title: Gated networks: an inventory

abstract: Gated networks are networks that contain gating connections, in which the outputs of at least two neurons are multiplied. Initially, gated networks were used to learn relationships between two input sources, such as pixels from two images. More recently, they have been applied to learning activity recognition or multi-modal representations. The aims of this paper are threefold: 1) to explain the basic computations in gated networks to the non-expert, while adopting a standpoint that insists on their symmetric nature. 2) to serve as a quick reference guide to the recent literature, by providing an inventory of applications of these networks, as well as recent extensions to the basic architecture. 3) to suggest future research directions and applications.


title: An FPGA-based digital class-D amplifier with power supply error correction

abstract: This paper presents the design and implementation of a Field Programmable Gate Array (FPGA)-based two-channel digital class-D amplifier with H-bridge power stages. Because in H-bridge power stages the power supply noise can intermodulate with the input signals to cause errors in the outputs, a power supply noise measurement circuit and two correction blocks are employed in the proposed amplifier to correct the errors. An experimental measurement platform with a 55 V linear unregulated power supply was built to measure the proposed amplifier. The results obtained show that the errors caused by the power supply noise can be corrected in the proposed amplifier and the Total Harmonic Distortion + Noise (THD+N) of the proposed amplifier is 0.039% with 1 kHz, −1 dBFS input.


title: Multilingual Multi-modal Embeddings for Natural Language Processing

abstract: We propose a novel discriminative model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to improve embedding quality. To that end, we introduce a modification of a pairwise contrastive estimation optimisation function as our training objective. We evaluate our embeddings on an image-sentence ranking (ISR), a semantic textual similarity (STS), and a neural machine translation (NMT) task. We find that the additional multilingual signals lead to improvements on both the ISR and STS tasks, and the discriminative cost can also be used in re-ranking $n$-best lists produced by NMT models, yielding strong improvements.


11. Reinventing Ourselves as Other: More New Agents of History and Knowledge


title: Soft set based association rule mining

abstract: Detailed insight into soft set based association rule mining is provided.Drawbacks of some existing definitions are pointed out and rectified.New concepts and algorithms for soft association rule mining are developed.Essentials of new concepts and algorithms are highlighted by two case studies. Association rules, one of the most useful constructs in data mining, can be exerted to capture interesting dependencies between variables in large datasets. Herawan and Deris initiated the investigation of mining association rules from transactional datasets using soft set theory. Unfortunately, some existing concepts in the literature were unable to realize properly Herawan and Deris's initial idea. This paper aims to offer further detailed insights into soft set based association rule mining. With regard to regular association rule mining using soft sets, we refine several existing concepts to improve the generality and clarity of former definitions. Regarding maximal association rule mining based on soft sets, we point out the drawbacks of some existing definitions and offer some way to rectify the problem. A number of new notions, such as transactional data soft sets, parameter-taxonomic soft sets, parameter cosets, realizations and M-realizations of parameter sets are proposed to facilitate soft set based association rule mining. Several algorithms are designed to find M-realizations of parameter sets or extract ź-M-strong and γ-M-reliable maximal association rules in parameter-taxonomic soft sets. We also present an example to illustrate potential applications of our method in clinical diagnosis. Moreover, two case studies are conducted to highlight the essentials of soft set based association rule mining approach.


title: Active-Braid, a Bioinspired Continuum Manipulator

abstract: This letter reports the concept and design of the active-braid, a novel bioinspired continuum manipulator with the ability to contract, extend, and bend in three-dimensional space with varying stiffness. The manipulator utilizes a flexible crossed-link helical array structure as its main supporting body, which is deformed by using two radial actuators and a total of six longitudinal tendons, analogously to the three major types of muscle layers found in muscular hydrostats. The helical array structure ensures that the manipulator behaves similarly to a constant volume structure (expanding while shortening and contracting while elongating). Numerical simulations and experimental prototypes are used in order to evaluate the feasibility of the concept.


title: Blind background calibration of harmonic distortion based on selective sampling

abstract: This paper proposes a blind calibration algorithm for suppressing harmonic distortion in analog to digital converters (ADCs). The proposed algorithm does not need any external calibration signal and is first of its kind. The proposed algorithm relies on the properties of downsampling and orthogonality of sinusoidal signals to estimate the harmonic distortion coefficients. The algorithm can be operated in both foreground and background modes to remove even and odd harmonics simultaneously. The algorithm is demonstrated on a first-order ring oscillator based ΔΣ ADC, whose performance is harmonic distortion limited. Built in 0.13μm, the algorithm improves the SNDR of the ADC by 39dB while improving SFDR by 45 dB.


Sequence to Better Sequence: Continuous Revision of Combinatorial Structures.


title: Opportunistic Routing in Intermittently Connected Mobile P2P Networks

abstract: Mobile P2P networking is an enabling technology for mobile devices to self-organize in an unstructured style and communicate in a peer-to-peer fashion. Due to user mobility and/or the unrestricted switching on/off of the mobile devices, links are intermittently connected and end-to-end paths may not exist, causing routing a very challenging problem. Moreover, the limited wireless spectrum and device resources together with the rapidly growing number of portable devices and amount of transmitted data make routing even harder. To tackle these challenges, the routing algorithms must be scalable, distributed, and light-weighted. Nevertheless, existing approaches usually cannot simultaneously satisfy all these three requirements. In this paper, we propose two opportunistic routing algorithms for intermittently connected mobile P2P networks, which exploit the spatial locality, spatial regularity, and activity heterogeneity of human mobility to select relays. The first algorithm employs a depth-search approach to diffuse the data towards the destination. The second one adopts a depth-width-search approach in a sense that it diffuses the data not only towards the destination but also to other directions determined by the actively moving nodes (activists) to find better relays. We perform both theoretical analysis as well as a comparison based simulation study. Our results obtained from both the synthetic data and the real world traces reveal that the proposed algorithms outperform the state-of-the-art in terms of delivery latency and delivery ratio.


title: Comparative evaluation of tungsten inert gas and laser welding of AZ31 magnesium alloy

abstract: AbstractWelding of AZ31 Mg alloy was conducted using various welding techniques, namely, tungsten inert gas (TIG) welding with Ar shielding gas, TIG welding with He shielding gas, CO2 laser welding, and YAG laser welding. The results were comparatively evaluated in terms of weld bead formation and microstructural characterisation. It was found that TIG welding with both Ar and He gas shielding produced good welds without major defects. The penetration capacity can be improved using He gas shielding. Owing to their high energy density, CO2 and YAG lasers can produce weld beads having high aspect ratio. Among the four techniques used, the YAG laser produced the finest weld microstructure, whereas TIG with Ar gas shielding produced the coarsest microstructure. Shielding is a key factor controlling the O contents in the welds. The more effective the shielding provided the lower the O content in the weld. Hardness reduction in the weld metals and heat affected zones was observed for all the techniques owing to...


Impact of agarose concentrations on electricity generation using hostel sludge based duel chambered microbial fuel cell.


title: Sustaining traditional food: consumers' perceptions on physical characteristics of keropok lekor or fish snack

abstract: Keropok Lekor or frankfurter-like snack has long existed in Malaysia and has been sold in commercial retail shops as well as on the streets. The aims of this study were to analyze consumer's perception and attitude towards keropok lekor and to determine consumer's behavior on the product. Survey questionnaire was employed as a data collection tool. A total of 212 respondents from a university participated in this study. Results showed that more than 70% of the respondents chose to eat keropok lekor because of the taste. Half of the respondents chose black pepper as an innovative supplementary flavor of keropok lekor. This study also explores and analyses preliminary consumer behavior about keropok lekor such as satisfaction level, factors that drive consumption, product's image from consumer perspectives and etc.


title: Bayesian Variable Selection in Linear Regression in One Pass for Large Datasets

abstract: Bayesian models are generally computed with Markov Chain Monte Carlo (MCMC) methods. The main disadvantage of MCMC methods is the large number of iterations they need to sample the posterior distributions of model parameters, especially for large datasets. On the other hand, variable selection remains a challenging problem due to its combinatorial search space, where Bayesian models are a promising solution. In this work, we study how to accelerate Bayesian model computation for variable selection in linear regression. We propose a fast Gibbs sampler algorithm, a widely used MCMC method that incorporates several optimizations. We use a Zellner prior for the regression coefficients, an improper prior on variance, and a conjugate prior Gaussian distribution, which enable dataset summarization in one pass, thus exploiting an augmented set of sufficient statistics. Thereafter, the algorithm iterates in main memory. Sufficient statistics are indexed with a sparse binary vector to efficiently compute matrix projections based on selected variables. Discovered variable subsets probabilities, selecting and discarding each variable, are stored on a hash table for fast retrieval in future iterations. We study how to integrate our algorithm into a Database Management System (DBMS), exploiting aggregate User-Defined Functions for parallel data summarization and stored procedures to manipulate matrices with arrays. An experimental evaluation with real datasets evaluates accuracy and time performance, comparing our DBMS-based algorithm with the R package. Our algorithm is shown to produce accurate results, scale linearly on dataset size, and run orders of magnitude faster than the R package.


title: OWL SAIQL - An OWL DL Query Language for Ontology Extraction

abstract: Existing approaches for querying OWL DL do either only operate on syntactic constructs without taking into account the semantics of OWL or do only have a restricted access to the T-Box. We present SAIQL, the novel Schema And Instance Query Language for OWL DL, that is well suited for ontology extraction. We describe its syntax and explain a basic evaluation strategy. We illustrate the use of SAIQL with an example for ontology extraction and re-use.


