title: Aligning Business and IT Strategies in Multi-Business Organizations.

abstract: The alignment of business and information technology (IT) strategies is an important and enduring theoretical challenge for the information systems discipline, remaining a top issue in practice over the past 20 years. Multi-business organizations (MBOs) present a particular alignment challenge because business strategies are developed at the corporate level, within individual strategic business units and across the corporate investment cycle. In contrast, the extant literature implicitly assumes that IT strategy is aligned with a single business strategy at a single point in time. This paper draws on resource-based theory and path dependence to model functional, structural, and temporal IT strategic alignment in MBOs. Drawing on Makadok’s theory of profit, we show how each form of alignment creates value through the three strategic drivers of competence, governance, and flexibility, respectively. We illustrate the model with examples from a case study on the Commonwealth Bank of Australia. We also explore the model’s implications for existing IT alignment models, providing alternative theoretical explanations for how IT alignment creates value.


title: 3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks

abstract: The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sensor data. Inspired by the nature of human perception of 3D shapes as a collection of simple parts, we explore such an abstract shape representation based on primitives. Given a single depth image of an object, we present 3D-PRNN, a generative recurrent neural network that synthesizes multiple plausible shapes composed of a set of primitives. Our generative model encodes symmetry characteristics of common man-made objects, preserves long-range structural coherence, and describes objects of varying complexity with a compact representation. We also propose a method based on Gaussian Fields to generate a large scale dataset of primitive-based shape representations to train our network. We evaluate our approach on a wide range of examples and show that it outperforms nearest-neighbor based shape retrieval methods and is on-par with voxel-based generative models while using a significantly reduced parameter space.


title: Self-citation and self-reference: credibility and promotion in academic publication

abstract: Author self-citation has long been of interest to those working in informetrics for what it reveals about the publishing behavior of individuals and their relationships within academic networks. While this research has produced interesting insights, it typically assumes either that self-citation is a neutral form of reporting not unlike references to others' work or an unsavory kind of academic egotism. By examining self-citation in a wider context of self-mention, however, the phenomenon can be seen as part of a more comprehensive rhetorical strategy for emphasizing a writer's personal contribution to a piece of research and strengthening his or her knowledge claims, research credibility, and wider standing in the discipline. These meanings are not easily revealed through quantitative bibliometric methods and require careful text analyses and discourse-based interviews with academics. In this paper I explore the use of self-citation and authorial mention in a corpus of 240 research articles and 800 abstracts in eight disciplines. Through an analysis of these texts and interviews with expert informants I show how self-mention is used and the ways these uses reflect both the promotional strategies of individuals and the epistemological practices of their disciplines.


title: Thelazia rhodesii in the African Buffalo, Syncerus caffer, in Zambia

abstract: We report 2 cases of Thelazia rhodesii infection in the African buffaloes, Syncerus caffer, in Zambia. African buffalo calves were captured from the livestock and wildlife interface area of the Kafue basin in the dry season of August 2005 for the purpose to translocate to game ranches. At capture, calves (n=48) were examined for the presence of eye infections by gently manipulating the orbital membranes to check for eye-worms in the conjunctival sacs and corneal surfaces. Two (4.3%) were infected and the mean infection burden per infected eye was 5.3 worms (n=3). The mean length of the worms was 16.4 mm (95% CI; 14.7-18.2 mm) and the diameter 0.41 mm (95% CI; 0.38-0.45 mm). The surface cuticle was made of transverse striations which gave the worms a characteristic serrated appearance. Although the calves showed signs of kerato-conjunctivitis, the major pathological change observed was corneal opacity. The calves were kept in quarantine and were examined thrice at 30 days interval. At each interval, they were treated with 200 µg/kg ivermectin, and then translocated to game ranches. Given that the disease has been reported in cattle and Kafue lechwe (Kobus lechwe kafuensis) in the area, there is a need for a comprehensive study which aims at determining the disease dynamics and transmission patterns of thelaziasis between wildlife and livestock in the Kafue basin.


title: 60-GHz 5-bit Phase Shifter With Integrated VGA Phase-Error Compensation

abstract: A 57-64-GHz low phase-error 5-bit switch-type phase shifter integrated with a low phase-variation variable gain amplifier (VGA) is implemented through TSMC 90-nm CMOS low-power technology. Using the phase compensation technique, the proposed VGA can provide appropriate gain tuning with almost constant phase characteristics, thus greatly reducing the phase-tuning complexity in a phased-array system. The measured root mean square (rms) phase error of the 5-bit phase shifter is 2° at 62 GHz. The phase shifter has a low group-delay deviation (phase distortion) of +/- 8.5 ps and an excellent insertion loss flatness of ±0.8 dB for a specific phase-shifting state, across 57-64 GHz. For all 32 states, the insertion loss is 14.6 ± 3 dB, including pad loss at 60 GHz. For the integrated phase shifter and VGA, the VGA can provide 6.2-dB gain tuning range, which is wide enough to cover the loss variation of the phase shifter, with only 1.86° phase variation. The measured rms phase error of the 5-bit phase shifter and VGA is 3.8° at 63 GHz. The insertion loss of all 32 states is 5.4 dB, including pad loss at 60 GHz, and the loss flatness is ±0.8 dB over 57-64 GHz. To the best of our knowledge, the 5-bit phase shifter presents the best rms phase error at center frequency among the V-band switch-type phase shifter.


title: FPGA-SOPC based motion controller with ACC/DEC using digital convolution

abstract: Acceleration/deceleration (ACC/DEC) motion planning is used to generate smooth motion command. This study focuses on realizing trapezoidal, S-curve and fourth-order ACC/DEC planning using digital convolution. To test and verify the proposed ACC/DEC method, a motion controller is designed and implemented. The FPGA (Field Programmable Gate Array) is used to implement the measurement unit and pulse generator, while the SOPC (System on a Programmable Chip) is used to realize the trajectory generator and PID controller. The FPGA-SOPC based motion controller can improve the integration level of the motion control system and meet the real-time requests. Simulation and experiment results verify that the digital convolution method is effective and the FPGA-SOPC based motion controller is feasible.


title: A biomechanical approach to iris normalization

abstract: The richness of the iris texture and its variability across individuals make it a useful biometric trait for personal authentication. One of the key stages in classical iris recognition is the normalization process, where the annular iris region is mapped to a dimensionless pseudo-polar coordinate system. This process results in a rectangular structure that can be used to compensate for differences in scale and variations in pupil size. Most iris recognition methods in the literature adopt linear sampling in the radial and angular directions when performing iris normalization. In this paper, a biomechanical model of the iris is used to define a novel nonlinear normalization scheme that improves iris recognition accuracy under different degrees of pupil dilation. The proposed biomechanical model is used to predict the radial displacement of any point in the iris at a given dilation level, and this information is incorporated in the normalization process. Experimental results on the WVU pupil light reflex database (WVU-PLR) indicate the efficacy of the proposed technique, especially when matching iris images with large differences in pupil size.


title: Activity Based Metadata for Semantic Desktop Search

abstract: With increasing storage capacities on current PCs, searching the World Wide Web has ironically become more efficient than searching one's own personal computer. The recently introduced desktop search engines are a first step towards coping with this problem, but not yet a satisfying solution. The reason for that is that desktop search is actually quite different from its web counterpart. Documents on the desktop are not linked to each other in a way comparable to the web, which means that result ranking is poor or even inexistent, because algorithms like PageRank cannot be used for desktop search. On the other hand, desktop search could potentially profit from a lot of implicit and explicit semantic information available in emails, folder hierarchies, browser cache contexts and others. This paper investigates how to extract and store these activity based context information explicitly as RDF metadata and how to use them, as well as additional background information and ontologies, to enhance desktop search.


title: Conceptual Framework for Internet of Things' Virtualization via OpenFlow in Context-aware Networks

abstract: A novel conceptual framework is presented in this paper with an aim to standardize and virtualize Internet of Things(IoT) infrastructure through deploying OpenFlow technology. The framework can receivee services based on context information leaving the current infrastructure unchanged. This framework allows the active collaboration of heterogeneous devices and protocols. Moreover it is capable to model placement of physical objects, manage the system and to collect information for services deployed on an IoT infrastructure. Our proposed IoT virtualization is applicable to a random topology scenario which makes it possible to 1) share flow sensors resources 2) establish multioperational sensor networks, and 3) extend reachability within the framework without establishing any further physical networks. Flow sensors achieve better results comparable to the typical sensors with respect to packet generation, reachability, simulation time, throughput, energy consumption point of view. Even better results are possible through utilizing multicast groups in large scale networks.


