title: How are habits formed: Modelling habit formation in the real world

abstract: To investigate the process of habit formation in everyday life, 96 volunteers chose an eating, drinking or activity behaviour to carry out daily in the same context (for example 'after breakfast') for 12 weeks. They completed the self-report habit index (SRHI) each day and recorded whether they carried out the behaviour. The majority (82) of participants provided sufficient data for analysis, and increases in automaticity (calculated with a sub-set of SRHI items) were examined over the study period. Nonlinear regressions fitted an asymptotic curve to each individual's automaticity scores over the 84 days. The model fitted for 62 individuals, of whom 39 showed a good fit. Performing the behaviour more consistently was associated with better model fit. The time it took participants to reach 95% of their asymptote of automaticity ranged from 18 to 254 days; indicating considerable variation in how long it takes people to reach their limit of automaticity and highlighting that it can take a very long time. Missing one opportunity to perform the behaviour did not materially affect the habit formation process. With repetition of a behaviour in a consistent context, automaticity increases following an asymptotic curve which can be modelled at the individual level. Copyright (C) 2009 John Wiley & Sons, Ltd.


title: A Braille Writing Tutor to Combat Illiteracy in Developing Communities

abstract: Less than 3% of the 145 million blind people living ::: in developing countries are literate. This low ::: literacy rate is in part due to the lack of trained ::: teachers and the challenges associated with learning ::: to write Braille on a traditional slate and stylus. ::: These challenges include writing from right ::: to left, writing mirrored images of letters, and receiving ::: significantly delayed feedback. Extensive ::: conversations with the Mathru School for the Blind ::: in Bangalore, India, revealed the need for a robust, ::: low-power, low-cost Braille writing tutor. In ::: this paper, we present the design and deployment ::: of our writing tutor system that uses a novel input ::: device to capture students activity on a slate and ::: stylus and uses a range of scaffolding techniques ::: and Artificial Intelligence to teach writing skills to ::: both beginner and the advanced students. The paper ::: also reports lessons learned in the implementation ::: of this project and from a six-week pilot study ::: at the Mathru school and outlines future directions ::: for improvement.


title: Tripolar pad for inductive power transfer systems

abstract: Inductive Power Transfer (TPT) systems have been proposed in recent years as a safe, convenient and robust method of battery charging in EVs and robots. In an IPT system, the magnetic design of the primary and secondary pads is an important factor that determines the power transfer capability. This paper proposes the Tripolar Pad (TPP), a novel three coil magnetic pad utilising mutually decoupled coils. The TPP maintains tolerance to rotational displacements similar to nonpolarised pads, but uses polarised fields to improve power transfer. Simulation results are first shown in the paper and then validated by a scaled prototype.


title: Micro-flow kinetics research on water invasion in tight sandstone reservoirs

abstract: Abstract The tight sandstone gas is a very critical unconventional gas resource. Nevertheless, liquid invasion may result in reduction of well productivity during the exploitation of tight gas. At present, the research on micro-flow kinetics in tight porous media is not very detailed, and there is no mathematical model for accurate description of liquid invasion in tight sandstone reservoirs. In this study, based on the micro-flow kinetics, the liquid invasion process is divided into two successive phases where the initial stage dominated by capillary and the pseudo steady stage influenced by pore pressure; a simplified kinetic model is proposed. The study shows that (i) the initial invasion stage is a transient process; (ii) during pseudo steady state, the key parameter becoming pore pressure drawdown, which should be considered in analysis of invasion in tight sandstone; (iii) the factor of permeability and static contact angle significantly influence invasion process, while viscosity has less impact on it.


title: ATSC 3.0 Transmitter Identification Signals and Applications

abstract: In the ATSC 3.0 PHY layer standard, a transmitter identification (TxID) signal is defined in order to provide the identification of an ATSC 3.0 transmitter. TxID signal can also be used to find the co-channel and adjacent interference signals, to assist accurate location finding calculation, and to obtain the channel estimation for each transmitter, which can help local service (e.g., targeted advertisement) reception, as well as for emergency alert. For efficient use of spectrum and quality of service improvement, single frequency network (SFN) where all transmitters share a single RF channel is often implemented. The recently developed ATSC 3.0 physical layer standard has been designed to support SFN. For efficient designing, fine-tuning and operating an SFN, it is crucial to identify each transmitter, as well as to adjust transmitting power and emission time of each transmitter. This paper presents TxID for ATSC 3.0, and analyzes its detection performance under very low signal to noise ratio environments and other applications, such as location finding, and channel estimation etc. for each SFN transmitter.


title: When Do Birds of a Feather Flock Together? k-Means, Proximity, and Conic Programming

abstract: Given a set of data, one central goal is to group them into clusters based on some notion of similarity between the individual objects. One of the most popular and widely-used approaches is k-means despite the computational hardness to find its global minimum. We study and compare the properties of different convex relaxations by relating them to corresponding proximity conditions, an idea originally introduced by Kumar and Kannan. Using conic duality theory, we present an improved proximity condition under which the Peng–Wei relaxation of k-means recovers the underlying clusters exactly. Our proximity condition improves upon Kumar and Kannan and is comparable to that of Awashti and Sheffet, where proximity conditions are established for projective k-means. In addition, we provide a necessary proximity condition for the exactness of the Peng–Wei relaxation. For the special case of equal cluster sizes, we establish a different and completely localized proximity condition under which the Amini–Levina relaxation yields exact clustering, thereby having addressed an open problem by Awasthi and Sheffet in the balanced case. Our framework is not only deterministic and model-free but also comes with a clear geometric meaning which allows for further analysis and generalization. Moreover, it can be conveniently applied to analyzing various data generative models such as the stochastic ball models and Gaussian mixture models. With this method, we improve the current minimum separation bound for the stochastic ball models and achieve the state-of-the-art results of learning Gaussian mixture models.


title: Cognitive and subjective dose-response effects of acute oral Δ9-tetrahydrocannabinol (THC) in infrequent cannabis users

abstract: Rationale: Although some aspects of memory functions are known to be acutely impaired by Delta(9)-tetrahydrocannabinol (Delta(9)-THC; the main active constituent of marijuana), effects on other aspects of memory are not known and the time course of functional impairments is unclear. Objective: The present study aimed to detail the acute and residual cognitive effects of Delta(9)-THC in infrequent cannabis users. Methods: A balanced, double-blind cross-over design was used to compare the effects of 7.5 mg and 15 mg Delta(9)-THC with matched placebo in 15 male volunteers. Participants were assessed pre and 1, 2, 4, 6, 8, 24 and 48 h post-drug. Results: Delta(9)-THC 15 mg impaired performance on two explicit memory tasks at the time of peak plasma concentration (2 h post-drug). At the same time point, performance on an implicit memory task was preserved intact. The higher dose of Delta(9)-THC resulted in no learning whatsoever occurring over a three-trial selective reminding task at 2 h. Working memory was generally unaffected by Delta(9)-THC. In several tasks, Delta(9)-THC increased both speed and error rates, reflecting "riskier" speed-accuracy trade-offs. Subjective effects were also most marked at 2 h but often persisted longer, with participants rating themselves as "stoned" for 8 h. Participants experienced a strong drug effect, liked this effect and, until 4 h, wanted more oral Delta(9)-THC. No effects of Delta(9)-THC were found 24 or 48 h following ingestion indicating that the residual effects of oral Delta(9)-THC are minimal. Conclusions: These data demonstrate that oral Delta(9)-THC impairs episodic memory and learning in a dose-dependent manner whilst sparing perceptual priming and working memory.


title: Basenji-Greyhound dog model of asthma: influence of atropine on antigen-induced bronchoconstriction

abstract: To test the relative importance of cholinergic effects on the Basenji-Greyhound (BG) model of asthma, we administered Ascaris antigen to Ascaris-sensitive BG dogs without other treatment, and following intravenous atropine. Pulmonary resistance (RL) increased from 1.4 +/- 0.43 cmH2O . l-1 . s (mean + SE) to 26.0 +/- 5.21 after Ascaris antigen exposure in seven untreated dogs. In the same dogs pretreated with 0.2 mg/kg atropine, RL increased from 1.5 +/- 0.31 to 18 +/- 5.00. Atropine 0.4 and 1.0 mg/kg did not result in increased protection. Although 0.2 mg/kg atropine markedly inhibited the airway responses to both inhaled citric acid and methacholine, it only slightly prevented antigen-induced airway constriction. We conclude that in BG dogs, cholinergic pathways elicit bronchomotor responses to irritants but that the major component of antigen-induced bronchoconstriction is not cholinergically mediated.


The Common Compensatory Pattern : Its Origin and Relationship to the Postural Model By


title: Migration of a SCADA system to IaaS clouds – a case study

abstract: SCADA systems allow users to monitor and/or control physical devices, processes, and events remotely and in real-time. As these systems are critical to industrial processes, they are often run on highly reliable and dedicated hardware. Moving these SCADA systems to an Infrastructure as a Service (IaaS) cloud allows for: cheaper deployments, system redundancy support, and increased uptime. The goal of this work was to present the results of our experimental study of moving/migrating a selected SCADA system to a cloud environment and present major lessons learned. To this end, EclipseSCADA was deployed to the NeCTAR research cloud using the "lift and shift" approach. Performance metrics of a unique nature and large scale of experimentation were collected from the deployed EclipseSCADA system under different loads to examine the effects cloud resources and public networks have on SCADA behavior.


Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics


title: Joint tracking and segmentation of multiple targets

abstract: Tracking-by-detection has proven to be the most successful strategy to address the task of tracking multiple targets in unconstrained scenarios [e.g. 40, 53, 55]. Traditionally, a set of sparse detections, generated in a preprocessing step, serves as input to a high-level tracker whose goal is to correctly associate these “dots” over time. An obvious short-coming of this approach is that most information available in image sequences is simply ignored by thresholding weak detection responses and applying non-maximum suppression. We propose a multi-target tracker that exploits low level image information and associates every (super)-pixel to a specific target or classifies it as background. As a result, we obtain a video segmentation in addition to the classical bounding-box representation in unconstrained, real-world videos. Our method shows encouraging results on many standard benchmark sequences and significantly outperforms state-of-the-art tracking-by-detection approaches in crowded scenes with long-term partial occlusions.


title: Counting in Anonymous Dynamic Networks under Worst-Case Adversary

abstract: In this paper we investigate the problem of counting the size of a network where processes are anonymous (i.e., they share the same identifier) and the network topology constantly changes controlled by an adversary able to look internal process states and add and remove edges in order to contrast the convergence of the algorithm to the correct count. It is easy to show that, if the adversary can generate graphs without any constraint on the connectivity (i.e. it can generate topologies where there exist nodes not able to influence the others), counting is impossible. In this paper we consider a synchronous round based computation and the dynamicity is governed by a worst-case adversary that generates a sequence of graphs, one for each round, with the only constraint that each graph must be connected (1-interval connectivity property). It has been conjectured that counting in a finite time against such adversary is impossible and the existing solutions consider that each process has some knowledge about network topologies generated by the adversary, i.e. at each round, each node has a degree lesser than D. Along the path of proving the validity (or not) of the conjecture, this paper presents an algorithm that counts in a finite time against the worst-case adversary assuming each process is equipped with an oracle. The latter provides a process at each round r with an estimation of the process degree in the graph generated by the adversary at round r. To the best of our knowledge, this is the first counting algorithm (terminating in a finite time) where processes exploit the minimal knowledge about the behavior of the adversary. Interestingly, such oracle can be implemented in a wide range of real systems.


title: Acquiring the reflectance field of a human face

abstract: We present a method to acquire the reflectance field of a human face and use these measurements to render the face under arbitrary changes in lighting and viewpoint. We first acquire images of the face from a small set of viewpoints under a dense sampling of incident illumination directions using a light stage. We then construct a reflectance function image for each observed image pixel from its values over the space of illumination directions. From the reflectance functions, we can directly generate images of the face from the original viewpoints in any form of sampled or computed illumination. To change the viewpoint, we use a model of skin reflectance to estimate the appearance of the reflectance functions for novel viewpoints. We demonstrate the technique with synthetic renderings of a person's face under novel illumination and viewpoints.


title: Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning

abstract: We propose a set of kinematic features that are derived from the optical flow for human action recognition in videos. The set of kinematic features includes divergence, vorticity, symmetric and antisymmetric flow fields, second and third principal invariants of flow gradient and rate of strain tensor, and third principal invariant of rate of rotation tensor. Each kinematic feature, when computed from the optical flow of a sequence of images, gives rise to a spatiotemporal pattern. It is then assumed that the representative dynamics of the optical flow are captured by these spatiotemporal patterns in the form of dominant kinematic trends or kinematic modes. These kinematic modes are computed by performing principal component analysis (PCA) on the spatiotemporal volumes of the kinematic features. For classification, we propose the use of multiple instance learning (MIL) in which each action video is represented by a bag of kinematic modes. Each video is then embedded into a kinematic-mode-based feature space and the coordinates of the video in that space are used for classification using the nearest neighbor algorithm. The qualitative and quantitative results are reported on the benchmark data sets.


title: Direct Feedback Alignment Provides Learning in Deep Neural Networks

abstract: Artificial neural networks are most commonly trained with the back-propagation algorithm, where the gradient for learning is provided by back-propagating the error, layer by layer, from the output layer to the hidden layers. A recently discovered method called feedback-alignment shows that the weights used for propagating the error backward don't have to be symmetric with the weights used for propagation the activation forward. In fact, random feedback weights work evenly well, because the network learns how to make the feedback useful. In this work, the feedback alignment principle is used for training hidden layers more independently from the rest of the network, and from a zero initial condition. The error is propagated through fixed random feedback connections directly from the output layer to each hidden layer. This simple method is able to achieve zero training error even in convolutional networks and very deep networks, completely without error back-propagation. The method is a step towards biologically plausible machine learning because the error signal is almost local, and no symmetric or reciprocal weights are required. Experiments show that the test performance on MNIST and CIFAR is almost as good as those obtained with back-propagation for fully connected networks. If combined with dropout, the method achieves 1.45% error on the permutation invariant MNIST task.


title: Everybody Dance Now

abstract: This paper presents a simple method for “do as I do” motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves. We approach this problem as video-to-video translation using pose as an intermediate representation. To transfer the motion, we extract poses from the source subject and apply the learned pose-to-appearance mapping to generate the target subject. We predict two consecutive frames for temporally coherent video results and introduce a separate pipeline for realistic face synthesis. Although our method is quite simple, it produces surprisingly compelling results (see video). This motivates us to also provide a forensics tool for reliable synthetic content detection, which is able to distinguish videos synthesized by our system from real data. In addition, we release a first-of-its-kind open-source dataset of videos that can be legally used for training and motion transfer.


title: Improved ArtGAN for Conditional Synthesis of Natural Image and Artwork

abstract: This paper proposes a series of new approaches to improve generative adversarial network (GAN) for conditional image synthesis and we name the proposed model as “ArtGAN. ” One of the key innovation of ArtGAN is that, the gradient of the loss function w.r.t. the label (randomly assigned to each generated image) is back-propagated from the categorical discriminator to the generator. With the feedback from the label information, the generator is able to learn more efficiently and generate image with better quality. Inspired by recent works, an autoencoder is incorporated into the categorical discriminator for additional complementary information. Last but not least, we introduce a novel strategy to improve the image quality. In the experiments, we evaluate ArtGAN on CIFAR-10 and STL-10 via ablation studies. The empirical results showed that our proposed model outperforms the state-of-the-art results on CIFAR-10 in terms of Inception score. Qualitatively, we demonstrate that ArtGAN is able to generate plausible-looking images on Oxford-102 and CUB-200, as well as able to draw realistic artworks based on style, artist, and genre. The source code and models are available at: https://github.com/cs-chan/ArtGAN .


title: Revisiting the Importance of Encoding Logic Rules in Sentiment Classification

abstract: We analyze the performance of different sentiment classification models on syntactically complex inputs like A-but-B sentences. The first contribution of this analysis addresses reproducible research: to meaningfully compare different models, their accuracies must be averaged over far more random seeds than what has traditionally been reported. With proper averaging in place, we notice that the distillation model described in arXiv:1603.06318v4 [cs.LG], which incorporates explicit logic rules for sentiment classification, is ineffective. In contrast, using contextualized ELMo embeddings (arXiv:1802.05365v2 [cs.CL]) instead of logic rules yields significantly better performance. Additionally, we provide analysis and visualizations that demonstrate ELMo's ability to implicitly learn logic rules. Finally, a crowdsourced analysis reveals how ELMo outperforms baseline models even on sentences with ambiguous sentiment labels.


title: Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification

abstract: Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the classification of remotely sensed images, discarding the other convolutional layer features which may also be helpful for classification purposes. In this paper, we propose a new adaptive deep pyramid matching (ADPM) model that takes advantage of the features from all of the convolutional layers for remote sensing image classification. To this end, the optimal fusing weights for different convolutional layers are learned from the data itself. In remotely sensed scenes, the objects of interest exhibit different scales in distinct scenes, and even a single scene may contain objects with different sizes. To address this issue, we select the CNN with spatial pyramid pooling (SPP-net) as the basic deep network, and further construct a multi-scale ADPM model to learn complementary information from multi-scale images. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.


title: A database for fine grained activity detection of cooking activities

abstract: While activity recognition is a current focus of research the challenging problem of fine-grained activity recognition is largely overlooked. We thus propose a novel database of 65 cooking activities, continuously recorded in a realistic setting. Activities are distinguished by fine-grained body motions that have low inter-class variability and high intra-class variability due to diverse subjects and ingredients. We benchmark two approaches on our dataset, one based on articulated pose tracks and the second using holistic video features. While the holistic approach outperforms the pose-based approach, our evaluation suggests that fine-grained activities are more difficult to detect and the body model can help in those cases. Providing high-resolution videos as well as an intermediate pose representation we hope to foster research in fine-grained activity recognition.