title: Laboratory scale prototype of a power electronic transformer for traction applications

abstract: Combining modern high-power semiconductor devices with constantly improving magnetic materials opens up the possibility to replace bulky low-frequency transformers with a new medium voltage medium frequency conversion structure. While there are still challenges to be addressed related to these, so called power electronic transformers, a steadily increasing development effort is evident and considered in various contexts. Traction applications seem to be the first ones, where proliferation of these new power electronic converters, are expected. In this application field substantial weight and volume reduction could be achieved, providing additional functionality at the same time. In this paper experimental results obtained from a laboratory scale prototype of a power electronic transformer are presented, focusing primarily on the control system design and also considering specific traction requirements. This prototype was built to support full-size converter development.


title: A music similarity function based on signal analysis

abstract: The present invention computer method and apparatus determines music similarity by generating a K-means (instead of Gaussian) cluster signature and a beat signature for each piece of music. The beat of the music is included in the subsequent distance measurement.


title: Investigating the Healthiness of Internet-Sourced Recipes: Implications for Meal Planning and Recommender Systems

abstract: Food recommenders have the potential to positively influence the eating habits of users. To achieve this, however, we need to understand how healthy recommendations are and the factors which influence this. Focusing on two approaches from the literature (single item and daily meal plan recommendation) and utilizing a large Internet sourced dataset from Allrecipes.com, we show how algorithmic solutions relate to the healthiness of the underlying recipe collection. First, we analyze the healthiness of Allrecipes.com recipes using nutritional standards from the World Health Organisation and the United Kingdom Food Standards Agency. Second, we investigate user interaction patterns and how these relate to the healthiness of recipes. Third, we experiment with both recommendation approaches. Our results indicate that overall the recipes in the collection are quite unhealthy, but this varies across categories on the website. Users in general tend to interact most often with the least healthy recipes. Recommender algorithms tend to score popular items highly and thus on average promote unhealthy items. This can be tempered, however, with simple post-filtering approaches, which we show by experiment are better suited to some algorithms than others. Similarly, we show that the generation of meal plans can dramatically increase the number of healthy options open to users. One of the main findings is, nevertheless, that the utility of both approaches is strongly restricted by the recipe collection. Based on our findings we draw conclusions how researchers should attempt to make food recommendation systems promote healthy nutrition.


title: Base stations placement optimization in wireless networks for emergency communications

abstract: Disaster relief operations rely on the rapid deployment of wireless network architectures to provide emergency communications. Future emergency networks will consist typically of terrestrial, portable base stations and base stations on-board low altitude platforms (LAPs). The effectiveness of network deployment will depend on strategically chosen station positions. In this paper a method is presented for calculating the optimal proportion of the two station types and their optimal placement. Random scenarios and a real example from Hurricane Katrina are used for evaluation. The results confirm the strength of LAPs in terms of high bandwidth utilisation, achieved by their ability to cover wide areas, their portability and adaptability to height. When LAPs are utilized, the total required number of base stations to cover a desired area is generally lower. For large scale disasters in particular, this leads to shorter response times and the requirement of fewer resources. This goal can be achieved more easily if algorithms such as the one presented in this paper are used.


title: New measurement results for the localization of UHF RFID transponders using an Angle of Arrival (AoA) approach

abstract: In this paper we present new measurement results for an Angle of Arrival (AoA) approach to localize RFID tags at 868 MHz. Self-designed three element antenna arrays, off-the-shelf IDS R901G RFID reader ICs, and UPM Raflatec DogBone RFID tags are used to generate and detect ISO 18000–6C compliant signals. The experimental setup comprises three antenna arrays and a test environment of size 3 times 3 square meters with 25 test points. The AoA is estimated using the phase differences in the complex baseband signals of adjacent antenna elements within one array. A mean positioning error of 0.21 m was achieved for the considered test grid.


title: An investigation of data and text mining methods for real world deception detection

abstract: Uncovering lies (or deception) is of critical importance to many including law enforcement and security personnel. Though these people may try to use many different tactics to discover deception, previous research tells us that this cannot be accomplished successfully without aid. This manuscript reports on the promising results of a research study where data and text mining methods along with a sample of real-world data from a high-stakes situation is used to detect deception. At the end, the information fusion based classification models produced better than 74% classification accuracy on the holdout sample using a 10-fold cross validation methodology. Nonetheless, artificial neural networks and decision trees produced accuracy rates of 73.46% and 71.60% respectively. However, due to the high stakes associated with these types of decisions, the extra effort of combining the models to achieve higher accuracy is well warranted.


title: A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges

abstract: Visual analytics employs interactive visualizations to integrate users’ knowledge and inference capability into numerical/algorithmic data analysis processes. It is an active research field that has applications in many sectors, such as security, finance, and business. The growing popularity of visual analytics in recent years creates the need for a broad survey that reviews and assesses the recent developments in the field. This report reviews and classifies recent work into a set of application categories including space and time, multivariate, text, graph and network, and other applications. More importantly, this report presents analytics space, inspired by design space, which relates each application category to the key steps in visual analytics, including visual mapping, model-based analysis, and user interactions. We explore and discuss the analytics space to add the current understanding and better understand research trends in the field.


title: BCD (Bipolar-CMOS-DMOS) technology trends for power management IC

abstract: This paper reviews the technology trends of BCD (Bipolar-CMOS-DMOS) technology in terms of voltage capability, switching speed of power transistor, and high integration of logic CMOS for SoC (System-on-Chip) solution requiring high-voltage devices. Recent trends such like modularity of the process, power metal routing, and high-density NVM (Non-Volatile Memory) are also discussed.


title: Support Vector Method for Function Approximation, Regression Estimation and Signal Processing

abstract: The Support Vector (SV) method was recently proposed for estimating regressions, constructing multidimensional splines, and solving linear operator equations [Vapnik, 1995]. In this presentation we report results of applying the SV method to these problems.


title: A Data Mode l for Integrating GIS and BIM for Assessment a nd 3D Visualisation of Flood Damage to Building

abstract: Floo d Damage Assessment (FDA) is a key component in modern risk management frameworks providing an effective basis for decision making and the treatment of the risks. Current FDA methods do not consider the distinctiveness of buildings in analysis and therefore, cannot analyse them on a case-by-case basis, which is necessary for a variety of applications like engineering and design evaluation. This is mainly due to the limited input data used in these methods. The information required for such micro-level FDA analysis includes on one hand, complete building information (well-represented in BIM) and on the other hand, flood information that is commonly managed by GIS. While the independent use of BIM and GIS cannot satisfy all the information requirements for detailed FDA, their integration can potentially be used for this purpose. However, existing integration methods are application-specific and their adoption for FDA is challenging. This paper presents a method for BIM-GIS integration to support the requirements of a detailed assessment and 3D visualisation of flood damage to buildings. The data modelling cycle was used to design a new data model as a profile of GML for this purpose. The model was evaluated using a case study and found effective to satisfy the required criteria for micro-level FDA.


title: Regression Facial Attribute Classification via simultaneous dictionary learning

abstract: Recently, many researchers have attempted to classify Facial Attributes (FAs) by representing characteristics of FAs such as attractiveness, age, smiling and so on. In this context, recent studies have demonstrated that visual FAs are a strong background for many applications such as face verification, face search and so on. However, Facial Attribute Classification (FAC) in a wide range of attributes based on the regression representation -predicting of FAs as real-valued labels- is still a significant challenge in computer vision and psychology. In this paper, a regression model formulation is proposed for FAC in a wide range of FAs (e.g. 73 FAs). The proposed method accommodates real-valued scores to the probability of what percentage of the given FAs is present in the input image. To this end, two simultaneous dictionary learning methods are proposed to learn the regression and identity feature dictionaries simultaneously. Accordingly, a multi-level feature extraction is proposed for FAC. Then, four regression classification methods are proposed using a regression model formulated based on dictionary learning, SRC and CRC. Convincing results are acquired to handle a wide range of FAs and represent the probability of FAs on the PubFig, LFW, Groups and 10k US Adult Faces databases compared to several state-of-the-art methods. This paper proposes a method for regression facial attributes classification.We propose two simultaneous optimization problems for Facial Attribute Classification.A multilevel feature extraction method was proposed to discriminate the facial features.Promising results were obtained to classify facial attributes on the PubFig, Groups, 10k US adult and LFW databases.


title: How bodies matter: five themes for interaction design

abstract: Our physical bodies play a central role in shaping human experience in the world, understandingof the world, and interactions in the world. This paper draws on theories of embodiment - from psychology, sociology, and philosophy - synthesizing five themes we believe are particularly salient for interaction design: thinking through doing, performance, visibility, risk, and thick practice. We intro-duce aspects of human embodied engagement in the world with the goal of inspiring new interaction design ap-proaches and evaluations that better integrate the physical and computational worlds.


title: A classification of all order-preserving homeomorphism groups of the reals that satisfy finite uniqueness

abstract: The only real relational structures of scale type (m, n) with 1 ≤ m ≤ n < ∞ are of scale type (1, 1), (1, 2), and (2, 2), and so are conjugate to structures whose automorphism groups are subgroups of the affines containing the group of translations. All real relational structures of scale type (0, n) with n < ∞ have automorphism groups abstractly isomorphic to; moreover, each contains subchain of order type theta, invariant under the action of the automorphism group, s.t. the action of the automorphisms on this subchain is conjugate to that of a sub-group of the affines.


title: On the security of the Winternitz one-time signature scheme

abstract: We show that the Winternitz one-time signature scheme is existentially unforgeable under adaptive chosen message attacks when instantiated with a family of pseudorandom functions. Our result halves the signature size at the same security level, compared to previous results, which require a collision resistant hash function. We also consider security in the strong sense and show that the Winternitz one-time signature scheme is strongly unforgeable assuming additional properties of the pseudorandom function family. In this context we formally define several key-based security notions for function families and investigate their relation to pseudorandomness. All our reductions are exact and in the standard model and can directly be used to estimate the output length of the hash function required to meet a certain security level.