Copyright © 2008 SIWN. Dynamic Experience Management in Virtual Worlds for Entertainment, Education, and Training


title: The sequence and phylogenetic analysis of avian reovirus genome segments M1, M2, and M3 encoding the minor core protein μA, the major outer capsid protein μB, and the nonstructural protein μNS

abstract: The sequences and phylogenetic analyses of the M-class genome segments of 12 avian reovirus strains are described. The S1133 M1 genome segment is 2283 base pairs long, encoding a protein μA consisted of 732 amino acids. Each M2 or M3 genome segment of 12 avian reovirus strains is 2158 or 1996 base pairs long, respectively, encoding a protein μB or μNS consisted of 676 and 635 amino acids, respectively. The S1133 genome segment has the 5′ GCUUUU terminal motif, but each M2 and M3 genome segment displays the 5′ GCUUUUU terminal motif which is common to other known avian reovirus genome segments. The UCAUC 3′-terminal sequences of the M-class genome segments are shared by both avian and mammalian reoviruses. Noncoding regions of both 5′- and 3′-termini of the S1133 M1 genome segment consist of 12 and 72 nucleotides, respectively, those of each M2 genome segment consist of 29 and 98 nucleotides, respectively, and those of each M3 genome segment are 24 and 64 nucleotides, respectively. Analysis of the average degree of the M-class gene and the deduced μ-class protein sequence identities indicated that the M2 genes and the μB proteins have the greatest level of sequence divergence. Computer searches revealed that the μA possesses a sequence motif (NH2-Leu-Ala-Leu-Asp-Pro-Pro-Phe-COOH) (residues 458–464) indicative of N-6 adenine-specific DNA methylase. Examination of the μB amino acid sequences indicated that the cleavage site of μB into μBN and μBC is between positions 42 and 43 near the N-terminus of the protein, and this site is conserved for each protein. During in vitro treatment of virions with trypsin to yield infectious subviral particles, both the N-terminal fragment δ and the C-terminal fragment φ were shown to be generated. The site of trypsin cleavage was identified in the deduced amino acid sequence of μB by determining the amino-terminal sequences of φ proteins: between arginine 582 and glycine 583. The predicted length of δ generated from μBC is very similar to that of δ generated from mammalian reovirus μ1C. Taken together, protein μB is structurally, and probably functionally, similar to its mammalian homolog, μ1. In addition, two regions near the C-terminal and with a propensity to form α-helical coiled-coil structures as previously indicated are observed for each protein μB. Phylogenetic analysis of the M-class genes revealed that the predicted phylograms delineated 3 M1, 5 M2, and 2 M3 lineages, no correlation with serotype or pathotype of the viruses. The results also showed that M2 lineages I–V consist of a mixture of viruses from the M1 and M3 genes of lineages I–III, reflecting frequent reassortment of these genes among virus strains.


title: AutoRand: Automatic Keyword Randomization to Prevent Injection Attacks

abstract: AutoRand automatically transforms Java applications to use SQL keyword randomization to defend against SQL injection vulnerabilities. AutoRand is completely automatic. Unlike previous approaches it requires no manual modifications to existing code and does not require source it works directly on Java bytecode. It can thus easily be applied to the large numbers of existing potentially insecure applications without developer assistance. Our key technical innovation is augmented strings. Augmented strings allow extra information such as random keys to be embedded within a string. AutoRand transforms string operations so that the extra information is transparent to the program, but is always propagated with each string operation. AutoRand checks each keyword at SQL statements for the random key. Experimental results on large, production Java applications and malicious inputs provided by an independent evaluation team hired by an agency of the United States government showed that AutoRand successfully blocked all SQL injection attacks and preserved transparent execution for benign inputs, all with low overhead.


title: Health status assessment for the feed system of CNC machine tool based on simulation

abstract: This paper proposes an approach of health assessment based on the ADAMS simulation for the feed system of the CNC machine tool. Firstly, the dynamics simulation model of the feed system has been built to collect the large number of the motion parameters, and to calculate the related performance parameters according to these motion parameters. Secondly, the BP neural network model is built for the performance mapping model between the structural parameters and the related performance parameters, which can provide a large number of reference data for health assessment of the CNC machine tool. Then the Hidden Markov Chain model is used to establish a health status assessment model, which has the observation sequence of multi-state and multi-performance. Based on the HMC model, the algorithms are used to solve the health status probability for the feed system of the CNC machine tool. Finally, a case study is discussed to verify the method of health status assessment.


title: Distributed Topology Inference for Electric Power Grids

abstract: This paper proposes an algorithm, including a multiagent-based system architecture, for distributed inference of power grid topologies. The core algorithm is based on processing measurement time series, or fingerprints, using statistical correlation to determine the connectivity between nodes. Notably, the method and proposed system architecture is completely distributed. The solution has been developed with contemporary IEC 61850 compliant substation automation systems in mind. This paper includes a presentation of the theoretical foundation for the distributed topology inference algorithm as well as the proposed system architecture. A case study utilizing the RBTS bus 4 model is included to demonstrate the capabilities of the method under static as well as transient situations. Furthermore, the results and analysis of the performance and scalability of the algorithm are presented. The main contribution of this paper is an application to infer the electrical connectivity of power grids without central functionality.


title: Fast parallel surface and solid voxelization on GPUs

abstract: This paper presents data-parallel algorithms for surface and solid voxelization on graphics hardware. First, a novel conservative surface voxelization technique, setting all voxels overlapped by a mesh's triangles, is introduced, which is up to one order of magnitude faster than previous solutions leveraging the standard rasterization pipeline. We then show how the involved new triangle/box overlap test can be adapted to yield a 6-separating surface voxelization, which is thinner but still connected and gap-free. Complementing these algorithms, both a triangle-parallel and a tile-based technique for solid voxelization are subsequently presented. Finally, addressing the high memory consumption of high-resolution voxel grids, we introduce a novel octree-based sparse solid voxelization approach, where only close to the solid's boundary finest-level voxels are stored, whereas uniform interior and exterior regions are represented by coarser-level voxels. This representation is created directly from a mesh without requiring a full intermediate solid voxelization, enabling GPU-based voxelizations of unprecedented size.


title: Decoupling Abstractions of Non-linear Ordinary Differential Equations

abstract: We investigate decoupling abstractions, by which we seek to simulate (i.e. abstract) a given system of ordinary differential equations (ODEs) by another system that features completely independent (i.e. uncoupled) sub-systems, which can be considered as separate systems in their own right. Beyond a purely mathematical interest as a tool for the qualitative analysis of ODEs, decoupling can be applied to verification problems arising in the fields of control and hybrid systems. Existing verification technology often scales poorly with dimension. Thus, reducing a verification problem to a number of independent verification problems for systems of smaller dimension may enable one to prove properties that are otherwise seen as too difficult. We show an interesting correspondence between Darboux polynomials and decoupling simulating abstractions of systems of polynomial ODEs and give a constructive procedure for automatically computing the latter.


title: Impact of Graphene Interface Quality on Contact Resistance and RF Device Performance

abstract: This letter demonstrates the importance of the graphene/metal interface on the ohmic contacts of high-frequency graphene transistors grown by chemical vapor deposition (CVD) on copper. Using an Al sacrificial layer during ohmic lithography, the graphene surface roughness underneath the ohmic contacts is reduced by fourfold, resulting in an improvement in the contact resistance from 2.0 to 0.2-0.5 kΩ·μm. Using this technology, top-gated CVD graphene transistors achieved direct-current transconductances of 200 mS/mm, maximum on current densities in excess of 1000 mA/mm, and hole mobilities ~ 1500-3000 cm2/(V·s) on silicon substrates. Radio-frequency device performance yielded an extrinsic current-gain cutoff frequency fT of 12 GHz after pad capacitance de-embedding resulting in an fT - LG product of 24 GHz·μm.


title: Free-riding in BitTorrent Networks with the Large View Exploit

abstract: This paper presents an experimental study on the behavior of BitTorrent networks when selfish peers attempt to maintain high download rates without uploading. We modified a BitTorrent client so that it acquires a larger than normal view of a BitTorrent swarm and connects to all peers in its view. At the same time, the modified client does not upload any data to its peers. Our experimental results show that: a) our modified freerider client can achieve better download rates than a compliant client in most common-case public torrents; b) when the percentage of our modified free-rider clients in PlanetLab-residing torrents with ∼300 leechers is less than 40%, free-riders on average outperform compliant clients; and c) as the number of free-riders increases, both free-riders and compliant clients incur substantial performance degradation. These results suggest that the large view exploit is effective, and it has the potential for wide adoption.


title: Social and mobile interaction design to increase the loyalty rates of young blood donors

abstract: Young adults represent the largest group of first time donors to the Australian Red Cross Blood Service, but they are also the least loyal group and often do not return after their first donation. At the same time, many young people use the internet and various forms of social media on a daily basis. Web and mobile based technological practices and communication patterns change the way that young people interact with one another, with their families, and communities. Combining these two points of departure, this study seeks to identify best practices of employing mobile apps and social media in order to enhance the loyalty rates of young blood donors. The findings reported in this paper are based on a qualitative approach presenting a nuanced understanding of the different factors that motivate young people to donate blood in the first place, as well as the obstacles or issues that prevent them from returning. The paper discusses work in progress with a view to inform the development of interactive prototypes trialling three categories of features: personal services (such as scheduling); social media (such as sharing the donation experience with friends to raise awareness); and data visualisations (such as local blood inventory levels). We discuss our translation of research findings into design implications.


title: Minimum Spanning Trees in Temporal Graphs

abstract: The computation of Minimum Spanning Trees (MSTs) is a fundamental graph problem with important applications. However, there has been little study of MSTs for temporal graphs, which is becoming common as time information is collected for many existing networks. We define two types of MSTs for temporal graphs, MSTa and MSTw, based on the optimization of time and cost, respectively. We propose efficient linear time algorithms for computing MSTa. We show that computing MSTw is much harder. We design efficient approximation algorithms based on a transformation to the Directed Steiner Tree problem (DST). Our solution also solves the classical DST problem with a better time complexity and the same approximation factor compared to the state-of-the-art algorithm. Our experiments on real temporal networks further verify the effectiveness of our algorithms. For MSTw, our solution is capable of shortening the runtime from 10 hours to 3 seconds.