title: Building trust in the electronic market through an economic incentive mechanism

abstract: Millions of dollars change hands daily through online auction markets. However, fraud has been on the rise in these markets. Using a game theoretic approach, we propose a design of an economic incentive mechanism, the trusted third party (TTP), to serve the online auction communities. The proposed model addresses both the economic and technological aspects of online auction transactions by assigning a digital certificate to each participant. Thus, each participant's identity as well as his or her reputation can be established by other market participants. The analytical results demonstrate that when online transactions take place with the assistance of digital certificates issued by a TTP, the most utilitarian course of action for a market participant is to behave honestly.


title: CPT+: Decreasing the Time/Space Complexity of the Compact Prediction Tree

abstract: Predicting next items of sequences of symbols has many applications in a wide range of domains. Several sequence prediction models have been proposed such as DG, All-k-order markov and PPM. Recently, a model named Compact Prediction Tree (CPT) has been proposed. It relies on a tree structure and a more complex prediction algorithm to offer considerably more accurate predictions than many state-of-the-art prediction models. However, an important limitation of CPT is its high time and space complexity. In this article, we address this issue by proposing three novel strategies to reduce CPT’s size and prediction time, and increase its accuracy. Experimental results on seven real life datasets show that the resulting model (CPT+) is up to 98 times more compact and 4.5 times faster than CPT, and has the best overall accuracy when compared to six state-of-the-art models from the literature: All-K-order Markov, CPT, DG, Lz78, PPM and TDAG.


title: FIGURE8: A Novel System for Generating and Evaluating Figurative Language

abstract: Similes are easily obtained from web-driven and casebased reasoning approaches. Still, generating thoughtful figurative descriptions with meaningful relation to narrative context and author style has not yet been fully explored. In this paper, the author prepares the foundation for a computational model which can achieve this level of aesthetic complexity. This paper also introduces and evaluates a possible architecture for generating and ranking figurative comparisons on par with humans: the FIGURE8 system.


title: Integrated longitudinal and lateral tire/road friction modeling and monitoring for vehicle motion control

abstract: A proper tire friction model is essential to model overall vehicle dynamics for simulation, analysis, or control purposes since a ground vehicle's motion is primarily determined by the friction forces transferred from roads via tires. Motivated by the developments of high-performance antilock brake systems (ABSs), traction control, and steering systems, significant research efforts had been put into tire/road friction modeling during the past 40 years. In this paper, a review of recent developments and trends in this area is presented, with attempts to provide a broad perspective of the initiatives and multidisciplinary techniques for related research. Different longitudinal, lateral, and integrated tire/road friction models are examined. The associated friction-situation monitoring and control synthesis are discussed with a special emphasis on ABS design


title: The Assessment of Present-Moment Awareness and Acceptance

abstract: The purpose of this project was to develop a bidimensional measure of mindfulness to assess its two key components: present-moment awareness and acceptance. The development and psychometric validation of the Philadelphia Mindfulness Scale is described, and data are reported from expert raters, two nonclinical samples (n = 204 and 559), and three clinical samples including mixed psychiatric outpatients (n = 52), eating disorder inpatients (n = 30), and student counseling center outpatients (n = 78). Exploratory and confirmatory factor analyses support a two-factor solution, corresponding to the two constituent components of the construct. Good internal consistency was demonstrated, and relationships with other constructs were largely as expected. As predicted, significant differences were found between the nonclinical and clinical samples in levels of awareness and acceptance. The awareness and acceptance subscales were not correlated, suggesting that these two constructs can be examined independently. Pot...


title: A first look at 802.11ac in action: Energy efficiency and interference characterization

abstract: This paper is first of its kind in presenting a detailed characterization of IEEE 802.11ac using real experiments. 802.11ac is the latest WLAN standard that is rapidly being adapted due to its potential to deliver very high throughput. The throughput increase in 802.11ac can be attributed to three factors - larger channel width (80/160 MHz), support for denser modulation (256 QAM) and increased number of spatial streams for MIMO. We provide an experiment evaluation of these factors and their impact using a 18-nodes 802.11ac testbed. Our findings provide numerous insights on benefits and challenges associated with using 802.11ac in practice. Since utilization of larger channel width is one of the most significant changes in 802.11ac, we focus our study on understanding its impact on energy efficiency and interference. Using experiments, we show that utilizing larger channel width is in general less energy efficient due to its higher power consumption in idle listening mode. Increasing the number of MIMO spatial streams is comparatively more energy efficient for achieving the same percentage increase in throughput. We also show that 802.11ac link witnesses severe unfairness issues when it coexists with legacy 802.11. We provide a detailed analysis to show how medium access in heterogeneous channel width environment leads to the unfairness issues. We believe that these and many other findings presented in this work will help in understanding and resolving various performance issues of next generation WLANs.


title: Design of a Disk Resonator Gyroscope With High Mechanical Sensitivity by Optimizing the Ring Thickness Distribution

abstract: In this paper, we present the mechanical sensitivity improvement of a disk resonator gyroscope (DRG) by optimizing the thickness distribution of the nested rings. The mechanical sensitivities of the DRGs with uniform, linearly changing, and step changing rings have been simulated. The results suggest that the ring thickness distribution has great influence on the performance of the DRG. Then, the optimized ring thickness distribution was obtained by using the traditional method of moving asymptotes (MMA), which result in a 24% improvement of the mechanical sensitivity. Finally, the bio-inspired particle swarm optimization (PSO) algorithm has also been used. The optimization results of PSO coincide well with that of MMA, and the optimized result is the global optimum. Meanwhile, the optimized distribution rules can be used on the series of DRGs and the optimization methods can be widely used on other microelectromechanical systems (MEMS) devices. [2015-0310]


title: Investigation of gender difference in thermal comfort for Chinese people

abstract: Gender difference in thermal comfort for Chinese people was investigated through two laboratory experiments. Both subjective assessment and objective measurement were taken during the experiment. Skin temperature (17 points) and heart rate variability (HRV) were measured in one of the experiment. Our results show that there are gender differences in thermal comfort for Chinese people. Correlation of thermal sensation votes and air temperature and vapor pressure shows that females are more sensitive to temperature and less sensitive to humidity than males. Subjective assessment, skin temperature and HRV analysis suggest that females prefer neutral or slightly warmer condition, due to their constantly lower skin temperature and the fact that mean skin temperature is a good predictor of sensation and discomfort below neutrality. Female comfortable operative temperature (26.3°C) is higher than male comfortable operative temperature (25.3°C), although males and females have almost the same neutral temperature and that there is no gender difference in thermal sensation near neutral conditions.


title: MapReduce-Based Data Processing on IoT

abstract: This paper presents a framework for data processing on the Internet of Things (IoT). It enables data on multiple nodes on IoT to be locally processed as much as possible by using the Map Reduce processing without transmitting the data to data-centers or clusters, and assembled into a few nodes. This paper describes the design and implementation of the framework, basic performance, and practical application.


title: Rules for an Ontology-based Approach to Adaptation

abstract: Adaptation addresses the complexity and overload caused by an increasing amount of available resources by the identification and presentation of relevant resources. Drawbacks of existing approaches to this end include the limited degree of customization, difficulties in the acquisition of model information and the lack of control and transparency of the system?s adaptive behavior. This paper proposes an approach which addresses these drawbacks to perform meaningful and effective adaptation by the application of semantic web technologies. This comprises the use of an ontology and adaptation rules for knowledge representation and inference engines for reasoning. The focus of this paper lies in the presentation of adaptation rules.


title: Burmese word segmentation with Character Clustering and CRFs

abstract: Word segmentation is one of the most fundamental processes for most natural language processing tasks. In particular, languages with no word boundary in writing such as Chinese, Japanese, Korean, Thai, and Burmese need it. However, the Burmese language still waits for a technique with good performance. In this paper, we propose a new technique for Burmese word segmentation employing the idea of Character Clustering for Conditional Random Fields. Character clusters are groups of some inseparable characters due to language characteristics. We proposed a set of 29 types of Burmese Character Clusters (BCCs) as rules, and Conditional Random Fields is applied as a sequential labelling machine learning method. We compared our proposed method with CRF without BCC and Syllable-based CRFs. The result shows that our proposed method achieved the highest performance.


title: An Open Source Prosodic Feature Extraction Tool

abstract: There has been an increasing interest in utilizing a wide variety of knowledge sources in order to perform automatic tagging of speech events, such as sentence boundaries and dialogue acts. In addition to the word spoken, the prosodic content of the speech has been proved quite valuable in a variety of spoken language processing tasks such as sentence segmentation and tagging, disfluency detection, dialog act segmentation and tagging, and speaker recognition. In this paper, we report on an open source prosodic feature extraction tool based on Praat, with a description of the prosodic features and the implementation details, as well as a discussion of its extension capability. We also evaluate our tool on a sentence boundary detection task and report the system performance on the NIST RT04 CTS data.