The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine


title: Mathematical modeling and simulation of an electric vehicle

abstract: As electric vehicles become promising alternatives for sustainable and cleaner energy emissions in transportation, the modeling and simulation of electric vehicles has attracted increasing attention from researchers. This paper presents a simulation model of a full electric vehicle on the Matlab-Simulink platform to examine power flow during motoring and regeneration. The drive train components consist of a motor, a battery, a motor controller and a battery controller; modeled according to their mathematical equations. All simulation results are plotted and discussed. The torque and speed conditions during motoring and regeneration were used to determine the energy flow, and performance of the drive. This study forms the foundation for further research and development.


title: DNA Secondary Structure

abstract: The term non-B-DNA refers to all secondary structures that the canonical, antiparallel right-handed double helix (B-DNA) can adopt, including left-handed duplexes and three- and four-stranded helices. Bioinformatic studies indicate that the number of sequences with the potential to form DNA secondary structures in the human genome is greater than expected by chance and probing in vivo supports the conclusion that a fraction of such sequences adopts non-B conformations. Non-B-DNA is found to elicit both physiological and pathological effects, including the regulation of transcription, telomere function, protein function, and the induction of genomic instability leading to human genetic disease.


title: Postgraduate medical students’ acceptance and understanding of scientific information databases and electronic resources

abstract: Introduction ::: The significance and validity of web-based scientific databases are increasing dramatically in the scientific community. Moreover, a great number of students use these resources without having sufficient and accurate knowledge and understanding. In order for students to use these databases and electronic resources optimally, identifying the factors that affect the understanding and acceptance of these resources seems necessary. The aim of this study was to determine postgraduate medical students’ acceptance and understanding of these resources.


title: Developing Targeted Health Service Interventions Using the PRECEDE-PROCEED Model: Two Australian Case Studies

abstract: Aims and Objectives. This paper provides an overview of the applicability of the PRECEDE-PROCEED Model to the development of targeted nursing led chronic illness interventions. Background. Changing health care practice is a complex and dynamic process that requires consideration of social, political, economic, and organisational factors. An understanding of the characteristics of the target population, health professionals, and organizations plus identification of the determinants for change are also required. Synthesizing this data to guide the development of an effective intervention is a challenging process. The PRECEDE-PROCEED Model has been used in global health care settings to guide the identification, planning, implementation, and evaluation of various health improvement initiatives. Design. Using a reflective case study approach, this paper examines the applicability of the PRECEDE-PROCEED Model to the development of targeted chronic care improvement interventions for two distinct Australian populations: a rapidly expanding and aging rural population with unmet palliative care needs and a disadvantaged urban community at higher risk of cardiovascular disease. Results. The PRECEDE-PROCEED Model approach demonstrated utility across diverse health settings in a systematic planning process. In environments characterized by increasing health care needs, limited resources, and growing community expectations, adopting planning tools such as PRECEDE-PROCEED Model at a local level can facilitate the development of the most effective interventions. Relevance to Clinical Practice. The PRECEDE-PROCEED Model is a strong theoretical model that guides the development of realistic nursing led interventions with the best chance of being successful in existing health care environments.


title: Single phase power factor correction: a survey

abstract: New recommendations and future standards have increased the interest in power factor correction circuits. There are multiple solutions in which line current is sinusoidal. In addition, a great number of circuits have been proposed with nonsinusoidal line current. In this paper, a review of the most interesting solutions for single phase and low power applications is carried out. They are classified attending to the line current waveform, energy processing, number of switches, control loops, etc. The major advantages and disadvantages are highlighted and the field of application is found.


title: An unsupervised deep domain adaptation approach for robust speech recognition

abstract: Abstract This paper addresses the robust speech recognition problem as a domain adaptation task. Specifically, we introduce an unsupervised deep domain adaptation (DDA) approach to acoustic modeling in order to eliminate the training–testing mismatch that is common in real-world use of speech recognition. Under a multi-task learning framework, the approach jointly learns two discriminative classifiers using one deep neural network (DNN). As the main task, a label predictor predicts phoneme labels and is used during training and at test time. As the second task, a domain classifier discriminates between the source and the target domains during training. The network is optimized by minimizing the loss of the label classifier and to maximize the loss of the domain classifier at the same time. The proposed approach is easy to implement by modifying a common feed-forward network. Moreover, this unsupervised approach only needs labeled training data from the source domain and some unlabeled raw data of the new domain. Speech recognition experiments on noise/channel distortion and domain shift confirm the effectiveness of the proposed approach. For instance, on the Aurora-4 corpus, compared with the acoustic model trained only using clean data, the DDA approach achieves relative 37.8% word error rate (WER) reduction.


The Design of Fuzzy Adaptive PID Controller of Two-Wheeled Self-Balancing Robot


title: Burnout among U.S. medical students, residents, and early career physicians relative to the general U.S. population.

abstract: PURPOSE ::: To compare the prevalence of burnout and other forms of distress across career stages and the experiences of trainees and early career (EC) physicians versus those of similarly aged college graduates pursuing other careers. ::: ::: ::: METHOD ::: In 2011 and 2012, the authors conducted a national survey of medical students, residents/fellows, and EC physicians (≤ 5 years in practice) and of a probability-based sample of the general U.S. population. All surveys assessed burnout, symptoms of depression and suicidal ideation, quality of life, and fatigue. ::: ::: ::: RESULTS ::: Response rates were 35.2% (4,402/12,500) for medical students, 22.5% (1,701/7,560) for residents/fellows, and 26.7% (7,288/27,276) for EC physicians. In multivariate models that controlled for relationship status, sex, age, and career stage, being a resident/fellow was associated with increased odds of burnout and being a medical student with increased odds of depressive symptoms, whereas EC physicians had the lowest odds of high fatigue. Compared with the population control samples, medical students, residents/fellows, and EC physicians were more likely to be burned out (all P < .0001). Medical students and residents/fellows were more likely to exhibit symptoms of depression than the population control samples (both P < .0001) but not more likely to have experienced recent suicidal ideation. ::: ::: ::: CONCLUSIONS ::: Training appears to be the peak time for distress among physicians, but differences in the prevalence of burnout, depressive symptoms, and recent suicidal ideation are relatively small. At each stage, burnout is more prevalent among physicians than among their peers in the U.S. population.


title: Learning to Generalize from Sparse and Underspecified Rewards

abstract: We consider the problem of learning from sparse and underspecified rewards, where an agent receives a complex input, such as a natural language instruction, and needs to generate a complex response, such as an action sequence, while only receiving binary success-failure feedback. Such success-failure rewards are often underspecified: they do not distinguish between purposeful and accidental success. Generalization from underspecified rewards hinges on discounting spurious trajectories that attain accidental success, while learning from sparse feedback requires effective exploration. We address exploration by using a mode covering direction of KL divergence to collect a diverse set of successful trajectories, followed by a mode seeking KL divergence to train a robust policy. We propose Meta Reward Learning (MeRL) to construct an auxiliary reward function that provides more refined feedback for learning. The parameters of the auxiliary reward function are optimized with respect to the validation performance of a trained policy. The MeRL approach outperforms our alternative reward learning technique based on Bayesian Optimization, and achieves the state-of-the-art on weakly-supervised semantic parsing. It improves previous work by 1.2% and 2.4% on WikiTableQuestions and WikiSQL datasets respectively.


title: Let’s go public! taking a spoken dialog system to the real world

abstract: In this paper, we describe how a research spoken dialog system was made available to the general public. The Let’s Go Public spoken dialog system provides bus schedule information to the Pittsburgh population during off-peak times. This paper describes the changes necessary to make the system usable for the general public and presents analysis of the calls and strategies we have used to ensure high performance.


title: Panda: Public Auditing for Shared Data with Efficient User Revocation in the Cloud

abstract: With data services in the cloud, users can easily modify and share data as a group. To ensure data integrity can be audited publicly, users need to compute signatures on all the blocks in shared data. Different blocks are signed by different users due to data modifications performed by different users. For security reasons, once a user is revoked from the group, the blocks, which were previously signed by this revoked user must be re-signed by an existing user. The straightforward method, which allows an existing user to download the corresponding part of shared data and re-sign it during user revocation, is inefficient due to the large size of shared data in the cloud. In this paper, we propose a novel public auditing mechanism for the integrity of shared data with efficient user revocation in mind. By utilizing proxy re-signatures, we allow the cloud to re-sign blocks on behalf of existing users during user revocation, so that existing users do not need to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the integrity of shared data without retrieving the entire data from the cloud, even if some part of shared data has been re-signed by the cloud. Experimental results show that our mechanism can significantly improve the efficiency of user revocation.