Laughing Metaphorically: Metaphor and Humour in Discourse


title: Reduction in gravitational torques of an industrial robot equipped with 2 DOF passive counterbalance mechanisms

abstract: In most 6 DOF robot arms, considerable amounts of gravitational torques due to the robot's own weight are applied to pitch joints of the robot, which causes most arms to use high capacity motors and speed reducers. A spring-based counterbalance mechanism can compensate for this gravitational torque, thus leading to a significant reduction in the effect of gravity. However, a simple installation of counterbalance mechanisms at each pitch joint does not work properly because the gravitational torque at each joint is dependent also on the other joints. To achieve multi-DOF counterbalancing, we propose a parallelogram linkage combined with dual counterbalance mechanisms, each being composed of a slider-crank mechanism and springs. Simulations and experimental results showed that the counterbalance robot arm based on the proposed counterbalance mechanisms effectively reduced the torques required to support the robot mass, thus allowing the prospective use of much smaller motors and speed reducers than traditional industrial robots.


title: A Novel Regression Based Clustering Technique for Wireless Sensor Networks

abstract: The objective of this paper is to develop a new adaptive iterative linear regression based clustering algorithm for wireless sensor network. According to this, the initial cluster is classified horizontally and vertically in parallel, each resulting in two sub-clusters. Of these two, the best is selected based on the proposed similarity index and with this selected cluster as reference, iteration continues until the convergence criterion `Delta' is met. The similarity index is designed based on the intra cluster similarity and inter cluster dissimilarity. Delta is the difference between the similarity index of the current iteration and the previous iteration. The proposal is implemented in MATLAB and simulations are carried out under different network scenarios. The cluster quality is evaluated through external and internal indices using the Cluster Validity Analysis Platform tool. The cluster obtained by the proposal is studied and its quality is compared with the well-established k-means and hierarchical clustering. The performance indices confirm the supremacy of the proposal.


title: Common effects of emotional valence, arousal and attention on neural activation during visual processing of pictures

abstract: Emotion and attention heighten sensitivity to visual cues. How neural activation patterns associated with emotion change as a function of the availability of attentional resources is unknown. We used positron emission tomography (PET) and 15O-water to measure brain activity in male volunteers while they viewed emotional picture sets that could be classified according to valence or arousal. Subjects simultaneously performed a distraction task that manipulated the availability of attentional resources. Twelve scan conditions were generated in a 3×2×2 factorial design involving three levels of valence (pleasant, unpleasant and neutral), two levels of arousal and two levels of attention (low and high distraction). ::: ::: Extrastriate visual cortical and anterior temporal areas were independently activated by emotional valence, arousal and attention. Common areas of activation derived from a conjunction analysis of these separate activations revealed extensive areas of activation in extrastriate visual cortex with a focus in right BA18 (12, −88, −2) (Z=5.73, P<0.001 corrected) and right anterior temporal cortex BA38 (42, 14, −30) (Z=4.03, P<0.05 corrected). These findings support an hypothesis that emotion and attention modulate both early and late stages of visual processing.


title: Multimodal fusion using dynamic hybrid models

abstract: We propose a novel hybrid model that exploits the strength of discriminative classifiers along with the representational power of generative models. Our focus is on detecting multimodal events in time varying sequences. Discriminative classifiers have been shown to achieve higher performances than the corresponding generative likelihood-based classifiers. On the other hand, generative models learn a rich informative space which allows for data generation and joint feature representation that discriminative models lack. We employ a deep temporal generative model for unsupervised learning of a shared representation across multiple modalities with time varying data. The temporal generative model takes into account short term temporal phenomena and allows for filling in missing data by generating data within or across modalities. The hybrid model involves augmenting the temporal generative model with a temporal discriminative model for event detection, and classification, which enables modeling long range temporal dynamics. We evaluate our approach on audio-visual datasets (AVEC, AVLetters, and CUAVE) and demonstrate its superiority compared to the state-of-the-art.


title: Venture Capital Syndication in Australia: Patterns and Implications

abstract: The paper investigates the patterns of organizational collaboration amongst the Australian venture capital firms (VCF) in local technology related venture which has been identified and measured through co-investments or investment syndications. I have used Thomson Reuter’s data and industry classification in the research. The paper has been presented in three parts. In part 1, I have studied 1156 rounds of venture capital investments in 367 biotechnologies, medical & health (BMH) industry and information & telecommunication (ICT) industry and derived the co-investment patterns and network using graph theory. The results have been presented as affiliation network graphs. In part 2, I have analyses individual co-investment points and the characteristics of participating investors at each point. The VCFs have been categorized in terms of capital under management, specialization, organizational forms and affiliations. The analysis suggests that the VCFs’ investment strategies are aligned with the resource position and specialization where syndication could arise endogenously as complementary resources are needed. In part 3, I have compared the performances of the ventures receiving investment from single VCF with those of from syndicates. The data shows that the ventures receiving investment from syndicates have higher probability of exiting successfully. While effectiveness of individual co-investment could depend on various issues, it could be argued that the co-investment network has been instrumental in market integration, resource and information mobilization.


title: Home network configuring scheme for all electric appliances using ZigBee-based integrated remote controller

abstract: Controlling the electric appliances is the essential technique in the home automation, and wireless communication between the residence gateway and electric appliances is one of the most important parts in the home network system. In these days, most of the electric appliances are controlled by infrared remote controllers. However, it is very difficult to connect most of the electric appliances to a home network, since the communication protocols are different. In this paper, we propose an integrated remote controller to control electric appliances in the home network with no extra attachment of communication device to the appliances using ZigBee protocol and infrared remote controller technology. The integrated remote controller system for home automation is composed of integrated remote controller, ZigBee to infrared converter, and ZigBee power adapter. ZigBee power adapter is introduced for some appliances which do not have even infrared remote device to be connected in home network. This paper presents a prototype of the proposed system and shows a scheme for the implementation. It provides high flexibility for the users to configure and manage a home network in order to control electric appliances.


title: Stereotype threat and women’s math performance

abstract: Abstract When women perform math, unlike men, they risk being judged by the negative stereotype that women have weaker math ability. We call this predicament stereotype threat and hypothesize that the apprehension it causes may disrupt women's math performance. In Study 1 we demonstrated that the pattern observed in the literature that women underperform on difficult (but not easy) math tests was observed among a highly selected sample of men and women. In Study 2 we demonstrated that this difference in performance could be eliminated when we lowered stereotype threat by describing the test as not producing gender differences. However, when the test was described as producing gender differences and stereotype threat was high, women performed substantially worse than equally qualified men did. A third experiment replicated this finding with a less highly selected population and explored the mediation of the effect. The implication that stereotype threat may underlie gender differences in advanced math performance, even those that have been attributed to genetically rooted sex differences, is discussed.


title: Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition

abstract: In this paper, we propose a novel Convolutional Neural Network (CNN) architecture for learning multi-scale feature representations with good tradeoffs between speed and accuracy. This is achieved by using a multi-branch network, which has different computational complexity at different branches. Through frequent merging of features from branches at distinct scales, our model obtains multi-scale features while using less computation. The proposed approach demonstrates improvement of model efficiency and performance on both object recognition and speech recognition tasks,using popular architectures including ResNet and ResNeXt. For object recognition, our approach reduces computation by 33% on object recognition while improving accuracy with 0.9%. Furthermore, our model surpasses state-of-the-art CNN acceleration approaches by a large margin in accuracy and FLOPs reduction. On the task of speech recognition, our proposed multi-scale CNNs save 30% FLOPs with slightly better word error rates, showing good generalization across domains.


title: Constrained Deep Metric Learning for Person Re-identification

abstract: Person re-identification aims to re-identify the probe image from a given set of images under different camera views. It is challenging due to large variations of pose, illumination, occlusion and camera view. Since the convolutional neural networks (CNN) have excellent capability of feature extraction, certain deep learning methods have been recently applied in person re-identification. However, in person re-identification, the deep networks often suffer from the over-fitting problem. In this paper, we propose a novel CNN-based method to learn a discriminative metric with good robustness to the over-fitting problem in person re-identification. Firstly, a novel deep architecture is built where the Mahalanobis metric is learned with a weight constraint. This weight constraint is used to regularize the learning, so that the learned metric has a better generalization ability. Secondly, we find that the selection of intra-class sample pairs is crucial for learning but has received little attention. To cope with the large intra-class variations in pedestrian images, we propose a novel training strategy named moderate positive mining to prevent the training process from over-fitting to the extreme samples in intra-class pairs. Experiments show that our approach significantly outperforms state-of-the-art methods on several benchmarks of person re-identification.