title: Infinite Hidden Semi-Markov Modulated Interaction Point Process

abstract: The correlation between events is ubiquitous and important for temporal events modelling. In many cases, the correlation exists between not only events' emitted observations, but also their arrival times. State space models (e.g., hidden Markov model) and stochastic interaction point process models (e.g., Hawkes process) have been studied extensively yet separately for the two types of correlations in the past. In this paper, we propose a Bayesian nonparametric approach that considers both types of correlations via unifying and generalizing the hidden semi-Markov model and interaction point process model. The proposed approach can simultaneously model both the observations and arrival times of temporal events, and automatically determine the number of latent states from data. A Metropolis-within-particle-Gibbs sampler with ancestor resampling is developed for efficient posterior inference. The approach is tested on both synthetic and real-world data with promising outcomes.


title: A systematic review of the clinical efficacy of transcranial direct current stimulation (tDCS) in psychiatric disorders

abstract: Abstract Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique, which can be used to selectively disrupt patterns of neural activity that are associated with symptoms of mental illness. tDCS has been implemented in numerous therapeutic trials across a range of patient populations, with a rapidly increasing number of studies being published each year. This systematic review aimed to evaluate the efficacy of tDCS in the treatment of psychiatric disorders. Four electronic databases were searched from inception until December 2015 by two independent reviewers, and 66 eligible studies were identified. Depression was the most extensively researched condition, followed by schizophrenia and substance use disorders. Data on obsessive compulsive disorder, generalised anxiety disorder, and anorexia nervosa were also obtained. The quality of included studies was appraised using a standardised assessment framework, which yielded a median score corresponding to “weak” on the three-point scale. This improved to “moderate” when case reports/series were excluded from the analysis. Overall, data suggested that tDCS interventions comprising multiple sessions can ameliorate symptoms of several major psychiatric disorders, both acutely and in the long-term. Nevertheless, the tDCS field is still in its infancy, and several methodological and ethical issues must be addressed before clinical efficacy can truly be determined. Studies probing the mechanisms of action of tDCS and those facilitating the definition of optimised stimulation protocols are warranted. Furthermore, evidence from large-scale, multi-centre randomised controlled trials is required if the transition of this therapy from the laboratory to the clinic is to be considered.


title: Anti-jamming performance of cognitive radio networks

abstract: In this paper, we study both the jamming capability of the cognitive-radio-based jammers and the anti-jamming capability of the cognitive radio networks. We first setup the models of cognitive-radio-based jammers and the cognitive radio network transmissions. We then analyze various jamming attack strategies where the jammer spends various powers in order to jam various transmission slots of the cognitive radio networks. Average throughput and jamming probability are derived and verified by simulations. Strength and weakness of jammer and cognitive radio networks are then discussed, which will be useful to guide the anti-jamming cognitive radio network design.


title: Key factors influencing adoption of an innovation in primary health care: a qualitative study based on implementation theory

abstract: Bridging the knowledge-to-practice gap in health care is an important issue that has gained interest in recent years. Implementing new methods, guidelines or tools into routine care, however, is a slow and unpredictable process, and the factors that play a role in the change process are not yet fully understood. There is a number of theories concerned with factors predicting successful implementation in various settings, however, this issue is insufficiently studied in primary health care (PHC). The objective of this article was to apply implementation theory to identify key factors influencing the adoption of an innovation being introduced in PHC in Sweden. A qualitative study was carried out with staff at six PHC units in Sweden where a computer-based test for lifestyle intervention had been implemented. Two different implementation strategies, implicit or explicit, were used. Sixteen focus group interviews and two individual interviews were performed. In the analysis a theoretical framework based on studies of implementation in health service organizations, was applied to identify key factors influencing adoption. The theoretical framework proved to be relevant for studies in PHC. Adoption was positively influenced by positive expectations at the unit, perceptions of the innovation being compatible with existing routines and perceived advantages. An explicit implementation strategy and positive opinions on change and innovation were also associated with adoption. Organizational changes and staff shortages coinciding with implementation seemed to be obstacles for the adoption process. When implementation theory obtained from studies in other areas was applied in PHC it proved to be relevant for this particular setting. Based on our results, factors to be taken into account in the planning of the implementation of a new tool in PHC should include assessment of staff expectations, assessment of the perceived need for the innovation to be implemented, and of its potential compatibility with existing routines. Regarding context, we suggest that implementation concurrent with other major organizational changes should be avoided. The choice of implementation strategy should be given thorough consideration.


title: Employee Motivation: The Key to Effective Organizational Management in Nigeria.

abstract: This article explores the indispensability of employee motivation in effective organizational management. In the style of a qualitative research, the paper x-rays, exhaustively, the merits, efficacy and relevance of motivation in the success of organizational management. It also identifies and explains the factors that affect employee motivation, the benefits of employee motivation in Nigerian organizations, some theories of motivation and the significantlink between effective organizational management and employee motivation in Nigerian organizations, both the public and the private. For the fact that employee motivation is an intricate and sophisticated subject, once applied in the right way an organization is bound to achieve a long-lasting success. However, if ignored an organization will fizzle out in the game. For Nigerian managers to have an understanding of employee motivation, they must know the concept of motivation, organizational management, differences in individual needs and the variety of motivational factors and the changes in priorities of these factors overtime. The necessity of this work is situated in it provision of solutions to Nigerian employees' redundant attitude to work and its consequences.


title: Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset

abstract: Recent research on problem formulations based on decomposition into low-rank plus sparse matrices shows a suitable framework to separate moving objects from the background. The most representative problem formulation is the Robust Principal Component Analysis (RPCA) solved via Principal Component Pursuit (PCP) which decomposes a data matrix in a low-rank matrix and a sparse matrix. However, similar robust implicit or explicit decompositions can be made in the following problem formulations: Robust Non-negative Matrix Factorization (RNMF), Robust Matrix Completion (RMC), Robust Subspace Recovery (RSR), Robust Subspace Tracking (RST) and Robust Low-Rank Minimization (RLRM). The main goal of these similar problem formulations is to obtain explicitly or implicitly a decomposition into low-rank matrix plus additive matrices. In this context, this work aims to initiate a rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation. For this, we first provide a preliminary review of the recent developments in the different problem formulations which allows us to define a unified view that we called Decomposition into Low-rank plus Additive Matrices (DLAM). Then, we examine carefully each method in each robust subspace learning/tracking frameworks with their decomposition, their loss functions, their optimization problem and their solvers. Furthermore, we investigate if incremental algorithms and real-time implementations can be achieved for background/foreground separation. Finally, experimental results on a large-scale dataset called Background Models Challenge (BMC 2012) show the comparative performance of 32 different robust subspace learning/tracking methods.


title: Modeling intra-label dynamics in connectionist temporal classification

abstract: Most sequence processing tasks can be cast as a problem of mapping a sequence of observations into a sequence of labels. This is a very difficult problem since the association between input data sequences and output label sequences is not given at the frame level. Recurrent neural networks (RNNs) equipped with connectionist temporal classification (CTC) are among the best tools devised to handle this problem and have been used to achieve state of the art results in many handwritten and speech recognition tasks. The reason that RNNs are used instead of feedforward networks in combination with CTC is that CTC does not model the dynamics of sequences. Specifically, the long short term memory (LSTM) RNN, which is excellent at memorizing information for a long time, is used in combination with CTC to overcome the limitations of CTC in modeling the dynamics of sequences. In this paper, we propose to model each label with a sequence of hidden sub-labels at the CTC level. The proposed framework allows CTC to learn the intra-label relations which transfers part of the load of learning dynamical sequences from RNN to CTC. Our experiments on handwriting recognition tasks show that the proposed method outperforms standard CTC in terms of accuracy.


title: Semantic Sentiment Analysis of Twitter Data

abstract: Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.


title: Potential Health Impact of Environmentally Released Micro- and Nanoplastics in the Human Food Production Chain: Experiences from Nanotoxicology.

abstract: High concentrations of plastic debris have been observed in the oceans. Much of the recent concern has focused on microplastics in the marine environment. Recent studies of the size distribution of the plastic debris suggested that continued fragmenting of microplastics into nanosized particles may occur. In this review we assess the current literature on the occurrence of environmentally released micro- and nanoplastics in the human food production chain and their potential health impact. The currently used analytical techniques introduce a great bias in the knowledge, since they are only able to detect plastic particles well above the nanorange. We discuss the potential use of the very sensitive analytical techniques that have been developed for the detection and quantification of engineered nanoparticles. We recognize three possible toxic effects of plastic particles: first due to the plastic particles themselves, second to the release of persistent organic pollutant adsorbed to the plastics, and third to the leaching of additives of the plastics. The limited data on microplastics in foods do not predict adverse effect of these pollutants or additives. Potential toxic effects of microplastic particles will be confined to the gut. The potential human toxicity of nanoplastics is poorly studied. Based on our experiences in nanotoxicology we prioritized future research questions.


title: Baseline prediction of point of sales data for trade promotion optimization

abstract: Baseline prediction is an important to devise marketing strategy for a consumer goods product. Simulation techniques, time series algorithms are often used to generate baseline for the future. However the algorithm that fits a particular point of sales (POS) data varies according to the datasets. Sample set of point of sales data were simulated under different conditions and constraints incorporating seasonal and non seasonal trends. This study has compared the performance of two time series models namely Winters model and linear exponential smoothening on the simulated datasets. Winters model was found to be a better fit for the point of sales data that were used for testing.