title: Dermatofibrosarcoma Protuberans: How Wide Should We Resect?

abstract: Background ::: Dermatofibrosarcoma protuberans (DFSP) is a rare dermal tumor with local recurrence rates ranging from 0 to 50%. Controversy exists regarding margin width and excision techniques, with some advocating Mohs surgery and others wide excision (WE). We reviewed the experience in two tertiary centers using WE with total peripheral margin pathologic evaluation.


title: Ontology-Based Expertise Finding

abstract: To accomplish knowledge intensive tasks, people in organizations must be able to find the knowledge or information needed to solve complex problems. For this, people often rely on their past experiences, explicit documents, and others who have the needed expertise. Knowledge Management Systems that enhance and facilitate the process of finding the right expert in an organization have gained much attention in recent years. This paper explores the potential benefits and challenges of using ontologies for improving existing systems. A modeling technique from requirements engineering is used to evaluate the proposed system and analyze the impact it would have on the goals of the stakeholders. This paper also discusses the organizational settings required for the successful deployment of the system in practice.


title: When it comes to Facebook there may be more to bad memory than just multitasking

abstract: Previous research has shown that high levels of Facebook use are associated with lower grades in college students. Divided attention in the form of trying to use Facebook during class or while studying has been suggested as a possible explanation for this finding. In the current study, 44 participants were divided into high and low Facebook users and completed a memory test for 72 words. Participants were not allowed to use Facebook, or any other electronic device, during the study thereby eliminating divided attention between Facebook and the task at hand as a possible explanation for the results. High Facebook users (defined as spending more than one hour a day on Facebook) scored significantly lower on the free recall test than low Facebook users. Possible explanations for this finding are discussed.


title: Structured Low-Rank Matrix Factorization for Point-Cloud Denoising

abstract: In this work we address the problem of point-cloud denoising where we assume that a given point-cloud comprises (noisy) points that were sampled from an underlying surface that is to be denoised. We phrase the point-cloud denoising problem in terms of a dictionary learning framework. To this end, for a given point-cloud we (robustly) extract planar patches covering the entire point-cloud, where each patch contains a (noisy) description of the local structure of the underlying surface. Based on the general assumption that many of the local patches (in the noise-free point-cloud) contain redundant information (e.g. due to smoothness of the surface, or due to repetitive structures), we find a low-dimensional affine subspace that (approximately) explains the extracted (noisy) patches. Computationally, this is achieved by solving a structured low-rank matrix factorization problem, where we impose smoothness on the patch dictionary and sparsity on the coefficients. We experimentally demonstrate that our method outperforms existing denoising approaches in various noise scenarios.


title: Click-through Prediction for Advertising in Twitter Timeline

abstract: We present the problem of click-through prediction for advertising in Twitter timeline, which displays a stream of Tweets from accounts a user choose to follow. Traditional computational advertising usually appears in two forms: sponsored search that places ads onto the search result page when a query is issued to a search engine, and contextual advertising that places ads onto a regular, usually static Web page. Compared with these two paradigms, placing ads into a Tweet stream is particularly challenging given the nature of the data stream: the context into which an ad can be placed updates dynamically and never replicates. Every ad is therefore placed into a unique context. This makes the information available for training a machine learning model extremely sparse. In this study, we propose a learning-to-rank method which not only addresses the sparsity of training signals but also can be trained and updated online. The proposed method is evaluated using both offline experiments and online A/B tests, which involve very large collections of Twitter data and real Twitter users. Results of the experiments prove the effectiveness and efficiency of our solution, and its superiority over the current production model adopted by Twitter.


title: Mining Longest Repeating Subsequences To Predict World Wide Web Surfing

abstract: Modeling and predicting user surfing paths involves tradeoffs between model complexity and predictive accuracy. In this paper we explore predictive modeling techniques that attempt to reduce model complexity while retaining predictive accuracy. We show that compared to various Markov models, longest repeating subsequence models are able to significantly reduce model size while retaining the ability to make accurate predictions. In addition, sharp increases in the overall predictive capabilities of these models are achievable by modest increases to the number of predictions made.


title: Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time

abstract: Distributional semantic models (DSMs) are often evaluated on artificial similarity datasets containing single words or fully compositional phrases. We present a large-scale multilingual evaluation of DSMs for predicting the degree of semantic compositionality of nominal compounds on 4 datasets for English and French. We build a total of 816 DSMs and perform 2,856 evaluations using word2vec, GloVe, and PPMI-based models. In addition to the DSMs, we compare the impact of different parameters, such as level of corpus preprocessing, context window size and number of dimensions. The results obtained have a high correlation with human judgments, being comparable to or outperforming the state of the art for some datasets (Spearman's ρ=.82 for the Reddy dataset).


title: Two populations of layer v pyramidal cells of the mouse neocortex: development and sensitivity to anesthetics

abstract: Previous studies have shown that layer V pyramidal neurons projecting either to subcortical structures or the contralateral cortex undergo different morphological and electrophysiological patterns of development during the first three postnatal weeks. To isolate the determinants of this differential maturation, we analyzed the gene expression and intrinsic membrane properties of layer V pyramidal neurons projecting either to the superior colliculus (SC cells) or the contralateral cortex (CC cells) by combining whole cell recordings and single-cell RT-PCR in acute slices prepared from postnatal day (P) 5–7 or P21–30 old mice. Among the 24 genes tested, the calcium channel subunits α1B and α1C, the protease Nexin 1, and the calcium-binding protein calbindin were differentially expressed in adult SC and CC cells and the potassium channel subunit Kv4.3 was expressed preferentially in CC cells at both stages of development. Intrinsic membrane properties, including input resistance, amplitude of the hyperpolarization-activated current, and action potential threshold, differed quantitatively between the two populations as early as from the first postnatal week and persisted throughout adulthood. However, the two cell types had similar regular action potential firing behaviors at all developmental stages. Surprisingly, when we increased the duration of anesthesia with ketamine–xylazine or pentobarbital before decapitation, a proportion of mature SC cells, but not CC cells, fired bursts of action potentials. Together these results indicate that the two populations of layer V pyramidal neurons already start to differ during the first postnatal week and exhibit different firing capabilities after anesthesia.


title: Analyzing and Preventing Bias in Text-Based Personal Trait Prediction Algorithms

abstract: Personality prediction based on textual data is one topic gaining attention recently for its potential in large-scale personalized applications such as social media-based marketing and political campaigning. However, when applying this technology in real-world applications, users often encounter situations in which the personality traits derived from different sources (e.g., social media posts versus emails) are inconsistent. Varying results for the same individual renders the tool ineffective and hard to trust. This paper demonstrates the impact of domain bias in automated text-based personality prediction, and proposes a novel method to correct domain bias. The proposed approach is generic since it requires neither retraining the system using examples from an application domain, nor any knowledge of the original training data used by a personal trait analysis tool. We conduct comprehensive experiments to evaluate the effectiveness of the method, and the findings indicate a significant improvement of prediction accuracy (e.g., a 20-30% relative error reduction) with the proposed method.


title: HaarHOG: Improving the HOG Descriptor for Image Classification

abstract: The Histograms of Oriented Gradients (HOG) descriptor represents shape information by storing the local gradients in an image. The Haar wavelet transform is a simple yet powerful technique that can separately enhance the horizontal and vertical local features in an image. In this paper, we enhance the HOG descriptor by subjecting the image to the Haar wavelet transform and then computing HOG from the result in a manner that enriches the shape information encoded in the descriptor. First, we define the novel HaarHOG descriptor for grayscale images and extend this idea for color images. Second, we compare the image recognition performance of the HaarHOG descriptor with the traditional HOG descriptor in four different color spaces and grayscale. Finally, we compare the image classification performance of the HaarHOG descriptor with some popular descriptors used by other researchers on four grand challenge datasets.


title: The economic burden of child maltreatment in the United States and implications for prevention

abstract: Objectives ::: To present new estimates of the average lifetime costs per child maltreatment victim and aggregate lifetime costs for all new child maltreatment cases incurred in 2008 using an incidence-based approach.


title: Assessing Security and Performances of Consensus algorithms for Permissioned Blockchains

abstract: Blockchain is a novel technology that is rising a lot of interest in the industrial and re- search sectors because its properties of decentralisation, immutability and data integrity. Initially, the underlying consensus mechanism has been designed for permissionless block- chain on trustless network model through the proof-of-work, i.e. a mathematical challenge which requires high computational power. This solution suffers of poor performances, hence alternative consensus algorithms as the proof-of-stake have been proposed. Conversely, for permissioned blockchain, where participants are known and authenti- cated, variants of distributed consensus algorithms have been employed. However, most of them comes out without formal expression of security analysis and trust assumptions because the absence of an established knowledge. Therefore the lack of adequate analysis on these algorithms hinders any cautious evaluation of their effectiveness in a real-world setting where systems are deployed over trustless networks, i.e. Internet ...


title: A sensor network for Glaciers

abstract: This chapter reports on the design, implementation and results from GlacsWeb, an environmental sensor network for glaciers installed in Summer 2004 at Briksdalsbreen, Norway. The importance of power control, hardware architecture and communication systems are discussed and research issues highlighted.


title: Optically transparent conductive polymer RFID meandering dipole antenna

abstract: In this paper, we present optically transparent flexible conductive polymer antennas for radio frequency identification systems. The designs for these antennas are presented along with simulated and measured results of antenna radiating properties. These conductive polymer antennas are compared to antennas with the same design fabricated out of copper. Finally, we include an analysis of the optical transparency of the conductive polymer antennas.


