vary over time from admission to the post-operative ward to their discharge home, but no significant changes in their distributions were observed from halfway through their stay on the ward to the time of discharge. The model of normality identified patient deterioration when tested with unseen "abnormal" data, suggesting that such techniques may be used to provide early warning of adverse physiological events.


title: Efficient Skyline Computation in MapReduce

abstract: Skyline queries are useful for nding interesting tuples from a large data set according to multiple criteria. The sizes of data sets are constantly increasing and the architecture of back-ends are switching from single-node environments to non-conventional paradigms like MapReduce. Despite the usefulness of skyline queries, existing works on skyline computation in MapReduce do not take full advantage of parallelism but still run signicant parts serially. In this paper, we propose a novel approach to compute skylines eciently in MapReduce. We design a grid partitioning scheme to divide the data space into partitions, and employ a bitstring to represent the partitions. The bitstring is eciently obtained in MapReduce, and it clearly helps prune partitions (and tuples) that cannot have skyline tuples. Based on the grid partitioning, we propose two MapReduce algorithms to compute skylines. Both algorithms utilize the bitstring and distribute the original tuples to multiple mappers and make use of them to compute local skylines in parallel. In particular, MapReduce Grid Partitioning based Single-Reducer Skyline Computation (MR-GPSRS) employs a single reducer to assemble the local skylines appropriately to compute the global skyline. In contrast, MapReduce Grid Partitioning based Multiple Reducer Skyline Computation (MR-GPMRS) further divides local skylines and distributes them to multiple reducers that compute the global skyline in an independent and parallel manner. The proposed algorithms are evaluated through extensive experiments, and the results show that MR-GPMRS signicantly outperforms the alternatives in various settings.


title: Graph Embedded Nonparametric Mutual Information for Supervised Dimensionality Reduction

abstract: In this paper, we propose a novel algorithm for dimensionality reduction that uses as a criterion the mutual information (MI) between the transformed data and their corresponding class labels. The MI is a powerful criterion that can be used as a proxy to the Bayes error rate. Furthermore, recent quadratic nonparametric implementations of MI are computationally efficient and do not require any prior assumptions about the class densities. We show that the quadratic nonparametric MI can be formulated as a kernel objective in the graph embedding framework. Moreover, we propose its linear equivalent as a novel linear dimensionality reduction algorithm. The derived methods are compared against the state-of-the-art dimensionality reduction algorithms with various classifiers and on various benchmark and real-life datasets. The experimental results show that nonparametric MI as an optimization objective for dimensionality reduction gives comparable and in most of the cases better results compared with other dimensionality reduction methods.


title: COMPUTER ORGANIZATION: Architecture 1. Architecture

abstract: The term Computer Architecture was first defined in the paper by Amdahl, Blaauw and Brooks of IBM Corporation announcing IBM System/360 computer family on April 7, 1964. On that day IBM Corporation introduced, in the words of IBM spokesman, "the most important product announcement that this corporation has made in its history". There were six models introduced originally, ranging in performance from 25 to 1. Six years later this performance range was increased to about 200 to 1. This was the key feature which prompted IBM's effort to design an architecture for a new line of computers that are to be code compatible with each other. The recognition that architecture and implementation could be separated and that one need not imply the other led to establishment of a common System/360 machine architecture implemented in the range of models.


title: HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences

abstract: We present a new descriptor for activity recognition from videos acquired by a depth sensor. Previous descriptors mostly compute shape and motion features independently, thus, they often fail to capture the complex joint shape-motion cues at pixel-level. In contrast, we describe the depth sequence using a histogram capturing the distribution of the surface normal orientation in the 4D space of time, depth, and spatial coordinates. To build the histogram, we create 4D projectors, which quantize the 4D space and represent the possible directions for the 4D normal. We initialize the projectors using the vertices of a regular polychoron. Consequently, we refine the projectors using a discriminative density measure, such that additional projectors are induced in the directions where the 4D normals are more dense and discriminative. Through extensive experiments, we demonstrate that our descriptor better captures the joint shape-motion cues in the depth sequence, and thus outperforms the state-of-the-art on all relevant benchmarks.


title: Style Transfer in Text: Exploration and Evaluation

abstract: Style transfer is an important problem in natural language processing (NLP). However, the progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data and principle evaluation metrics. In this paper, we propose to learn style transfer with non-parallel data. We explore two models to achieve this goal, and the key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. We also propose novel evaluation metrics which measure two aspects of style transfer: transfer strength and content preservation. We access our models and the evaluation metrics on two tasks: paper-news title transfer, and positive-negative review transfer. Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.