title: Cloud database systems: A model conversion in databases

abstract: Relational databases have controlled the Information Technology businesses for more than four decades. However, there has been a revolution widely accepted in the way Information Technology is being used and observed. Mostly, Standalone systems used to access using internet-based software, network with huge bandwidth and dedicated servers with multiple distributed servers. Cloud Database emphasizes the applications of actual traditional databases specifically to scalability, dynamic appliances and ease of use facility. Databases in Cloud are basically used for data through tools such as business intelligence, data mining and data warehousing. These tools provide capability to create new business strategies and also show scalability and elasticity nature. They are able to handle large amount of data as well. The main purpose of this paper is to review the requirements of the standard way in the cloud databases and multiple frameworks. Furthermore, it determines the contest to develop and maintain cloud databases that fulfill the user requirements.


title: Visualization of Eye Tracking Data: A Taxonomy and Survey

abstract: This survey provides an introduction into eye tracking visualization with an overview of existing techniques. Eye tracking is important for evaluating user behaviour. Analysing eye tracking data is typically done quantitatively, applying statistical methods. However, in recent years, researchers have been increasingly using qualitative and exploratory analysis methods based on visualization techniques. For this state-of-the-art report, we investigated about 110 research papers presenting visualization techniques for eye tracking data. We classified these visualization techniques and identified two main categories: point-based methods and methods based on areas of interest. Additionally, we conducted an expert review asking leading eye tracking experts how they apply visualization techniques in their analysis of eye tracking data. Based on the experts' feedback, we identified challenges that have to be tackled in the future so that visualizations will become even more widely applied in eye tracking research.


title: Cybersecurity, women and minorities: findings and recommendations from a preliminary investigation

abstract: This paper presents the work done by the ACM ITiCSE, 2013 Conference Working Group (WG) on Cybersecurity, Women and Minorities: How to Succeed in the Career! The ITiCSE 2013 conference was held July 1-3, 2013, in Canterbury, United Kingdom. The overall goal of the WG was to conduct a preliminary investigation into the reasons behind the lack of women and minorities within the field of Cybersecurity. This is not just an issue of academic or research interest, but is important in ensuring that a greater number of women and minorities progress through a full career in Cybersecurity. There are currently no statistics available on the numbers of women and minorities either currently enrolled in or graduated from these programs. There is a need to explore the full range of factors that influence women and minority's decisions not to consider a career in Cybersecurity.


title: Portable Lumipen: Dynamic SAR in Your Hand

abstract: Based on the concept of spatial augmented reality (SAR), a number of applications have been developed by extending the type of target objects to include dynamically moving objects; however, the systems themselves still remain fixed. In this paper, we propose a new paradigm for dynamic SAR implemented with a mobile device and a practical architecture using a 3D-stacked vision chip and a high-speed optical gaze controller. We evaluated the prototype system that we developed, called “Portable Lumipen”, and confirmed that image capturing at 1000 fps, rapid processing and a 3 ms response time for projection were achieved on a mobile device. We also propose potential applications using this portable dynamic SAR system and demonstrated miniaturization of a high-speed visual feedback system.


title: Fast Combinational Architecture for a Vedic Divider

abstract: This paper proposes a fast, purely combinational implementation of a divider, based on the Dhwajanka Sutra of Vedic mathematics. Vedic mathematics offers algorithms that are computationally efficient over conventional arithmetic algorithms. Dhwajanka Sutra has been chosen as it is an algorithm efficient for all possible cases, unlike other sutras for division (Nikhilam and Paravartya) which are case specific. The simplicity of the Vedic algorithm implemented in combinational form has reduced computation time significantly The proposed design is compared with existing divider architectures implemented on an FPGA namely Non-restoring Algorithm based Divider, Vedic Divider (Paravartya Algorithm), Vedic Divider (Nikhilam Algorithm), Decimal Divider based on Newton Raphson algorithm, Decimal Divider based on SRT algorithm and Combination divider algorithms. The proposed vedic divider shows a maximum of 273.72% speed improvement while the minimum is 99.95%. The results have been validated using the Xilinx ISE Design Suite 14.7, on the Virtex5 (xc5vlx20T-2ff323) FPGA Target Technology and the design has been implemented in Verilog.


title: Implementation of 32-Bit Arithmetic Logic Unit on Xilinx using VHDL

abstract: In the present day knowledge, there is an massive requisite of developing appropriate data communication interfaces for real time embedded systems. Field Programmable Gate Array (FPGA) gives various means, which can be programmed for constructing an effective embedded unit. The FPGA configuration is generally specified using a hardware description language (HDL). VHDL (VHSIC hardware description language) is a hardware description language used in electronic design automation to explain digital and mixed-signal structures such as field programmable gate arrays (FPGA) and integrated circuits. This work proposes a technique to design and implement a 32 bit ALU which is a digital circuit that performs arithmetic and logical operations on Xilinx ISE using VHDL.


title: Razzer: Finding Kernel Race Bugs through Fuzzing

abstract: A data race in a kernel is an important class of bugs, critically impacting the reliability and security of the associated system. As a result of a race, the kernel may become unresponsive. Even worse, an attacker may launch a privilege escalation attack to acquire root privileges. In this paper, we propose Razzer, a tool to find race bugs in kernels. The core of Razzer is in guiding fuzz testing towards potential data race spots in the kernel. Razzer employs two techniques to find races efficiently: a static analysis and a deterministic thread interleaving technique. Using a static analysis, Razzer identifies over-approximated potential data race spots, guiding the fuzzer to search for data races in the kernel more efficiently. Using the deterministic thread interleaving technique implemented at the hypervisor, Razzer tames the non-deterministic behavior of the kernel such that it can deterministically trigger a race. We implemented a prototype of Razzer and ran the latest Linux kernel (from v4.16-rc3 to v4.18-rc3) using Razzer. As a result, Razzer discovered 30 new races in the kernel, with 16 subsequently confirmed and accordingly patched by kernel developers after they were reported.


title: On the interdependency between multi-channel scheduling and tree-based routing for WSNs in smart grid environments

abstract: Abstract Field tests show that the link-quality of wireless links in different smart grid environments, such as outdoor substation, varies greatly both in space and time because of various factors, including multi-path, fading, node contentions, radio frequency (RF) interference, and noise. This leads to both time and location dependent capacity limitations of wireless links in smart grid environments. To improve network capacity in such environments, multichannel communication and the use of proper routing topologies emerge as efficient solutions to achieve simultaneous, interference-free transmissions over multiple channels. In this paper, we explore the impact of multi-channel communication and the selection of efficient routing topologies on the performance of wireless sensors networks in different smart grid spectrum environments. Particularly, we evaluate the network performance using a receiver-based channel selection method and using different routing trees, including routing trees constructed considering the link qualities, Capacitated Minimum Spanning Trees (CMSTs), capacitated minimum spanning tree considering link qualities and Minimum Hop Spanning Trees (MHSTs). We focus on performance measures such as delay and throughput that can benefit from the simultaneous parallel transmissions and show that the use multiple channels together with routing trees that consider network capacity and link quality, i.e., capacitated minimum spanning tree considering link qualities, substantially improve the network performance in harsh smart-grid environments compared to single-channel communication and minimum-hop routing trees.


title: Traffic sign detection based on AdaBoost color segmentation and SVM classification

abstract: This paper aims to present a new approach to detect traffic signs which is based on color segmentation using AdaBoost binary classifier and circular Hough Transform. The Adaboost classifier was trained to segment traffic signs images according to the desired color. A voting mechanism was invoked to establish a property curve for each of the candidates. SVM classifier was trained to classify the property curves of each object into their corresponding classes. Experiments conducted on Adaboost color segmentation under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 95%. The proposed system was tested on two different groups of traffic signs; the warning and the prohibitory signs. In the case of warning signs, a recognition rate of 98.4% was achieved while it was 97% for prohibitory traffic signs. This test was carried out under a wide range of environmental conditions.


title: FingerCode: a filterbank for fingerprint representation and matching

abstract: With the identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic positive personal identification applications, biometrics-based identification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a significant fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information, available in the fingerprints. The proposed filter-based algorithm uses a bank of Gabor filters to capture both the local and the global details in a fingerprint as a compact 640-byte fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. Our initial results show identification accuracies comparable to the best results of minutiae-based algorithms published in the open literature. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary fingerprint information.


title: Security Notions and Advanced Method for Human Shoulder-Surfing Resistant PIN-Entry

abstract: The personal identification number (PIN) is a well-known authentication method used in various devices, such as ATMs, mobile devices, and electronic door locks. Unfortunately, the conventional PIN-entry method is vulnerable to shoulder-surfing attacks. Consequently, various shoulder-surfing resistant methods have been proposed. However, the security analyses used to justify these proposed methods are not based on rigorous quantitative analysis, but instead on the results of experiments involving a limited number of human attackers. In this paper, we propose new theoretical and experimental techniques for quantitative security analysis of PIN-entry methods. We first present new security notions and guidelines for secure PIN-entry methods by analyzing the existing methods under the new framework. On the basis of these guidelines, we develop a new PIN-entry method that effectively obviates human shoulder-surfing attacks by significantly increasing the amount of short-term memory required in an attack.