title: Proximate, Nutrient and Mineral Composition of Cuttlefish (Sepia recurvirostra)

abstract: The proximate, nutrient and mineral analyses were conducted on the head and mantle of cuttlefish (Sepia recurvirostra). Proximate analysis showed both head and mantle of cuttlefish contained 13.16 -13.51% protein, 0.7- 0.9% ash, 0.8% fat and1-1.4% carbohydrate. Amino acid analysis revealed cuttlefish contained essential and non essential amino acid with arginine (0.97%) and glutamate (0.81%) was the highest in the head, respectively, while lysine (0.7%) and glutamate (0.7%) was the highest in the mantle, respectively. Lipid of cuttlefish contained high Percentage of Polyunsaturated Fatty Acid (PUFA) with a high content of DHA (17.5-20.5%). The C16:0 and C18:0 were the most abundant saturated fatty acid in the head and mantle. Mineral analysis showed high contents of sodium, phosphorus, potassium and calcium, with sodium content being the highest (1532.7-1610.4 mg/kg). Zinc and copper were the dominant trace minerals in both portions. This study showed that cuttlefish contain essential nutrients for human good health with high in protein and mineral content.


title: Validation of photoplethysmography as a method to detect heart rate during rest and exercise

abstract: AbstractDespite their enhanced marketplace visibility, validity of wearable photoplethysmographic heart rate monitoring is scarce. Forty-seven healthy participants performed seven, 6-min exercise bouts and completed a valid skin type scale. Participants wore an Omron HR500U (OHR) and a Mio Alpha (MA), two commercial wearable photoplethysmographic heart rate monitors. Data were compared to a Polar RS800CX (PRS). Means and error were calculated between devices using minutes 2–5. Compared to PRS, MA data was significantly different in walking, biking (2.41 ± 3.99 bpm and 3.26 ± 11.38 bpm, p < 0.05) and weight lifting (23.30 ± 31.94 bpm, p < 0.01). OHR differed from PRS in walking (4.95 ± 7.53 bpm, p < 0.05) and weight lifting (4.67 ± 8.95 bpm, p < 0.05). MA during elliptical, stair climbing and biking conditions demonstrated a strong correlation between jogging speed and error (r = 0.55, p < 0.0001), and showed differences in participants with less photosensitive skin.


OPPORTUNITIES AND CHALLENGES OF WIRELESS COMMUNICATION TECHNOLOGIES FOR SMART GRID APPLICATION


title: MVCWalker: Random Walk-Based Most Valuable Collaborators Recommendation Exploiting Academic Factors

abstract: In academia, scientific research achievements would be inconceivable without academic collaboration and cooperation among researchers. Previous studies have discovered that productive scholars tend to be more collaborative. However, it is often difficult and time-consuming for researchers to find the most valuable collaborators (MVCs) from a large volume of big scholarly data. In this paper, we present MVCWalker, an innovative method that stands on the shoulders of random walk with restart (RWR) for recommending collaborators to scholars. Three academic factors, i.e., coauthor order, latest collaboration time, and times of collaboration, are exploited to define link importance in academic social networks for the sake of recommendation quality. We conducted extensive experiments on DBLP data set in order to compare MVCWalker to the basic model of RWR and the common neighbor-based model friend of friends in various aspects, including, e.g., the impact of critical parameters and academic factors. Our experimental results show that incorporating the above factors into random walk model can improve the precision, recall rate, and coverage rate of academic collaboration recommendations.


Identifying and Understanding Effective High Schools: Personalization for Academic and Social Learning & Student Ownership and Responsibility.


title: Is my daughter still a virgin? Can you, please, check it, doctor?

abstract: Gender-based violence is a pervasive public health problem, let alone a violation of human rights. The World Health Organization (WHO) has estimated that, overall, 35% of women worldwide have experienced physical and/or sexual intimate partner violence or nonpartner sexual violence. In 2016, the author had the privilege to work with an outstanding group of people that helps sexual and gender-based violence (SGBV) survivors in Port-au-Prince, Haiti. The clinic, run by an international nongovernmental organizations, open walk-in to anyone in need, is functioning to the present day. In its 1st year along, the clinic helped almost 1000 survivors, mainly underaged girls. At the clinic, a highly qualified staff of local doctors and nurses, psychologists, and social workers is available, free of charge, 24 h a day, 7 days a week, and accessible to SGBV survivors in absolute confidentiality. Survivors are offered all the support they might need as per the WHO guidelines, from medical assistance (treating of physical injuries, testing and preventing sexually transmitted diseases such as HIV or hepatitis B, and also gynecological and obstetric care) to psychological counseling, as well as specific referrals to shelters, legal aid, or secondary care as needed. Virginity still is highly valued in Haiti, and the father, mother, the beau-mare, or aunt of the survivor will often approach the doctor and anxiously whisper the question: “Is my daughter (goddaughter, niece) still a virgin?”, “Can you, please, check it, Doctor?” This article presents the perspective of SGBV survivors clinic in Port-au-Prince, Haiti.


Near Earth Asteroid (NEA) Scout Solar Sail Contingency Trajectory Design and Analysis


title: Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks

abstract: Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related death among females worldwide. In this article, we investigate the applicability of densely connected convolutional neural networks to the problems of histology image classification and whole slide image segmentation in the area of computer-aided diagnoses for breast cancer. To this end, we study various approaches for transfer learning and apply them to the data set from the 2018 grand challenge on breast cancer histology images (BACH).


title: GPU-acceleration of optimal permutation-puzzle solving

abstract: We first investigate parallelization of Rubik's cube optimal solver, especially for acceleration by GPU. To examine its efficacy, we implement a simple solver based on Korf's algorithm, with which CPU and GPU collaborate in IDA* algorithm and a large number of GPU cores are utilized for speedup instead of a huge distance table used for pruning. Empirical studies succeeded to attain sufficient speedup by GPU-acceleration. There are many other similar puzzles of so-called permutation puzzles. The puzzle solving, i.e., restoring the original ordered state from a scrambled one is equivalent to the path-finding in the Cayley graph of the permutation group. We generalize the method used for Rubik's cube to much smaller problems, and examine its efficacy. The focus of our research interest is how efficient the parallel path-finding can be and whether the use of a large number of cores substitutes for a large distance table used for pruning, in general.


title: Characterizing a software process maturity model for small organizations

abstract: Software process refers to the set of tools, methods, and practices used to produce a software artifact. The objective of a software process management model is to produce software artifacts according to plans while simultaneously improving the organization's capability to produce better artifacts. The SEI's Capability Maturity Model (CMM) is a software process management model; it assists organizations to provide the infrastructure for achieving a disciplined and mature software process. There is a growing concern that the CMM is not applicable to small firms because it requires a huge investment. In fact, detailed studies of the CMM show that its applications may cost well over $100,000. This article attempts to address the above concern by studying the feasibility of a scaled-down version of the CMM for use in small software firms. The logic for a scaled-down CMM is that the same quantitative quality control principles that work for larger projects can be scaled-down and adopted for smaller ones. Both the CMM and the Personal Software Process (PSP) are briefly described and are used as basis.


title: Hyperfine aperiodic optical superlattice optimized by iterative domino algorithm for phase-matching engineering

abstract: We proposed and experimentally demonstrated the iterative domino algorithm to optimize optical superlattice with >10(5) unit blocks to achieve arbitrary target phase-matching power spectrum. This scheme can achieve unprecedented overall conversion efficiency and spectral fidelity with extremely high computation efficiency.


title: A General BRDF Representation Based on Tensor Decomposition

abstract: Generating photo-realistic images through Monte Carlo rendering requires efficient representation of light–surface interaction and techniques for importance sampling. Various models with good representation abilities have been developed but only a few of them have their importance sampling procedure. In this paper, we propose a method which provides a good bidirectional reflectance distribution function (BRDF) representation and efficient importance sampling procedure. Our method is based on representing BRDF as a function of tensor products. Four-dimensional measured BRDF tensor data are factorized using Tucker decomposition. A large data set is used for comparing the proposed BRDF model with a number of well-known BRDF models. It is shown that the underlying model provides good approximation to BRDFs.


title: Workspace mapping and control of a teleoperated endoscopic surgical robot

abstract: This paper presents the experimental implementation of a teleoperated endoscopic surgical manipulator system that uses PHANTOM Omni haptic device as the master. The 4-DOF, 2-PUU 2-PUS, endoscopic surgical parallel manipulator design is carried out using screw theory and Parallel virtual chain methodology to have larger bending angles and workspace volume. The master and slave devices of the teleoperation system are dissimilar in their kinematics and workspace volumes. A workspace mapping technique is implemented based on Position with Modied Rate Control to navigate through the slave workspace without annoying the user. To control the motion of the slave robot, a PID controller is used. The experimental results show the feasibility of the teleoperation surgical system using the 4-DOF parallel manipulator. Also, they indicate the efficiency of the implemented mapping technique and the designed controller to span the slave workspace with high dexterity and good tracking which allows the surgeon to perform the operation with high accuracy.