CYUT Short Text Conversation System for NTCIR-12 STC


title: Metric Learning-based Generative Adversarial Network

abstract: Generative Adversarial Networks (GANs), as a framework for estimating generative models via an adversarial process, have attracted huge attention and have proven to be powerful in a variety of tasks. However, training GANs is well known for being delicate and unstable, partially caused by its sig- moid cross entropy loss function for the discriminator. To overcome such a problem, many researchers directed their attention on various ways to measure how close the model distribution and real distribution are and have applied dif- ferent metrics as their objective functions. In this paper, we propose a novel framework to train GANs based on distance metric learning and we call it Metric Learning-based Gener- ative Adversarial Network (MLGAN). The discriminator of MLGANs can dynamically learn an appropriate metric, rather than a static one, to measure the distance between generated samples and real samples. Afterwards, MLGANs update the generator under the newly learned metric. We evaluate our ap- proach on several representative datasets and the experimen- tal results demonstrate that MLGANs can achieve superior performance compared with several existing state-of-the-art approaches. We also empirically show that MLGANs could increase the stability of training GANs.


abstract: Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted. The fundamental reason is the difficulty of back-propagation through discrete random variables combined with the inherent instability of the GAN training objective. To address these problems, we propose Maximum-Likelihood Augmented Discrete Generative Adversarial Networks. Instead of directly optimizing the GAN objective, we derive a novel and low-variance objective using the discriminator's output that follows corresponds to the log-likelihood. Compared with the original, the new objective is proved to be consistent in theory and beneficial in practice. The experimental results on various discrete datasets demonstrate the effectiveness of the proposed approach.


title: Lesions of dorsolateral striatum preserve outcome expectancy but disrupt habit formation in instrumental learning

abstract: Habits are controlled by antecedent stimuli rather than by goal expectancy. Interval schedules of feedback have been shown to generate habits, as revealed by the insensitivity of behaviour acquired under this schedule to outcome devaluation treatments. Two experiments were conducted to assess the role of the dorsolateral striatum in habit learning. In Experiment 1, sham operated controls and rats with dorsolateral striatum lesions were trained to press a lever for sucrose under interval schedules. After training, the sucrose was devalued by inducing taste aversion to it using lithium chloride, whereas saline injections were given to the controls. Only rats given the devaluation treatment reduced their consumption of sucrose and this reduction was similar in both the sham and the lesioned groups. All rats were then returned to the instrumental chamber for an extinction test, in which the lever was extended but no sucrose was delivered. In contrast to sham operated controls, rats with dorsolateral striatum lesions refrained from pressing the lever if the outcome was devalued. To assess the specificity of the role of dorsolateral striatum in this effect a second experiment was conducted in which a group with lesions of dorsomedial striatum was added. In relation now to both the sham and the dorsomedial lesioned groups, only rats with lesions of dorsolateral striatum significantly reduced responding after outcome devaluation. In conclusion, this study provides direct evidence that the dorsolateral striatum is necessary for habit formation. Furthermore, it suggests that, when the habit system is disrupted, control over instrumental performance reverts to the system controlling the performance of goal-directed instrumental actions.


title: Stress effects on memory: An update and integration

abstract: It is well known that stressful experiences may affect learning and memory processes. Less clear is the exact nature of these stress effects on memory: both enhancing and impairing effects have been reported. These opposite effects may be explained if the different time courses of stress hormone, in particular catecholamine and glucocorticoid, actions are taken into account. Integrating two popular models, we argue here that rapid catecholamine and non-genomic glucocorticoid actions interact in the basolateral amygdala to shift the organism into a 'memory formation mode' that facilitates the consolidation of stressful experiences into long-term memory. The undisturbed consolidation of these experiences is then promoted by genomic glucocorticoid actions that induce a 'memory storage mode', which suppresses competing cognitive processes and thus reduces interference by unrelated material. Highlighting some current trends in the field, we further argue that stress affects learning and memory processes beyond the basolateral amygdala and hippocampus and that stress may pre-program subsequent memory performance when it is experienced during critical periods of brain development.


Annotating Relation Inference in Context via Question Answering


title: High step-up converter with coupled-inductor

abstract: In this study, a high step-up converter with a coupled-inductor is investigated. In the proposed strategy, a coupled inductor with a lower-voltage-rated switch is used for raising the voltage gain (whether the switch is turned on or turned off). Moreover, a passive regenerative snubber is utilized for absorbing the energy of stray inductance so that the switch duty cycle can be operated under a wide range, and the related voltage gain is higher than other coupled-inductor-based converters. In addition, all devices in this scheme also have voltage-clamped properties and their voltage stresses are relatively smaller than the output voltage. Thus, it can select low-voltage low-conduction-loss devices, and there are no reverse-recovery currents within the diodes in this circuit. Furthermore, the closed-loop control methodology is utilized in the proposed scheme to overcome the voltage drift problem of the power source under the load variations. As a result, the proposed converter topology can promote the voltage gain of a conventional boost converter with a single inductor, and deal with the problem of the leakage inductor and demagnetization of transformer for a coupled-inductor-based converter. Some experimental results via examples of a proton exchange membrane fuel cell (PEMFC) power source and a traditional battery are given to demonstrate the effectiveness of the proposed power conversion strategy.


title: Utility max-min fair resource allocation for communication networks with multipath routing

abstract: This paper considers the flow control and resource allocation problem as applied to the generic multipath communication networks with heterogeneous applications. We propose a novel distributed algorithm, show and prove that among all the sources with positive increasing and bounded utilities (no need to be concave) in steady state, the utility max-min fairness is achieved, which is essential for balancing Quality of Service (QoS) for different applications. By combining the first order Lagrangian method and filtering mechanism, the adopted approach eliminates typical oscillation behavior in multipath networks and possesses a rapid convergence property. In addition, the algorithm is capable of deciding the optimal routing strategy and distributing the total traffic evenly out of the available paths. The performance of our utility max-min fair flow control algorithm is evaluated through simulations under two representative case studies, as well as the real implementation issues are addressed deliberately for the practical purpose.


title: More Robust Doubly Robust Off-policy Evaluation

abstract: We study the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of a policy from the data generated by another policy(ies). In particular, we focus on the doubly robust (DR) estimators that consist of an importance sampling (IS) component and a performance model, and utilize the low (or zero) bias of IS and low variance of the model at the same time. Although the accuracy of the model has a huge impact on the overall performance of DR, most of the work on using the DR estimators in OPE has been focused on improving the IS part, and not much on how to learn the model. In this paper, we propose alternative DR estimators, called more robust doubly robust (MRDR), that learn the model parameter by minimizing the variance of the DR estimator. We first present a formulation for learning the DR model in RL. We then derive formulas for the variance of the DR estimator in both contextual bandits and RL, such that their gradients w.r.t.~the model parameters can be estimated from the samples, and propose methods to efficiently minimize the variance. We prove that the MRDR estimators are strongly consistent and asymptotically optimal. Finally, we evaluate MRDR in bandits and RL benchmark problems, and compare its performance with the existing methods.


A Study on Job Satisfaction Factors in Retention and Turnover Groups using Dominance Analysis and LDA Topic Modeling with Employee Reviews on Glassdoor.com


title: A Study of WhatsApp Usage Patterns and Prediction Models without Message Content

abstract: Internet social networks have become a ubiquitous application allowing people to easily share text, pictures, and audio and video files. Popular networks include WhatsApp, Facebook, Reddit and LinkedIn. We present an extensive study of the usage of the WhatsApp social network, an Internet messaging application that is quickly replacing SMS messaging. In order to better understand people's use of the network, we provide an analysis of over 6 million messages from over 100 users, with the objective of building demographic prediction models using activity data. We performed extensive statistical and numerical analysis of the data and found significant differences in WhatsApp usage across people of different genders and ages. We also inputted the data into the Weka data mining package and studied models created from decision tree and Bayesian network algorithms. We found that different genders and age demographics had significantly different usage habits in almost all message and group attributes. We also noted differences in users' group behavior and created prediction models, including the likelihood a given group would have relatively more file attachments, if a group would contain a larger number of participants, a higher frequency of activity, quicker response times and shorter messages. We were successful in quantifying and predicting a user's gender and age demographic. Similarly, we were able to predict different types of group usage. All models were built without analyzing message content. We present a detailed discussion about the specific attributes that were contained in all predictive models and suggest possible applications based on these results.


title: Dynamic factor volatility modeling: A Bayesian latent threshold approach,” Working Paper 11-16

abstract: We discuss dynamic factor modeling of financial time series using a latent threshold approach to factor volatility. This approach models time-varying patterns of occurrence of zero elements in factor loadings matrices, providing adaptation to changing relationships over time and dynamic model selection. We summarize Bayesian methods for model fitting and discuss analyses of several FX, commodities, and stock price index time series. Empirical results show that the latent threshold approach can define interpretable, data-driven, dynamic sparsity, leading to reduced estimation uncertainties, improved predictions, and portfolio performance in increasingly high-dimensional dynamic factor models. Copyright The Author, 2012. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.