title: HadoopViz: A MapReduce framework for extensible visualization of big spatial data

abstract: This paper introduces HadoopViz; a MapReduce-based framework for visualizing big spatial data. HadoopViz has three unique features that distinguish it from other techniques. (1) It exposes an extensible interface which allows users to define a new visualization types, e.g., scatter plot, road network, or heat map, by defining five abstract functions, without delving into the implementation details of the MapReduce algorithms. As it is open source, HadoopViz allows algorithm designers to focus on how the data should be visualized rather than performance or scalability issues. (2) HadoopViz is capable of generating big images with giga-pixel resolution by employing a three-phase technique, partition-plot-merge. (3) HadoopViz provides a smoothing functionality which can fuse nearby records together as the image is plotted. This makes it capable of generating more types of images with high quality as compared to existing work. Experimental results on real datasets of up to 14 Billion points show the extensibility, scalability, and efficiency of HadoopViz to handle different visualization types of spatial big data.


title: Knowing Indonesia: Intersections of Self, Discipline, and Nation ed. by Jemma Purdy (review)

abstract: This review considers debates in the study of Indonesia, in particular the politics of such study.


title: Smart gateway based communication for cloud of things

abstract: With the increasing applications in the domains of ubiquitous and context-aware computing, Internet of Things (IoT) are gaining importance. In IoTs, literally anything can be part of it, whether it is sensor nodes or dumb objects, so very diverse types of services can be produced. In this regard, resource management, service creation, service management, service discovery, data storage, and power management would require much better infrastructure and sophisticated mechanism. The amount of data IoTs are going to generate would not be possible for standalone power-constrained IoTs to handle. Cloud computing comes into play here. Integration of IoTs with cloud computing, termed as Cloud of Things (CoT) can help achieve the goals of envisioned IoT and future Internet. This IoT-Cloud computing integration is not straight-forward. It involves many challenges. One of those challenges is data trimming. Because unnecessary communication not only burdens the core network, but also the data center in the cloud. For this purpose, data can be preprocessed and trimmed before sending to the cloud. This can be done through a Smart Gateway, accompanied with a Smart Network or Fog Computing. In this paper, we have discussed this concept in detail and present the architecture of Smart Gateway with Fog Computing. We have tested this concept on the basis of Upload Delay, Synchronization Delay, Jitter, Bulk-data Upload Delay, and Bulk-data Synchronization Delay.


title: A Novel Connectionist System for Unconstrained Handwriting Recognition

abstract: Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. In experiments on two large unconstrained handwriting databases, our approach achieves word recognition accuracies of 79.7 percent on online data and 74.1 percent on offline data, significantly outperforming a state-of-the-art HMM-based system. In addition, we demonstrate the network's robustness to lexicon size, measure the individual influence of its hidden layers, and analyze its use of context. Last, we provide an in-depth discussion of the differences between the network and HMMs, suggesting reasons for the network's superior performance.


title: Smart layers and dumb result: IO characterization of an android-based smartphone

abstract: In this paper, we offer an in-depth IO characterization of the Android-based smartphone. We analyze the IO behaviors of a total of 14 Android applications from six different categories. We examine the correlations among seven IO attributes: originating application, file type, IO size, IO type (read/write), random/sequential, block semantics (Data/Metadata/Journal), and session type (buffered vs. synchronous IO). For the purposes of our study, we develop Mobile Storage Analyzer (MOST), a framework for collecting IO attributes across layers. Let us summarize our findings briefly. SQLite, which is the most popular tool for maintaining persistent data in Android, puts too much burden on the storage. For example, a single SQLite operation (update or insert) results in at least 11 write operations being sent to the storage. These are for creating short-lived files, updating database tables, and accessing EXT4 Journal. From the storage point of view, more than 50% of writes are for EXT4 Journal updating. Excluding Metadata and Journal accesses, 60-80% of the writes are random. More than 50% of the writes are synchronous. 4KB IO accounts for 70% of all writes. In the Android platform, each SQLite and EXT4 filesystem requires a great amount of effort to ensure reliability in supporting transactions and journaling, respectively. When they are combined, the results are rather dumb. The operations of SQLite and EXT4, when combined, generate unnecessarily excessive write operations to the NAND-based storage. This not only degrades IO performance but also significantly reduces the lifetime of the underlying NAND flash storage. The results of this study clearly suggest that SQLite, EXT4, and the underlying NAND-based storage need to be completely overhauled and vertically integrated so as to properly and effectively incorporate their respective characteristics.


title: In vitro anthelmintic activity of crude extracts of five medicinal plants against egg-hatching and larval development of Haemonchus contortus.

abstract: Abstract Ethnopharmacological relevance Senna occidentalis , Leonotis ocymifolia , Leucas martinicensis , Rumex abyssinicus , and Albizia schimperiana are traditionally used for treatment of various ailments including helminth infection in Ethiopia. Materials and Methods In vitro egg hatch assay and larval development tests were conducted to determine the possible anthelmintic effects of crude aqueous and hydro-alcoholic extracts of the leaves of Senna occidentalis , aerial parts of Leonotis ocymifolia , Leucas martinicensis , Rumex abyssinicus , and stem bark of Albizia schimperiana on eggs and larvae of Haemonchus contortus . Results Both aqueous and hydro-alcoholic extracts of Leucas martinicensis , Leonotis ocymifolia and aqueous extract of Senna occidentalis and Albizia schimperiana induced complete inhibition of egg hatching at concentration less than or equal to 1 mg/ml. Aqueous and hydro-alcoholic extracts of all tested medicinal plants have shown statistically significant and dose dependent egg hatching inhibition. Based on ED 50 , the most potent extracts were aqueous and hydro-alcoholic extracts of Leucas martinicensis (0.09 mg/ml), aqueous extracts of Rumex abyssinicus (0.11 mg/ml) and Albizia schimperiana (0.11 mg/ml). Most of the tested plant extracts have shown remarkable larval development inhibition. Aqueous extracts of Leonotis ocymifolia , Leucas martinicensis , Albizia schimperiana and Senna occidentalis induced 100, 99.85, 99.31, and 96.36% inhibition of larval development, respectively; while hydro-alcoholic extracts of Albizia schimperiana induced 99.09 inhibition at the highest concentration tested (50 mg/ml). Poor inhibition was recorded for hydro-alcoholic extracts of Senna occidentalis (9%) and Leonotis ocymifolia (37%) at 50 mg/ml. Conclusions The overall findings of the current study indicated that the evaluated medicinal plants have potential anthelmintic effect and further in vitro and in vivo evaluation is indispensable to make use of these plants.


title: Projected Newton methods for optimization problems with simple constraints

abstract: We consider the problem $\min \{ f(x)|x \geqq 0\} $, and propose algorithms of the form $x_{k + 1} = [x_k - \alpha _k D_k \nabla f(x_k )]^ + $, where $[ \cdot ]^ + $ denotes projection on the positive orthant, $\alpha _k $ is a stepsize chosen by an Armijo-like rule and $D_k $ is a positive definite symmetric matrix which is partly diagonal. We show that $D_k $ can be calculated simply on the basis of second derivatives of f so that the resulting Newton-like algorithm has a typically superlinear rate of convergence. With other choices of $D_k $ convergence at a typically linear rate is obtained. The algorithms are almost as simple as their unconstrained counterparts. They are well suited for problems of large dimension such as those arising in optimal control while being competitive with existing methods for low-dimensional problems. The effectiveness of the Newton-like algorithm is demonstrated via computational examples involving as many as 10,000 variables. Extensions to general linearly constrained pr...


title: Ensemble Distillation for Neural Machine Translation

abstract: Knowledge distillation describes a method for training a student network to perform better by learning from a stronger teacher network. Translating a sentence with an Neural Machine Translation (NMT) engine is time expensive and having a smaller model speeds up this process. We demonstrate how to transfer the translation quality of an ensemble and an oracle BLEU teacher network into a single NMT system. Further, we present translation improvements from a teacher network that has the same architecture and dimensions of the student network. As the training of the student model is still expensive, we introduce a data filtering method based on the knowledge of the teacher model that not only speeds up the training, but also leads to better translation quality. Our techniques need no code change and can be easily reproduced with any NMT architecture to speed up the decoding process.


title: Sentiment classification using Comprehensive Attention Recurrent models

abstract: Sentiment classification has been a very hot topic in the field of natural language processing (NLP) and understanding in recent years. Recurrent neural networks (RNN) is a widely used tool to deal with the classification problem of variable-length sentences. The standard RNN can only access the preceding context of a sentence. In this paper, a new architecture termed Comprehensive Attention Recurrent Neural Networks (CA-RNN) which can store preceding, succeeding and local contexts of any position in a sequence is developed. The bidirectional recurrent neural networks (BRNN) is used to access the past and future information while a convolutional layer is employed to capture local information. The standard RNN is also replaced by two recently emerged RNN variants, namely long short-term memory (LSTM) and gated recurrent unit (GRU), to enhance the effectiveness of the new architecture. Another salient feature of the proposed model is that it can be trained end-to-end without any human intervention. It is very easy to be implemented. We conduct experiments on several sentiment-labeled datasets and analysis tasks. Experiment results demonstrate that capturing comprehensive contextual information can significantly enhance the classification accuracy compared with the standard recurrent models and the new models can achieve competitive performance compared with the state-of-the-art approaches.