Determinants and outcome of repeat continuous electroencephalogram monitoring—A case‐control study


title: Jigsaw panel: a tangible approach for delightful human-computer interaction

abstract: In this paper, we propose jigsaw panel system that aims to build a delightful human-computer interaction in the multimedia authoring. Jigsaw panel employs the concept of tangible user interface (TUI). In the most of the current TUI researches, the physical objects to operate virtual objects or functions are designed in abstract shapes and they pay very few attentions to the affordances that the physical objects have. In Jigsaw panel, user can edit multimedia data by connecting or layout the physical transparent panels which are assigned multimedia data or computational functions. These panels have shapes just like a jigsaw puzzle, and users can receive the affordances by their shapes this allows the users to intuitively find how to use computational functions and what conditions are required to use them. In the experiments to evaluate the jigsaw panel, we found internal delight obtained by the interaction with tasks or contents are very important to accomplish the tasks, while external delight provided by the interaction of the computers functions only in the beginning of the task. We also discussed, in this paper, about the factors to make the internal delight spring up in users' minds.


title: Automatic vehicle identification for Argentinean license plates using intelligent template matching

abstract: The problem of automatic number plate recognition (ANPR) has been studied from different aspects since the early 90s. Efficient approaches have been recently developed, particularly based on the features of the license plate representation used in different countries. This paper focuses on a novel approach to solving the ANPR problem for Argentinean license plates, called Intelligent Template Matching (ITM). We compare the performance obtained with other competitive approaches to robust pattern recognition (such as artificial neural networks), showing the advantages both in classification accuracy and training time. The approach can also be easily extended to other license plate representation systems different from the one used in Argentina.


title: Tensor decomposition via joint matrix schur decomposition

abstract: We describe an approach to tensor decomposition that involves extracting a set of observable matrices from the tensor and applying an approximate joint Schur decomposition on those matrices, and we establish the corresponding first-order perturbation bounds. We develop a novel iterative Gauss-Newton algorithm for joint matrix Schur decomposition, which minimizes a nonconvex objective over the manifold of orthogonal matrices, and which is guaranteed to converge to a global optimum under certain conditions. We empirically demonstrate that our algorithm is faster and at least as accurate and robust than state-of-the-art algorithms for this problem.


title: Impact of Sentiment Detection to Recognize Toxic and Subversive Online Comments

abstract: The presence of toxic content has become a major problem for many online communities. Moderators try to limit this problem by implementing more and more refined comment filters, but toxic users are constantly finding new ways to circumvent them. Our hypothesis is that while modifying toxic content and keywords to fool filters can be easy, hiding sentiment is harder. In this paper, we explore various aspects of sentiment detection and their correlation to toxicity, and use our results to implement a toxicity detection tool. We then test how adding the sentiment information helps detect toxicity in three different real-world datasets, and incorporate subversion to these datasets to simulate a user trying to circumvent the system. Our results show sentiment information has a positive impact on toxicity detection against a subversive user.


title: A Neural Network-Based Ensemble Approach for Spam Detection in Twitter

abstract: As the social networking sites get more popular, spammers target these sites to spread spam posts. Twitter is one of the most popular online social networking sites where users communicate and interact on various topics. Most of the current spam filtering methods in Twitter focus on detecting the spammers and blocking them. However, spammers can create a new account and start posting new spam tweets again. So there is a need for robust spam detection techniques to detect the spam at tweet level. These types of techniques can prevent the spam in real time. To detect the spam at tweet level, often features are defined, and appropriate machine learning algorithms are applied in the literature. Recently, deep learning methods are showing fruitful results on several natural language processing tasks. We want to use the potential benefits of these two types of methods for our problem. Toward this, we propose an ensemble approach for spam detection at tweet level. We develop various deep learning models based on convolutional neural networks (CNNs). Five CNNs and one feature-based model are used in the ensemble. Each CNN uses different word embeddings (Glove, Word2vec) to train the model. The feature-based model uses content-based, user-based, and n-gram features. Our approach combines both deep learning and traditional feature-based models using a multilayer neural network which acts as a meta-classifier. We evaluate our method on two data sets, one data set is balanced, and another one is imbalanced. The experimental results show that our proposed method outperforms the existing methods.