title: Predicting the Maximum Earthquake Magnitude from Seismic Data in Israel and Its Neighboring Countries

abstract: This paper explores several data mining and time series analysis methods for predicting the magnitude of the largest seismic event in the next year based on the previously recorded seismic events in the same region. The methods are evaluated on a catalog of 9,042 earthquake events, which took place between 01/01/1983 and 31/12/2010 in the area of Israel and its neighboring countries. The data was obtained from the Geophysical Institute of Israel. Each earthquake record in the catalog is associated with one of 33 seismic regions. The data was cleaned by removing foreshocks and aftershocks. In our study, we have focused on ten most active regions, which account for more than 80% of the total number of earthquakes in the area. The goal is to predict whether the maximum earthquake magnitude in the following year will exceed the median of maximum yearly magnitudes in the same region. Since the analyzed catalog includes only 28 years of complete data, the last five annual records of each region (referring to the years 2006–2010) are kept for testing while using the previous annual records for training. The predictive features are based on the Gutenberg-Richter Ratio as well as on some new seismic indicators based on the moving averages of the number of earthquakes in each area. The new predictive features prove to be much more useful than the indicators traditionally used in the earthquake prediction literature. The most accurate result (AUC = 0.698) is reached by the Multi-Objective Info-Fuzzy Network (M-IFN) algorithm, which takes into account the association between two target variables: the number of earthquakes and the maximum earthquake magnitude during the same year.


title: Performance comparison between an UHF RFID antenna for portable reader and its UHF RFID/WLAN dual-band version

abstract: A low-cost single-band circularly polarized antenna for the UHF RFID band and its extended dual-band configuration for UHF RFID/WLAN applications are here compared, in terms of both electromagnetic parameters (reflection coefficient, axial ratio and gain, in the UHF band) and operational performances when used in a commercial RFID reader (Received Signal Strength Indicator, RSSI, and reading range). Thanks to their high compactness and low-profile (60mm×60mm×7mm), both antennas can be easily integrated into a commercial portable UHF RFID reader. An array composed by four inverted-F meandered monopoles appears in both antennas, which covers the 902–928 MHz frequency band. In the dual-band configuration, a miniaturized patch has been added on the same FR4 substrate, in the middle of the four monopoles, so providing the portable reader with an extra wireless functionality, namely a WLAN connectivity (2400–2480 MHz). Results in terms of reflection coefficient, gain, axial ratio, as well as measured RSSI values are shown in this paper. The dual-band antenna performances are comparable with those of the original singleband version, despite of the remarkable mutual coupling between the RFID array and the WLAN patch, which are very close to each other and printed on the same side of the FR4 substrate.


Recent Trends in Applied Artificial Intelligence


title: Demystifying Fog Computing: Characterizing Architectures, Applications and Abstractions

abstract: Internet of Things (IoT) has accelerated the deployment of millions of sensors at the edge of the network, through Smart City infrastructure and lifestyle devices. Cloud computing platforms are often tasked with handling these large volumes and fast streams of data from the edge. Recently, Fog computing has emerged as a concept for low-latency and resource-rich processing of these observation streams, to complement Edge and Cloud computing. In this paper, we review various dimensions of system architecture, application characteristics and platform abstractions that are manifest in this Edge, Fog and Cloud eco-system. We highlight novel capabilities of the Edge and Fog layers, such as physical and application mobility, privacy sensitivity, and a nascent runtime environment. IoT application case studies based on first-hand experiences across diverse domains drive this categorization. We also highlight the gap between the potential and the reality of Fog computing, and identify challenges that need to be overcome for the solution to be sustainable. Taken together, our article can help platform and application developers bridge the gap that remains in making Fog computing viable.


title: LOCO: an interactive code (De)obfuscation tool

abstract: This paper presents LOCO, a graphical, interactive environment to experiment with code obfuscation and deobfuscation transformations, which can be applied automatically, semi-automatically and by hand. LOCO is an extension of the multi-platform visualization tool LANCET, combined with an obfuscation infrastructure in the underlying link-time program rewriter DIABLO. By use of LOCO, a developer can easily navigate through the control flow graph of a program and do fine-grained obfuscation, test new obfuscation transformations, test the robustness of existing transformations or improve existing transformations.


title: Half-bridge resonant inverter with SiC cascode applied to domestic induction heating

abstract: This paper presents an evaluation of a SiC MOSFET-JFET n-ON cascode structure for induction heating home appliances. In particular, the SiC cascode is applied in a resonant half-bridge inverter (HB-SCR). The cascode under test is comprised of a high power JFET n-ON in series with a low voltage MOSFET with a reduced conduction resistance. This small signal auxiliary device controls the conduction status of the main power JFET device. Operation modes for Zero Voltage Switching (ZVS) are analyzed. The obtained results are compared with those obtained with a similar resonant HB with n-ON SiC JFETs. The cascode structure applied to induction heating yields similar results in terms of conduction and switching than the classical resonant half-bridge. The improvement consists on a direct normally-off operation avoiding the need for additional security features and simplifying the driver circuit. Experimental verification of this stage applied to induction heating is presented in this paper opening future lines for driverless and snubberless inverters.


title: Semi-Supervised Image-to-Video Adaptation for Video Action Recognition

abstract: Human action recognition has been well explored in applications of computer vision. Many successful action recognition methods have shown that action knowledge can be effectively learned from motion videos or still images. For the same action, the appropriate action knowledge learned from different types of media, e.g., videos or images, may be related. However, less effort has been made to improve the performance of action recognition in videos by adapting the action knowledge conveyed from images to videos. Most of the existing video action recognition methods suffer from the problem of lacking sufficient labeled training videos. In such cases, over-fitting would be a potential problem and the performance of action recognition is restrained. In this paper, we propose an adaptation method to enhance action recognition in videos by adapting knowledge from images. The adapted knowledge is utilized to learn the correlated action semantics by exploring the common components of both labeled videos and images. Meanwhile, we extend the adaptation method to a semi-supervised framework which can leverage both labeled and unlabeled videos. Thus, the over-fitting can be alleviated and the performance of action recognition is improved. Experiments on public benchmark datasets and real-world datasets show that our method outperforms several other state-of-the-art action recognition methods.


title: Clustering to determine predictive model for news reports analysis and econometric modeling

abstract: A tree model is constructed for the econometric problem domain and for topic modeling of news reports using a clustering approach. Here segments are represented as discretized intervals defined on econometric variables for speeding up the construction of regression tree. This discretization is achieved from variances defined on variables with predictability for that generated for calculating category utility values defined on correlated variables where the discretization method proposed has the aim to satisfy a constraint of minimum entropy distribution of values of the predictor variable among the categories. An algorithm is proposed for tree merging which is used for incrementally incorporating information for new time intervals with the existing model to generate updated tree model for maintaining logical consistency. The tree merging algorithm has been shown to be suitable for applying to news report documents or econometric information. This is accomplished with a proposed Pruning procedure for maintaining logical consistency in the merged tree which is applied together with existing approaches for limiting pruning and access costs for reducing misclassification error.


title: Environmental abstraction and path planning techniques for realistic crowd simulation

abstract: This paper treats two linked subjects underlying behavioural simulation. First, the way to describe a virtual environment through an informed hierarchical abstract graph. This graph stores some pre-computations such as potential visibility sets, oriented grids, or densities of people, which can be used individually by simulated entities. Second, the way to use this abstract graph to perform a realistic and efficient path planning, which takes care of individual preferences as well as individual knowledge of the environment. Moreover, the path planning method we propose is reactive to some events, reflecting the perceived modifications of the environment, which allows the entity to adapt its behaviour in consequence. Copyright © 2006 John Wiley & Sons, Ltd.


title: Clustering method for CoMP with limited backhaul data transfer using convex relaxation

abstract: Joint transmission (JT) is an attractive coordinated multipoint (CoMP) downlink transmission technique in multi-cell networks. It offers high system throughput but needs to distribute each user data to multiple base stations (BSs), which can lead to huge backhaul signaling overhead when the number of users is large. In this paper, we address the problem of jointly optimizing the BS assignment for each user data and the BS transmit beamformer design subject to per-BS power constraints and given quality-of-service (QoS) requirements. Our aim is to minimize the backhaul user data transfer from the data center to the BSs for the JT approach. We formulate such a problem into a sparsity-maximization problem. However, this problem is NP-hard. We propose to tackle it by first applying convex relaxation to an equivalent form of the original problem and improving the obtained results by solving a series of convex l1-norm minimization problems. Compared to full BS cooperation, the proposed method saves about 13%-35% backhaul user data transfer in our simulations for moderate number of users and QoS requirement.


title: Dynamic Models of Segregation in Small-World Networks

abstract: Schelling (1969, 1971a,b, 1978) considered a simple proximity model of segregation where individual agents only care about the types of people living in their own local geographical neighborhood, the spatial structure being represented by one- or two-dimensional lattices. In this paper, we argue that segregation might occur not only in the geographical space, but also in social environments. Furthermore, recent empirical studies have documented that social interaction structures are well-described by small-world networks. We gen- eralize Schelling's model by allowing agents to interact in small-world networks instead of regular lattices. We study two alternative dynamic models where agents can decide to move either arbitrarily far away (global model) or are bound to choose an alternative location in their social neighborhood (local model). Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. Thus, Schelling's original results seem to be robust to the structural properties of the network.