title: Handover within 3GPP LTE: Design Principles and Performance

abstract: The 3GPP LTE system has been designed to offer significantly higher data rates, higher system throughput, and lower latency for delay critical services. This improved performance has to be provided and guaranteed under various mobility conditions. Hence, handover (HO) and its performance are of high importance. This paper investigates the performance of the handover procedure within 3GPP LTE in terms of HO failure rate and the delay of the whole procedure. System level simulations within a typical urban propagation environment, with different User Equipment (UE) speeds, cell radii and traffic loads per cell have been performed. The entire layer 3 signalling exchanged via air interface is considered in the simulations. In addition, errors at the Layer 1 (L1) control channels are taken into account. Simulation results show that the handover procedure within 3GPP satisfies the goal of high performance mobility. Namely for cell radii up to 1 km and for UE speeds up to 120 km/h, the HO failure rate lies within the range of 0-2.2% even in high loaded systems. For medium and low loads even at speeds of 250 km/h, HO failure is below 1.3 %. In addition, simulation results show that the handover procedure is robust against L1 control channel errors.


title: Building Energy Modelling and Monitoring by Integration of IoT Devices and Building Information Models

abstract: In recent years, the research about energy waste and CO2 emission reduction has gained a strong momentum, also pushed by European and national funding initiatives. The main purpose of this large effort is to reduce the effects of greenhouse emission, climate change to head for a sustainable society. In this scenario, Information and Communication Technologies (ICT) play a key role. From one side, advances in physical and environmental information sensing, communication and processing, enabled the monitoring of energy behaviour of buildings in real-time. The access to this information has been made easy and ubiquitous thank to Internet-of-Things (IoT) devices and protocols. From the other side, the creation of digital repositories of buildings and districts (i.e. Building Information Models - BIM) enabled the development of complex and rich energy models that can be used for simulation and prediction purposes. As such, an opportunity is emerging of mixing these two information categories to either create better models and to detect unwanted or inefficient energy behaviours. In this paper, we present a software architecture for management and simulation of energy behaviours in buildings that integrates heterogeneous data such as BIM, IoT, GIS (Geographical Information System) and meteorological services. This integration allows: i) (near-) real-time visualisation of energy consumption information in the building context and ii) building performance evaluation through energy modelling and simulation exploiting data from the field and real weather conditions. Finally, we discuss the experimental results obtained in a real-world case-study.


title: Social attachment and aversion in human moral cognition

abstract: Modern neuroscience is beginning to substantiate Darwin's notion that the roots of human morality lie in social instincts, present in several species. The role of primitive motivational-emotional systems in human morality still remains under-recognized, however. Based on recent experimental evidence and classic neuroanatomical data, we here portray a view of how "ancient" limbic-neurohumoral systems of social attachment and aversion are crucially involved in human moral behaviors, including altruism, empathic concern and aggression. Rather than being a mere evolutionary remnant of our ancestors, such limbic-neurohumoral systems are tightly integrated with cortical mechanisms to enable complex moral sentiments and values, which powerfully influence our choices in socio-cultural settings. Exploring the underlying mechanisms of human social attachment and aversion will provide new insights and foster novel experimental paradigms for the study of moral cognition and behavior. Language: en


title: ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements

abstract: The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs an intermediate reconstruction. We call this network, ReconNet. The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image. On a standard dataset of images we show significant improvements in reconstruction results (both in terms of PSNR and time complexity) over state-of-the-art iterative CS reconstruction algorithms at various measurement rates. Further, through qualitative experiments on real data collected using our block single pixel camera (SPC), we show that our network is highly robust to sensor noise and can recover visually better quality images than competitive algorithms at extremely low sensing rates of 0.1 and 0.04. To demonstrate that our algorithm can recover semantically informative images even at a low measurement rate of 0.01, we present a very robust proof of concept real-time visual tracking application.


title: A path space extension for robust light transport simulation

abstract: We present a new sampling space for light transport paths that makes it possible to describe Monte Carlo path integration and photon density estimation in the same framework. A key contribution of our paper is the introduction of vertex perturbations, which extends the space of paths with loosely coupled connections. The new framework enables the computation of path probabilities in the same space under the same measure, which allows us to use multiple importance sampling to combine Monte Carlo path integration and photon density estimation. The resulting algorithm, unified path sampling, can robustly render complex combinations and glossy surfaces and caustics that are problematic for existing light transport simulation methods.


title: 15 Bimodal Emotion Recognition using Speech and Physiological Changes

abstract: With exponentially evolving technology it is no exaggeration to say that any interface for human-robot interaction (HRI) that disregards human affective states and fails to pertinently react to the states can never inspire a user’s confidence, but they perceive it as cold, untrustworthy, and socially inept. Indeed, there is evidence that HRI is more likely to be accepted by the user if it is sensitive towards the user’s affective states, as expression and understanding of emotions facilitate to complete the mutual sympathy in human communication. To approach the affective human-robot interface, one of the most important prerequisites is a reliable emotion recognition system which guarantees acceptable recognition accuracy, robustness against any artifacts, and adaptability to practical applications. Emotion recognition is an extremely challenging task in several respects. One of the main difficulties is that it is very hard to uniquely correlate signal patterns with a certain emotional state because even it is difficult to define what emotion means in a precise way. Moreover, it is the fact that emotion-relevant signal patterns may widely differ from person to person and from situation to situation. Gathering “ground-truth” dataset is also problematical to build a generalized emotion recognition system. Therefore, a number of assumptions are generally required for engineering approach to emotion recognition. Most research on emotion recognition so far has focused on the analysis of a single modality, such as speech and facial expression (see (Cowie et al., 2001) for a comprehensive overview). Recently some works on emotion recognition by combining multiple modalities are reported, mostly by fusing features extracted from audiovisual modalities such as facial expression and speech. We humans use several modalities jointly to interpret emotional states in human communication, since emotion affects almost all modes, audiovisual (facial expression, voice, gesture, posture, etc.), physiological (respiration, skin temperature etc.), and contextual (goal, preference, environment, social situation, etc.) states. Hence, one can expect higher recognition rates through the integration of multiple modalities for emotion recognition. On the other hand, however, more complex classification and fusion problems arise. In this chapter, we concentrate on the integration of speech signals and physiological measures (biosignals) for emotion recognition based on a short-term observation. Several advantages can be expected when combining biosensor feedback with affective speech. First


Severe atopic dermatitis associated with transient hypogammaglobulinemia of infancy


title: A Survey of Software Estimation Techniques and Project Planning Practices

abstract: Paper provides in depth review of software and project estimation techniques existing in industry and literature, its strengths and weaknesses. Usage, popularity and applicability of such techniques are elaborated. In order to improve estimation accuracy, such knowledge is essential. Many estimation techniques, models, methodologies exists and applicable in different categories of projects. None of them gives 100% accuracy but proper use of them makes estimation process smoother and easier. Organizations should automate estimation procedures, customize available tools and calibrate estimation approaches as per their requirements. Proposed future work is to study factors involved in Software Engineering Approaches (Software Estimation in focus) for Offshore and Outsourced Software Development taking Pakistani IT Industry as a Case Study.


title: Single-stage, single-phase, ac–dc buck–boost converter for low-voltage applications

abstract: The suitability of a single-stage ac–dc buck–boost converter for low-voltage applications is investigated. In-depth discussion and analysis of the converter's operating principle, basic relationships that govern converter steady-state operation and details of the necessary control structures needed to comply with the grid code are provided. The validity of the proposed system is confirmed using power system computer aided design (PSCAD)/electromagnetic transients including DC (EMTDC) simulations, and is substantiated experimentally. The buck–boost converter under investigation has good dynamic performance in both buck and boost modes, and ensures near unity input power factor over the full operating range, whilst having fewer devices and passive elements than other published versions of the buck–boost converter.


title: Nanoscale optical communications modulator and acousto-optic transduction with vibrating graphene and resonance energy transfer

abstract: Graphene resonators are future promising in terms of ultra-low weight, high Young's modulus, strength and wideband resonance frequencies. Besides that, nanoscale optical wireless channels including visible light spectrum are alternatives to radio-frequency communications promising energy efficiency and high data rates. In this article, vibrating multi-layer graphene nanoelectromechanical resonators are combined with designed vibrating Forster resonance energy transfer (VFRET) mechanism to achieve a nanoscale acousto-optic modulator converting vibrations to multi-color photon emissions. The frequency, color and the vibration sensitivity of emission are tunable while vibrations are realized either passively or actively by exploiting acoustic, thermo-acoustic or opto-acoustic properties of graphene. The light is generated by FRET mechanism with oscillating donor-acceptor distance where donor molecules attached on graphene are chosen as CdSe/ZnS core-shell QDs with significant properties of broad absorption spectrum, large cross-sections, tunable emission spectra, size dependent emission wavelength, high photochemical stability and improved quantum yield. The designed modulator achieves acoustic and ultrasound frequencies between several KHz and tens of MHz and radiation power reaching several nanowatts with resonator sizes of hundreds of micrometers for ambient light intensity of 0.1 W/m2/nm. The proposed system promises significant applications including nanoscale acousto-optic communication, transduction, sensing, energy harvesting and biomedical nanoscale communications.


title: Modality Effects in Deception Detection and Applications in Automatic-Deception-Detection

abstract: Modality is an important context factor in deception, which is context-dependent. In order to build a reliable and flexible tool for automatic-deception-detection (ADD), we investigated the characteristics of verbal cues to deceptive behavior in three modalities: text, audio and face-to-face communication. Seven categories of verbal cues (21 cues) were studied: quantity, complexity, diversity, verb nonimmediacy, uncertainty, specificity and affect. After testing the interaction effects between modality and condition (deception or truth), we found significance only with specificity and observed that differences between deception and truth were in general consistent across the three modalities. However, modality had strong effects on verbal cues. For example, messages delivered face-to-face were largest in quantity (number of words, verbs, and sentences), followed by the audio modality. Text had the sparsest examples. These modality effects are an important factor in building baselines in ADD tools, because they make it possible to use them to adjust the baseline for an unknown modality according to a known baseline, thereby simplifying the process of ADD. The paper discusses in detail the implications of these findings on modality effects in three modalities.