title: FORECASTING TIME SERIES DATA USING HYBRID GREY RELATIONAL ARTIFICIAL NEURAL NETWORK AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE MODEL

abstract: In business, industry and government agencies, anticipating future behavior that involves many critical variables for nation wealth creation is vitally important, thus the necessity to make precise decision by the policy makers is really essential. Consequently, an accurate and reliable forecast system is needed to compose such predictions. Accordingly, the aim of this research is to develop a new hybrid model by combining a linear and nonlinear model for forecasting time series data. The proposed model (GRANN_ARIMA) integrates nonlinear grey relational articial neural network (GRANN) and linear ARIMA model, combining new features such as multivariate time series data as well as grey relational analysis to select the appropriate inputs and hybridization succession. To validate the performance of the proposed model, small and large scale data sets are used. The forecasting performance was compared with several models, and these include: individual models (ARIMA, multiple regression, grey relational articial neural network), several hybrid models (MARMA, MR_ANN, ARIMA_ANN), and articial neural network (ANN) trained using levenberg marquardt algorithm. The experiments have shown that the proposed model has outperformed other models with 99.5% forecasting accuracy for small-scale data and 99.84% for large-scale data. The empirical results obtained have proved that the GRANN_ARIMA model can provide a better alternative for time series forecasting due to its promising performance and capability in handling time series data for both small and large scale data.


title: Symbiotic Composition and Evolvability

abstract: Several of the Major Transitions in natural evolution, such as the symbiogenic origin of eukaryotes from prokaryotes, share the feature that existing entities became the components of composite entities at a higher level of organisation. This composition of pre-adapted extant entities into a new whole is a fundamentally different source of variation from the gradual accumulation of small random variations, and it has some interesting consequences for issues of evolvability. In this paper we present a very abstract model of 'symbiotic composition' to explore its possible impact on evolvability. A particular adaptive landscape is used to exemplify a class where symbiotic composition has an adaptive advantage with respect to evolution under mutation and sexual recombination. Whilst innovation using conventional evolutionary algorithms becomes increasingly more difficult as evolution continues in this problem, innovation via symbiotic composition continues through successive hierarchical levels unimpeded.


title: Speaking in tongues: SQL access to NoSQL systems

abstract: Non-relational data stores, which are usually called NoSQL systems, have become an important class of data management systems. They often outperform the relational systems. Yet there is no common way to interface with NoSQL systems. The Structured Query Language (SQL) has already proven to be useful to provide a uniform query language for all the relational systems. We identify a subset of SQL for access to NoSQL systems. Our extensible middleware translates SQL queries to the query languages of the connected NoSQL systems. The migration between these systems is thereby greatly simplified as well as the comparison of the supported NoSQL systems.


title: Accelerated cone beam CT reconstruction based on OpenCL

abstract: Open Computing Language (OpenCL) is a fundamental technology for cross-platform parallel programming. The emerging of OpenCL provides portable and efficient access to the power of modern processors. This revolutionary new technology is applied to accelerate the reconstruction of cone beam computed tomography (CBCT) on Graphics Processing Unit (GPU) in this paper. An OpenCL-based implementation of the Feldkamp-Davis-Kress (FDK) algorithm is presented. The required transformations to parallelize the algorithm for the OpenCL architecture are also explained. Comparing to the conventional CPU-based implementation, the proposed method reaches an over 57 times speedup. Experimental results show a great performance boost, which can pave the way for widespread application and new conceptual innovation of CBCT. Besides, the feasibility and potential of OpenCL-based implementation are also indicated.


title: AgriSys: A smart and ubiquitous controlled-environment agriculture system

abstract: With new technological advancement in controlled-environment agriculture systems, the level of productivity has significantly increased. Agriculture systems are now more capable, reliable, and provide enhanced productivity. An agriculture environment can range from a single plant in a house, a backyard garden, a small farm, to a large farming facility. These agricultural automated systems will help in managing and maintain safe environment especially the agricultural areas. In this paper, we propose a smart Agriculture System (AgriSys) that can analyze an agriculture environment and intervene to maintain its adequacy. The system deals with general agriculture challenges, such as, temperature, humidity, pH, and nutrient support. In addition, the system deals with desert-specific challenges, such as, dust, infertile sandy soil, constant wind, very low humidity, and the extreme variations in diurnal and seasonal temperatures. The system interventions are mainly intended to maintain the adequacy of the agriculture environment. For a reduced controller complexity, the adoption of fuzzy control is considered. The system implementation relies on state-of-art computer interfacing tools from National Instruments as programmed under LabVIEW.


title: Lexical Entrainment In Spontaneous Dialog

abstract: It is often desirable to predict or constrain the lexical choices people make with spoken language systems. I discuss lexical variability in language use; while there is a great deal of variability across conversations, there is little within. In conversation, people achieve conceptual pacts, or shared conceptualizations, which they mark by using the same terms; this process of lexical entrainment limits and systematizes lexical variability (Brennan & Clark, 1996). In text and speech dialogs with computer systems, people often adopt the systems' terms; this process shares some aspects in common with lexical entrainment, and it differs in some ways as well. After summarizing five experiments on lexical variability with human partners and computers, I present some implications of this work for spoken language systems.


title: Novel Electrodes for Underwater ECG Monitoring

abstract: We have developed hydrophobic electrodes that provide all morphological waveforms without distortion of an ECG signal for both dry and water-immersed conditions. Our electrode is comprised of a mixture of carbon black powder (CB) and polydimethylsiloxane (PDMS). For feasibility testing of the CB/PDMS electrodes, various tests were performed. One of the tests included evaluation of the electrode-to-skin contact impedance for different diameters, thicknesses, and different pressure levels. As expected, the larger the diameter of the electrodes, the lower the impedance and the difference between the large sized CB/PDMS and the similarly-sized Ag/AgCl hydrogel electrodes was at most 200 kΩ, in favor of the latter. Performance comparison of CB/PDMS electrodes to Ag/AgCl hydrogel electrodes was carried out in three different scenarios: a dry surface, water immersion, and postwater immersion conditions. In the dry condition, no statistical differences were found for both the temporal and spectral indices of the heart rate variability analysis between the CB/PDMS and Ag/AgCl hydrogel (p > 0.05) electrodes. During water immersion, there was significant ECG amplitude reduction with CB/PDMS electrodes when compared to wet Ag/AgCl electrodes kept dry by their waterproof adhesive tape, but the reduction was not severe enough to obscure the readability of the recordings, and all morphological waveforms of the ECG signal were discernible even when motion artifacts were introduced. When water did not penetrate tape-wrapped Ag/AgCl electrodes, high fidelity ECG signals were observed. However, when water penetrated the Ag/AgCl electrodes, the signal quality degraded to the point where ECG morphological waveforms were not discernible.


title: The Relationship between Subthreshold Autistic Traits, Ambiguous Figure Perception and Divergent Thinking.

abstract: This research investigates the paradox of creativity in autism. That is, whether people with subclinical autistic traits have cognitive styles conducive to creativity or whether they are disadvantaged by the implied cognitive and behavioural rigidity of the autism phenotype. The relationship between divergent thinking (a cognitive component of creativity), perception of ambiguous figures, and self-reported autistic traits was evaluated in 312 individuals in a non-clinical sample. High levels of autistic traits were significantly associated with lower fluency scores on the divergent thinking tasks. However autistic traits were associated with high numbers of unusual responses on the divergent thinking tasks. Generation of novel ideas is a prerequisite for creative problem solving and may be an adaptive advantage associated with autistic traits.


title: Comparing business modeling methods: creating and applying a comparison framework for meta-business models

abstract: A business model is very important to any company and especially for e-businesses. Over the past few years, several authors have created different meta-business models, methods for business modeling. However, the authors rarely use existing literature for their conceptual design. Because of this, there are no clear semantics in the field of business model research. The elements used in meta-business models are not clearly defined, or differ wildly from one model to the next. As business models are important to companies, they must add some value. The use of business models certainly does that for just about any company. Nevertheless, this value is strongly limited by the lack of cohesion in the field of business model research. In order to identify the elements used in meta-business models and their role, 10 meta-business models are compared. First a comparison framework is constructed, consisting of 19 criteria. After that, the framework is applied to compare the different meta-business models. After the comparison, the elements are categorized in five groups. These groups provide a clear overview of the metabusiness models and the role that different elements play.