title: Developing Vehicular Data Cloud Services in the IoT Environment

abstract: The advances in cloud computing and internet of things (IoT) have provided a promising opportunity to resolve the challenges caused by the increasing transportation issues. We present a novel multilayered vehicular data cloud platform by using cloud computing and IoT technologies. Two innovative vehicular data cloud services, an intelligent parking cloud service and a vehicular data mining cloud service, for vehicle warranty analysis in the IoT environment are also presented. Two modified data mining models for the vehicular data mining cloud service, a Naive Bayes model and a Logistic Regression model, are presented in detail. Challenges and directions for future work are also provided.


title: Are Specific Language Impairment and Dyslexia Distinct Disorders?

abstract: Purpose: The purpose of this study was to determine whether specific language impairment (SLI) and dyslexia are distinct developmental disorders. Method: Study 1 investigated the overlap between SL...


title: Diagnostic Feedback in the Classroom

abstract: Diagnostic assessment enables teachers to make inferences about learners’ strengths and weaknesses in the skills being taught. Teachers may provide students with diagnostic feedback to make positive changes in their learning. This pedagogical desire for classroom diagnostic assessment resonates well with formative assessment and differentiates it from clinical diagnosis. However, this formative potential to advance student learning is only realized when diagnostic feedback is used by teachers and learners. The ways in which this feedback is interpreted and utilized depend on a range of variables at individual and structural (e.g., classroom, schools, and community) levels. Questioning the assumption that learners are passive recipients of feedback, the chapter probes conditions and variables that enable or inhibit the maximal use of diagnostic feedback. While the focus of diagnosis is students’ cognitive competence, the parameters of diagnostic assessment involve more than a cognitive dimension. Viewing students as change agents in diagnostic assessment requires consideration of noncognitive learner characteristics such as students’ attitude to learning and assessment, especially their goal orientations. Students’ mastery goals are shown to be related to adaptive outcomes, persistence, and effective self-regulatory strategies that facilitate performance on assessment tasks. Few studies have examined whether there is a direct link between students’ goal orientations and their attitudes to and use of diagnostic feedback. The chapter examines whether students’ goal orientations serve as a gateway to understanding how students may interpret assessment feedback and use it in their learning. Other structural conditions, beyond individual orientations, are also explored in order to understand the role of context, where individual learners’ attitudes and goal orientations are shaped and influenced through the interaction with other learners, teachers, parents, and social norms. The chapter examines these relationships by drawing from multiple small-scale studies engaging English language learners, and argues that the effectiveness of diagnostic feedback is achieved when the complex nature of language learners’ goal structures situated in sociocultural learning contexts is considered. ::: ::: ::: Keywords: ::: ::: Assessment evaluation; ::: Classroom assessment; ::: Cognitive psychology; ::: ESL/EFL


title: The BigDAWG Polystore System and Architecture

abstract: Organizations are often faced with the challenge of providing data management solutions for large, heterogenous datasets that may have different underlying data and programming models. For example, a medical dataset may have unstructured text, relational data, time series waveforms and imagery. Trying to fit such datasets in a single data management system can have adverse performance and efficiency effects. As a part of the Intel Science and Technology Center on Big Data, we are developing a polystore system designed for such problems. BigDAWG (short for the Big Data Analytics Working Group) is a polystore system designed to work on complex problems that naturally span across different processing or storage engines. BigDAWG provides an architecture that supports diverse database systems working with different data models, support for the competing notions of location transparency and semantic completeness via islands and a middleware that provides a uniform multi-island interface. Initial results from a prototype of the BigDAWG system applied to a medical dataset validate polystore concepts. In this article, we will describe polystore databases, the current BigDAWG architecture and its application on the MIMIC II medical dataset, initial performance results and our future development plans.


title: Experiences of Time Loss among Videogame Players: An Empirical Study

abstract: Playing videogames is now a major leisure pursuit, yet research in the area is comparatively sparse. Previous correlational evidence suggests that subjective time loss occurs during playing videogames. This study examined experiences of time loss among a relatively large group of gamers (n = 280). Quantitative and qualitative data were collected through an online survey. Results showed that time loss occurred irrespective of gender, age, or frequency of play, but was associated with particular structural characteristics of games such as their complexity, the presence of multi-levels, missions and/or high scores, multiplayer interactions, and plot. Results also demonstrated that time loss could have both positive and negative outcomes for players. Positive aspects of time loss included helping players to relax and temporarily escape from reality. Negative aspects included the sacrificing of other things in their lives, guilty feelings about wasted time, and social conflict. It is concluded that for many ga...


title: Nuclei segmentation of fluorescence microscopy images using convolutional neural networks

abstract: Fluorescence microscopy has emerged as a powerful tool for studying cell biology because it enables the acquisition of 3D image volumes deeper into tissue and the imaging of complex subcellular structures. Quantitative analysis of these structures, which is needed to characterize the structure and constitution of tissue volumes, is facilitated by nuclei segmentation. However, manual segmentation is a laborious and intractable process due to the size and complexity of the data. In this paper, we describe a nuclei segmentation method using a deep convolutional neural network, data augmentation to generate training images of different shapes and contrasts, a refinement process combining segmentation results of horizontal, frontal, and sagittal planes in a volume, and a watershed technique to count the number of nuclei. Our results indicate that compared to 3D ground truth data, our method is able to successfully segment and count 3D nuclei.


title: Phase synchrony among neuronal oscillations in the human cortex.

abstract: Synchronization of neuronal activity, often associated with network oscillations, is thought to provide a means for integrating anatomically distributed processing in the brain. Neuronal processing, however, involves simultaneous oscillations in various frequency bands. The mechanisms involved in the integration of such spectrally distributed processing have remained enigmatic. We demonstrate, using magnetoencephalography, that robust cross-frequency phase synchrony is present in the human cortex among oscillations with frequencies from 3 to 80 Hz. Continuous mental arithmetic tasks demanding the retention and summation of items in the working memory enhanced the cross-frequency phase synchrony among alpha (approximately 10 Hz), beta (approximately 20 Hz), and gamma (approximately 30-40 Hz) oscillations. These tasks also enhanced the "classical" within-frequency synchrony in these frequency bands, but the spatial patterns of alpha, beta, and gamma synchronies were distinct and, furthermore, separate from the patterns of cross-frequency phase synchrony. Interestingly, an increase in task load resulted in an enhancement of phase synchrony that was most prominent between gamma- and alpha-band oscillations. These data indicate that cross-frequency phase synchrony is a salient characteristic of ongoing activity in the human cortex and that it is modulated by cognitive task demands. The enhancement of cross-frequency phase synchrony among functionally and spatially distinct networks during mental arithmetic tasks posits it as a candidate mechanism for the integration of spectrally distributed processing.


title: Efficient semantic image segmentation with superpixel pooling

abstract: In this work, we evaluate the use of superpixel pooling layers in deep network architectures for semantic segmentation. Superpixel pooling is a flexible and efficient replacement for other pooling strategies that incorporates spatial prior information. We propose a simple and efficient GPU-implementation of the layer and explore several designs for the integration of the layer into existing network architectures. We provide experimental results on the IBSR and Cityscapes dataset, demonstrating that superpixel pooling can be leveraged to consistently increase network accuracy with minimal computational overhead. Source code is available at https://github.com/bermanmaxim/superpixPool


title: EMI filter design methodology taking into account the static converter impedance

abstract: The classic procedure design of EMI filters determines the insertion loss of the common mode and differential mode filters under standardized 50© at the input and output impedances. In order to choose the more adapted filter topology for a static converter, it's necessary to consider the exact impedances connected to the input and output port in operating conditions. These impedances represent the LISN and the static converter which are measured with the current injection method. For simulation purpose, equivalent electrical circuit models are obtained and then applied in the filter design process.


title: PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks

abstract: Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present two privacy-preserving data aggregation schemes for additive aggregation functions. The first scheme -cluster-based private data aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme -Slice-Mix-AggRegaTe (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme -TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes. To the best of our knowledge, this paper is among the first on privacy-preserving data aggregation in wireless sensor networks.


title: Learning Sentiment Lexicons in Spanish

abstract: In this paper we present a framework to derive sentiment lexicons in a target language by using manually or automatically annotated data available in an electronic resource rich language, such as English. We show that bridging the language gap using the multilingual sense-level aligned WordNet structure allows us to generate a high accuracy (90%) polarity lexicon comprising 1,347 entries, and a disjoint lower accuracy (74%) one encompassing 2,496 words. By using an LSA-based vectorial expansion for the generated lexicons, we are able to obtain an average F-measure of 66% in the target language. This implies that the lexicons could be used to bootstrap higher-coverage lexicons using in-language resources.


title: A Compact Dual-Band Dual-Polarized Antenna for Base Station Application

abstract: A compact dual-band dual-polarized antenna is proposed in this paper. The two pair dipoles with strong end coupling are used for the lower frequency band, and cross-placed patch dipoles are used for the upper frequency band. The ends of the dipoles for lower frequency band are bent to increase the coupling between adjacent dipoles, which can benefit the compactness and bandwidth of the antenna. Breaches are introduced at the ends of the dipoles of the upper band, which also benefit the compactness and matching of the antenna. An antenna prototype was fabricated and measured. The measured results show that the antenna can cover from 790MHz to 960MHz (19.4%) for lower band and from 1710MHz to 2170MHz (23.7%) for upper band with VSWR < 1.5. It is expected to be a good candidate design for base station antennas.