Symmetry in 3D Geometry: Extraction and Applications


title: Concept Based Short Text Stream Classification with Topic Drifting Detection

abstract: Short text stream classification is a challengingand significant task due to the characteristics of short length, weak signal, high velocity and especially topic drifting in short text stream. However, this challenge has received little attention from the research community. Motivated by this, we propose a new feature extension approach for short text stream classification using a large scale, general purpose semantic network obtained from a web corpus. Our approach is built on an incremental ensemble classification model. First, in terms of the open semantic network, we introduce more semantic contexts in short texts to make up of the data sparsity. Meanwhile, we disambiguate terms by their semantics to reduce the noise impact. Second, to effectively track hidden topic drifts, we propose a concept cluster based topic drifting detection method. Finally, extensive experiments demonstratethat our approach can detect topic drifts effectively compared to several well-known concept drifting detection methods in data streams. Meanwhile, our approach can perform best in the classification of text data streams compared to several stateof-the-art short text classification approaches.


Description Logic for Vision-Based Intersection Understanding


title: POLLY — PERFORMING POLYHEDRAL OPTIMIZATIONS ON A LOW-LEVEL INTERMEDIATE REPRESENTATION

abstract: The polyhedral model for loop parallelization has proved to be an effective tool for advanced optimization and automatic parallelization of programs in higher-level languages. Yet, to integrate such optimizations seamlessly into production compilers, they must be performed on the compiler's internal, low-level, intermediate representation (IR). With Polly, we present an infrastructure for polyhedral optimizations on such an IR. We describe the detection of program parts amenable to a polyhedral optimization (so-called static control parts), their translation to a Z-polyhedral representation, optimizations on this representation and the generation of optimized IR code. Furthermore, we define an interface for connecting external optimizers and present a novel way of using the parallelism they introduce to generate SIMD and OpenMP code. To evaluate Polly, we compile the PolyBench 2.0 benchmarks fully automatically with PLuTo as external optimizer and parallelizer. We can report on significant speedups.


title: Biometric-oriented Iris Identification Based on Mathematical Morphology

abstract: A new method for biometric identification of human irises is proposed in this paper. The method is based on morphological image processing for the identification of unique skeletons of iris structures, which are then used for feature extraction. In this approach, local iris features are represented by the most stable nodes, branches and end-points extracted from the identified skeletons. Assessment of the proposed method was done using subsets of images from the University of Bath Iris Image Database (1000 images) and the CASIA Iris Image Database (500 images). Compelling experimental results demonstrate the viability of using the proposed morphological approach for iris recognition when compared to a state-of-the-art algorithm that uses a global feature extraction approach.


title: Compact two-layer slot array antenna with SIW for 60GHz wireless applications

abstract: In a variety of microwave and millimeter-wave applications where high performance antennas are required, waveguide slot arrays have received considerable attention due to their high aperture efficiency, low side lobe levels, and low cross polarization. Resonant slot arrays usually suffer from narrow bandwidth and high cost due to high precision required in manufacturing. Furthermore, because of using standard rectangular waveguides, the antenna array is thick and heavy and is not suitable for monolithic integration with high frequency printed circuits.


title: Internet Research: Privacy, Ethics and Alienation: An Open Source Approach

abstract: This paper examines some of the ethical problems involved in undertaking Internet research and draws on historical accounts as well as contemporary studies to offer an analysis of the issues raised. It argues that privacy is a misleading and confusing concept to apply to the Internet, and that the concept of non‐alienation is more resourceful in addressing the many ethical issues surrounding Internet research. Using this as a basis, the paper then investigates the Free/Libre and Open Source research model and argues for the principles of “open source ethics” in researching the online world, which includes a participatory and democratic research method.


title: Intellectual disability and its relationship to autism spectrum disorders

abstract: Intellectual disability (ID) and autism spectrum disorders (ASDs) covary at very high rates. Similarly, greater severity of one of these two disorders appears to have effects on the other disorder on a host of factors. A good deal of research has appeared on the topic with respect to nosology, prevalence, adaptive functioning, challenging behaviors, and comorbid psychopathology. The purpose of this paper was to provide a critical review and status report on the research published on these topics. Current status and future directions for better understanding these two covarying disorders was reviewed along with a discussion of relevant strengths and weaknesses of the current body of research.


title: Towards a truly mobile auditory brain-computer interface: exploring the P300 to take away.

abstract: Abstract In a previous study we presented a low-cost, small, and wireless 14-channel EEG system suitable for field recordings (Debener et al., 2012, psychophysiology). In the present follow-up study we investigated whether a single-trial P300 response can be reliably measured with this system, while subjects freely walk outdoors. Twenty healthy participants performed a three-class auditory oddball task, which included rare target and non-target distractor stimuli presented with equal probabilities of 16%. Data were recorded in a seated (control condition) and in a walking condition, both of which were realized outdoors. A significantly larger P300 event-related potential amplitude was evident for targets compared to distractors ( p


MetaNET - a web-accessible interactive platform for biological metabolic network analysis


title: The path to personalized medicine.

abstract: Advances in personalized medicine, or the use of an individual's molecular profile to direct the practice of medicine, have been greatly enabled through human genome research. This research is leading to the identification of a range of molecular markers for predisposition testing, disease screening and prognostic assessment, as well as markers used to predict and monitor drug response. Successful personalized medicine research programs will not only require strategies for developing and validating biomarkers, but also coordinating these efforts with drug discovery and clinical development.


A future for intelligent autonomous ocean observing systems


title: GOLOG: A Logic Programming Language for Dynamic Domains

abstract: Abstract This paper proposes a new logic programming language called GOLOG whose interpreter automatically maintains an explicit representation of the dynamic world being modeled, on the basis of user supplied axioms about the preconditions and effects of actions and the initial state of the world. This allows programs to reason about the state of the world and consider the effects of various possible courses of action before committing to a particular behavior. The net effect is that programs may be written at a much higher level of abstraction than is usually possible. The language appears well suited for applications in high level control of robots and industrial processes, intelligent software agents, discrete event simulation, etc. It is based on a formal theory of action specified in an extended version of the situation calculus. A prototype implementation in Prolog has been developed.


title: Prevalence of pain and dysfunction in the cervical and thoracic spine in persons with and without lateral elbow pain

abstract: Abstract The purpose of this study was to survey the prevalence of pain in the cervical and thoracic spine (C2–T7) in persons with and without lateral elbow pain. Thirty-one subjects with lateral elbow pain and 31 healthy controls participated in the study. The assessment comprised a pain drawing, provocation tests of the cervical and thoracic spine, a neurodynamic test of the radial nerve, and active cervical range of motion. Seventy percent of the subjects with lateral elbow pain indicated pain in the cervical or thoracic spine, as compared to 16% in the control group ( p p p p


title: Optimal Mass Transport: Signal processing and machine-learning applications

abstract: Transport-based techniques for signal and data analysis have received increased attention recently. Given their ability to provide accurate generative models for signal intensities and other data distributions, they have been used in a variety of applications including content-based retrieval, cancer detection, image super-resolution, and statistical machine learning, to name a few, and shown to produce state of the art results in several applications. Moreover, the geometric characteristics of transport-related metrics have inspired new kinds of algorithms for interpreting the meaning of data distributions. Here we provide a practical overview of the mathematical underpinnings of mass transport-related methods, including numerical implementation, as well as a review, with demonstrations, of several applications. Software accompanying this tutorial is available at [43].


title: Detecting Text Similarity Over Short Passages: Exploring Linguistic Feature Combinations Via Machine Learning

abstract: We present a new composite similarity metric that combines information from multiple linguistic indicators to measure semantic distance between pairs of small textual units. Several potential features are investigated and an optimal combination is selected via machine learning. We discuss a more restrictive definition of similarity than traditional, document-level and information retrieval-oriented, notions of similarity, and motivate it by showing its relevance to the multi-document text summarization problem. Results from our system are evaluated against standard information retrieval techniques, establishing that the new method is more effective in identifying closely related textual units.


title: Concept Hierarchy Extraction from Textbooks

abstract: Concept hierarchies have been useful tools for presenting and organizing knowledge. With the rapid growth in the number of online knowledge resources, automatic concept hierarchy extraction is increasingly attractive. Here, we focus on concept extraction from textbooks based on the knowledge in Wikipedia. Given a book, we extract important concepts in each book chapter using Wikipedia as a resource and from this construct a concept hierarchy for that book. We define local and global features that capture both the local relatedness and global coherence embedded in that textbook. In order to evaluate the proposed features and extracted concept hierarchies, we manually construct concept hierarchies for three well used textbooks by labeling important concepts for each book chapter. Experiments show that our proposed local and global features achieve better performance than using only keyphrases to construct the concept hierarchies. Moreover, we observe that incorporating global features can improve the concept ranking precision and reaffirms the global coherence in the book.