title: Joint 3D Tracking of a Deformable Object in Interaction with a Hand

abstract: We present a novel method that is able to track a complex deformable object in interaction with a hand. This is achieved by formulating and solving an optimization problem that jointly considers the hand, the deformable object and the hand/object contact points. The optimization evaluates several hand/object contact configuration hypotheses and adopts the one that results in the best fit of the object’s model to the available RGBD observations in the vicinity of the hand. Thus, the hand is not treated as a distractor that occludes parts of the deformable object, but as a source of valuable information. Experimental results on a dataset that has been developed specifically for this new problem illustrate the superior performance of the proposed approach against relevant, state of the art solutions.


title: Cutaneous granulomatous reaction to injectable hyaluronic acid gel

abstract: Injectable hyaluronic acid (HA) derivatives are the most used reabsorbable dermal fillers for soft tissue augmentation today and their utilization is considered safe. We report a cutaneous granulomatous reaction that developed in a woman 5 weeks after the first treatment with a nonanimal HA derivative for the correction of facial wrinkling. We describe the clinicopathological findings and course of the cutaneous reaction. The adverse reaction showed clinical and histopathological characteristics comparable to the few previously reported cases. All cutaneous lesions spontaneously disappeared without scars within 3 months. We conclude that even nonanimal injectable HA derivatives can be associated with delayed granulomatous reactions. The patient should be informed of this potential long-term complication.


title: Learning to Track Colored Objects with Log-Polar Vision

abstract: An approach bringing together space-variant vision through a simple color segmentation technique and learning is presented. The proposed approach is employed to control the movement of a 5 degree of freedom (d.o.f.) robotic head. Color information is used to determine the position of the object of interest in the image plane and, consequently, to track it during its motion. The distance of the target from the center of the image is used to feed both a closed-loop and an open-loop controller. Most important, the parameters of the controllers are learnt on-line in a self-supervised fashion. Experiments are presented to demonstrate empirically the feasibility of the approach and its application to a real world control problem.


title: Abstract Understanding Movement as Input for Interaction – A Study of Two Eyetoy ™ Games

abstract: Interaction between people and computers can now be driven by movements of the human body without the need for mediation by other input devices. We present a way of conceptualising movement-based interaction. Our approach uses two existing frameworks for investigation of the relationship between bodily actions and the corresponding response from technology. The first framework examines characteristics of an interface in terms of “Sensible, Sensable, and Desirable” movement properties. In the second framework movement is seen as a form of “communication” between the user and technology, and the analysis looks at the implications this has for realising the interaction.


"Leadership in Action: How Top Hackers Behave" A Big-Data Approach with Text-Mining and Sentiment Analysis


title: The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information

abstract: We present a new visualization, called the Table Lens, for visualizing and making sense of large tables. The visualization uses a focus+context (fisheye) technique that works effectively on tabular information because it allows display of crucial label information and multiple distal focal areas. In addition, a graphical mapping scheme for depicting table contents has been developed for the most widespread kind of tables, the cases-by-variables table. The Table Lens fuses symbolic and graphical representations into a single coherent view that can be fluidly adjusted by the user. This fusion and interactivity enables an extremely rich and natural style of direct manipulation exploratory data analysis.


title: A new waveform enabling enhanced QAM-FBMC systems

abstract: Multiple filter-banks enable QAM transmission and reception in filter-bank multi-carrier (FBMC) systems. In this paper, we propose a new method to design a set of simultaneously used base filters for QAM-FBMC by optimizing the trade-off between spectrum confinement and self-interference. The increased freedom in multiple base filter designs brings merits of a tailor-made spectrum confinement and desirable bit error performance simultaneously. Simulations results show the superiority of the proposed QAM-FBMC systems to other post-OFDM waveform candidates.


title: ParSym: Parallel symbolic execution

abstract: Scaling software analysis techniques based on source-code, such as symbolic execution and data flow analyses, remains a challenging problem for systematically checking software systems. The increasing availability of clusters of commodity machines provides novel opportunities to scale these techniques using parallel algorithms. This paper presents ParSym, a novel parallel algorithm for scaling symbolic execution using a parallel implementation. In every iteration ParSym explores multiple branches of a path condition in parallel by distributing them among available workers resulting in an efficient parallel version of symbolic execution. Experimental results show that symbolic execution is highly scalable using parallel algorithms: using 512 processors, more than two orders of magnitude speedup are observed.


Font and background color independent text binarization


abstract: Multidimensional projection techniques have experienced many improvements lately, mainly regarding computational times and accuracy. However, existing methods do not yet provide flexible enough mechanisms for visualization-oriented fully interactive applications. This work presents a new multidimensional projection technique designed to be more flexible and versatile than other methods. This novel approach, called Local Affine Multidimensional Projection (LAMP), relies on orthogonal mapping theory to build accurate local transformations that can be dynamically modified according to user knowledge. The accuracy, flexibility and computational efficiency of LAMP is confirmed by a comprehensive set of comparisons. LAMP's versatility is exploited in an application which seeks to correlate data that, in principle, has no connection as well as in visual exploration of textual documents.


title: Extracting Ontological Knowledge from Textual Descriptions

abstract: Authoring of OWL-DL ontologies is intellectually challenging and to make this process simpler, many systems accept natural language text as input. A text-based ontology authoring approach can be successful only when it is combined with an effective method for extracting ontological axioms from text. Extracting axioms from unrestricted English input is a substantially challenging task due to the richness of the language. Controlled natural languages (CNLs) have been proposed in this context and these tend to be highly restrictive. In this paper, we propose a new CNL called TEDEI (TExtual DEscription Identifier) whose grammar is inspired by the different ways OWL-DL constructs are expressed in English. We built a system that transforms TEDEI sentences into corresponding OWL-DL axioms. Now, ambiguity due to different possible lexicalizations of sentences and semantic ambiguity present in sentences are challenges in this context. We find that the best way to handle these challenges is to construct axioms corresponding to alternative formalizations of the sentence so that the end-user can make an appropriate choice. The output is compared against human-authored axioms and in substantial number of cases, human-authored axiom is indeed one of the alternatives given by the system. The proposed system substantially enhances the types of sentence structures that can be used for ontology authoring.


title: Towards a secure network architecture for smart grids in 5G era

abstract: Smart grid introduces a wealth of promising applications for upcoming fifth-generation mobile networks (5G), enabling households and utility companies to establish a two-way digital communications dialogue, which can benefit both of them. The utility can monitor real-time consumption of end users and take proper measures (e.g., real-time pricing) to shape their consumption profile or to plan enough supply to meet the foreseen demand. On the other hand, a smart home can receive real-time electricity prices and adjust its consumption to minimize its daily electricity expenditure, while meeting the energy need and the satisfaction level of the dwellers. Smart Home applications for smart phones are also a promising use case, where users can remotely control their appliances, while they are away at work or on their ways home. Although these emerging services can evidently boost the efficiency of the market and the satisfaction of the consumers, they may also introduce new attack surfaces making the grid vulnerable to financial losses or even physical damages. In this paper, we propose an architecture to secure smart grid communications incorporating an intrusion detection system, composed of distributed components collaborating with each other to detect price integrity or load alteration attacks in different segments of an advanced metering infrastructure.


title: Efficient Road Detection and Tracking for Unmanned Aerial Vehicle

abstract: An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground–vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut–based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 $\times$ 595 videos on average.


title: Globalizing selectively: shared-memory efficiency with address-space separation

abstract: It has become common for MPI-based applications to run on shared-memory machines. However, MPI semantics do not allow leveraging shared memory fully for communication between processes from within the MPI library. This paper presents an approach that combines compiler transformations with a specialized runtime system to achieve zero-copy communication whenever possible by proving certain properties statically and globalizing data selectively by altering the allocation and deallocation of communication buffers. The runtime system provides dynamic optimization, when such proofs are not possible statically, by copying data only when there are write-write or read-write conflicts. We implemented a prototype compiler, using ROSE, and evaluated it on several benchmarks. Our system produces code that performs better than MPI in most cases and no worse than MPI, tuned for shared memory, in all cases.


title: A New Concept of Marketing: The Emotional Marketing

abstract: Nowadays, in the marketing area, a new concept of marketing is emerging: the emotional marketing. The emotional marketing studies how to arouse emotions in people to induce them to buy that particular produc/service. Recent studies shown how purchasing choices and decisions are the result of a careful analysis of rational and emotional aspects. Psychological literature recognizes that the emotional conditions influence every stage of decision-making in purchasing process. Emotions play a key role in any kind of social or business decision. The emotions are manifested in verbal, facial and textual expressions. People when speak, interact and write, convey emotions.


title: On-road driver eye movement tracking using head-mounted devices

abstract: It is now evident from anecdotal evidence and preliminary research that distractions can hinder the task of operating a vehicle, and consequently reduce driver safety. However with increasing wireless connectivity and the portability of office devices, the vehicle of the future is visualized as an extension of the static work place - i.e. an office-on-the-move, with a phone, a fax machine and a computer all within the reach of the vehicle operator. For this research a Head mounted Eye-tracking Device (HED), is used for tracking the eye movements of a driver navigating a test route in an automobible while completing various driving tasks. Issues arising from data collection of eye movements during the completion of various driving tasks as well as the analysis of this data are discussed. Methods for collecting video and scan-path data, as well as difficulties and limitations are also reported.