title: AES-Based Security Coprocessor IC in 0.18-$muhbox m$CMOS With Resistance to Differential Power Analysis Side-Channel Attacks

abstract: Security ICs are vulnerable to side-channel attacks (SCAs) that find the secret key by monitoring the power consumption or other information that is leaked by the switching behavior of digital CMOS gates. This paper describes a side-channel attack resistant coprocessor IC fabricated in 0.18- $muhbox m$ CMOS consisting of an Advanced Encryption Standard (AES) based cryptographic engine, a fingerprint-matching engine, template storage, and an interface unit. Two functionally identical coprocessors have been fabricated on the same die. The first coprocessor was implemented using standard cells and regular routing techniques. The second coprocessor was implemented using a logic style called wave dynamic differential logic (WDDL) and a layout technique called differential routing to combat the differential power analysis (DPA) side-channel attack. Measurement-based experimental results show that a DPA attack on the insecure coprocessor requires only 8000 encryptions to disclose the entire 128-bit secret key. The same attack on the secure coprocessor does not disclose the entire secret key even after 1 500 000 encryptions.


title: Classification of ECG signals using machine learning techniques: A survey

abstract: Classification of electrocardiogram (ECG) signals plays an important role in diagnoses of heart diseases. An accurate ECG classification is a challenging problem. This paper presents a survey of ECG classification into arrhythmia types. Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient. Different classifiers are available for ECG classification. Amongst all classifiers, artificial neural networks (ANNs) have become very popular and most widely used for ECG classification. This paper discusses the issues involved in ECG classification and presents a detailed survey of preprocessing techniques, ECG databases, feature extraction techniques, ANN based classifiers, and performance measures to address the mentioned issues. Furthermore, for each surveyed paper, our paper also presents detailed analysis of input beat selection and output of the classifiers.


title: A Latent Source Model for Patch-Based Image Segmentation

abstract: Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation. Our analysis relies on a new local property for how similar nearby patches are, and fuses existing lines of work on modeling natural imagery patches and theory for nonparametric classification. We use the model to derive a new patch-based segmentation algorithm that iterates between inferring local label patches and merging these local segmentations to produce a globally consistent image segmentation. Many existing patch-based algorithms arise as special cases of the new algorithm.


title: Rumor detection on twitter

abstract: Twitter is useful in a situation of disaster for communication, announcement, request for rescue and so on. On the other hand, it causes a negative by-product, spreading rumors. This paper describe how rumors have spread after a disaster of earthquake, and discuss how can we deal with them. We first investigated actual instances of rumor after the disaster. And then we attempted to disclose characteristics of those rumors. Based on the investigation we developed a system which detects candidates of rumor from twitter and then evaluated it. The result of experiment shows the proposed algorithm can find rumors with acceptable accuracy.


title: Tighter Linear Program Relaxations for High Order Graphical Models

abstract: Graphical models with High Order Potentials (HOPs) have received considerable interest in recent years. While there are a variety of approaches to inference in these models, nearly all of them amount to solving a linear program (LP) relaxation with unary consistency constraints between the HOP and the individual variables. In many cases, the resulting relaxations are loose, and in these cases the results of inference can be poor. It is thus desirable to look for more accurate ways of performing inference. In this work, we study the LP relaxations that result from enforcing additional consistency constraints between the HOP and the rest of the model. We address theoretical questions about the strength of the resulting relaxations compared to the relaxations that arise in standard approaches, and we develop practical and efficient message passing algorithms for optimizing the LPs. Empirically, we show that the LPs with additional consistency constraints lead to more accurate inference on some challenging problems that include a combination of low order and high order terms.


title: CapsNet comparative performance evaluation for image classification

abstract: Image classification has become one of the main tasks in the field of computer vision technologies. In this context, a recent algorithm called CapsNet that implements an approach based on activity vectors and dynamic routing between capsules may overcome some of the limitations of the current state of the art artificial neural networks (ANN) classifiers, such as convolutional neural networks (CNN). In this paper, we evaluated the performance of the CapsNet algorithm in comparison with three well-known classifiers (Fisher-faces, LeNet, and ResNet). We tested the classification accuracy on four datasets with a different number of instances and classes, including images of faces, traffic signs, and everyday objects. The evaluation results show that even for simple architectures, training the CapsNet algorithm requires significant computational resources and its classification performance falls below the average accuracy values of the other three classifiers. However, we argue that CapsNet seems to be a promising new technique for image classification, and further experiments using more robust computation resources and re-fined CapsNet architectures may produce better outcomes.


title: Essentia: An Audio Analysis Library for Music Information Retrieval

abstract: Comunicacio presentada a la 14th International Society for Music Information Retrieval Conference, celebrada a Curitiba (Brasil) els dies 4 a 8 de novembre de 2013.


title: Feature-Fused SSD: Fast Detection for Small Objects

abstract: Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off as base architecture. We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects. In detailed fusion operation, we design two feature fusion modules, concatenation module and element-sum module, different in the way of adding contextual information. Experimental results show that these two fusion modules obtain higher mAP on PASCAL VOC2007 than baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points improvement on some small objects categories. The testing speed of them is 43 and 40 FPS respectively, superior to the state of the art Deconvolutional single shot detector (DSSD) by 29.4 and 26.4 FPS.


title: Conceptual Model Development of Big Data Analytics Implementation Assessment Effect on Decision-Making

abstract: The significance of big data advancement has benefited various organizations to leverage the potential insights ::: and capabilities of big data in organizational performance and decision-making. However, the analytics outcome ::: and quality of big data analytics (BDA) implementation has yet to be addressed. Therefore the aims of this ::: paper are to identify and analyze the affecting factors and elements of BDA implementation and to propose ::: a conceptual model for effective decision-making through BDA implementation assessment. The model is ::: developed based on three dimensions such as performing data strategy (organization), collaborative knowledge ::: worker (people) and executing data analytics (technology). The findings of this ongoing study proceeds with ::: designing a proposed conceptual model with the research hypothesis and may provide a better assessment ::: model for effective decision-making in the long run.


title: Problems in dealing with missing data and informative censoring in clinical trials

abstract: A common problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to bias. There are no universally applicable methods for handling missing data. We recommend the following: (1) Report reasons for dropouts and proportions for each treatment group; (2) Conduct sensitivity analyses to encompass different scenarios of assumptions and discuss consistency or discrepancy among them; (3) Pay attention to minimize the chance of dropouts at the design stage and during trial monitoring; (4) Collect post-dropout data on the primary endpoints, if at all possible; and (5) Consider the dropout event itself an important endpoint in studies with many.


Datification and its human, organizational and societal effects: The strategic opportunities and challenges of algorithmic decision-making


Learning to Predict Denotational Probabilities For Modeling Entailment


title: Timber quality after thinning from above and below in stands of Pinus sylvestris

abstract: Timber quality and logging damage after different thinning types in Scots pine (Pinus sylvestris L.) stands were studied in a field experiment in north Sweden. Thinning operations were mechanized and carried out according to normal Swedish practise. The treatments were thinning from below and thinning from above. The quality of the residual stand was evaluated using variables such as diameter of the thickest branch, stem taper, annual ring width, straightness and lean of trees. The only variables showing differences between treatments were lean of tree and stem straightness. No difference in the frequency of trees with logging damage was found. The conclusion of this study is that differences between thinning types in first thinning may be evened out when carried out as in commercial forestry due to extraction of strip road and damaged trees. Consequently, differences in timber quality and logging damage in the residual stand may be small.


title: Yara Parser: A Fast and Accurate Dependency Parser

abstract: Dependency parsers are among the most crucial tools in natural language processing as they have many important applications in downstream tasks such as information retrieval, machine translation and knowledge acquisition. We introduce the Yara Parser, a fast and accurate open-source dependency parser based on the arc-eager algorithm and beam search. It achieves an unlabeled accuracy of 93.32 on the standard WSJ test set which ranks it among the top dependency parsers. At its fastest, Yara can parse about 4000 sentences per second when in greedy mode (1 beam). When optimizing for accuracy (using 64 beams and Brown cluster features), Yara can parse 45 sentences per second. The parser can be trained on any syntactic dependency treebank and dierent options are provided in order to make it more flexible and tunable for specific tasks. It is released with the Apache version 2.0 license and can be used for both commercial and academic purposes. The parser can be found at https: //github.com/yahoo/YaraParser.


title: Friend Inspector: A Serious Game to Enhance Privacy Awareness in Social Networks

abstract: Currently, many users of Social Network Sites are insufficiently aware of who can see their shared personal items. Nonetheless, most approaches focus on enhancing privacy in Social Networks through improved privacy settings, neglecting the fact that privacy awareness is a prerequisite for privacy control. Social Network users first need to know about privacy issues before being able to make adjustments. In this paper, we introduce Friend Inspector, a serious game that allows its users to playfully increase their privacy awareness on Facebook. Since its launch, Friend Inspector has attracted a significant number of visitors, emphasising the need for better tools to understand privacy settings on Social Networks.