title: Scale and rotation invariant OCR for Pashto cursive script using MDLSTM network

abstract: Optical Character Recognition (OCR) of cursive scripts like Pashto and Urdu is difficult due the presence of complex ligatures and connected writing styles. In this paper, we evaluate and compare different approaches for the recognition of such complex ligatures. The approaches include Hidden Markov Model (HMM), Long Short Term Memory (LSTM) network and Scale Invariant Feature Transform (SIFT). Current state of the art in cursive script assumes constant scale without any rotation, while real world data contain rotation and scale variations. This research aims to evaluate the performance of sequence classifiers like HMM and LSTM and compare their performance with descriptor based classifier like SIFT. In addition, we also assess the performance of these methods against the scale and rotation variations in cursive script ligatures. Moreover, we introduce a database of 480,000 images containing 1000 unique ligatures or sub-words of Pashto. In this database, each ligature has 40 scale and 12 rotation variations. The evaluation results show a significantly improved performance of LSTM over HMM and traditional feature extraction technique such as SIFT.


title: Purine: A bi-graph based deep learning framework

abstract: In this paper, we introduce a novel deep learning framework, termed Purine. In Purine, a deep network is expressed as a bipartite graph (bi-graph), which is composed of interconnected operators and data tensors. With the bi-graph abstraction, networks are easily solvable with event-driven task dispatcher. We then demonstrate that different parallelism schemes over GPUs and/or CPUs on single or multiple PCs can be universally implemented by graph composition. This eases researchers from coding for various parallelization schemes, and the same dispatcher can be used for solving variant graphs. Scheduled by the task dispatcher, memory transfers are fully overlapped with other computations, which greatly reduce the communication overhead and help us achieve approximate linear acceleration.


title: Control of Higher Order Harmonics and Spurious Modes for Microstrip Patch Antennas

abstract: In rectifying or active antenna systems, the higher order modes can hamper the system efficiency, and thus, the filter antennas are used to eliminate the harmonic radiation. In this paper, the distribution of standing wave fields in patch antenna having been presented and then the characteristic mode theory having been employed, the influences of feeding location and the indentation of patch edges on the suppression of higher order harmonics and their spurious modes are investigated. It is indicated that indenting the radiating edges of the patch element works more effectively on the control of spurious modes while indenting the non-radiating edges can eliminate the harmonics by shifting down the fundamental mode. By selecting feed position and indenting the patch edges, a microstrip patch antenna with both harmonic and spurious mode controls is proposed.


Clustering Paraphrases by Word Sense


title: Filipino Americans and racism: A multiple mediation model of coping.

abstract: Although the literature on Asian Americans and racism has been emerging, few studies have examined how coping influences one's encounters with racism. To advance the literature, the present study focused on the psychological impact of Filipino Americans' experiences with racism and the role of coping as a mediator using a community-based sample of adults (N = 199). Two multiple mediation models were used to examine the mediating effects of active, avoidance, support-seeking, and forbearance coping on the relationship between perceived racism and psychological distress and self-esteem, respectively. Separate analyses were also conducted for men and women given differences in coping utilization. For men, a bootstrap procedure indicated that active, support-seeking, and avoidance coping were mediators of the relationship between perceived racism and psychological distress. Active coping was negatively associated with psychological distress, whereas both support seeking and avoidance were positively associated with psychological distress. A second bootstrap procedure for men indicated that active and avoidance coping mediated the relationship between perceived racism and self-esteem such that active coping was positively associated with self-esteem, and avoidance was negatively associated with self-esteem. For women, only avoidance coping had a significant mediating effect that was associated with elevations in psychological distress and decreases in self-esteem. The results highlight the importance of examining the efficacy of specific coping responses to racism and the need to differentiate between the experiences of men and women.


title: TensorFlow-Serving: Flexible, High-Performance ML Serving

abstract: We describe TensorFlow-Serving, a system to serve machine learning models inside Google which is also available in the cloud and via open-source. It is extremely flexible in terms of the types of ML platforms it supports, and ways to integrate with systems that convey new models and updated versions from training to serving. At the same time, the core code paths around model lookup and inference have been carefully optimized to avoid performance pitfalls observed in naive implementations. Google uses it in many production deployments, including a multi-tenant model hosting service called TFS^2.


title: On the Feasibility of Breast Cancer Imaging Systems at Millimeter-Waves Frequencies

abstract: Medical imaging currently relies on several techniques, including X-rays, magnetic resonance, and echography. However, these techniques exhibit drawbacks, and alternative approaches are required. Microwave imaging has been proposed as a possible solution, especially for breast cancer imaging. However, most of these systems work with a central frequency of a few gigahertz, and this leads to a suboptimum resolution, which can jeopardize the image quality. Millimeter waves can provide superior resolutions, at the cost of a lower penetration depth within the breast tissue. In addition, a significant fraction of the power generated by a mm-wave imaging system would be reflected back from the skin. For these reasons, and also considering that mm-wave transmitters and receivers have been historically outperformed by microwave counterparts in terms of available power and sensitivity, mm-wave imaging has not been considered a possible solution. This paper contributes to demonstrate a paradigm shift toward the possible use of mm-waves for breast cancer imaging of targets a few centimeter below the skin, a useful penetration depth for several cases. All key points are addressed using analytical, full-wave, and multiphysics simulations, including the system architecture (linear and conformal), the safety aspects (power density, specific absorption rate, and temperature increase), and the use of realistic breast models derived from ex vivo measurements.


title: A Compact Quasi-Isotropic Shorted Patch Antenna

abstract: A compact shorted patch antenna with quasi-isotropic radiation pattern is proposed in this paper. The antenna consists of a radiating patch, a small ground plane that has the same dimensions with the top patch, and a metallic sidewall which connects the former two. A coaxial probe is used to feed the antenna and excite its fundamental TEM mode, whose magnetic field generates surface electric current on the shorted sidewall and electric field generates surface magnetic current on the open-ended aperture. Due to the inherent properties of the electric and magnetic fields, the corresponding currents are found not only perpendicular but also quadrature with each other, and, therefore, the patch antenna can provide a quasi-isotropic radiation pattern without involving complex feeding circuit. To verify the theory, a prototype operating at 2.4-GHz WLAN band was designed, fabricated, and measured. Reasonable agreement between the calculated, simulated, and measured results is obtained. It has been shown that the difference between the maximum and minimum radiation power densities is ~2 dB over the entire spherical radiating surface, and the difference can be further reduced to ~0.9 dB by using a lower profile.


title: A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization

abstract: Taking both convergence and diversity into consideration, this paper suggests a vector angle-based evolutionary algorithm for unconstrained (with box constraints only) many-objective optimization problems. In the proposed algorithm, the maximum-vector-angle-first principle is used in the environmental selection to guarantee the wideness and uniformity of the solution set. With the help of the worse-elimination principle, worse solutions in terms of the convergence (measured by the sum of normalized objectives) are allowed to be conditionally replaced by other individuals. Therefore, the selection pressure toward the Pareto-optimal front is strengthened. The proposed method is compared with other four state-of-the-art many-objective evolutionary algorithms on a number of unconstrained test problems with up to 15 objectives. The experimental results have shown the competitiveness and effectiveness of our proposed algorithm in keeping a good balance between convergence and diversity. Furthermore, it was shown by the results on two problems from practice (with irregular Pareto fronts) that our method significantly outperforms its competitors in terms of both the convergence and diversity of the obtained solution sets. Notably, the new algorithm has the following good properties: 1) it is free from a set of supplied reference points or weight vectors; 2) it has less algorithmic parameters; and 3) the time complexity of the algorithm is low. Given both good performance and nice properties, the suggested algorithm could be an alternative tool when handling optimization problems with more than three objectives.


title: Revision of the Competency Standards for Occupational Therapy Driver Assessors: An overview of the evidence for the inclusion of cognitive and perceptual assessments within fitness‐to‐drive evaluations

abstract: Background/aim ::: Determination of fitness-to-drive after illness or injury is a complex process typically requiring a comprehensive driving assessment, including off-road and on-road assessment components. The competency standards for occupational therapy driver assessors (Victoria, Australia) define the requirements for performance of a comprehensive driving assessment, and we are currently revising these. Assessment of cognitive and perceptual skills forms an important part of the off-road assessment. The aim of this systematic review of systematic reviews (known as an overview) is to identify what evidence exists for including assessment of cognitive and perceptual skills within fitness-to-drive evaluations to inform revision of the competency standards. ::: ::: Methods ::: Five electronic databases (MEDLINE, CINAHL, PsycINFO, The Cochrane Library, OT Seeker) were systematically searched. Systematic review articles were appraised by two authors for eligibility. Methodological quality was independently assessed using the AMSTAR tool. Narrative analysis was conducted to summarise the content of eligible reviews. ::: ::: Results ::: A total of 1228 results were retrieved. Fourteen reviews met the inclusion criteria. Reviews indicated that the components of cognition and perception most frequently identified as being predictive of fitness-to-drive were executive function (n = 13), processing speed (n = 12), visuospatial skills, attention, memory and mental flexibility (n = 11). Components less indicative were perception, concentration (n = 10), praxis (n = 9), language (n = 7) and neglect (n = 6). ::: ::: Conclusion ::: This overview of systematic reviews supports the inclusion of assessment of a range of cognitive and perceptual skills as key elements in a comprehensive driver assessment and therefore should be included in the revised competency standards for occupational therapy driver assessors.