title: MARBLE: Modernization approach for recovering business processes from legacy information systems

abstract: The volatile IT industry often tempts companies to replace legacy information systems with new ones. However, these systems cannot always be completely discarded because they gradually store a significant amount of valuable business knowledge as a result of progressive maintenance over time. MARBLE semi-automatically rebuilds the hidden business processes embedded in legacy information systems. MARBLE supports a business process archeology method which allows business experts to attain a rapid and meaningful understanding of the organization's business processes. MARBLE-framed techniques are based on static and dynamic analysis by considering different legacy software artifacts (e.g., source code, event logs, etc.). Through the validation of MARBLE with several industrial systems, the proposal proved to be less time-consuming and more exhaustive (since it considers the embedded business knowledge) than a manual process redesigned by experts from scratch. The main implications are that MARBLE provides maintainers with a mechanism with which to modernize legacy information systems in line with the actual business processes of an organization.


title: Continuing education in nursing: A concept analysis

abstract: Summary The importance of continuing education for nurses has been increasingly emphasized in the nursing literature since the beginning of the profession. The concept of continuing education is often used as a substitute for associated terms such as continuing professional development and lifelong learning, thus highlighting a need for its clarification. The purpose of this article is to explain and describe continuing education, in order to encourage a broader understanding of the concept among nurses. The concept analysis is directed by Rodgers' [Rodgers, B.L., 1989. Concept analysis and the development of nursing knowledge: the evolutionary cycle. Journal of Advanced Nursing 14, 330–335] ‘evolutionary approach' which is viewed as an ongoing dynamic process, and one that identifies the shared meaning of concepts. Examining everyday discourse used in the nursing literature identified the critical attributes, antecedents and consequence of continuing education in nursing. As a result, the emerging attributes of the concept are synthesised into a conceptual model. The article concludes with an exploration of the application of the concept of continuing education within nursing and its implications for professional development.


title: Uniform Indexing for Geospatial Data and Authorizations

abstract: High-resolution satellites are promising technologies to bolster global transparency by providing unprecedented access to accurate and timely information. A significant number in this satellite fleet are owned by private organizations, which can collect images of human activities and the environment, and make them commercially available around the globe. Uncontrolled dissemination of this information may have grave implications on national security and personal privacy, as some governments and private groups may exploit this information for aggressive purposes. Formal policies for prohibiting the release of imagery beyond a certain resolution, and notifying when an image crosses an international boundary or when such a request is made, are beginning to emerge. As such, there is a need for effective and efficient schemes for facilitating controlled dissemination of satellite imagery and the information products generated from it. Unlike conventional authorizations that can be implemented as access control lists, since authorizations on geospatial imagery involve spatial attributes, managing the authorization base and searching for authorizations based on the spatial extent require a spatial indexing structure. An access request thus requires a search on two index structures, one for the authorizations, and the other for the image database. In this paper, we propose an indexing structure, called RMX-quadtree, that is capable of indexing multi-resolution satellite imagery and additionally allow representing authorizations on top of it. By employing a uniform index for both image database and authorization base, access requests can be processed more efficiently as only one index structure need to be traversed. We demonstrate how authorizations with privilege modes such as view and zoom-in can be implemented using the RMX-quadtree.


title: Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition

abstract: 3D action recognition – analysis of human actions based on 3D skeleton data – becomes popular recently due to its succinctness, robustness, and view-invariant representation. Recent attempts on this problem suggested to develop RNN-based learning methods to model the contextual dependency in the temporal domain. In this paper, we extend this idea to spatio-temporal domains to analyze the hidden sources of action-related information within the input data over both domains concurrently. Inspired by the graphical structure of the human skeleton, we further propose a more powerful tree-structure based traversal method. To handle the noise and occlusion in 3D skeleton data, we introduce new gating mechanism within LSTM to learn the reliability of the sequential input data and accordingly adjust its effect on updating the long-term context information stored in the memory cell. Our method achieves state-of-the-art performance on 4 challenging benchmark datasets for 3D human action analysis.


abstract: The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control, a fleet of quadrotor helicopters, has been developed as a testbed for novel algorithms that enable autonomous operation of aerial vehicles. The testbed has been used to validate multiple algorithms such as reactive collision avoidance, collision avoidance through Nash Bargaining, path planning, cooperative search and aggressive maneuvering. This article briefly describes the algorithms presented and provides references for a more in-depth formulation, and the accompanying movie shows the demonstration of the algorithms on the testbed.


title: An Overview of Speaker Identification: Accuracy and Robustness Issues

abstract: This paper presents the main paradigms for speaker identification, and recent work on missing data methods to increase robustness. The feature extraction, speaker modeling and system classification are discussed. Evaluations of speaker identification performance subject to environmental noise are presented. While performance is impressive in clean speech conditions, there is rapid degradation with mismatched additive noise. Missing data methods can compensate against arbitrary disturbances and remove environmental mismatches. An overview of missing data methods is provided and applications to robust speaker identification summarized. Finally combined approaches involving bottom-up estimation and top-down processing are reviewed, and their significance discussed.


title: PiLoc: a self-calibrating participatory indoor localization system

abstract: While location is one of the most important context information in mobile and ubiquitous computing, large-scale deployment of indoor localization system remains elusive. In this work, we propose PiLoc, an indoor localization system that utilizes opportunistically sensed data contributed by users. Our system does not require manual calibration, prior knowledge and infrastructure support. The key novelty of PiLoc is that it merges walking segments annotated with displacement and signal strength information from users to derive a map of walking paths annotated with radio signal strengths. We evaluate PiLoc over 4 different indoor areas. Evaluation shows that our system can achieve an average localization error of 1.5m.


title: Learning through System Dynamics as Preparation for the 21st Century

abstract: Compounds are described of formula (I) in which: Ar represents a aryl, possibly substituted; R represents a C1-C4 alkyl; R' represents a hydroxyl, an alkoxy, an amino group possibly mono or di-alkyl substituted, or an O-M+ group where M+ represents the cation of an alkaline metal; X represents a hydrogen, chlorine, bromine or iodine atom, a hydroxyl, or an acyloxy, alkylsulphonyloxy or arylsulphonyloxy group. The compounds of formula I can be easily transformed into alphaarylalkanoic acids of formula (III) in which Ar and R have the aforesaid meanings.


title: Assessing The Feasibility Of Self-organizing Maps For Data Mining Financial Information

abstract: Analyzing financial performance in today’s information-rich society can be a daunting task. With the evolution of the Internet, access to massive amounts of financial data, typically in the form of financial statements, is widespread. Managers and stakeholders are in need of a data-mining tool allowing them to quickly and accurately analyze this data. An emerging technique that may be suited for this application is the self-organizing map. The purpose of this study was to evaluate the performance of self-organizing maps for analyzing financial performance of international pulp and paper companies. For the study, financial data, in the form of seven financial ratios, was collected, using the Internet as the primary source of information. A total of 77 companies, and six regional averages, were included in the study. The time frame of the study was the period 1995-00. An example analysis was performed, and the results analyzed based on information contained in the annual reports. The results of the study indicate that self-organizing maps can be feasible tools for the financial analysis of large amounts of financial data.


title: The applicability of blockchain in the Internet of Things

abstract: In this paper, we address the applicability of Blockchain technology to ensure security of data transmitted and received by the nodes in an Internet of Things (IoT) network. We propose a Blockchain consensus model that is suitable for resource constrained devices. We also propose a model for implementing IoT security on top of the Blockchain model. We simulate our proposed model to understand its feasibility.


title: Synthesis and modification of epoxy resins using recycled poly(ethylene terephthalate)

abstract: Development of a method of degradation of poly(ethylene terephthalate) (PET) wastes for the preparation of materials for the synthesis and modification of epoxy resins, development of a method of preparation of epoxy resins, characterization of epoxy resins so prepared, and optimization of the composition of epoxy resins modified with recycled materials were the goals of this work. Therefore, the conditions required for the strong degradation of waste PET by glycolysis reaction were chosen. Glycols with different length of alkyl chain were tested. Composition and structure of the degradation products were determined by GPC and spectroscopic methods (i.e. FT-IR, 1H-NMR, and 13C-NMR). Next, the products of waste PET glycolysis were used as polyhydroxylic reagents for the synthesis of epoxy resins by their reaction with epichlorohydrin. Bis(2-hydroxyalkyl)-terephthalate was used as a model compound. The products were applied in the preparation of compositions with Bisphenol A-based low-molecular-weight epoxy resins. It was found that modification of epoxy resins with the products of PET degradation led to the improvement of their tensile and flexural strengths, hardness, and thermal stability. Better results were obtained for the products with longer alkyl fragments. The incorporation of alkylether chains from PET glycolysis not only increased water absorption of the modified resins, but also improved their chemical stability against 10% HNO3, 75% H2SO4, and ethyl acetate. Copyright © 2008 John Wiley & Sons, Ltd.


A Study of Third-Party Tracking by Mobile Apps in the Wild


Approximation Algorithms for Offline Risk-averse Combinatorial Optimization